Reviewerly/exHarmony huggingface dataset:
Local-linear-fitting-based matting for joint hole filling and depth upsampling of RGB-D images

We propose an approach for jointly filling holes and upsampling depth information for RGB-D images captured with common acquisition systems, where RGB color information is available at all pixel locations whereas depth information is only available at lower resolution and entirely missing in small regions referred to as “holes.” Depth information completion is formulated as a minimization of an objective function composed of two additive terms. The first data fidelity term penalizes disagreement with the observed low-resolution data. The second regularization term penalizes weighted depth deviations from a local linear model in spatial coordinates, where the weights are experimentally determined to ensure consistency between the RGB color image and the estimated depth image. Analogous to techniques used for optimization formulations of image matting, the completed depth image is then obtained by solving a large sparse linear system of equations. We also propose a memory-efficient implementation of the proposed method based on the conjugate gradient method. Visual evaluation of results obtained with the proposed algorithm demonstrates that the method provides high-resolution depth maps that are consistent with the color images. Furthermore, the memory-efficient implementation significantly reduces memory requirements, allowing for computation of the upsampled, hole-filled depth maps for typical RGB-D images on normal workstation hardware. Quantitative comparisons demonstrate that the method offers an improvement in accuracy over the current state-of-the-art techniques for depth information completion. Importantly, statistical analysis, which we present in this paper, also reveals that prior evaluations of depth upsampling accuracy are potentially biased because the evaluations inappropriately used preprocessed hole-filled data as “ground truth.” An implementation of the proposed algorithm can be accessed and executed through Code Ocean: https://codeocean.com/capsule/5103691/tree/v1.
Peppy

Over the years, researchers have found an increase in learning problem among young children especially related to writing. Among these is a drop in dexterity or developmental dysgraphia due to a lack of fine motor skills. Also discovered is that handwritten activities on paper help work out these problems and with the advancement in technology in today's day and age, better combative measures can be taken against these problems. With Peppy, a mobile application using augmented reality (AR), our aim is to fight these problems using technology as well as paper by augmenting it into something that is both interactive and useful for child development. Although educational AR applications already exist, they don't focus on improving children's fine motor skills using paper-based exercises. Peppy brings enjoyable, thought-provoking and intriguing paper prototypes consisting of colouring, games, and puzzles to life through AR.
Investigation based on quantified spatial relationships between gold deposits and ore genesis factors in northeast Malaysia

This research demonstrates an effective procedure for assessing the impact of ore-controlling factors of orogenic gold. In this paper, ore genesis factors such as heat source, migration pathways and depositional environment were investigated. The procedure subsequently applied the weight of evidence model to weigh and quantify the spatial relationship between the control factors and 30 known gold deposits in Kelantan, Malaysia. The results show a strong correlation pattern between the deposits and the considered factors such as the NE-SW faults, the intrusive rocks and the sedimentary rocks. The NW-SE fault shows a weak correlation pattern with the deposits.
An Efficient Index for RDF Query Containment

Query containment is a fundamental operation used to expedite query processing in view materialisation and query caching techniques. Since query containment has been shown to be NP-complete for arbitrary conjunctive queries on RDF graphs, we introduce a simpler form of conjunctive queries that we name f-graph queries. We first show that containment checking for f-graph queries can be solved in polynomial time. Based on this observation, we propose a novel indexing structure, named mv-index, that allows for fast containment checking between a single f-graph query and an arbitrary number of stored queries. Search is performed in polynomial time in the combined size of the query and the index. We then show how our algorithms and structures can be extended for arbitrary conjunctive queries on RDF graphs by introducing f-graph witnesses, i.e., f-graph representatives of conjunctive queries. F-graph witnesses have the following interesting property, a conjunctive query for RDF graphs is contained in another query only if its corresponding f-graph witness is also contained in it. The latter allows to use our indexing structure for the general case of conjunctive query containment. This translates in practice to microseconds or less for the containment test against hundreds of thousands of queries that are indexed within our structure.
A Modified Generation of S-Box for Advanced Encryption Standards

Data security is one of the pressing issues that needs to be addressed in this data-driven time. Enhancing techniques in data security can prevent attackers in launching attacks. AES is one of many cryptography tools that can be used in data security. S-box is one of the key parts and the only nonlinear part of AES. The paper enhanced S-box generation by combining its original S-box and an S-box generated by Logistic Map. Evaluation using avalanche effect shows that the results of encrypted texts using the modified S-box is 51% on average, better than 48% on average of the original.
Document Model with Attention Bidirectional Recurrent Network for Gender Identification

Author profiling is an important statistical and semantic processing task Author profiling is an important statistical and semantic processing task in the field of natural language processing (NLP). It refers to the extraction of information from author's texts such as gender, age and other kinds of personality traits. Author profiling can be applied in various fields like marketing, security and forensics. In this work, we explore how bi-directional deep learning architectures can be used to learn the abstract and higher-level features of the document, which could be employed to identify the author's gender. To deal with this, we extend Bidirectional Long Short-Term Memory Networks Language Models with an attention mechanism. The originality of our approach lays in its ability to capture the most important semantic information in a sentence. The experimental results on Facebook and twitter corpus show that our method outperformed the majority of the existing methods.
Exploration vs. Data Refinement via Multiple Mobile Sensors

We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we propose a general framework that makes value-laden decisions for the trajectory of mobile sensors. The framework employs a Gaussian process regression model to predict the distribution of the physical quantity of interest at unseen locations. Then, the decision-making on the trajectories of sensors is performed using an epistemic utility controller. An example is provided to illustrate the merit and applicability of the proposed framework.
A new Robust Heterogeneous Multi-Robot Approach Based on Cloud for Task Allocation

In the field of multi-robots, various research works have addressed the problem of coordination and task division between robots. Coordination in a multi-robot system is necessary when they collaborate to perform a given task or simultaneously share the same resources. The coordination aims to ensure that each robot in the multi-robot system (MRS) performs its tasks safely. As well as a good method of distribution and assignment of tasks must be effective, reliable and well-defined. In this work, we will present a conceptual architecture, that ensures coordination between the entire team of heterogeneous robots and then a new strategy based on artificial intelligence algorithms to assign tasks for a heterogeneous MRS in the optimal way.
Optimization of Convolutional Neural Network Ensemble Classifiers by Genetic Algorithms

Breast cancer exhibits a high mortality rate and it is the most invasive cancer in women. An analysis from histopathological images could predict this disease. In this way, computational image processing might support this task. In this work a proposal which employes deep learning convolutional neural networks is presented. Then, an ensemble of networks is considered in order to obtain an enhanced recognition performance of the system by the consensus of the networks of the ensemble. Finally, a genetic algorithm is also considered to choose the networks that belong to the ensemble. The proposal has been tested by carrying out several experiments with a set of benchmark images.
A New Strategy Based on GSABAT to Solve Single Objective Optimization Problem

This article proposes a new strategy based on a hybrid method that combines the gravitational search algorithm (GSA) with the bat algorithm (BAT) to solve a single-objective optimization problem. It first runs GSA, followed by BAT as the second step. The proposed approach relies on a parameter between 0 and 1 to address the problem of falling into local research because the lack of a local search mechanism increases intensity search, whereas diversity remains high and easily falls into the local optimum. The improvement is equivalent to the speed of the original BAT. Access speed is increased for the best solution. All solutions in the population are updated before the end of the operation of the proposed algorithm. The diversification feature of BAT has solved the problem of weakness in diversity observed in the algorithm by applying the parameters used in BAT. Moreover, balance is achieved through the intensification properties of the algorithms.
GraphWrangler

Existing data stores of enterprises are full of connected data and users are increasingly finding value in performing graph querying, analytics and visualization on this data. This process involves a labor-intensive ETL pipeline, where users write scripts to extract graphs from data stored in legacy stores, often an RDBMS, and import these graphs into a graph-specific software. We demonstrate GraphWrangler, a system that allows users to connect to an RDBMS and within a few clicks extract graphs out of their tabular data, visualize and explore these graphs, and automatically generate scripts for their ETL pipelines. GraphWrangler adopts the predictive interaction framework and internally uses a data transformation language that is a limited subset of SQL. Our demonstration video can be found here: https://youtu.be/k92Qk6vuIsU
A Teaching Experience of the Human-Computer Interaction Course in a Master Program

Learning-by-doing is an educational theory which establishes that the students learn in a more effective way if they are involved in experiences of the real world instead of passively listening. In the HCI course of the Master Program in Informatics, of the Pontifical Catholic University of Peru, the professors proposed the active-learning approach for the teaching of several topics related to HCI such as usability, user-centered design, human factors, accessibility, ergonomics and internationalization that are nowadays relevant concepts for the design process of graphical user interfaces. For this purpose, we received the committed collaboration of a local financial entity, the BBVA Continental Bank, which voluntarily agreed to establish a set of requirements for the design of GUIs that support the implementation of new features in ATMs. This proposed exercise has allowed the students to apply what they learned in real work situations, and to consolidate the discussed concepts from practice.
Designing to Support Teen Mental Health Using Asynchronous Online Groups

The majority of teenagers experience high levels of stress and technology can help increase access and engagement for mental health support. While it is important to incorporate teens' input in designing for their mental health, it is challenging to involve teens in design research due to constraints in access and time burden on teens. Researchers have feasibly used the method of Asynchronous Remote Communities (ARC) to involve adults in design research on private online groups. In my dissertation, I aim to use the ARC method to (1) understand the design needs of teens and clinicians to support stress management and (2) develop and evaluate technology probes based on evidence-based support for stress management. My work will contribute to the research and design of future technologies to scaffold teens in learning healthy coping strategies for their mental wellbeing.
HyPar: A divide-and-conquer model for hybrid CPU–GPU graph processing

Efficient processing of graph applications on heterogeneous CPU–GPU systems require effectively harnessing the combined power of both the CPU and GPU devices. This paper presents HyPar, a divide-and-conquer model for processing graph applications on hybrid CPU–GPU systems. Our strategy partitions the given graph across the devices and performs simultaneous independent computations on both the devices. The model provides a simple and generic API, supported with efficient runtime strategies for hybrid executions. The divide-and-conquer model is demonstrated with five graph applications and using experiments with these applications on a heterogeneous system it is shown that our HyPar strategy provides equivalent performance to the state-of-art, optimized CPU-only and GPU-only implementations of the corresponding applications. When compared to the prevalent BSP approach for multi-device executions of graphs, our HyPar method yields 74%–92% average performance improvements.
Handwritten Signature Authentication using Color Coherence Vector and Signing Behavior

Nowadays, many applications are constructed for supporting the daily human activities. Authentication is a crucial process to access the sensitive data on any applications. This study, we developed a method to verify user identity by using handwritten signature. Color Coherence Vector (CCV) and signing behavior parameters with the stylus pen on android smartphones are used. The experimental results showed that the verification by using signing behavior parameters and image processing based on CCV gives authentication accuracy level of 94%. The verification by signing behavior parameters improves efficiency to detect the mimic handwritten signature.
Implementation of Object Tracking Augmented Reality Markerless using FAST Corner Detection on User Defined-Extended Target Tracking in Multivarious Intensities

Abstract This paper presents a FAST Corner Detection object scanning to improve SLAM technique, which is SLAM, has a weakness in extracting features in the real-world object. The use of User Defined Target and Extended Tracking are for making this work more convenient and reliable. We can trace the object even though the object does not exist, so this improves the function of markerless itself. The use of Raycast is for the make labeling the objects or features in the scanned object. In this research, we executed multivarious intensity to test the FAST Corner Detection to increase function in real world feature extraction and prove it better than SLAM. Then, we got the result where is a brighter condition will get faster recognition. The best environments for augmentation are in the range of 80-190, they took in less than 1 second. On the contrary, the intensity outside of the range such as ≤50 or ≥200, has a deficiency of augmentation. The range of ≤50, there was no augmentation cause of low intensity. For the range of ≥200, we haven’t made measurements as we don’t have the resources yet, but we hypothesize that the object would be corrupt or we may call it was overexposure cause of the intensity is too high. So, this could also lead to augmentation will not occur.
C <sup>2</sup> Metadata

Datasets are often derived by manipulating raw data with statistical software packages. The derivation of a dataset must be recorded in terms of both the raw input and the manipulations applied to it. Statistics packages typically provide limited help in documenting provenance for the resulting derived data. At best, the operations performed by the statistical package are described in a script. Disparate representations make these scripts hard to understand for users. To address these challenges, we created Continuous Capture of Metadata (C2Metadata), a system to capture data transformations in scripts for statistical packages and represent it as metadata in a standard format that is easy to understand. We do so by devising a Structured Data Transformation Algebra (SDTA), which uses a small set of algebraic operators to express a large fraction of data manipulation performed in practce. We then implement SDTA, inspired by relational algebra, in a data transformation specification language we call SDTL. In this demonstration, we showcase C2metadata's capture of data transformations from a pool of sample transformation scripts in at least two languages: SPSS and Stata (SAS and R are under development), for social science data in a large academic repository. We will allow the audience to explore C2Metadata using a web-based interface, visualize the intermediate steps and trace the provenance and changes of data at different levels for better understanding of the process.
Arbitrary Length Perfect Integer Sequences Using All-Pass Polynomial

A novel method is proposed to construct arbitrary length perfect integer sequences based on all-pass polynomial. It uses a fact that associative sequence of an all-pass polynomial is guaranteed to be perfect. In addition, geometric series method is our special case. Moreover, perfect Gaussian integer sequences can also be constructed. Furthermore, this approach can be applied to symmetric perfect sequences design. Finally, perfect sequence with low degree of freedom can be achieved by the difference set. Concrete examples are illustrated.
Deviance information criteria for mixtures of distributions

As a Bayesian criterion for model comparison, Spiegelhalter et al. proposed the deviance information criterion (DIC) which consists of two parts: a classical estimate of fit and an effective number of parameters. This model comparison method is based on generalized linear models, and it may be inappropriate to be used for comparison in the case of mixture of distributions mainly due to the label switching and multimodality issues. For this purpose, Celeux et al. proposed several modified DIC constructions and assessed their behaviors under a mixture of distributions, however they did not fully explore the properties of alternative DICs. Here, we study and provide the properties of DIC3, one of the variations Celeux et al. proposed, and propose our modified DIC to lessen the issue raised by using the dataset twice. We compare our proposed criterion to other model selection criteria based on two numerical examples, the Galaxy dataset and the simulated dataset.
Multi-input CNN for Text Classification in Commercial Scenarios

In this work we describe a multi-input Convolutional Neural Network for text classification which allows for combining text preprocessed at word level, byte pair encoding level and character level. We conduct experiments on different datasets and we compare the results obtained with other classifiers. We apply the developed model to two different practical use cases: (1) classifying ingredients into their corresponding classes by means of a corpus provided by Northfork; and (2) classifying texts according to the English level of their corresponding writers by means of a corpus provided by ProvenWord. Additionally, we perform experiments on a standard classification task using Yahoo! Answers and GermEval2017 task A datasets. We show that the developed architecture obtains satisfactory results with these corpora, and we compare results obtained for each dataset with different state-of-the-art approaches, obtaining very promising results.
A flexible probabilistic framework for large-margin mixture of experts

Mixture-of-Experts (MoE) enable learning highly nonlinear models by combining simple expert models. Each expert handles a small region of the data space, as dictated by the gating network which generates the (soft) assignment of input to the corresponding experts. Despite their flexibility and renewed interest lately, existing MoE constructions pose several difficulties during model training. Crucially, neither of the two popular gating networks used in MoE, namely the softmax gating network and hierarchical gating network (the latter used in the hierarchical mixture of experts), have efficient inference algorithms. The problem is further exacerbated if the experts do not have conjugate likelihood and lack a naturally probabilistic formulation (e.g., logistic regression or large-margin classifiers such as SVM). To address these issues, we develop novel inference algorithms with closed-form parameter updates, leveraging some of the recent advances in data augmentation techniques. We also present a novel probabilistic framework for MoE, consisting of a range of gating networks with efficient inference made possible through our proposed algorithms. We exploit this framework by using Bayesian linear SVMs as experts on various classification problems (which has a non-conjugate likelihood otherwise generally), providing our final model with attractive large-margin properties. We show that our models are significantly more efficient than other training algorithms for MoE while outperforming other traditional non-linear models like Kernel SVMs and Gaussian Processes on several benchmark datasets.