Reviewerly/exHarmony huggingface dataset:
On Image Segmentation Based on Local Entropy Fitting Under Nonconvex Regularization Term Constraints

The energy functional of the CV and LBF model is single, which makes the curve to get into the local minimum easily during the evolution process, and results inaccurate segmentation of the images with nonuniform grayscale and nonsmooth edges. The proposed algorithm, which is based on local entropy fitting under the constraint of nonconvex regularization term, is used to deal with such problems. In this algorithm, global information and local entropy are fitted to avoid segmentation falling into local optimum, and nonconvex regularization term is imported for constraint to protect edge smoothing. First, global information is used to evolve the approximate contour curve of the target segmentation. Then, a local energy functional with local entropy information is constructed to avoid the segmentation process from falling into a local minimum, and to precisely segment the image. Finally, nonconvex regularization terms are used in the energy functional to protect the smoothness of edge information during image segmentation process. The experimental results clearly indicate that the new algorithm can effectively resist noise, precisely segment images with nonuniform grayscale, and achieve the global optimal.
Predicting Reading Speed from Eye-Movement Measures

Examining eye-movement measures makes understanding the intricacies of reading processes possible. Previous studies have identified some eye-movement measures such as fixation time, number of progressive and regressive saccades as possible major indices for measuring silent reading speed, however, not quite intensively and systematically investigated. The purpose of this study was to exhaustively reveal the functions of different global eye-movement measures and their contribution to reading speed using linear regression analysis. Twenty-four young adults underwent an eye-tracking experiment while reading text paragraphs. Reading speed and a set of twenty-three eye-movement measures including properties of saccades, glissades and fixations were estimated. Correlation analysis indicated multicollinearity between several eye-movement measures, and accordingly, linear regression with elastic net regularization was used to model reading speed with eye-movement explanatory variables. Regression analyses revealed the capability of progressive saccade frequency and the number of progressive saccades normalized by the number of words in predicting reading speed. Furthermore, the results supported claims in the existing literature that reading speed depends on fixation duration, as well as the amplitude, number and percentage of progressive saccades, and also indicated the potential importance of glissade measures in deeper understanding of reading processes. Our findings indicate the possibility of the applied linear regression modeling approach to eventually identify important eye-movement measures related to different reading performance metrics, which could potentially improve the assessment of reading abilities.
Diacritics restoration based on word n-grams for Slovak texts

Abstract Despite the modern boom in technology, we are still faced with the fact that people write texts without diacritics. There are two main reasons for this. The first, historical reason stems from the past when the use of diacritics was troublesome and people would write text without them. The second one is the speed - typing without diacritics is usually faster. Text without diacritics is easy to understand for people, but for some types of documents, missing diacritics can cause a problem. This is also an issue when computers process such text. In this paper, we propose an algorithm based on word n-grams (a contiguous sequence of n words) that can restore diacritics of text written in the Slovak language. We also compare and evaluate our results with other algorithms developed for Slovak text.
Quality assessment of digital speckle patterns for the single-shot speckle projection profilometry based on a visualised simulation platform

As a high-speed 3D surface imaging technique, the single-shot speckle projection profilometry (SPP) is widely applied to various fields, where the design of speckle pattern plays a critical role in the fundamental issue of correspondence matching. However, the assessment of speckle patterns in the real imaging system is a time consuming and tedious procedure and their performance might vary with the operators and testing environment. To solve this problem, a visualised simulation platform based on 3ds Max engine is proposed in the paper. Furthermore, the performance of digital speckle patterns (DSPs) on the SPP technique is comprehensively evaluated in terms of 3D reconstruction precision, algorithm efficiency and pattern uniqueness. Firstly, the influence of the noise levels of the projection and imaging processes is considered. Then, the 3D profile reconstruction using five types of speckle pattern is performed in a real experiment. Afterward, a comparative analysis with the optimal DSP is conducted. In addition, the effectiveness of the typical metric of the painted speckle pattern in the field of digital image correlation (DIC) is investigated; results demonstrate its insufficient capability in the SPP field. Moreover, the scaling model of speckle size, influence of speckle deformation and material surface are also discussed to guide the design of DSP. This study can potentially facilitate the selection of high-quality DSPs and the standardisation of the SPP technique. The virtual experiment platform executable file of 3ds Max and the recommended DSP are uploaded as reference.
Massive-Scale Aerial Photo Categorization by Cross-Resolution Visual Perception Enhancement

Categorizing aerial photographs with varied weather/lighting conditions and sophisticated geomorphic factors is a key module in autonomous navigation, environmental evaluation, and so on. Previous image recognizers cannot fulfill this task due to three challenges: 1) localizing visually/semantically salient regions within each aerial photograph in a weakly annotated context due to the unaffordable human resources required for pixel-level annotation; 2) aerial photographs are generally with multiple informative attributes (e.g., clarity and reflectivity), and we have to encode them for better aerial photograph modeling; and 3) designing a cross-domain knowledge transferal module to enhance aerial photograph perception since multiresolution aerial photographs are taken asynchronistically and are mutually complementary. To handle the above problems, we propose to optimize aerial photograph's feature learning by leveraging the low-resolution spatial composition to enhance the deep learning of perceptual features with a high resolution. More specifically, we first extract many BING-based object patches (Cheng et al., 2014) from each aerial photograph. A weakly supervised ranking algorithm selects a few semantically salient ones by seamlessly incorporating multiple aerial photograph attributes. Toward an interpretable aerial photograph recognizer indicative to human visual perception, we construct a gaze shifting path (GSP) by linking the top-ranking object patches and, subsequently, derive the deep GSP feature. Finally, a cross-domain multilabel SVM is formulated to categorize each aerial photograph. It leverages the global feature from low-resolution counterparts to optimize the deep GSP feature from a high-resolution aerial photograph. Comparative results on our compiled million-scale aerial photograph set have demonstrated the competitiveness of our approach. Besides, the eye-tracking experiment has shown that our ranking-based GSPs are over 92% consistent with the real human gaze shifting sequences.
Bug Severity Prediction System Using XGBoost Framework

The field of Bug Reporting and Triaging has of late been a hot area of study among researchers trying to improve system management techniques. There is an increasing importance for developers to consider and address the various issues faced by users in order to not only ensure the delivery of quality service but also to understand the performance of the system under real-life scenarios. Hence, in this project, there is an attempt to develop a system that can improve the process of handling bug reports submitted by users of a software. This will be done through Bug Severity Prediction using the eXtreme Gradient Boosting (XGBoost) algorithm and the inclusion of a class balancing function to offset the bias due to the presence of majority and minority classes. The project would also include a study on the work that has already been done along with a proposal of the system architecture, methodologies used and the various hardware and software requirements. The main aim of the project is to shed light on the advantages of developing a Bug Severity Prediction system that can help reduce the dependence on users for providing accurate information. With the help of models built based on the history of bug reports received till date, the system should be able to take up some of the responsibilities of the user reporting the bug.
A unified framework on node classification using graph convolutional networks

Graphs contain a plethora of valuable information about the underlying data which can be extracted, analyzed, and visualized using Machine Learning (ML). The challenge is that graphs are non-Euclidean structures, and cannot be directly used with ML techniques. In order to overcome this challenge, one way is to encode nodes into an equivalent Euclidean representation in the form of a low-dimensional vector, also called an embedding vector, and the encoding process is called node embedding. During the recent years, various ML techniques have been developed that learn the encoding of the nodes automatically. Some of these techniques, called Graph Convolutional Networks (GCN), use variants of the convolutional neural networks adapted for graphs. The focus of this paper is two-fold. Firstly, to develop a unified framework focusing on three major GCN techniques in order to analyze, evaluate, and compare their performance on select benchmark datasets for the task of node classification. And secondly, to implement a new attention aggregator for GraphSAGE, and compare the performance of the aggregator with the existing GCN methods as well as the other aggregators provided by GraphSAGE.
Neighbours on Line (NoL): An Approach to Balance Skewed Datasets

The real-world datasets in many domains like medical, intrusion detection, fraud transactions and bioinformatics are highly imbalanced. In classification problems, imbalanced datasets negatively affect the accuracy of class predictions. This skewness can be handled either by oversampling minority class examples or by undersampling majority class. In this paper, a new undersampling approach, namely Neighbours on Line (NOL) has been proposed to improve class distribution. The approach performed better than the existing Condensed Nearest Neighbour approach of undersampling.
‘Orrery Arcana’: An Esoteric System for Improvisational Performance

Orrery Arcana is a system for real-time improvisational performance designed to facilitate a process analogous to automatic writing. The system includes custom software for real-time signal processing written in Max/MSP and Python and a self-made hardware controller that is integrated with a planetary gear train, which gives the performer control over timing and sequenced events through manual gear rotations. Each gear is equipped with a sensor plate with embedded light, magnetic, and capacitive-touch sensors. The sensors are primarily manipulated via tactile modular control objects in the form of concentric rings of colored acrylic and inset magnets that correspond to Tarot cards.
Extending MapReduce framework with locality keys

This paper extends the existing MapReduce framework to allow the user programmer to control data locality and reduce communication costs of the shuffle operations in iterative in-memory computation. The programming extension is fully consistent with the style of MapReduce and allows straightforward fast implementation.
TTR-Based Reward for Reinforcement Learning with Implicit Model Priors

Model-free reinforcement learning (RL) is a powerful approach for learning control policies directly from high-dimensional state and observation. However, it tends to be data-inefficient, which is especially costly in robotic learning tasks. On the other hand, optimal control does not require data if the system model is known, but cannot scale to models with high-dimensional states and observations. To exploit benefits of both model-free RL and optimal control, we propose time-to-reach-based (TTR-based) reward shaping, an optimal control-inspired technique to alleviate data inefficiency while retaining advantages of model-free RL. This is achieved by summarizing key system model information using a TTR function to greatly speed up the RL process, as shown in our simulation results. The TTR function is defined as the minimum time required to move from any state to the goal under assumed system dynamics constraints. Since the TTR function is computationally intractable for systems with high-dimensional states, we compute it for approximate, lower-dimensional system models that still captures key dynamic behaviors. Our approach can be flexibly and easily incorporated into any model-free RL algorithm without altering the original algorithm structure, and is compatible with any other techniques that may facilitate the RL process. We evaluate our approach on two representative robotic learning tasks and three well-known model-free RL algorithms, and show significant improvements in data efficiency and performance.
Research on Digital Currency Supervision Model Based on Blockchain Technology

Abstract With the rapid iterative development of computer information technology, financial technology is also constantly innovating. In this context, the digital currency which should be used in blockchain technology has been developed and popularized rapidly. However, effective supervision of digital currency has become a big problem in the rapid development of blockchain and digital currency. Based on this, this paper first studies the blockchain technology and application, analyzes the technology principle and application of blockchain, secondly, analyzes the digital currency supervision model based on blockchain technology, and studies the necessity of the construction of the supervision system and the strategy of supervision.
MRN: Moment Relation Network for Natural Language Video Localization with Transfer Learning

In this paper, we tackle the task of natural language video localization (NLVL): given an untrimmed video and a description language query, the goal is to localize the temporal segment within the video that best describes the natural language description. NLVL is challenging at the intersection of language and video understanding because a video may contain multiple segments of interests and the language may describe complicated temporal dependencies. Though existing approaches have achieved good performance, most of them did not fully consider the inherent differences between language and video modalities. Here, we propose Moment Relation Network (MRN) to reduce the divergence of the probability distribution of these two modalities. Specifically, MRN trains video and language subnets, and then uses transfer learning techniques to map the extracted features into an embedding-shared space where we calculate the similarity of two modalities using Mahalanobis distance metric, which is used to localize moments. Extensive experiments on benchmark datasets show that the proposed MRN significantly outperforms the state-of-the-art under the widely used metrics by a large margin.
Using Multiple Deep Neural Networks Platform to Detect Different Types of Potential Faults in Unmanned Aerial Vehicles

Many researchers developed new algorithms to predict the faults of unmanned aerial vehicles (UAV).These algorithms detect anomalies in the streamed data of the UAV and label them as potential faults.Most of these algorithms consider neither the complex relationships among the UAV variables nor the temporal patterns of the previous instances, which leaves a potential opportunity for new ideas.A new method for analyzing the relationships and the temporal patterns of every two variables to detect the potentially defected sensors.The proposed method depends on a new platform, which is composed of multiple deep neural networks.The method starts by building and training this platform.The training step requires reshaping the dataset into a set of subdatasets.Each new subdataset is used to train one deep neural network.In the testing phase, the method reads new instances of the UAV testing dataset.The output of the algorithm is the predicted potential faults.The proposed approach is evaluated and compared it with other well-known algorithms.The proposed approach showed promising results in predicting different kinds of faults.
Efficient data deduplication scheme for scale-out distributed storage

Nowadays, there are many sources that generate a large amount of data since almost all devices around our environment are well connected and communicate with each other. Data storage becomes more and more important due to exponentially increasing demand of data over time. Besides this, storing and managing the growth of unstructured data is one of the great challenges. More optimized data storage is essential with the increase of computing power. On the other hand, space efficiency is also one of the primary concerns in the storage system. Recent storage systems such as scale-out distributed storage systems, modern datacenters, and cloud storage environments control their storage costs by eliminating storage waste and the requirement for dedicated storage capacity. The compression techniques become essential in the storage system because large storage space is saved and reliability of the system is maintained. If the storage space is large then it creates challenges related to reliability and space requirement. As computer systems are taking more and more workload responsibilities in critical processes, the yearning for a better understanding of the system’s reliability is ever increasing. This chapter highlights a system that emphasizes on the storage capacity utilization and reliability improvement by balancing data deduplication scheme.
Universal Qudit Hamiltonians

Abstract A family of quantum Hamiltonians is said to be universal if any other finite-dimensional Hamiltonian can be approximately encoded within the low-energy space of a Hamiltonian from that family. If the encoding is efficient, universal families of Hamiltonians can be used as universal analogue quantum simulators and universal quantum computers, and the problem of approximately determining the ground-state energy of a Hamiltonian from a universal family is QMA-complete. One natural way to categorise Hamiltonians into families is in terms of the interactions they are built from. Here we prove universality of some important classes of interactions on qudits ( d -level systems): We completely characterise the k -qudit interactions which are universal, if augmented with arbitrary Hermitian 1-local terms. We find that, for all $$k \geqslant 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>⩾</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> and all local dimensions $$d \geqslant 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>⩾</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> , almost all such interactions are universal aside from a simple stoquastic class. We prove universality of generalisations of the Heisenberg model that are ubiquitous in condensed-matter physics, even if free 1-local terms are not provided. We show that the SU ( d ) and SU (2) Heisenberg interactions are universal for all local dimensions $$d \geqslant 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>⩾</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> (spin $$\geqslant 1/2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>⩾</mml:mo> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> ), implying that a quantum variant of the Max- d -Cut problem is QMA-complete. We also show that for $$d=3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> all bilinear-biquadratic Heisenberg interactions are universal. One example is the general AKLT model. We prove universality of any interaction proportional to the projector onto a pure entangled state.
Bootstrap aggregated classification for sparse functional data

Sparse functional data are commonly observed in real-data analyzes. For such data, we propose a new classification method based on functional principal component analysis (FPCA) and bootstrap aggregating. Bootstrap aggregating is believed to improve the single classifier. In this paper, we apply this belief to an FPCA based classification, and compare the classification performance with that of the single classifiers. The simulation results show that the proposed method performs better than the conventional single classifiers. We then conduct two real-data analyzes.
Capacity Optimization and Economic Evaluation of Battery Energy Storage System for Qinghai Province based on Time Series Simulation Technology

With the rapid development of renewable energy power in Qinghai province, the utilization rate of renewable energy has become to a new challenge. Large-scale battery energy storage system (BESS) is an effective way to promote the utilization of renewable energy. In this paper, a method on capacity optimization and economic evaluation of BESS is proposed based on sequential production simulation technology. An optimization model is established to maximize the utilization of renewable energy, considering constraints of system power balance, operation mode of BESS and conventional units. The annual power outputs of renewable energy are incorporated as inputs for optimizing the hourly charging and discharging power of BESS. The discount cash flow method is adopted to evaluate the economic benefits of BESS. In case studies, a set of scenarios for Qinghai province with different power rating and energy capacity of BESS are tested, and the best plans of BESS in Qinghai province is obtained. The testing results verify the effectiveness of the proposed method.
Human Activity Recognition in Ambient Sensing Using Sequential Networks

In the past few years, with the boost in the ambient-assisted living environment using the Internet of Things (IoT), there is a need to analyze human activities using automated intelligent mechanisms. Machine learning has proved that a great resource is efficiently supervising activities of daily living (ADL), and especially in multi-resident environments where activities are more complicated and need involvements of multiple residents. Public healthcare departments are significantly helped by these automated systems, as they play a vital role in examining patients, old-age peoples, and behavioral habits. In this paper, we have presented a state-of-the-art machine learning algorithms on the real-world ARAS multi-resident dataset, which consists of data from two houses, each with two residents. We have used deep LSTM and mini-batch LSTM, and each is applied to different residents with slightly different architecture. We tried to improve the work previously done by other researchers, using the machine learning sequence models.