Reviewerly/exHarmony huggingface dataset:
Subspace Clustering and Feature Extraction Based on Latent Sparse Low-Rank Representation

Robust recovery of multiple subspace structures from high-dimensional data with noise has received considerable attention in computer vision and pattern recognition. Low-Rank Representation (LRR) as a typical method has made satisfactory results in subspace clustering. Latent Low-Rank Representation (LLRR) is an advanced version of LRR, which considers the row and column of data to solve the insufficient samples problem. However, they fail to exploit the local structures of data. To address this problem, Latent Sparse Low-Rank Representation (LSLRR) is proposed to capture the local and global structures of data by considering sparse and low-rank constraints simultaneously. In this way, LSLRR not only solves the clustering problem, but also extracts significant features for classification. Inexact Augmented Lagrange Multiplier method (IALM) is utilized to solve its objective function. Experimental results in subspace clustering and salient features extraction demonstrate the proposed LSLRR have a favorable performance.
Quantum correlation dynamics of two qubits in independent reservoirs with initial system-reservoir correlations

The dynamics of entanglement and quantum discord (QD) between two two-level atoms interacting with two dissipative coupled cavities in the presence of initial atom-cavity correlations is investigated. In comparison with the result of the initial factorized state, we show that the initial state contained quantum correlation of atom-cavity is most robust against the dissipative environment, and the initial atom-cavity correlations, especially the quantum correlation, play a constructive role in the generation of atomic entanglement and QD. Simultaneously, the comparison between Markovian and non-Markovian dynamics, and the influences of inter-cavity hopping rate are also taken into account and analyzed.
A Scalable Data Augmentation and Training Pipeline for Logo Detection

Logo detection in images is particularly challenging due to limited access to well-labelled data. Many existing logo detection methods are not scalable to larger datasets due to tedious bounding-box annotation work. As a result, with only a small number of logo classes and limited well-labelled images per class, their performance deteriorates on real-world applications. In this work, we propose a data augmentation and training pipeline to tackle these challenges. Specifically, we develop an incremental learning approach that starts training using synthetic data, followed by iteratively obtaining real training images from a given source and updating the current model with the newly-obtained data. To avoid model drift, we add a human curation step where incorrect detections (false-positives) are filtered out by simple-clicks using a User Interface, we designed. With this approach, we were able to generate a large (173,000 images of 173 logo classes) dataset termed Logo173 where all images are annotated with bounding-boxes. This image dataset can also be used to train a frame-by-frame baseline logo detector for videos. We demonstrate with extensive experiments that the proposed pipeline significantly saves time and effort for tedious data annotation and outperforms a current state-of-the-art logo detection method.
Parameterizing higher-order processes on names and processes

Parameterization extends higher-order processes with the capability of abstraction and application (like those in lambda-calculus). As is well-known, this extension is strict, meaning that higher-order processes equipped with parameterization are strictly more expressive than those without parameterization. This paper studies strictly higher-order processes ( i.e. , no name-passing) with two kinds of parameterization: one on names and the other on processes themselves. We present two main results. One is that in presence of parameterization, higher-order processes can interpret first-order (name-passing) processes in a quite elegant fashion, in contrast to the fact that higher-order processes without parameterization cannot encode first-order processes at all. We present two such encodings and analyze their properties in depth, particularly full abstraction. In the other result, we provide a simpler characterization of the standard context bisimilarity for higher-order processes with parameterization, in terms of the normal bisimilarity that stems from the well-known normal characterization for higher-order calculus. As a spinoff, we show that the bisimulation up-to context technique is sound in the higher-order setting with parameterization.
Intelligent Monitoring and Simulation of the Whole Life Cycle of Coal Mining Process and Water Quantity and Quality in Coal and Water Coordinated Development Under Large Data

Coal is China’s main energy, and this situation in the future in a long period of time is unlikely to have fundamental changes. As an underground activity, coal mining inevitably causes local damage and pollution to underground water cut system due to improper application of coal mining technology. And a large number of mine drainage, not only need to spend a lot of drainage costs, increase the cost of coal production, but also waste very valuable water resources. In this paper, based on the water - coal, on the basis of coordination development, through big data analysis methods, proposes the establishment process of coal mining and mine water quality, and the whole life cycle of the coal - water coordination development precision control technology system, through a series of research on the laws of the evolution of time and space and nonlinear multiobjective model building and simulation development plan, research and development of the evaluation method of mine water resource utilization efficiency and benefit, help to realize the coordinated development of the coal - water control in whole life cycle of technology. So as to realize the exploitation and utilization of mine drainage and turn waste into treasure, it can not only relieve the tight situation of water supply in coal mines, but also reduce the environmental pollution caused by coal mining to the mining areas and surrounding cities, and realize the unity of economic and environmental benefits.
A Real-Time Multimedia Data Quality Assessment Framework

Nowadays, social media runs a significant portion of people's daily lives. Millions of people use social media applications to share photos. The huge volume of images shared on social media presents serious challenges and requires large computational infrastructure to ensure successful data processing. However, image gets distorted somehow during the processing, transmission, sharing or from a combination of many factors. So, there is a need to guarantee an acceptable delivery content, especially for image processing applications. In this paper, we present a framework developed to process a large amount of images in real-time while estimating the image quality. Our quality evaluation is measured based on two methods: Perceptual Coherence Measure and Structural Similarity Index. A set of experiments is conducted to evaluate our proposed approach.
RGB-D Camera Based Collision Prediction and Avoidance for X-ray Rotational Angiography

Optimal clinical workflow leads to faster treatment times and a potential to cater to a larger number of patients. A key part of this is preventing collisions between moving medical systems and patient. For interventional environments, high-speed rotational angiography (RA) scans are prone to potential collisions between the C-arm X-ray system and the patient. To ensure safety, a low speed safety-run is executed before the actual high-speed scan. However, several iterations of the safety- run are often required before a scan is collision-free leading to a suboptimal clinical work-flow. This work proposes a RGB-D camera based collision prediction system which detects collisions before the actual RA scan. Additionally, a motion planner is designed to provide appropriate patient repositioning such that the collision is avoided. The system is introduced as a first proof- of-concept to eliminate the safety-run and improve clinical safety and workflow.
User interface redesign of technology based learning service using usability testing method

Along with the development of technology, the use of the internet in the field of education has also progressed rapidly. Indonesia has reached the 8th position out of the 10 countries with the highest growth of e-learning in the world. One of the e-learning platforms developed by the Ministry of Education and Culture of Indonesia is Rumah Belajar, a free e-learning platform for all students in Indonesia. However, based on preliminary studies and customer's complaints data, it is known that there are still many users who find it difficult when using the Rumah Belajar service. Therefore, this study was conducted to evaluate and measure the usability level of the current Rumah Belajar website using the usability testing method, then redesign the user interface. The results of this study indicate that the usability level is still low, as indicated by low performance indicators and low SUS and QUIS scores. After redesigning, the design of the new user interface proved to be significantly easier for the user, as indicated by the higher SUS and QUIS values, and the time to complete tasks reduced.
Live Broadcast Data Processing &amp; Security by Software Based Encryption

There are various types of digital content protection techniques available in the market. Effectively securing the content is the main challenge as we have various advance tricks & methods available that can easily hack our premium content. So, delivering the content like OTT & Digital TV services to end-user makes advance data security a very interesting topic. This paper focuses on a key concept to use software-based encryption for Broadcast data security. This paper covers complete information about live data processing through broadcast equipment. The main point is securing the content through softwarebased encryption that will reduce the additional hardware needed to encrypt & decrypt the live content. The simplified approach of securing the content via open-source tools & platform makes the entire work interesting & cost-effective.
Implementation of Cryptography Module Security Certification Based on SNI ISO/IEC 19790:2012 - Security Requirements For Cryptography Module

Utilization of cryptography in information and communication technology devices in the form of cryptographic modules can guarantee confidentiality, integrity, and anti-denial of the information. In 2015 the National Standardization Agency (BSN) has issued SNI ISO/IEC 19790 : 2015 - Persyaratan Keamanan untuk Modul Kriptografi which is identical adoption of ISO/IEC 19790:2012 - Security Requirements for Cryptographic Module. SNI ISO/IEC 19790:2015 is a national standard that can support national security and digital economic development by providing security guarantees of the cryptographic modules. Unfortunatelly until now there are still no certification schemes, certification bodies, and testing laboratories that built for this standard, so the certification of the cryptographic module security based on SNI ISO/IEC 19790:2015 is cannot be held yet. This paper proposes that the cryptographic module security certification scheme that can be implemented in Indonesia is type 2 scheme with some adjustments, the Certification Body that runs the recommended cryptographic module security certification scheme is BSSN, and the Testing Laboratory that runs the recommended cryptographic module security testing is BSSN internal testing laboratory.
A Short-term Motion Prediction Approach for Guaranteed Collision-Free Planning

To enable safe and efficient human-robot collaboration in shared workspaces, it is important for the robot to take possible future movements into account and predict the reachable occupancy of human when performing a task. While human motion is fast and changeable, predicting human motion for tasks unknown a priori is very challenging. However, the existing methods lack the ability to adapt to time-varying of human behaviours. Moreover, many of them do not quantify uncertainties in the prediction. This paper proposes a simple and fast approach calculating the reachable occupancy of human arms in Cartesian space. We use a second order kinematic model which is based on the constraints of human motion such as the maximum velocity and acceleration constraints collected from the demonstrations of different people. The constraint prediction model is conservative and can accommodate the time-varying behaviours of human. Finally, this model has been used to predict the motion of different people. The experiment results show that the proposed method is computationally efficient and robust for all relevant movement.
Machine Translation Evaluation: Manual Versus Automatic—A Comparative Study

The quality of machine translation (MT) is best judged by humans well versed in both source and target languages. However, automatic techniques are often used as these are much faster, cheaper and language independent. The goal of this paper is to check for correlation between manual and automatic evaluation, specifically in the context of Indian languages. To the extent automatic evaluation methods correlate with the manual evaluations, we can get the best of both worlds. In this paper, we perform a comparative study of automatic evaluation metrics—BLEU, NIST, METEOR, TER and WER, against the manual evaluation metric (adequacy), for English-Hindi translation. We also attempt to estimate the manual evaluation score of a given MT output from its automatic evaluation score. The data for the study was sourced from the Workshop on Statistical Machine Translation WMT14.
A Non-local Rank-Constraint Hyperspectral Images Denoising Method with 3-D Anisotropic Total Variation

Abstract Hyperspectral Images (HSIs) are usually degraded by many kinds of noise called mixed noise, which greatly limits the subsequent applications of HSIs. Many researches have proved the patch-based low-rank methods and the total variation (TV) based approaches have a good effect on reducing noise in HSIs. Here, we propose a non-local patch based rank-constraint HSIs noise suppression methods with a global 3-D anisotropic total variation (NLRATV). Differing from previous patch-based methods which usually ignore spatial structural information, we add more structural constraints with the non-local similarity across patches for suppressing the structural noise that exists at the same location of many bands. Besides, we utilize the global 3-D anisotropic total variation to ensure its smoothness in spatial and spectral dimensionalities while reconstructing the image. The augmented Lagrange multiplier method is adopted to optimize the proposed algorithm. The real data experiments have proved the superiority of NLRATV in decreasing mixed and dense noise.
A new preprocessing method reduces the dimensionality of classification models

In data mining classification problems, the higher the number of features, the harder the visualization of the training set is. Working on a dataset with varied factors sounds a big problem for data analyst even after applying classification models, especially when we could not identify the features that have generated such classification. Sometimes, most of these features are correlated, and hence redundant. Dimensionality reduction is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables.
K-NN Similarity Measure based on Fourier Descriptors for Hyperspectral Images Classification

This paper proposes a new similarity measure for K-Nearest Neighbor (K-NN) classifier based on Fourier Descriptors of pixels spectral curves. This similarity measures the resemblance between Fourier Descriptors of candidate pixel and reference pixels spectral curves in the frequency domain. The proposed similarity measure is evaluated on real hyperspectral dataset against the three commonly used distances by the K-NN classifier, which are Euclidean distance, Cosine distance, and Correlation distance. The experiment results indicate that the proposed similarity measure exhibits superior performance compared with the other state-of-the-art K-NN metrics, with robustness to the sensitivity of different choices of the neighborhood size k.
Object Removal Software Test Automation

This paper considers the problem of designing a test method and robust test automation for evaluating the functionality of computer vision algorithms for object removal and image reconstruction of the removed area. An object removal algorithm is used to select and remove a region in an image. After the removal of the object, the object removal algorithm regenerates the area in the image from which the object was removed, similar to photoshop. We developed an algorithm in MATLAB that accesses the features of the macroblocks from the region that adjoins the removed object and correlates these macroblocks with the actual area that is regenerated. The SSIM (structural similarity index) score is calculated by comparing the ground truth with the image generated from image reconstruction after removal of the object. Finally, we calculate the artifact area present in the regenerated region to determine the algorithm quality. To validate this method, the authors compare a 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> -party object removal solution with an in-house developed solution using the proposed approach. This comparison shows that the SSIM score is 1.55% higher than the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> -party solution and 26.3% lower in artifact areas. Besides the results, this paper also describes in detail the procedure to automate the use case of object removal test automation to scale up the test coverage.
Semantic Relationship Study between Citing and Cited Scientific Articles Using Topic Modeling

In the evaluation of scientific research, it is clear that 'scientific quality' is proving to be one of the most important criteria, and in the case of research for development, it is on an equal footing with another key dimension: relevance for development. The evaluation criteria and procedures are very similar to those applied in the evaluation of research in general: peer reviews, panels, bibliometric indices, etc ... The practical application of these tools in the case of research for development can sometimes be quite difficult. There is, however, a general consensus that quality should be expressed identically for any scientific research, with the same problems and constraints. In this article and to evaluate a scientific article, we thought to study the semantic relationship between the citing article and the other cited, we based on the Topic modeling theme with the choice of LDA and LDA2VEC algorithms applied to the corpus Nips (1987-2017) then on the citations of each article of the corpus.
Multi-source User Attribute Inference based on Hierarchical Auto-encoder

With the rapid development of Online Social Networks (OSNs), it is crucial to construct users' portraits from their dynamic behaviors to address the increasing needs for customized information services. Previous work on user attribute inference mainly concentrated on developing advanced features/models or exploiting external information and knowledge but ignored the contradiction between dynamic behaviors and stable demographic attributes, which results in deviation of user understanding
Lateral Positioning Method for Unmanned Roller Compactor Based on Visual Feature Extraction

Accurate and real-time lateral positioning is the key to path tracking control of unmanned roller compactor. In order to ensure the accuracy and real-time of lateral positioning, a lateral positioning method based on visual feature extraction is proposed. Firstly, the camera calibration was carried out to obtain its internal parameters and external parameters. Then the image processing method was used to detect and fit the ground markings in the working area. Finally, in order to achieve the lateral positioning, the visual positioning model was used to calculate the distance between the look-ahead point in the moving direction of the roller compactor and the left and right ground markings. The experimental results show that the average absolute error of static positioning is 0.04 meters, the average relative error is 2.31%, the average absolute error of dynamic experiment is 0.06 meters, the average relative error is 3.51%, and the average processing speed of the algorithm reaches 70 frames/second. It fully meets the needs of positioning measurement accuracy and real-time in engineering applications. In addition, the method is less subject to the interference of electromagnetic signal and requires less hardware cost.
Artificial Bee Colony Algorithm and an Application to Software Defect Prediction

The term swarm refers to any restrained collection of interacting agents or individuals. For survival, creatures need to live or perform some tasks collectively such as defending against predators, foraging, mating, etc. An intelligent swarm optimizes a goal or task by responding adaptively to the local and/or global environmental changes in a collective manner. Swarm intelligence research field deals with designing algorithms inspired by the collective behavior of social creatures. Task division and self-organization abilities lead swarm intelligence to occur in a colony and the self-organization is characterized by positive-feedback, negative feedback, fluctuation and multiple interactions in order to use the local information to form a global pattern without a supervision. Ants, termites, birds, and fishes are some examples of social animals that have swarm intelligence and inspire researchers to design problem solving techniques. Bees also are a typical example of creatures performing tasks collectively in nest site selection, nest building, mating, and foraging. Among these activities, foraging might be the most crucial one for the survival of a bee colony. The swarm intelligence in foraging of a honey bee colony inspired Karaboga to design an optimization algorithm, Artificial Bee Colony (ABC), in which the search is guided by the bees’ exploration and exploitation mechanisms to maximize the quality of the honey within the hive. In this chapter, first, the foraging behavior of real honey bees is summarized and then, the details of Artificial Bee Colony algorithm about how it mimics the foraging behavior is provided. In the third section, an application of ABC algorithm is carried out on a software engineering problem, software defect prediction.