Reviewerly/exHarmony huggingface dataset:
Development of E-Learning Application using Web-Based Tools to Improve Learning Effectiveness (Case Study: STT Mandala bandung)

E-leaming system with an assorted advantage that currently steal attention to various parties, especially the academic circles, has opened new horizons in learning process in the fields of education. And it is an alternative that is suited to be applied within the framework of our understanding of a teaching materials in the mass communication now. Delivery of information, communication, education and training online. This does not mean the e-learning replacing conceptual model of learning in the classroom, but only reinforce the learning model through the development of educational technology. The research done by directly observe so that we get the results of an analysis of the system which needs to be created. A literature study was also conducted to seek other sources that can be the basis and reference in making e-learning applications. E-learning system is made by using the programming language PHP and uses a MySQL database. It can be concluded that the application of e-learning has been designed and implemented with facilities covering downloading materials, assignments, quizzes and upload the material.
Bayesian Uncertainty Quantification with Synthetic Data

Image semantic segmentation systems based on deep learning are prone to making erroneous predictions for images affected by uncertainty influence factors such as occlusions or inclement weather. Bayesian deep learning applies the Bayesian framework to deep models and allows estimating so-called epistemic and aleatoric uncertainties as part of the prediction. Such estimates can indicate the likelihood of prediction errors due to the influence factors. However, because of lack of data, the effectiveness of Bayesian uncertainty estimation when segmenting images with varying levels of influence factors has not yet been systematically studied. In this paper, we propose using a synthetic dataset to address this gap. We conduct two sets of experiments to investigate the influence of distance, occlusion, clouds, rain, and puddles on the estimated uncertainty in the segmentation of road scenes. The experiments confirm the expected correlation between the influence factors, the estimated uncertainty, and accuracy. Contrary to expectation, we also find that the estimated aleatoric uncertainty from Bayesian deep models can be reduced with more training data. We hope that these findings will help improve methods for assuring machine-learning-based systems.
Greedy Sampling for Approximate Clustering in the Presence of Outliers

Greedy algorithms such as adaptive sampling (k-means++) and furthest point traversal are popular choices for clustering problems. One the one hand, they possess good theoretical approximation guarantees, and on the other, they are fast and easy to implement. However, one main issue with these algorithms is the sensitivity to noise/outliers in the data. In this work we show that for k-means and k-center clustering, simple modifications to the well-studied greedy algorithms result in nearly identical guarantees, while additionally being robust to outliers. For instance, in the case of k-means++, we show that a simple thresholding operation on the distances suffices to obtain an O(\log k) approximation to the objective. We obtain similar results for the simpler k-center problem. Finally, we show experimentally that our algorithms are easy to implement and scale well. We also measure their ability to identify noisy points added to a dataset.
A Smart Home Simulation Tool to Support the Recognition of Activities of Daily Living

The prevalence of Internet of Things (IoT) technologies and internet-connected devices introduces the potential of providing intelligence to a wide range of applications. The smart home is a popular application of IoT. Smart homes are perceived as a promising solution for providing support to inhabitants in completing daily activities, prolonging independence and enhancing quality of life. Access to sensor datasets is essential for smart home research. For example, activity recognition research requires sensor datasets to assess the performance of activity recognition models and algorithms. There are several limitations that can prevent researchers from acquiring sensor datasets, such as cost, time, and inflexibility of smart homes. The limitations also extend to both the limited quality and quantity of existing sensor datasets. Smart home simulation is identified as a potential solution to mitigate these limitations. This paper proposes Smart Environment Simulation (SESim), a simulation tool that supports smart home simulation for generating synthetic sensor datasets. The paper discusses the design, implementation, and validation of SESim. SESim was validated by conducting three phases of validation. The first validation phase validated the utility of SESim by addressing several use-cases of smart home simulation. The second validation phase validated the utility of sensor datasets generated using SESim by conducting an experiment. The experiment involved the generation of synthetic sensor datasets using SESim, training of an activity recognition model using the generated sensor datasets, and the evaluation of the activity recognition model using the generated sensor datasets. The third validation phase assessed and validated the extensibility of the SESim tool by conducting an interview with an expert user who extended the functionality of SESim.
A Roadmap to User-Controllable Social Exploratory Search

Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial and error, and on-the-fly selections, gathers and organizes information (resources). A range of innovative interfaces with increased user control has been developed to support the exploratory search process. In this work, we present our attempt to increase the power of exploratory search interfaces by using ideas of social search—for instance, leveraging information left by past users of information systems. Social search technologies are highly popular today, especially for improving ranking. However, current approaches to social ranking do not allow users to decide to what extent social information should be taken into account for result ranking. This article presents an interface that integrates social search functionality into an exploratory search system in a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates control features that allow the user to (i) express information needs by selecting keywords and (ii) to express preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes search transparency through color-coded stacked bars and rich tooltips. This work presents the full series of evaluations conducted to, first, assess the value of the social models in contexts independent to the user interface, in terms of objective and perceived accuracy. Then, in a study with the full-fledged system, we investigated system accuracy and subjective aspects with a structural model revealing that when users actively interacted with all of its control features, the hybrid system outperformed a baseline content-based–only tool and users were more satisfied.
Development of a Remote Supervisory Control and Data Acquisition System for Offshore Waste Final Disposal Facility

Koo, S.; Song, Y.J.; Lim, S.H.; Oh, M.H.; Seo, S.N., and Baek, S.J., 2019. Development of a remote supervisory control and data acquisition system for offshore waste final disposal facility. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 205-213. Coconut Creek (Florida), ISSN 0749-0208.In this paper, a case study implementation of a remote supervisory control and data acquisition system (RSCDA) for offshore waste final disposal facilities is presented. The RSCDA system is composed of three main parts—a fixed sensor-communication-integrated interface that controls multiple sensors and conducts wireless transmission of the data at fixed points, a mobile sensing platform that measures bathymetry and landfill status at irregular points, and a central control center for data collection, storage, analysis, and display. Field tests on water tanks and open water sites are conducted to verify that the proposed RSCDA can effectively collect and maintain the status of offshore waste disposal facilities.
A Pheromonal Artificial Bee Colony (pABC) Algorithm for Discrete Optimization Problems

The Artificial Bee Colony (ABC) algorithm, which simulates the intelligent foraging behavior of the honeybee colony, is one of the most preferred swarm intelligence-based metaheuristic methods for combinatorial optimization problems. In this study, the local search ability of the ABC algorithm, which can be spread to different regions of the solution space, is developed with the pheromone approach of ant colony optimization (ACO). The effects of the method, named pheromonal ABC (pABC), to the standard ABC and its competitiveness with other metaheuristic methods was presented with testing with popular benchmark problems in the NP-hard problem class. For 40 different benchmark problems, while 15 results with ABC have reached the most successful results were obtained in the literature, 25 results obtained with pABC have reached to literature. While ABC best results were behind literature with a percentage of up to 1.12%, pABC best results were behind the percentage of up to 0.63%
Inferring Context from Pixels for Multimodal Image Classification

Image classification models take image pixels as input and predict labels in a predefined taxonomy. While contextual information (e.g. text surrounding an image) can provide valuable orthogonal signals to improve classification, the typical setting in literature assumes the unavailability of text and thus focuses on models that rely purely on pixels. In this work, we also focus on the setting where only pixels are available in the input. However, we demonstrate that if we predict textual information from pixels, we can subsequently use the predicted text to train models that improve overall performance. We propose a framework that consists of two main components: (1) a phrase generator that maps image pixels to a contextual phrase, and (2) a multimodal model that uses textual features from the phrase generator and visual features from the image pixels to produce labels in the output taxonomy. The phrase generator is trained using web-based query-image pairs to incorporate contextual information associated with each image and has a large output space. We evaluate our framework on diverse benchmark datasets (specifically, the WebVision dataset for evaluating multi-class classification and OpenImages dataset for evaluating multi-label classification), demonstrating performance improvements over approaches based exclusively on pixels and showcasing benefits in prediction interpretability. We additionally present results to demonstrate that our framework provides improvements in few-shot learning of minimally labeled concepts. We further demonstrate the unique benefits of the multimodal nature of our framework by utilizing intermediate image/text co-embeddings to perform baseline zero-shot learning on the ImageNet dataset.
High efficiency continuous-variable quantum key distribution based on QC-LDPC codes

Seeking good error correcting codes to improve the efficiency of continuous-variable quantum key distribution (CVQKD) reconciliation is a concerning issue. Due to the introduction of multidimensional reconciliation, the error correcting techniques in the classical binary-input additive white Gaussian noise channel are applicable to CVQKD. In this Letter, we apply the quasi-cyclic low-density parity-check (QC-LDPC) codes, which are specified in 5G protocol, to the reconciliation process. Simulation results show that the reconciliation efficiency can reach 92.6% when the code rate is 22/68 and the signal-to-noise ratio is 0.623. Such a new error correcting code points out a new direction for the development of CVQKD.
Finding Fake News Key Spreaders in Complex Social Networks by Using Bi-Level Decomposition Optimization Method

Focal structure analysis explores the smallest possible sets of individuals can influence maximum number of users in social networks. These sets of individuals, when coordinating together, maximize information diffusing, influencing operations, or mobilizing crowds. Focal structure sets have enough resources at their disposal to regulate the flow of information in the network. Due to drawbacks in general Node-based and group-based community detection algorithms, these small influential sets are not discovered, and they remain hidden or forgotten in large communities. In this research, we propose a two-level decomposition optimization method to discover these intensive groups in complex social networks. We utilized a two-level decomposition problem maximizing the influencer nodes and the network’s global sparsity/modularity measures, subjected to small real-world network metrics. Later, we demonstrate the efficacy of our model by applying it to a YouTube network. The dataset was collected by identifying a YouTube channel that had more than 15 million views and was spreading fake news or conspiracy theory videos related to the conflict in the South China Sea. The dataset consisted of 47,265 comments on 5,095 videos by 8,477 commenters. We applied focal structure analysis to co-commenter network, where two commenters were connected if they commented on the same video, to identify the sets of individuals that are coordinating to manipulate YouTube’s recommendation algorithm to maximize the spread of fake news. The proposed method in this research identified the smallest entities that had high influence, interactions, and higher reachability for information dissemination. Also, a multi-criteria optimization problem is deployed to rank the identified sets for in-depth explorations.
Word sense disambiguation using implicit information

Abstract Humans proficiently interpret the true sense of an ambiguous word by establishing association among words in a sentence. The complete sense of text is also based on implicit information, which is not explicitly mentioned. The absence of this implicit information is a significant problem for a computer program that attempts to determine the correct sense of ambiguous words. In this paper, we propose a novel method to uncover the implicit information that links the words of a sentence. We reveal this implicit information using a graph, which is then used to disambiguate the ambiguous word. The experiments show that the proposed algorithm interprets the correct sense for both homonyms and polysemous words. Our proposed algorithm has performed better than the approaches presented in the SemEval-2013 task for word sense disambiguation and has shown an accuracy of 79.6 percent, which is 2.5 percent better than the best unsupervised approach in SemEval-2007.
A brief review of Bayesian belief network

Bayesian belief network (BBN) is a very potential graphical network based on probabilistic reasoning. This paper briefly introduces the development history of BBN and its successful applications in engineering including fault diagnosis and reliability analysis. The basic principles of the Bayesian belief network and the method of establishing a Bayesian belief network from practical engineering problems are described too. Finally, some current challenges and future development directions of BBN applications are summarized.
Fast Mode Decision Based on Gradient Information in 3D-HEVC

The 3D extension of High Efficiency Video Coding (3D-HEVC) introduce Depth Modeling Mode (DMM) and 35 conventional intra modes to enhance the quality of coding, while bringing unacceptable computational complexity during the process of rough mode decision (RMD) and most probable mode (MPM). In this paper, we proposed a fast mode decision algorithm for texture map and depth map coding based on gradient information. Firstly, analyzing the characteristics of predict units (PU) in different intra mode applications that obtain the lowest RD-cost,then extracting the gradient information of PU to classify the PU into three types of gradient blocks and selecting appropriate candidate modes for the PU,thereby avoiding search each mode in coding process and skip the process of RMD and MPM early. Experimental results show that the proposed algorithm can achieve average 30.6% time saving with negligible reduction of coding performance.
Improving Neural Machine Translation Using Rule-Based Machine Translation

The world is united socially and technologically with means of languages. Hence there is a big requirement for transfer of information from one language to another. Sanskrit is considered as an important language in the Indo-European family. A lot of work is still required to explore the potential of this language to open vistas in the computational linguistic domain. Currently, Sanskrit-Hindi translation system uses rule-based and statistical approaches. These approaches are not adequate for extending the system to generic and huge domains. In order to remove this problem, an efficient system is required to be developed which would cover various domains. Therefore, a hybrid system combining the best of Neural Machine Translation (NMT) and Rule-Based Machine Translation (RBMT) is developed and presented in this paper. The proposed hybrid model has a BLEU score of 61.2% which is higher than other existing systems i.e 41%. This approach uses deep learning feature to overcome drawbacks of the existing systems. Experimental results show that the proposed hybrid system using deep learning model has a high accuracy of 99%. It is also evaluated that it has less response time and more speed than existing systems.
Word frequency, predictability, and return-sweep saccades: Towards the modeling of eye movements during paragraph reading.

Models of eye movement control during reading focus on the reading of single lines of text. Within these models, word frequency and predictability are important input variables which influence fixation probabilities and durations. However, a comprehensive model of eye movement control will have to account for readers' eye movements across multiline texts. Line-initial words are unlike those presented midline; they are routinely unavailable for parafoveal preprocessing. Therefore, it is unclear whether and how word frequency and predictability influence reading times on line-initial words. To address this, we present an analysis of the Provo Corpus (Luke & Christianson, 2018) followed by a novel eye-movement experiment. We conclude that word frequency and predictability impact single-fixation and gaze durations on line-initial words. We also observed that return-sweep error (undersweep-fixations) may, among several other possibilities, allow for parafoveal processing of line-initial words prior to their direct fixation. Implications for models of eye movement control during reading are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
A Novel LSTM Approach for Asynchronous Multivariate Time Series Prediction

Long-short-term-memory (LSTM) recurrent neural networks have difficulty in representing temporal and non-temporal inputs simultaneously, due to the sequential emphasis of the architecture. This limits the LSTM applicability in settings where multivariate data is difficult to align. In this paper, a modified hierarchical approach is proposed where a set of univariate LSTM’s is trained for asynchronous temporal sequences. The resulting representation is jointly trained with an encoding of multivariate input for prediction. The approach is generalized to combine with non-sequential inputs. The proposed architecture is verified experimentally to improve prediction performance and convergence over conventional LSTM interpolation approaches on simulated Lorenz data and pipeline flow prediction.
A novel IFCM integrated distance based hierarchical intuitionistic decision making procedure for agile supplier selection

Companies should cope with uncertain and unpredictable changes while improving responsiveness capability to survive in dynamic market conditions. In recent years, agility concept becomes more and more popular to meet these requirements in management as well as manufacturing. From supply chain viewp oint, collaborating with agile suppliers is principal for constructing an agile supply chain, which is flexible, quick, and responsive. This paper aims to identify the most suitable agile supplier alternative by introducing a novel distance based hierarchical intuitionistic decision making procedure. The proposed methodology, which deals with uncertain and vague data, allows to represent hesitation by using intuitionistic fuzzy numbers rather than fuzzy numbers that fail to take into account hesitancy. The causal links among evaluation criteria are expressed with intuitionistic fuzzy cognitive map tool that enables to yield the importance degree of each criterion. Moreover, the hierarchical representation of the decision making problem provides an effective analysis with the presence of numerous criteria that are to be considered. The distance based framework is to minimize the distance to the ideal solution while maximizing the distance from the anti-ideal solution in order to determine the most appropriate agile supplier alternative. The case study is conducted in a dye manufacturer that performs in Turkish dye industry.
POEM+: Pricing Longer for Mobile Blockchain Computation Offloading with Edge Computing

Blockchain as an effective security solution has been applied in many mobile devices. But due to the limits of storage and computational capacities, it is hard for mobile devices to run blockchain applications locally. To solve this challenge, blockchain applications are offloaded to edge servers with mobile edge computing (MEC). However, most existing auction mechanisms on mobile blockchain fail to utilize parallel execution and the long term performance has not been well addressed. This paper investigates the mobile blockchain computation task offloading problem to improve total utility of auction participants. We propose an auction mechanism named POEM+ by solving an NP-hard multiple-choice multi-dimensional knapsack problem. POEM+ can offload tasks of one mobile user to edge servers on heterogeneous edge serves. We theoretically prove that POEM+ satisfies economic properties, such as individual rationality, budget balance, truthfulness and computation efficiency. Both simulation and real world experiments demonstrate that POEM+ achieves 130.6% higher utility than the existing heterogeneous tasks double auction WBD and 138.77% higher allocation efficiency averagely. Besides, the long-term performance of POEM+ can be guaranteed.
Single-photon Bell state measurement based on a quantum random walk

Bell state measurement (BSM) plays an important role in quantum information processing. However, a complete BSM cannot be achieved with linear optics with current technology. In this paper, we present a scheme for realizing single-photon BSM via a one-dimensional photonic quantum random walk. In our scheme, the maximally entangled states with polarization and path degrees of freedom are used as Bell states; then a complete BSM based on quantum random walk can be implemented in single-photon Hilbert space. Detecting single-photon count at different positions can provide unambiguous discrimination of all four Bell states to verify our scheme. Furthermore, we also discuss how to apply our BSM scheme to the proposed single-photon cryptography system.