Reviewerly/exHarmony huggingface dataset:
Seismic Data Denoising Based on Sparse and Low-Rank Regularization

Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications. For the past ten years, there have mainly been two classes of methods for seismic denoising. One is based on the sparsity of seismic data. This kind of method can make use of the sparsity of seismic data in local area. The other is based on nonlocal self-similarity, and it can utilize the spatial information of seismic data. Sparsity and nonlocal self-similarity are important prior information. However, there is no seismic denoising method using both of them. To jointly use the sparsity and nonlocal self-similarity of seismic data, we propose a seismic denoising method using sparsity and low-rank regularization (called SD-SpaLR). Experimental results showed that the SD-SpaLR method has better performance than the conventional wavelet denoising and total variation denoising. This is because both the sparsity and the nonlocal self-similarity of seismic data are utilized in seismic denoising. This study is of significance for designing new seismic data analysis, processing and inversion methods.
Object-based attention generalizes to multisurface objects

When a part of an object is cued, targets presented in other locations on the same object are detected more rapidly and accurately than are targets on other objects. Often in object-based attention experiments, cues and targets appear not only on the same object but also on the same surface. In four psychophysical experiments, we examined whether the "object" of attentional selection was the entire object or one of its surfaces. In Experiment 1, facilitation effects were found for targets on uncued, adjacent surfaces on the same object, even when the cued and uncued surfaces were oriented differently in depth. This suggests that the "object-based" benefits of attention are not restricted to individual surfaces. Experiments 2a and 2b examined the interaction of perceptual grouping and object-based attention. In both experiments, cuing benefits extended across objects when the surfaces of those objects could be grouped, but the effects were not as strong as in Experiment 1, where the surfaces belonged to the same object. The cuing effect was strengthened in Experiment 3 by connecting the cued and target surfaces with an intermediate surface, making them appear to all belong to the same object. Together, the experiments suggest that the objects of attention do not necessarily map onto discrete physical objects defined by bounded surfaces. Instead, attentional selection can be allocated to perceptual groups of surfaces and objects in the same way as it can to a location or to groups of features that define a single object.
Deployment of Deep Learning Models to Mobile Devices for Spam Classification

The advent of deep learning brings the possibility of better and faster applications in real world. In this work, deep learning models are used for application of spam classification in mobile devices. A Binary Classification model is trained with deep learning and is transformed to a graph using tensorflow and then, is converted to a protobuf file to be deployed on mobile devices. Instead of looking into the spam messages in an algorithmic way i.e. just with keywords, binary model deals with experience of learning and predicts if a text message is spam. The training was performed multiple times on resource-deficient devices and hyper-parameter optimization was performed to enhance the training accuracy to 99.87 %. The test accuracy of mobile application is 98.7 % and testing happens in real-time without any internet access. Our simulation shows that a model with an embedding layer (size 128), an LSTM layer (size 64, dropout 0.2) and a dense layer (sigmoid) yields the highest performance. Also, the comparative evaluation with state-of-the-art methods displayed that our model achieves higher accuracy.
Research on Region Selection Strategy for Visible Watermark Embedding

Visible watermarking is one of the most direct and effective copyright protection methods for digital images and videos, but it faces the problems of easy detection of watermarking location and watermarking removal attacks. It requires that the visible watermarking not only can not affect visual quality of original digital products but also can resist accidental editing and malicious attacks. In this paper, an adaptive region selection method for visible watermark embedding is proposed. First, an improved region selection method based on the model of saliency-based visual attention (MSVA) is used to more accurately distinguish salient and non-salient regions of host image; then, the non-salient regions of image are divided into sub-blocks to calculate texture complexity respectively; finally, a moderate image block is selected as the embedding region of visible watermark image according to the distribution of image texture complexity. The experimental results show that the proposed method can identify saliency regions more accurately, thus ensuring that the important contents of host image will not be occluded. The adaptive selection of embedding region based on image complexity not only ensures the recognizability and aesthetics of visible watermark embedding effect but also takes into account the security of watermark embedding, and especially reduces the risk of batch removal attacks.
Overlays and Goggles and Projections, Oh My!

Augmented reality (AR) technologies have been available to the general public in varying formats for several years, but confusion remains about what AR actually is, and what it can do. This paper explores how well mental models of the general public align with the standing definitions of AR from an academic perspective. We also seek to understand whether individual experience with augmented reality technologies, or self-rated willingness to adopt new technologies, correlate with the accuracy of an individual's understanding of AR. A pilot survey asking participants to describe augmented reality revealed a variety of mental models, some of which aligned with academically defined characteristics of AR. The accuracy of the responses decreased in participants with no hands-on AR experience, and willingness to adopt new technology proved to have little to no influence on response accuracy. This paper presents some initial trends in public perceptions of augmented reality technologies, but also highlights the need for more research to establish a better understanding of mental models of AR.
Mining Adverse Drug Events Using Multiple Feature Hierarchies and Patient History Windows

We study the problem of detecting adverse drug events in electronic health records. The challenge is this work is to aggregate heterogeneous data types involving lab measurements, diagnoses codes and medications codes. An earlier framework proposed for the same problem demonstrated promising predictive performance for the random forest classifier by using only lab measurements as data features. We extend this framework, by additionally including diagnosis and drug prescription codes, concurrently. In addition, we employ the concept of hierarchies of clinical codes as proposed by another work, in order to exploit the inherently complex nature of the medical data. Moreover, we extended the state-of-the-art by considering variable patient history lengths before the occurrence of an ADE event rather than a patient history of an arbitrary length. Our experimental evaluation on eight medical datasets of adverse drug events, five different patient history lengths, and six different classifiers, suggests that the integration of these additional features on the different window lengths provides significant improvements in terms of AUC while employing medically relevant features.
Fuzzy Alternating Refinement Relations Under the Gödel Semantics

Refinement relations, such as trace containment, simulation preorder, and their alternating versions, have been successfully applied in formal verification of concurrent systems. Recently, trace containment and simulation preorder have been adopted and developed in fuzzy systems, but the generalization of their alternating versions to fuzzy systems has not been investigated. To satisfy the need for modeling and analyzing fuzzy systems, this article proposes two types of refinement relations called fuzzy alternating trace containment and fuzzy alternating simulation preorder, based on fuzzy concurrent game structures (FCGSs) under the Gödel semantics. These two fuzzy notions inherit properties from the corresponding classical setting. For example, fuzzy alternating simulation preorder for finite-state FCGSs can be computed in polynomial time; fuzzy alternating simulation preorder is a fuzzy subset of fuzzy alternating trace containment, and both relations can be logically characterized in terms of fuzzy version of alternating-time temporal logic. These properties make the theory developed here suitable for the modeling and verification of fuzzy systems.
Multiple premises entailment recognition based on attention and gate mechanism

Multi-premise natural language inference provides important technical support for automatic question answering, machine reading comprehension and other application fields. Existing approaches for Multiple Premises Entailment (MPE) task are to convert MPE data into Single Premise Entailment (SPE) data format, then MPE is handled in the same way as SPE. This process ignores the unique characteristics of multi-premise, which will result in loss of semantics. This paper proposes a mechanism based on Attention and Gate Fusion Network (AGNet). AGNet adopts a “Local Matching-Integration” strategy to consider the characteristics of multi-premise. In this process, an attention mechanism combined with a matching gate mechanism can fully describe the relationship between the premise and hypothesis. A self-attention mechanism and a fusion gate mechanism can deeply exploit the relationship from the multi-premise. In order to avoid over-fitting problem, we propose a pre-training method for our model. In terms of computational complexity, AGNet has good parallelism, reduces the time complexity to O(1) in the process of matching. The experiments show that our model has achieved new state-of-the-art results on MPE test set.
Searching Region of Interest from News Website using Steepest Ascent Hill Climbing Algorithm

Extracting content from online news websites, typically known as web scraping, is challenging due to the variety of presentation layout employed by those websites. To facilitate data collection from these websites, this paper introduces a method to correctly identify a Region of Interest (ROI) of a news article by using web scraping techniques. A ROI should consists of title, content and main picture. A search algorithm based on the Steepest Ascent Hill Climbing (SAHC) was employed by define a heuristic function from common features in the HTML tag of a news article. There were five features that are used in the heuristic function, namely heading 1 (h1), image (img), line break (br), paragraph (p), and the length of textual content. The proposed SAHC achieved a success rate of 88,9% for searching a ROI in a news article. We also identified several limitations of our algorithm, especially when a feature did not exist or when multiple similar features did exist.
Factor graph fragmentization of expectation propagation

Expectation propagation is a general approach to fast approximate inference for graphical models. The existing literature treats models separately when it comes to deriving and coding expectation propagation inference algorithms. This comes at the cost of similar, long-winded algebraic steps being repeated and slowing down algorithmic development. We demonstrate how factor graph fragmentization can overcome this impediment. This involves adoption of the message passing on a factor graph approach to expectation propagation and identification of factor graph sub-graphs, which we call fragments, that are common to wide classes of models. Key fragments and their corresponding messages are catalogued which means that their algebra does not need to be repeated. This allows compartmentalization of coding and efficient software development.
Path Planning for Differential Drive Mobile Robot to Avoid Static Obstacles Collision using Modified Crossover Genetic Algorithm

Autonomous mobile robots such as differential drive mobile robots really need an intelligent navigation system that is able to avoid collisions with any obstacle from the starting point to the destination point. These collisions can be avoided by planning a collision-free path first. This study uses a modified genetic algorithm method to planning collision-free paths with static obstacles. Modification of the genetic algorithm is carried out on the crossover process by making rules that the fitness value of the progeny should be compared with fitness value of the parents. The chromosome with the best fitness value will be taken and used for the mutation process. Meanwhile the chromosome with poor fitness value will be completely ignored. Several experiments were carried out to achieve the desired criterion such as shortest path and fast execution time. The simulation result shows that the modified crossover genetic algorithm is able to generate optimal solutions based on the desired criterion.
Accelerating Fourier–Motzkin elimination using bit pattern trees

The paper concerns the elimination of a set of variables from a system of linear inequalities. We employ the widely used Fourier–Motzkin elimination method extended with the Chernikov rules. A straightforward implementation of the algorithm results in extensive enumeration during the most computationally demanding stage. We propose a new way of checking Chernikov rules using bit pattern trees as an accelerating data structure to avoid extensive enumeration. The bit pattern tree is a data structure based on k-d tree used to accelerate the double description method. First we describe an adaptation of that approach to check the second Chernikov rule in Fourier–Motzkin elimination. We also propose a new algorithm that employs bit pattern trees to accelerate both Chernikov rules. Presented results of computational evaluation prove competitiveness of the proposed algorithms.
On designing MWIR and visible band based DeepFace detection models

In this work, we propose an optimal solution for face detection when operating in the thermal and visible bands. Our aim is to train, fine tune, optimize and validate preexisting object detection models using thermal and visible data separately. Thus, we perform an empirical study to determine the most efficient band specific DeepFace detection model in terms of detection performance. The original object detection models that were selected for our study are the Faster R-CNN (Region based Convolutional Neural Network), SSD (Single-shot Multi-Box Detector) and R-FCN (Region-based Fully Convolutional Network). Also, the dual-band dataset used for this work is composed of two challenging MWIR and visible band face datasets, where the faces were captured under variable conditions, i.e. indoors, outdoors, different standoff distances (5 and 10 meters) and poses. Experimental results show that the proposed detection model yields the highest accuracy independent of the band and scenario used. Specifically, we show that a modified and tuned Faster R-CNN architecture with ResNet 101 is the most promising model when compared to all the other models tested. The proposed model yields accuracy of 99.2% and 98.4% when tested on thermal and visible face data respectively. Finally, while the proposed model is relatively slower than its competitors, our further experiments show that the speed of this network can be increased by reducing the number of proposals in RPN (Region Proposal Network), and thus, the computational complexity challenge is significantly minimized.
Machining accuracy detection of PCB hole by X-ray micro-CT

Over the past few years, non-destructive testing plays an increasingly important role in the accuracy detection of PCB manufacturing. Due to the complexity of PCB materials and the development of PCB towards high density. So far, no method has been described which can semi-automatically detect the machining accuracy of PCB array holes based on three-dimensional structural data. Here, we propose an efficient method to detect the machining accuracy of PCB array holes by X-ray micro-CT. Firstly, the overall three-dimensional structural data of the sample was obtained by CT scanning and three-dimensional reconstruction. And then all the information of array holes could be extracted and visual analyzed. Finally, the measured values of PCB machining accuracy were gained according to the designed algorithms, such as straightness error, process capability index, cylindricity error and three-dimensional surface roughness. The method that based on X-ray micro-CT can get the results of PCB array holes' machining accuracy precisely and efficiently. The same principle also provides a direction for promoting the development of micro-CT in PCB industry.
The 2D Dependency Pair Framework for Conditional Rewrite Systems—Part II: Advanced Processors and Implementation Techniques

Proving termination of programs in ‘real-life’ rewriting-based languages like CafeOBJ, Haskell, Maude, etc., is an important subject of research. To advance this goal, faithfully capturing the impact in the termination behavior of the main language features (e.g., conditions in program rules) is essential. In Part I of this work, we have introduced a 2D Dependency Pair Framework for automatically proving termination properties of Conditional Term Rewriting Systems. Our framework relies on the notion of processor as the main practical device to deal with proofs of termination properties of conditional rewrite systems. Processors are used to decompose and simplify the proofs in a divide and conquer approach. With the basic proof framework defined in Part I, here we introduce new processors to further improve the ability of the 2D Dependency Pair Framework to deal with proofs of termination properties of conditional rewrite systems. We also discuss relevant implementation techniques to use such processors in practice.
Fallacies and Pitfalls on the Road to DevOps: A Longitudinal Industrial Study

DevOps has come into play to help companies in improving their product delivery. This paper offers an overview of the fallacies and pitfalls faced in this context by engineers and operators in an industrial case-study. We reveal a total of 8 key fallacies and pitfalls that span the organisational structure, technical structures, as well as software process and delivery mechanisms in the target case-study. Practitioners can use these challenges as references for diagnosing their own scenario while planning their own potential DevOps process migration strategy.
Homomorphic Encryption: Review and Applications

In a recent decade, homomorphic encryption became more popular because of its special properties. Homomorphic encryption is identified to be the key candidate for providing security on the cloud data. The cloud-based applications and service providers have given an extreme importance to homomorphic encryption when compared to other security techniques. In this work, an effort is made to present an overview of the homomorphic encryption and its different types. At a broad level, the various potential applications of homomorphic encryption are discussed to demonstrate the importance of homomorphic encryption in the various areas.
Insights from a Bibliometric Analysis of Vividness and Its Links with Consciousness and Mental Imagery

We performed a bibliometric analysis of the peer-reviewed literature on vividness between 1900 and 2019 indexed by the Web of Science and compared it with the same analysis of publications on consciousness and mental imagery. While we observed a similarity between the citation growth rates for publications about each of these three subjects, our analysis shows that these concepts rarely overlap (co-occur) in the literature, revealing a surprising paucity of research about these concepts taken together. A disciplinary analysis shows that the field of Psychology dominates the topic of vividness, even though the total number of publications containing that term is small and the concept occurs in several other disciplines such as Computer Science and Artificial Intelligence. The present findings suggest that without a coherent unitary framework for the use of vividness in research, important opportunities for advancing the field might be missed. In contrast, we suggest that an evidence-based framework (such as the bibliometric analytic methods as exemplified here) will help to guide research from all disciplines that are concerned with vividness and help to resolve the challenge of epistemic incommensurability amongst published research in multidisciplinary fields.
A Comprehensive Review on Unsupervised Feature Selection Algorithms

Technological advancements have generated huge amount of data in recent times. The objective is to analyze this data to get meaningful insights from them. One of the techniques is to perform clustering, an unsupervised learning technique. The performance of clustering is dependent on the size of data (number of features, number of records), a well-known fact. Feature selection/extraction algorithms not only eliminate irrelevant features from the data but also improve the performance of the clustering algorithm applied. Knowing your data sets (type, nature, and size), the appropriate algorithm to use and the appropriate evaluation metrics that will be insightful to improve the quality of clusters is important. No such correlation has been established as per author’s knowledge. In this paper, we report the results of various internal and external evaluation metrics on application of distinct unsupervised feature selection/extraction algorithms on a variety of data sets. The output of various algorithms indicates that retaining 100 features give best results. The experiments prove that Principal Component Analysis (PCA) algorithm is suitable for linear and low-dimensional data sets. Laplacian Score, Non-negative Discriminative Feature Selection (NDFS), and Spectral Feature Selection (SPEC) work well with high dimensional, binary data sets. Low variance algorithm and Unsupervised Discriminative Feature Selection (UDFS) do not perform well on any type of data sets.
MacWilliams type identities for linear codes on certain pomsets: Chain, direct and ordinal sum of pomsets

For codes over Zm, pomset metric is a generalization to poset metric, in some sense. We define pomset weight enumerator of a code C and establish MacWilliams type identities for linear codes with respect to certain pomsets. The identities for a particular type of linear codes (those that can be described as direct sum of linear codes) are established by considering direct and ordinal sum of pomsets on them. By induction, this result is extended for more than two pomsets. Moreover, MacWilliams type identities for any linear code with chain pomset is derived which paved a way to arrive at those identities for direct sum of codes with disjoint union of chains.
Errors and Poor Practices of Software Engineering Students in Using Git

Git is one of the most popular version control systems used by software developers to work collaboratively. Using Git in an educational context allows students to gain skills for real-life projects but they also face challenges when interacting with Git. In this paper, we present a list of errors and poor practices obtained through a focus group study with teaching staff. This list can be used to improve the learning experience and to help teach Git in a more efficient way. The list includes many more items, and has a wider coverage of the elements of collaborative software development, than other studies in this field.