Reviewerly/exHarmony huggingface dataset:
Colorization of Depth Map via Disentanglement

Vision perception is one of the most important components for a computer or robot to understand the surrounding scene and achieve autonomous applications. However, most of the vision models are based on the RGB sensors, which in general are vulnerable to the insufficient lighting condition. In contrast, the depth camera, another widely-used visual sensor, is capable of perceiving 3D information and being more robust to the lack of illumination, but unable to obtain appearance details of the surrounding environment compared to RGB cameras. To make RGB-based vision models workable for the low-lighting scenario, prior methods focus on learning the colorization on depth maps captured by depth cameras, such that the vision models can still achieve reasonable performance on colorized depth maps. However, the colorization produced in this manner is usually unrealistic and constrained to the specific vision model, thus being hard to generalize for other tasks to use. In this paper, we propose a depth map colorization method via disentangling appearance and structure factors, so that our model could 1) learn depth-invariant appearance features from an appearance reference and 2) generate colorized images by combining a given depth map and the appearance feature obtained from any reference. We conduct extensive experiments to show that our colorization results are more realistic and diverse in comparison to several image translation baselines.
Deep Energy Factorization Model for Demographic Prediction

Demographic information is important for various commercial and academic proposes, but in reality, few of these data are accessible for analysis and research. To solve this problem, several studies predict demographic attributes from users’ behavioral data. However, previous works suffer from different kinds of disadvantages. Handling data sparseness and defining useful features remain especially challenge tasks. In this article, we propose a novel Deep Energy Factorization Model to address these two drawbacks. The model is a designed network that performs multi-label classification and feature representation. Experiments are conducted on four datasets with four evaluation metrics. The empirical results show that our Deep Energy Factorization Model significantly outperforms state-of-the-art models.
Embedding-Augmented Generalized Matrix Factorization for Recommendation With Implicit Feedback

Learning effective representations of users and items is crucially important to recommendation with implicit feedback. Matrix factorization is the basic idea to derive the representations of users and items by decomposing the given interaction matrix. However, existing matrix factorization based approaches share the limitation in that the interaction between user embedding and item embedding is only weakly enforced by fitting the given individual rating value, which may lose potentially useful information. In this article, we propose a novel augmented generalized matrix factorization approach that is able to incorporate the historical interaction information of users and items for learning effective representations of users and items. Despite the simplicity of our proposed approach, extensive experiments on four public implicit feedback datasets demonstrate that our approach outperforms state-of-the-art counterparts. Furthermore, the ablation study demonstrates that by using the historical interactions to enrich user embedding and item embedding for generalized matrix factorization, better performance, faster convergence, and lower training loss can be achieved.
Bundled Causal History Interaction

Complex systems arise as a result of the nonlinear interactions between components. In particular, the evolutionary dynamics of a multivariate system encodes the ways in which different variables interact with each other individually or in groups. One fundamental question that remains unanswered is: How do two non-overlapping multivariate subsets of variables interact to causally determine the outcome of a specific variable? Here, we provide an information-based approach to address this problem. We delineate the temporal interactions between the bundles in a probabilistic graphical model. The strength of the interactions, captured by partial information decomposition, then exposes complex behavior of dependencies and memory within the system. The proposed approach successfully illustrated complex dependence between cations and anions as determinants of pH in an observed stream chemistry system. In the studied catchment, the dynamics of pH is a result of both cations and anions through mainly synergistic effects of the two and their individual influences as well. This example demonstrates the potentially broad applicability of the approach, establishing the foundation to study the interaction between groups of variables in a range of complex systems.
Proposing a conceptual model for cloud computing adoption in upstream oil &amp; gas sector

Cloud computing is a paradigm which offers IT services such as storage, network and processing power via the internet. The technology has gained popularity in recent years with adoption in different sectors due to the numerous benefits it offers such as scalability, flexibility and cost reduction. Although some are quick adopters, others are considered cautious adaptors. The upstream oil and gas industry fall under the latter category due to some challenges with regards to adoption decision. Migrating to a cloud platform depends on a number of factors. A clear understanding of these factors is necessary to enable decision makers in the industry to be more proactive and appropriately guided in their plan towards adoption. Therefore, this study aims to identify the factors that may influence cloud adoption in the industry. A literature review was conducted in order to propose an integrated model, which is a combination of the Technology environment organisation (TOE) framework, institutional theory, and diffusion of innovation. The model groups the factors into three fundamental categories. In addition, the study reports benefits of the cloud technology in the upstream oil and gas sector, challenges hindering adoption, as well as approaches by earlier researchers to support cloud migration in the industry.
A note on constituent parsing for Korean

Abstract This study deals with widespread issues on constituent parsing for Korean including the quantitative and qualitative error analyses on parsing results. The previous treebank grammars have been accepted as being interpretable in the various annotation schemes, whereas the recent parsers turn out to be much harder for humans to interpret. This paper, therefore, intends to find the concrete typology of parsing errors, to describe how these parsers deal with sentences and to show their statistical distribution, using state-of-the-art statistical and neural parsers. For doing this work, we train and evaluate the phrase structure Sejong treebank using statistical and neural parsing systems and obtain results up to a 89.18% F $_1$ score, which outperforms previous constituent parsing results for Korean. We also define best practices for correct comparison to future work by proposing the standard corpus division for the Sejong treebank.
Continuous practices and technical debt: a systematic literature review

Technical debt in software development is a common problem that is overlooked by many development teams. This debt can be generated from a variety of reasons, including time pressure and complexity in software. Technical debt in simple terms is when a simple and less optimized solution is carried out in order to gain short term benefits, which leads to refactoring and reworking code later on, costing both time and money. The issue is present in both big, established companies and small startups, and is the reason why many of these small startups never get enough economic grip before debt catch up and they go bankrupt. This paper aims to address this problem by exploring how continuous practices including DevOps could help resolve this issue by adopting the right approaches into the software development cycle and workflow. So as to collect information about these topics, a systematic literature review has been conducted, covering both positive and negative impacts these practices can have on technical debt. The findings will present the current practices used to manage and reduce the accumulation of technical debt, if and how these approaches can be used to reduce already existing technical debt and which of these practices that have the biggest impact on technical debt. The paper concludes that there's potential for continuous practices including DevOps to possibly reduce technical debt if applied appropriately.
MultiTune: Adaptive Integration of Multiple Fine-Tuning Models for Image Classification

Transfer learning has been widely used as a deep learning technique to solve computer vision related problems, especially when the problem is image classification employing Convolutional Neural Networks (CNN). In this paper, a novel transfer learning approach that can adaptively integrate multiple models with different fine-tuning settings is proposed, which is denoted as MultiTune. To evaluate the performance of MultiTune, we compare it to SpotTune, a state-of-the-art transfer learning technique. Two image datasets from the Visual Decathlon Challenge are used to evaluate the performance of MultiTune. The FGVC-Aircraft dataset is a fine-grained task and the CIFAR100 dataset is a more general task. Results obtained in this paper show that MultiTune outperforms SpotTune on both tasks. We also evaluate MultiTune on a range of target datasets with smaller numbers of images per class. MultiTune outperforms SpotTune on most of these smaller-sized datasets as well. MultiTune is also less computational than SpotTune and requires less time for training for each dataset used in this paper.
Dynamically improved bounds bidirectional search

This paper presents a bidirectional search algorithm that dynamically improves the bounds during its execution. It has the property that it always terminates on or before the forward search meets the backward search. Computational experiments on the pancake problem, the sliding tile puzzle, and the topspin problem demonstrate that it is capable of solving problems using significantly fewer node expansions than A⁎ or state-of-the-art bidirectional algorithms such as MMϵ and GBFHS.
Correlation of Single Nucleotide Gene Polymorphisms and Gastric Cancer Based on Magnetic Nanoparticles

Gastric cancer (GC) is a serious threat to the health and lives of people around the world. In China, the incidence and mortality of gastric cancer are much higher than the world average, coupled with its low early diagnosis rate, low survival rate, poor prognosis, and complex etiology, especially the serious lack of effective early warning methods, which has become the main constraint on the diagnosis and treatment of gastric cancer factor. Therefore, finding reliable, effective, and specific markers that can be applied to early warning and diagnosis of gastric cancer has been a hot issue in gastric cancer research. Magnetic nanoparticles are an ideal molecular carrier for gene separation because they have many advantages such as easy operation, fast, high efficiency, and non-destructive non-recognition biological entities. Changes in gene levels can detect the development of early diagnosis and treatment of prognosis in patients with gastric cancer by affecting susceptibility, clinical phenotype, and drug response. PcG protein can modify chromatin and affect tumorigenesis. The experimental results show that the introduction of magnetic nanoparticles can improve the sensing signal, detection sensitivity and gene differentiation. Combined with the latest magnetic nanoparticle technology to analyze the relationship between SNPs of some genes in the pathways involved in gastric cancer treatment and DNA specificity, screening and identifying specific SNP markers are helpful to the mechanism of gastric cancer development. Understand to achieve the purpose of individualized treatment. By introducing the RAS-BRAF gene on the surface of magnetic nanoparticles, the surface of the magnetic particles was biologically functionalized and used for the separation and detection of proteins and pathogens, respectively. The results show that the system has excellent detection sensitivity and separation selectivity. At present, the research results of susceptible genes screened by coding gene association studies are inconsistent. In this study, PLCE1 gene was found to be used as a DNA gene identification method through high expression of cells to analyze that polymorphisms are closely related to the incidence of gastric cancer. In addition, the study suggests that PLCE1 gene may be a susceptible gene for tumor cells. The signaling pathways involved in the regulation play an important role in tumorigenesis, development, migration, and apoptosis, and are closely related to disease prognosis. Therefore, at the gene level More analysis of the role of these genes in gastric cancer is needed.
Geobook: Mobile App with Augmented Reality for Learning Geometry

This study shows the results obtained after the application of augmented reality - AR in the learning of descriptive geometry in elementary level students. The use of Mobile-D methodology facilitated the rapid development of mobile applications with augmented reality called Geobook. The statistical sample involved two sections in 6th-grade elementary school students from a public school in Lima city, where the experimental group improved by 82.18% with respect to the learning levels of the control group. It is concluded that the use of an AR-based mobile application significantly enhances learning in three aspects of descriptive geometry, included in the experimental and test dimensions.
Online Structured Meta-learning.

Learning quickly is of great importance for machine intelligence deployed in online platforms. With the capability of transferring knowledge from learned tasks, meta-learning has shown its effectiveness in online scenarios by continuously updating the model with the learned prior. However, current online meta-learning algorithms are limited to learn a globally-shared meta-learner, which may lead to sub-optimal results when the tasks contain heterogeneous information that are distinct by nature and difficult to share. We overcome this limitation by proposing an online structured meta-learning (OSML) framework. Inspired by the knowledge organization of human and hierarchical feature representation, OSML explicitly disentangles the meta-learner as a meta-hierarchical graph with different knowledge blocks. When a new task is encountered, it constructs a meta-knowledge pathway by either utilizing the most relevant knowledge blocks or exploring new blocks. Through the meta-knowledge pathway, the model is able to quickly adapt to the new task. In addition, new knowledge is further incorporated into the selected blocks. Experiments on three datasets demonstrate the effectiveness and interpretability of our proposed framework in the context of both homogeneous and heterogeneous tasks.
Spectral Features based Speech Recognition for Speech Interfacing to Control PC Windows

In this research work Speech interfacing for computer control has been developed in MATLAB platform that enables physically disabled people also to gain access to computer. This speech interfacing enables users to execute common computer commands by spoken utterances and not by using standard input devices such as keyboards and mouse. The approach used, in this work for computer control is direct execution of recognized vocal command rather than the mouse movement control or the text matching of text between one from application menu and the other that is produced by speech recognition system. The work is based on PC Windows (windows 7), and is carried out in English language. In this work, recognition of speech is carried out by Gaussian Mixture Model (GMM). Mel Frequency Cepstral Coefficients (MFCC) are used for transferring input speech into spectral components. Optimum recognition is achieved with the performance comparison of the GMMs by varying the number of MFCC, Gausses (components) and iterations. Also, the average speech command recognition performance is observed to be 74.38 percent.
A New Construction of Nonlinear Codes via Rational Function Fields

It is well known that constructing codes with good parameters is one of the most important and fundamental problems in coding theory. Though a great many of good codes have been produced, most of them are defined over alphabets of sizes equal to prime powers. In this article, we provide a new explicit construction of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(q+1)$ </tex-math></inline-formula> -ary nonlinear codes via rational function fields, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> is a prime power. Our codes are constructed by evaluations of rational functions at all rational places (including the place of “infinity”) of the rational function field. Compared to the rational algebraic geometry codes, the main difference is that we allow rational functions to be evaluated at pole places. After evaluating rational functions from a union of Riemann-Roch spaces, we obtain a family of nonlinear codes with length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q+1$ </tex-math></inline-formula> over the alphabet <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q}\cup \{\infty \}$ </tex-math></inline-formula> . As a result, our codes have reasonable parameters as they are rather close to the Singleton bound. Furthermore, our codes have better parameters than those obtained from MDS codes via code alphabet restriction or extension. Amazingly, an efficient decoding algorithm can be provided for our codes.
Quadratic open quantum harmonic oscillator

We study the quantum open system evolution described by a Gorini–Kossakowski–Sudarshan–Lindblad generator with creation and annihilation operators arising in Fock representations of the $$\mathfrak {sl}_2$$ Lie algebra. We show that any initial density matrix evolves to a fully supported density matrix and converges towards a unique equilibrium state. We show that the convergence is exponentially fast and we exactly compute the rate for a wide range of parameters. We also discuss the connection with the two-photon absorption and emission process.
A Densely Connected Face Super-Resolution Network Based on Attention Mechanism

Super resolution reconstruction of human face is a cost effective way to obtain high resolution images from its corresponding low resolution face. It is also known as face illusion. In order to obtain clearer texture details, this paper proposes a densely connected super-resolution algorithm based on attention mechanism which consists of feature extraction and image reconstruction. By integrating channel and spatial domain information of the feature map, the Multi Attention Domain Module (MADM) is proposed: Features are weighted and recombined by analyzing the relationship between channels and spatial information of feature maps. The features of different layers are fused using dense connections. Experimental results show that the proposed algorithm can improve by up to 0.5dB in PSNR and the reconstructed face image has clearer texture details compared to existing algorithms.
Inverse square root level-crossing quantum two-state model

We introduce a new unconditionally solvable level-crossing two-state model given by a constant-amplitude optical field configuration for which the detuning is an inverse-square-root function of time. This is a member of one of the five families of bi-confluent Heun models. We prove that this is the only non-classical exactly solvable field configuration among the bi-confluent Heun classes, solvable in terms of finite sums of the Hermite functions. The general solution of the two-state problem for this model is written in terms of four Hermite functions of a shifted and scaled argument (each of the two fundamental solutions presents an irreducible combination of two Hermite functions). We present the general solution, rewrite it in terms of more familiar physical quantities and analyze the time dynamics of a quantum system subject to excitation by a laser field of this configuration.
Turkish handwriting recognition system using multi-layer perceptron

Recently, handwriting recognition has found many application areas along with technological advances. Handwriting recognition systems can greatly simplify human life by reading tax returns, forwarding mail, reading bank checks, and so on. On the other hand, these systems can reduce the need for human interaction. Therefore, academic and commercial studies of handwriting characters have recently become an important research topic in pattern recognition. In this study, Turkish handwritten letter recognition system from A to Z was developed in C++ environment by using Artificial Neural Networks (ANNs). After the feature data were extracted, handwriting images were presented to the network, the training process of ANN was completed, and different handwriting images were classified with trained ANN. In this study, MLP (Multi-Layered Perceptron: MLP) type ANN and back-propagation learning algorithm were used. The ANN used has 35 inputs and 23 outputs. In the hidden layer, ANNs with different numbers of artificial neural cells (neurons) were evaluated and the most appropriate neural number ANN was selected. As a result, ANN with 24 neurons was selected in the hidden layer and handwriting images was classified with an accuracy rate of 94.90 %.
SODA²:Salient Object Detection With Structure-Adaptive &amp; Scale-Adaptive Receptive Field

Salient objects with complex shapes and arbitrary sizes are generally hard to detect, especially in cluttered background and complex scenes. Noticing the deficiency of single-feature-based methods, recent methods fused multiple features from Deep Neural Networks (DNNs) and obtained better performance thanks to the hierarchical feature representations. However, existing works processed the features on each semantic level with convolutions of certain fixed scale and regular sampling locations, deteriorating the predicted salient objects with complex shapes and arbitrary sizes. To tackle these problems, in this article, we propose a novel dual-adaptive salient object detection algorithm with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">structure-adaptive</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scale-adaptive</i> receptive field (abbreviated as SODA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). In particularly, the deformable convolution is introduced to transform the kernels’ sampling locations by augmenting extra offsets. Upon the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">structure-adaptive</i> features on different semantic level, novel spatial context-aware modules (SCAMs) are devised to capture multi-scale contexts with dilated convolutions of deliberately designed dilation rates and kernel sizes. To further emphasize on saliency-relevant semantic information (e.g., humans), we proposed a region-of-interest-aware channel attention module (ROI-CAM) to redistribute the feature maps on different channels. Extensive experiments have been carried out on five challenging datasets and our algorithm performs favorably compared with state-of-the-art algorithms, validating the efficiency of the proposed models.