Reviewerly/exHarmony huggingface dataset:
Numerical rainfall-runoff model of Cimanuk Watershed before and after the operation of Jatigede Reservoir

Abstract Cimanuk watershed is one of the watersheds in West Java that has critical land. Critical land resulted in a Cimanuk watershed becomes a flood and drought. The management of Cimanuk Watershed water availability is done by regulating water supply from the Cimanuk River with the contruction of Jatigede Reservoir. The watershed hydrological process is the relationship between inputs in the form of rainfall, process, and output in the form of runoff. The process of converting rainfall into runoff is very complex, therefore, simplifying the rainfall-runoff model approach is needed. Hydrologic Engineering Center-hydrologic Modeling System (HEC-HMS) is one of the models for simulating rainfall into runoff. The purpose of the study is to simulate the availability of water surface at Cimanuk Hulu watershed using HEC-HMS hydrological modeling system based on before and after the operation of Jatigede reservoir. The results showed that the calibration of the model produced an NSE value of 0,640. On the other hand, the best validation results was in 2018 with NSE of 0,576 with the peak discharge value before the operation of the reservoir was of 456,8 m 3 / s , and after modified with the presence of Jatigede Reservoir, peak discharge became 131,9 m 3 / s .
Novel Mutation Operators of a Variable-Length Representation for EC-Based Feature Selection in High-Dimensional Data

Because of their ability to explore large and complex search spaces, evolutionary algorithms have been widely used to solve feature selection problems. Many of them, e.g. genetic algorithms, provide a natural way to represent feature subsets, however, when the number of features is high, these representations may make the evolutionary process inefficient. In a previous paper, we presented a novel variable-length representation scheme for encoding subsets of features efficiently as well as a crossover operator to cope with the variable length representation. In this paper, we present two novel mutation operators that can generate offspring that may have a length different from that of the parent. We aim to simplify the approach presented previously, by using a single operator. We have tested the proposed approach on six datasets and the results compared with those achieved by a standard GA and two state-of-art algorithms. The results of comparisons demonstrated the effectiveness of the proposed approach when thousand of features are involved.
Design and Research on Big Data Application of Web Convergence Media

As a new data processing solution, big data technology can well complete the processing and value mining of massive data of various types and large scale. The media form formed by the integration of the Internet and traditional radio and television, as an important field of media communication and media data source, big data application will have a profound impact on it. Through the elaboration of the development and characteristics of Internet convergence media in the era of big data, this paper reveals the importance and necessity of the application of big data in Internet convergence media. At the same time, the application technology of big data in Internet convergence media is briefly analyzed.
A k-means-based and no-super-parametric Improvement of AdaBoost and its Application to Transaction Fraud Detection

AdaBoost is a well-known effective boosting algorithm for classification and has achieved successful applications in many fields. The existing studies show that it is very sensitive to noisy points, resulting in a decline of classification performance. We have proposed an improved algorithm called CAdaBoost in order to overcome the weakness. However, our CAdaBoost uses a set of super-parameters. In this paper, we propose a no-super-parametric improvement to CAdaBoost and it is applied to the problem of detecting credit card fraud. Although the performance of this CAdaBoost without super-parameters is a little worse than the original CAdaBoost, it still outperforms others including the original AdaBoost and several existing improvements of AdaBoost. Our design without super-parameters provides a helpful idea for other similar problems.
Knowledge-Infused Pre-trained Models for KG Completion

Knowledge graphs (KG) are the basis for many artificial intelligence applications but still suffer from incompleteness. In this paper, we introduce a novel method for KG completion task by knowledge-infused pre-trained language models. We represent each triple in the KG as textual sequences and transform the KG completion task into a sentence classification task that fits the input of the language model. Our KG completion framework based on the knowledge-infused pre-trained language model which can capture both linguistic information and factual knowledge to compute the plausible of the triples. Experiments show that our method achieves better results than previous state-of-the-art on multiple benchmark datasets.
Robust object tracking based on adaptive multicue feature fusion

Object tracking is challenging due to unconstrained variations in the target’s appearance and complex environmental variations. Appearance models based on a single cue are inefficient in addressing the various tracking challenges. To address this, we propose a discriminative visual tracking approach in which complementary multicue features viz. RGB cue and histogram of gradient are integrated to build an efficient appearance model. The multicue feature fusion ensures that the limitations of the individual cue are suppressed and complementary multicue information are appropriately captured in the unified feature. These unified features are robust to scale variation, rotation, and background clutter. In addition, random forest classifier not only creates clear decision boundary between the foreground fragments and the background fragments but also aids in adaptive update of the reference dictionary. This adaptive update strategy avoids the eventual drift of the tracker during illumination variation, rotation, and deformation. Extensive qualitative and quantitative analyses on benchmark OTB-2015 and VOT2017 datasets demonstrate the robustness and accuracy of the proposed tracker against seven other state-of-the-art tracker.
A Momentum-incorporated Fast Parallelized Stochastic Gradient Descent for Latent Factor Model in Shared Memory Systems

Latent factor (LF) model is an effective method for extracting useful knowledge from high-dimensional and sparse (HiDS) data generated by various industrial applications. Parallelized stochastic gradient descent (SGD) is widely used in building a parallelized LF model for handling large-scale HiDS data, but parallelized SGD suffers from slow convergence and considerable time cost. To address this issue, this study incorporates the principle of momentum into parallelized SGD, where momentum decay coefficient and learning rate are adjusted dynamically, and proposes a momentum-incorporated fast parallelized SGD (MFSGD) method to discover latent patterns from large-scale HiDS data. The experiments on two datasets show that the proposed MFSGD method outperforms state-of-the-art parallel SGD methods in terms of computational efficiency.
AURA

AURA is a speculative design project that criticizes the social problem, body image obsession, which is influenced by consumer culture, social media etc. In this regard, AURA consists of three woven light emitting interactive textile artifacts which aims to alter people's perception of self, which is limited only to a material level, by creating a fictional spatial experience based on the stimulation of the sense of movement. The project asks 1) what if there would be no mirror or screen to reflect the body image but textiles that emit light according to the existence of body and 2) how this would influence the perception of self. By asking these questions, the project created a space for corporeal experience where individuals are able to experience their body movements as sensory beings and in return receive color and brightness changes corresponding to the existence of their bodies. The project is discussed from the viewpoint of phenomenology and creates the interactive sensory experience in relation to speculative design through use of electronics i.e. sensors, microcontrollers and with digital light elements to investigate the pointed-out issue.
Gaze-Contingent Rendering in Virtual Reality

Virtual reality (VR) is a technology that relies on a computer graphics system and other external display and control interfaces, to create an immersive experience by generating an interactive three-dimensional environment on a computer. Currently, however, most virtual reality scenes are far behind the real world in naturalism. One of the limitations is the insufficient graphics computing performance of the computer. It is difficult for mainstream consumer GPUs to meet the requirements of high picture quality and high fluency at the same time when running VR scenes, resulting in a reduction in the game’s visual experience and even human discomfort. In order to balance the quality and fluency of the picture, the areas within and outside the focus range of the user’s sight can be rendered hierarchically, so as to efficiently use computing resources. In order to achieve this goal, the following article proposes a model that combines the saliency information of the virtual scene and the head motion information to predict the focus of the field of view in real time. The model can assign different rendering priorities to objects in the field of view according to the prediction results, and give different priorities, use different rendering algorithms to provide a flexible VR scene rendering optimization solution.
Integrating a Data Mining Engine into Recommender Systems

History could be epitomised to a handful of events that changed the course of human evolution. Now, we found ourselves amid another revolution: the data revolution. Easily unnoticeable, this new outlook is shifting in every possible way how we interact with the internet and, for the first time in history, how the internet interacts with us. This new kind of interactions is defined by connections between users and consumable goods (products, articles, movies, etc.). And through these connections, knowledge can be found. This is the definition of data mining. Buying online has become mainstream due to its convenience and variety, but the enormous offering options affect negatively the user experience. Millions of products are displayed online, and frequently the search for the craved product is long and tiring. This process can lead to a loss of interest from the customers and, consequentially, losing profits. The competition is increasing, and personalisation is considered the game-changer for platforms. This article follows the research and implementation of a recommender engine in a well-known Portuguese e-commerce platform specialised in clothing and sports apparel, aiming the increase in customer engagement, by providing a personalised experience with multiple types of recommendations across the platform. First, we address the reason why implementing recommender systems can benefit online platforms and the state of the art in that area. Then, a proposal and implementation of a customised system are presented, and its results discussed.
The Making of Accessible Android Applications: An Empirical Study on the State of the Practice

Context. Nowadays, mobile applications represent the principal means to enable human interaction. Being so pervasive, these applications should be made usable for all users: accessibility collects the guidelines that developers should follow to include features allowing users with disabilities (e.g., visual impairments) to better interact with an application. Problem. While research in this field is gaining interest, there is still a notable lack of knowledge on how developers practically deal with the problem: (i) whether they are aware and take accessibility guidelines into account when developing apps, (ii) which guidelines are harder for them to implement, and (iii) which tools they use to be supported in this task. Objective. To bridge the gap of knowledge on the state of the practice concerning the accessibility of mobile applications. Method. Adopting a mixed-method research approach, we aim to (i) verify how accessibility guidelines are implemented in mobile applications through a coding strategy and (ii) survey mobile developers on the issues and challenges of dealing with accessibility in practice. Limitations. Threats are represented by the size of the app sample and the number of answers to our survey study.
Satellite Image Small Target Application Based on Deep Segmented Residual Neural Network

This study employs a deep segmented residual neural network model to analyze the super-resolution of a single satellite image. A deep convolutional neural network model was analyzed, and its performance was improved. We proposed two residual layers to divide the deep network into two groups, the sum of the two residuals is the total residual, which can minimize the residual loss function and enhance the network performance. The experimental model achieved high peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) than networks without the proposed improvements when tested on satellite images. Considering these results, the application of this technology will be significant for further research on satellite images.
Towards Inclusive Privacy Protections in the Cloud

The adoption of cloud computing has created tremendous prospects and cost savings for a variety of organizations. Although increasing resources and effort have been devoted to fighting cyber-threats in cloud environments, cloud computing continues to be associated with a range of severe and complex security and privacy issues that may challenge the overall benefits that CSPs offer. While security protections in cloud computing has been widely discussed and comprehensive guidelines have been established, privacy protections in the cloud does not have the same level of focus or set of guideline or framework that has been established. In this paper, we present a systematic review of previous literature related to privacy issues in cloud computing, analyze multiple general privacy frameworks, policies, and principles to highlight the critical need for creating privacy protection criteria’s for cloud computing. Our research and review illustrates that privacy protections focused on cloud computing is deficient and it can serve as an initial guide towards building and improving privacy protections in order to meet cloud privacy requirements and ensure data protection.
New Adaptive Morphological Geodesic Active Contour Method for Segmentation of Hemorrhagic Stroke in Computed Tomography Image

This work proposes a new approach to segmentation of hemorrhagic stroke (HS) based on morphological geodesic active contour method, with automatic initialization near to lesion region, without previous training, called Adaptive Morphological Geodesic Active Contour (Ada-MGAC). To evaluate the performance, we used 100 computed tomography images with HS of volunteers. These samples were compared against segmentation methods from specialized literature. A manual segmentation from a medical specialist was considered as the gold standard. The results indicate a competitive potential of Ada-MGAC and method showed a mean convergency time about 3 s, indicating a fast result to medical analysis. Thus, it is possible to conclude that the proposed approach can be used to aid medical diagnosis in the cerebral vascular accident.
Investigating and Modeling the Web Elements’ Visual Feature Influence on Free-viewing Attention

User attentional analyses on web elements help in synthesis and rendering of webpages. However, majority of the existing analyses are limited in incorporating the intrinsic visual features of text and images. This study aimed to analyze the influence of elements’ visual features (font-size, font-family, color, etc., for text; and brightness, color, intensity, etc., for images) besides their position on users’ free-viewing visual attention. The investigation includes: (i) user’s position-based attention allocation on text and image web elements, (ii) identification of informative visual features with respect to the attention, (iii) performance of informative visual features in predicting the ordinal visual attention (fixation-indices). Towards the study, an eye-tracking experiment was conducted with 42 participants on 36 real-world webpages. The analyses revealed: (i) Though users predominantly allocate the initial attention to MiddleCenter}, MiddleLeft, TopCenter, TopLeft regions, the elements in Right and Bottom regions are not completely ignored; (ii) Space -related (column-gap, line-height, padding) and font Size -related (font-size, font-weight) intrinsic text features, and Mid-level Color Histogram intrinsic image features are informative, while position and size are informative for both the types; (iii) the informative visual features predict the ordinal visual attention on an element with 90% average accuracy and 70% micro-F1 score. Our approach finds applications in element-granular web-designing and user attention prediction.
A blessing in disguise? Assessing the Relationship between Code Smells and Sustainability

Code smells have always been associated with bad code source quality and maintenance problems by the research community. However, a few code smells remain within code for years without influence a project, and some of the developers do not even recognize them. This late-breaking idea paper wants to investigate the role of code smells within the context of software quality and the sustainability of projects. For this reason, we first plan to understand the impact of code smells on sustainability and then perform a fine-grain analysis to analyze their evolution, focusing on the possible benefits of their presence.
Multi-hop Reading Comprehension Incorporating Sentence-Based Reasoning

Multi-hop machine reading comprehension (MRC) requires models to mine and utilize relevant information from multiple documents to predict the answer to a semantically related question. Existing work resorts to either document-level or entity-level inference among relevant information, which can be too coarse or too subtle, resulting less accurate understanding of the texts. To mitigate the issue, this research proposes a sentence-based multi-hop reasoning approach named SMR. SMR starts with sentences of documents, and unites the question to establish several reasoning chains based on sentence-level representations. In addition, to resolve the complication of pronouns on sentence semantics, we concatenate two sentences, if necessary, to assist in constructing reasoning chains. The model then synthesizes the information existed in all the reasoning chains, and predicts a probability distribution for selecting the correct answer. In experiments, we evaluate SMR on two popular multi-hop MRC benchmark datasets - WikiHop and MedHop. The model achieves 68.3 and 62.9 in terms of accuracy, respectively, exhibiting a remarkable improvement over state-of-the-art option. Additionally, qualitative analysis also demonstrates the validity and interpretability of SMR.
New technology interventions including blockchain technology in land record and registry management in India

The paper examines the adoption of technology interventions such as Geographic Information System (GIS) mapping and web enablement, integrated land management information systems, Ultra Resolution Unmanned Aerial Vehicles (UAV) based mapping for preparation and maintenance of maps for Land record management. It also covers a blockchain based architecture for Land record and registry management. Such architecture can enhance transparency, trust and traceability and increase efficiency of processes and is examined in more detail. The vulnerability of a centralized land management system is also discussed. The paper discusses different initiatives taken under the modernization of land records programme in three different states of India. It also examines the architectural and governance requirements, issues and challenges in introducing the new technology concepts to Land Record and Registry management domain.
Empowering Elementary Schools on Learning Math: A Development of Gamified Educational Mobile Application for Grade 3 Students

Mathematics is one of the essential subjects that need to be learned and apply in daily lives. But, it must admit that learning math is not easy especially in some children these days. Since most of the children today are exposed to mobile games, they spend most of their time playing than studying. That is why educational games are developed to help the children learn and play at the same time. The researchers developed the Math's Going On, an educational game application to empower elementary students' especially third graders in learning math. The game can be used as an additional tool for teaching mathematics as well as to identify how it will affect the learning style of children using technology. With its features, teachers can monitor the progress of the students, create quizzes and access games' website. The game is developed to further maximize the use of technology since it is slowly capturing the hearts of many children and students today. This study utilized ISO 9126 to evaluate the functionality, reliability, and usability of the system. Based on the evaluation, an overall excellent rating was obtained. Likewise, findings also showed that the students who played this mobile game obtained a significant scores in Math.
Adaptive Compositing and Navigation of Variable Resolution Images

Abstract We present a new, high‐quality compositing pipeline and navigation approach for variable resolution imagery. The motivation of this work is to explore the use of variable resolution images as a quick and accessible alternative to traditional gigapixel mosaics. Instead of the common tedious acquisition of many images using specialized hardware, variable resolution images can achieve similarly deep zooms as large mosaics, but with only a handful of images. For this approach to be a viable alternative, the state‐of‐the‐art in variable resolution compositing needs to be improved to match the high‐quality approaches commonly used in mosaic compositing. To this end, we provide a novel, variable resolution mosaic seam calculation and gradient domain color correction. This approach includes a new priority order graph cuts computation along with a practical data structure to keep memory overhead low. In addition, navigating variable resolution images is challenging, especially at the zoom factors targeted in this work. To address this challenge, we introduce a new image interaction for variable resolution imagery: a pan that automatically, and smoothly, hugs available resolution. Finally, we provide several real‐world examples of our approach producing high‐quality variable resolution mosaics with deep zooms typically associated with gigapixel photography.