Reviewerly/exHarmony huggingface dataset:
A Simple and Enhanced Low-Light Image Enhancement Process Using Effective Illumination Mapping Approach

When an image is captured in low-light, it gets the low visibility. To overcome the low visibility of the image, some operations are to be performed. But in this paper, image enhancement is introduced using illumination mapping. First, R, G, B maximum values in each pixel of the considered image are to be calculated and then convert it into a grey scale image by applying the formulae. Some filters are used to remove the noise, the choice of filter depends on the type of noise, and then the image is preprocessed. The logarithmic transformation helps to increase the brightness and contrast of the image with a certain amount. Earlier there were some methods to enhance the low-light image, but illumination map existence is chosen. In this illumination, the image will be enhanced with the good quality and efficiency. The illumination technique will be the more efficient and more quality. The illumination corrects the R, G, B values to get the desired image, then Gamma Correction is applied. The Gamma Correction is a non-linear power transform, it helps to increase or decrease the brightness of the desired image when a low value of gamma is taken, the brightness will be increased and when a high value of gamma is taken, and the brightness will be decreased. The proposed system is implemented using MATLAB software. When different types of images are applied, different contrast and brightness levels that depend on the type of image are observed.
Collaborative Learning for Answer Selection in Question Answering

Answer selection is an essential step in a question answering (QA) system. Traditional methods for this task mainly focus on developing linguistic features that are limited in practice. With the great success of deep learning method in distributed text representation, deep learning-based answer selection approaches have been well investigated, which mainly employ only one neural network, i.e., convolutional neural network (CNN) or long short term memory (LSTM), leading to failures in extracting some rich sentence features. Thus, in this paper, we propose a collaborative learning-based answer selection model (QA-CL), where we deploy a parallel training architecture to collaboratively learn the initial word vector matrix of the sentence by CNN and bidirectional LSTM (BiLSTM) at the same time. In addition, we extend our model by incorporating the sentence embedding generated by the QA-CL model into a joint distributed sentence representation using a strong unsupervised baseline weight removal (WR), i.e., the QA-CLWR model. We evaluate our proposals on a popular QA dataset, InsuranceQA. The experimental results indicate that our proposed answer selection methods can produce a better performance compared with several strong baselines. Finally, we investigate the models’ performance with respect to different question types and find that question types with a medium number of questions have a better and more stable performance than those types with too large or too small number of questions.
General multi-view learning with maximum entropy discrimination

Maximum entropy discrimination (MED) is an excellent discriminative classification algorithm in view of maximum entropy and maximum margin principles, and can generate hard-margin support vector machines (SVMs) under certain condition. Recently, many versions of multi-view maximum entropy discrimination are proved to be effective in multi-view learning. However, those methods are designed only for the two-view classification problem. For example, alternative multi-view maximum entropy discrimination (AMvMED) and multi-kernel multi-view maximum entropy discrimination (MkMvMED) belong to the two-view classification method. In this paper, we extend AMvMED and MkMvMED to the general multi-view classification problems by jointly learning multiple different views in a non-pairwise way called general alternative multi-view maximum entropy discrimination (GAMvMED) and general multi-kernel multi-view maximum entropy discrimination (GM3ED). In GAMvMED, combination weight for the reconstruction of each view in regularization terms is learned to explore complementarity information among different views. Finally, we provide an efficient iteration algorithm with the classical convex quadratic programming for optimization. Simultaneously, we derive that GAMvMED and GM3ED are equivalent in form under the similar prior assumption. Experimental results confirm the effectiveness of our proposed methods.
Quasi-Streaming Graph Partitioning: A Game Theoretical Approach

Graph partitioning is a fundamental problem to enable scalable graph computation on large graphs. Existing partitioning models are either streaming based or offline based. In the streaming model, the current edge needs all previous edges' partition choices to make a decision. As a result, it is hard to carry out partitioning in parallel. Besides, offline based partitioning requires full knowledge about the input graph which may not suit well for large graphs. In this work, we propose a quasi-streaming partitioning model and a game theory based solution for the edge partitioning problem. Specifically, we separate the whole edge stream into a series of batches where the batch size is a constant multiple of the number of partitions. In each batch, we model the graph edge partitioning problem as a game process, where the edge's partition choice is regarded as a rational strategy choice of the player in the game. As a result, the edge partitioning problem is decomposed into finding Nash Equilibriums in a series of game processes. We mathematically prove the existence of Nash Equilibrium in such a game process, and analyze the number of rounds needed to converge into a Nash Equilibrium. We further measure the quality of these Nash Equilibriums via computing the PoA (Price of Anarchy), which is bounded by the number of partitions. Then we evaluate the performance of our strategy via comprehensive experiments on both real-world graphs and random graphs. Results show that our solution achieves significant improvements on load balance and replication factor when compared with five exsiting streaming partitioning strategies.
Estimating diffuse solar radiation in India: performance characterization of generalized single-input empirical models

Solar radiation data obtained from Indian Meteorological Department, Pune over the span of 1986–2000 were used to establish generalized models for estimation of monthly mean diffuse solar radiation over India. Four categories of single input models were established by rationalizing the diffuse component which correlated diffuse fraction and diffuse coefficient with relative sunshine period and clearness index. 52 models (13 models under each defined category) were selected from the literature. 28 new generalized model with 7 in each category were also developed from the data. The accuracy of all the 80 models was assessed using error analysis. Global Performance Indicator was calculated and models were ranked accordingly with the highest value representing best performance. GPI variation under first category ranged from −6.1035 to 0.5416, where top five models were T4, T3, T2, T1 and M13. In second category, GPI varied from −7.8812 to 0.0839 with top five models as T11, T10, T9, T13 and T12. Third category exhibited variation of GPI from −6.8551 to 0.4354 and top five model were T18, T17, T16, T15 and T19. The last category, involved GPI variation from −6.1323 to 0.1221 with T24, T23, T22, T25 and T26 as top five models.
Analyzing the Role of Human Computer Interaction Principles for E-Learning Solution Design

Education technology is one of the key application areas of Multimedia. Interactive multimedia also called as Hypermedia plays a vital role in enhancing the teaching and learning experience in higher education sector. Various e-learning solutions are already available in the market and many of them are even available for free. These solutions may include, E-books, interactive lessons, video-based tutorials, interactive exercises for revision, educational games, mobile-based educational tools, and so on. The Higher education sector has students of varying age groups. Therefore, in order to make sure that appropriate e-learning solution is adapted with suitable interface design, it is important to keep all the principles of Human-Computer Interaction (HCI) in consideration. This research paper analyzes the role of HCI in the design of e-learning solution. Authors have performed a thorough literature survey on various resources related to this topic. The result of this research will provide guidelines for better e-learning solution design, which could be considered appropriate for respective users.
YouTube Video Ranking by Aspect-Based Sentiment Analysis on User Feedback

In the current big data and Internet of things world, a huge amount of data is available in the form of multimedia, shared on Web 2.0 platforms. Today, in the online smart learning process, the most widely used online video repository is YouTube, which also provides a place for users to express their views about videos in the form comments and rating. These user comments are still unexplored in the context of ranking in retrieval. Majorly, rating based on like/dislike is the only criterion considered to decide the relevancy and quality and in determining the ranking of the videos. Sometimes, the relevancy of the video is unveiled after watching the video, and as a result of this, irrelevant videos may be ranked higher. In this paper, we have investigated the impact of different aspects of the video’s subject from the user comments in the video retrieval using aspect-based sentiment analysis.
HAP: A Hybrid QoS Prediction Approach in Cloud Manufacturing Combining Local Collaborative Filtering and Global Case-Based Reasoning

With the rapid development of cloud manufacturing (CMfg), a large number of functionally equivalent cloud services are available on the Internet, thus quality of service (QoS) prediction has become a hot issue. Since the QoS of service varies widely among users, many Collaborative Filtering (CF) approaches are recently proposed to predict the unknown QoS by extending the Pearson Correlation Coefficient (PCC) similarity. However, most existing approaches either enhance the similarity computation inadequately or ignore the data sparsity problem, and are thus vulnerable to the unreliable result. To address this problem, we propose a hybrid approach HAP for QoS prediction. First, to achieve high prediction accuracy, we employ a similarity-enhanced CF (L-CF) for local QoS prediction, in which the personalized influence of similar users and services are considered when computing PCC similarity. Second, to overcome the data sparsity problem, we propose a global QoS prediction approach based on the case-based reasoning (G-CBR) which could make full use of both user and service information. Finally, we establish an ensemble model to combine the results of two approaches. Comprehensive real-world experiments are conducted to demonstrate the effectiveness of our approach compared with other state-of-the-arts approaches, particularly when the QoS data is very sparse.
Learning concept drift with ensembles of optimum-path forest-based classifiers

Concept drift methods learn patterns in non-stationary environments. Although such behavior is usually not expected in traditional classification problems, in real-world scenarios one can face them very much easier. In such a context, classifiers can be fooled and their effectiveness affected as well. Some examples include theft detection in energy distribution systems, where the consumer's behavior may change suddenly or smoothly, or even churn prediction in mobile companies. In this paper, we introduce the Optimum-Path Forest (OPF) classifier in the context of concept drift, using decisions for concept drift handling based on a committee of OPF classifiers. We consider three distinct perspectives (three rounds of experiments with variations of streaming managements) over publics datasets, being the results compared to the ones obtained by standard OPF. We consider OPF ensemble suitable to work under these dynamic scenarios since its recognition rates were considerably better when compared to traditional OPF.
On the difference in control performance of interval type-2 fuzzy PI control system with different FOU shapes

Interval type-2 fuzzy logic controllers (IT2-FLCs) have been attracting a lot of attention. However, challenges in designing IT2-FLCs still remain. One of the main challenges is to choose the appropriate FOU shape for interval type-2 fuzzy sets (IT2-FSs). This paper aims to analyse the differences in control performance between three IT2 fuzzy PI controllers (IT2-F-PICs) with different FOU shapes as antecedent sets, namely the triangular top wide IT2 fuzzy set, the triangular bottom wide IT2 fuzzy set and the trapezoidal (also called parallel) IT2 fuzzy set. First, the analytical structures of these IT2-FLCs are derived and the mathematical input–output equations are obtained. Three interesting differences between the analytical structures and input–output relationship of the IT2-F-PICs are then presented. From the differences in the analytical structures of the three IT2-F-PICs and numerical simulation results, it is demonstrated that IT2-F-PICs with trapezoidal (IT2-F-PI-P) and triangular bottom wide (IT2-F-PI-BW) antecedent sets with the potential to provide faster transient response and faster settling time than the IT2-F-PICs with triangular top wide (IT2-F-PI-TW). In addition, IT2-F-PI-P is better able to handle plant uncertainties and disturbances than IT2-F-PI-BW and IT2-F-PI-TW. The contribution of this paper is to provide insights into the performance differences between different FOU shaped controllers, which in turns allowing control designers to select the appropriate FOU shape in order to meet design requirements.
Ontology-based conditional random fields for object recognition

Object recognition is a cornerstone task in autonomous and/or assistance systems like robots, autonomous vehicles, or those assisting to visually impaired, aiming to achieve a certain level of understanding of their surroundings. Probabilistic models, such as Conditional Random Fields (CRFs), have been successfully applied to this end given their ability to exploit contextual and situation information, e.g. a bowl is typically found in a cabinet and not in a night-stand. In this work we propose to evolve CRFs into Ontology-based Conditional Random Fields (obCRFs ), which define a multi-level structure where each level assigns a category with different granularity to the same set of objects. For example, a level could assign to an object the category appliance or furniture, while the next one could categorize it into the tv, microwave, cabinet, or table types. In this way, general categorizations can guide the classification into more specialized ones (and vice versa), improving recognition success, and mitigating the CRFs limitations when modeling a high number of object categories (shared, in general, by Machine Learning techniques). To set the categories in each level we propose to mimic the hierarchical structure of ontologies, where categories are naturally codified following a subsumption ordering. This leads us to the second advantage of obCRFs : the multi-labeling of objects provides a richer understanding of the scene, which can be leveraged for accomplishing high-level tasks (e.g. object search or scheduling). Our approach has been tested with scenes from two state-of-the-art datasets: [email protected] and Cornell-RGBD, outperforming the results provided by standard CRFs.
Robust and accurate 2D-tracking-based 3D positioning method: Application to head pose estimation

Head pose estimation (HPE) is currently a growing research field, mainly because of the proliferation of human–computer interfaces (HCI) in the last decade. It offers a wide variety of applications, including human behavior analysis, driver assistance systems or gaze estimation systems. This article aims to contribute to the development of robust and accurate HPE methods based on 2D tracking of the face, enhancing performance of both 2D point tracking and 3D pose estimation. We start with a baseline method for pose estimation based on POSIT algorithm. A novel weighted variant of POSIT is then proposed, together with a methodology to estimate weights for the 2D–3D point correspondences. Further, outlier detection and correction methods are also proposed in order to enhance both point tracking and pose estimation. With the aim of achieving a wider impact, the problem is addressed using a global approach: all the methods proposed are generalizable to any kind of object for which an approximate 3D model is available. These methods have been evaluated for the specific task of HPE using two different head pose video databases; a recently published one that reflects the expected performance of the system in current technological conditions, and an older one that allows an extensive comparison with state-of-the-art HPE methods. Results show that the proposed enhancements improve the accuracy of both 2D facial point tracking and 3D HPE, with respect to the implemented baseline method, by over 15% in normal tracking conditions and over 30% in noisy tracking conditions. Moreover, the proposed HPE system outperforms the state of the art on the two databases.
Quantifying risk associated with clinical trial termination: A text mining approach

Clinical trials that terminate prematurely without reaching conclusions raise financial, ethical, and scientific concerns. Scientific studies in all disciplines are initiated with extensive planning and deliberation, often by a team of highly trained scientists. To assure that the quality, integrity, and feasibility of funded research projects meet the required standards, research-funding agencies such as the National Institute of Health and the National Science Foundation, pass proposed research plans through a rigorous peer review process before making funding decisions. Yet, some study proposals successfully pass through all the rigorous scrutiny of the scientific peer review process, but the proposed investigations end up being terminated before yielding results. This study demonstrates an algorithm that quantifies the risk associated with a study being terminated based on the analysis of patterns in the language used to describe the study prior to its implementation. To quantify the risk of termination, we use data from the clinicialTrials.gov repository, from which we extracted structured data that flagged study characteristics, and unstructured text data that described the study goals, objectives and methods in a standard narrative form. We propose an algorithm to extract distinctive words from this unstructured text data that are most frequently used to describe trials that were completed successfully vs. those that were terminated. Binary variables indicating the presence of these distinctive words in trial proposals are used as input in a random forest, along with standard structured data fields. In this paper, we demonstrate that this combined modeling approach yields robust predictive probabilities in terms of both sensitivity (0.56) and specificity (0.71), relative to a model that utilizes the structured data alone (sensitivity = 0.03, specificity = 0.97). These predictive probabilities can be applied to make judgements about a trial's feasibility using information that is available before any funding is granted.
The Personality of AI Systems in Education: Experiences with the Watson Tutor, a one-on-one virtual tutoring system

As artificially intelligent systems make their foray into the day-to-day educational experiences of students, we need to pay careful attention to the relationship between the system and the student. In this article, the authors discuss designing the personality of a virtual tutoring system called IBM Watson Tutor. The AI personality is key to the students' level of engagement and satisfaction with their experience. The designers have examined the qualities of effective human tutors, and used that insight as inspiration.
The role of ICTs in refugee lives

Given that the number of refugees and displaced populations around the world is reaching calamitous proportions and given the high penetration of mobile phones and internet among refugee populations, there is an imperative need to understand the role of information and communication technologies (ICTs) in serving their information needs. Using five key themes from ICTD research - digital divide, social inclusion and community development, economic well-being and financial inclusion, education, and health, this poster provides an in-depth literature review of the nascent ICT for Refugees research field. We then discuss the gaps identified in existing research, provide some future research questions for the field and conclude that future lies in the interdisciplinary and collaborative interaction between researchers, policy makers and refugees themselves.
A Wikidata-based tool for building and visualising narratives

In this paper we present a semi-automatic tool for constructing and visualising narratives, intended as networks of events related to each other by semantic relations. The tool obeys an ontology for narratives that we developed. It retrieves and assigns internationalised resource identifiers to the instances of the classes of the ontology using Wikidata as an external knowledge base and also facilitates the construction and contextualisation of events, and their linking to form the narratives. The knowledge collected by the tool is automatically saved as an Web ontology language graph. The tool also allows the visualisation of the knowledge included in the graph in simple formats like tables, network graphs and timelines. We have carried out an initial qualitative evaluation of the tool. As case study, an historian from the University of Pisa has used the tool to build the narrative of Dante Alighieri's life. The evaluation has regarded the effectiveness of the tool and the satisfaction of the users' requirements.
Identification of technical analysis patterns with smoothing splines for bitcoin prices

This research studies automatic price pattern search procedure for bitcoin cryptocurrency based on 1-min price data. To achieve this, search algorithm is proposed based on nonparametric regression method of smoothing splines. We investigate some well-known technical analysis patterns and construct algorithmic trading strategy to evaluate the effectiveness of the patterns. We found that method of smoothing splines for identifying the technical analysis patterns and that strategies based on certain technical analysis patterns yield returns that significantly exceed results of unconditional trading strategies.