pwc-archive/papers-with-abstracts huggingface dataset:
An ensemble of Density based Geometric One-Class Classifier and Genetic Algorithm
Authors: Do Gyun Kim, Jin Young Choi
Date: 2020-10-02 00:00:00
Tasks: Classification, General Classification, Multi-class Classification, One-Class Classification, One-class classifier

One of the most rising issues in recent machine learning research is One-Class Classification which considers data set composed of only one class and outliers. It is more reasonable than traditional Multi-Class Classification in dealing with some problematic data set or special cases. Generally, classification accuracy and interpretability for user are considered as trade-off in OCC methods. Classifier based on Hyper-Rectangle (H-RTGL) is a sort of classifier that can be a remedy for such trade-off and uses H-RTGL formulated by conjunction of geometric rules called interval. This interval can be basis of interpretability since it can be easily understood by user. However, existing H-RTGL based OCC classifiers have limitations that (i) most of them cannot reflect density of target class and (ii) that considering density has primitive interval generation method, and (iii) there exists no systematic procedure for hyperparameter of H-RTGL based OCC classifier, which influences classification performance of classifier. Based on these remarks, we suggest One-Class Hyper-Rectangle Descriptor based on density (1-HRD_d) with more elaborate interval generation method including parametric and nonparametric approaches. In addition, we designed Genetic Algorithm (GA) that consists of chromosome structure and genetic operators for systematic generation of 1-HRD_d by optimization of hyperparameter. Our work is validated through a numerical experiment using actual data set with comparison of existing OCC algorithms along with other H-RTGL based classifiers.
Subgroup-based Rank-1 Lattice Quasi-Monte Carlo
Authors: Yueming Lyu, Yuan Yuan, Ivor W. Tsang
Date: 2020-10-29 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Bayesian Inference

Quasi-Monte Carlo (QMC) is an essential tool for integral approximation, Bayesian inference, and sampling for simulation in science, etc. In the QMC area, the rank-1 lattice is important due to its simple operation, and nice properties for point set construction. However, the construction of the generating vector of the rank-1 lattice is usually time-consuming because of an exhaustive computer search. To address this issue, we propose a simple closed-form rank-1 lattice construction method based on group theory. Our method reduces the number of distinct pairwise distance values to generate a more regular lattice. We theoretically prove a lower and an upper bound of the minimum pairwise distance of any non-degenerate rank-1 lattice. Empirically, our methods can generate a near-optimal rank-1 lattice compared with the Korobov exhaustive search regarding the $l_1$-norm and $l_2$-norm minimum distance. Moreover, experimental results show that our method achieves superior approximation performance on benchmark integration test problems and kernel approximation problems.
Dynamic Power Balancing Algorithm for Single-Phase Energy Storage Systems in LV Distribution Network with Unbalanced PV Systems Distribution
Authors: Watcharakorn Pinthurat, Branislav Hredzak
Date: 2020-11-12 00:00:00
Tasks: Clustering

Unbalanced power, due to high penetration of single-phase PV rooftops into a four-wire multi-grounded LV distribution system, can result in significant rise in the neutral current and neutral voltage. This preprint proposes a distributed clustering algorithm for dynamic power balancing, using single-phase battery storage systems distributed in the LV distribution system, in order to reduce the neutral current and neutral voltage rise. The distributed clustering algorithm aggregates households connected to the same phase into clusters. Within each cluster, another distributed clustering algorithm is applied to calculate the total grid power exchanged but the corresponding phase. Then, the dynamic power balancing control is applied to balance the powers at the bus, based on battery storage systems' charge/discharge constraints, power minimization and willingness of the households to participate in the power balancing control.
导师评价网数据已在Github备份，可直接检索导师个人信息！
Authors: 导师评价网
Date: 2020-08-25 00:00:00
Tasks: 2D Human Pose Estimation

永久免费开源的导师评价数据、数据爬虫、无需编程基础的展示网页以及新信息补充平台。从导师评价网获取的原始数据，本平台开源存储数据以备各种你懂得的不可抗力因素。 项目名称：RateMySupervisor，链接： https://github.com/kgco/RateMySupervisor

在线浏览：可以直接访问这个GitHub Pages页面，打开即可在线浏览数据。导师评价信息 https://kgco.github.io/RateMySupervisor/html/index.html  网页前端加载出来之后，要加载一个20MB左右的js数据文件，由于网络原因可能速度比较慢，所以会有一小段时间下拉列表里没有数据，请耐心等待。

离线浏览：点击右上方Code按钮中的Download ZIP，下载文件，然后打开html/index.html即可浏览数据。由于调用了bootstrap和jquery，所以打开的时候最好保持网络连接（不打开也行啦，就是UI可能有点乱）。
Automatic human sleep stage scoring using Deep Neural Networks
Authors: Alexander Malafeev, Dmitry Laptev, Stefan Bauer, Ximena Omlin, Aleksandra Wierzbicka, Adam Wichniak, Wojciech Jernajczyk, Robert Riener, Joachim M. Buhmann and Peter Achermann
Date: 2018-11-06 00:00:00
Tasks: Classification, General Classification

The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods in healthy subjects and in patients. Most algorithms yielded good results comparable to human interrater agreement. Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. We also demonstrated that taking the local temporal structure of sleep into account a priori is important. Our results demonstrate the utility of neural network architectures for the classification of sleep.
Universal evolutionary model for periodical species
Authors: Eric Goles, Ivan Slapničar, Marco A. Lardies
Date: 2020-09-25 00:00:00
Tasks: model

Real-world examples of periods of periodical organisms range from cicadas whose life-cycles are larger prime numbers, like 13 or 17, to bamboos whose periods are large multiples of small primes, like 40 or even 120. The periodicity is caused by interaction of species, be it a predator-prey relationship, symbiosis, commensialism, or competition exclusion principle. We propose a simple mathematical model which explains and models all those principles, including listed extremel cases. This, rather universal, qualitative model is based on the concept of a local fitness function, where a randomly chosen new period is selected if the value of the global fitness function of the species increases. Arithmetically speaking, the different observed interactions are related to only four principles: given a couple of integer periods either (1) their greatest common divisor is one, (2) one of the periods is prime, (3) both periods are equal, or (4) one period is an integer multiple of the other.
Improved Price Oracles: Constant Function Market Makers
Authors: Guillermo Angeris, Tarun Chitra
Date: 2020-03-22 00:00:00
Tasks: scoring rule

Automated market makers, first popularized by Hanson's logarithmic market scoring rule (or LMSR) for prediction markets, have become important building blocks, called 'primitives,' for decentralized finance. A particularly useful primitive is the ability to measure the price of an asset, a problem often known as the pricing oracle problem. In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers (or CFMMs) which includes existing popular market makers such as Uniswap, Balancer, and Curve, whose yearly transaction volume totals to billions of dollars. We give sufficient conditions such that, under fairly general assumptions, agents who interact with these constant function market makers are incentivized to correctly report the price of an asset and that they can do so in a computationally efficient way. We also derive several other useful properties that were previously not known. These include lower bounds on the total value of assets held by CFMMs and lower bounds guaranteeing that no agent can, by any set of trades, drain the reserves of assets held by a given CFMM.
A Novel Discourse Parser Based on Support Vector Machine Classification
Authors: David duVerle, Helmut Prendinger
Date: 2009-08-02 00:00:00
Tasks: Classification, Discourse Parsing, General Classification, Text Generation

This paper introduces a new algorithm to parse discourse within the framework of Rhetorical Structure Theory (RST). Our method is based on recent advances in the field of statistical machine learning (multivariate capabilities of Support Vector Machines) and a rich feature space. RST offers a formal framework for hierarchical text organization with strong applications in discourse analysis and text generation. We demonstrate automated annotation of a text with RST hierarchically organised relations, with results comparable to those achieved by specially trained human annotators. Using a rich set of shallow lexical, syntactic and structural features from the input text, our parser achieves, in linear time, 73.9% of professional annotators’ human agreement F-score. The parser is 5% to 12% more accurate than current state-of-the-art parsers.
Sparsity Learning Based Multiuser Detection in Grant-Free Massive-Device Multiple Access
Authors: Tian Ding, Xiaojun Yuan, Soung Chang Liew
Date: 2018-07-28 00:00:00
Tasks: User Identification

In this work, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS). Specifically, we put forth two MUD schemes, termed random sparsity learning multiuser detection (RSL-MUD) and structured sparsity learning multiuser detection (SSL-MUD) for the time-slotted and non-time-slotted grant-free MaDMA systems, respectively. In the time-slotted RSL-MUD scheme, active users generate and transmit data packets with random sparsity. In the non-time-slotted SSL-MUD scheme, we introduce a sliding-window-based detection framework, and the user signals in each observation window naturally exhibit structured sparsity. We show that by exploiting the sparsity embedded in the user signals, we can recover the user activity state, the channel, and the user data in a single phase, without using pilot signals for channel estimation and/or active user identification. To this end, we develop a message-passing based statistical inference framework for the BS to blindly detect the user data without any prior knowledge of the identities and the channel state information (CSI) of the active users. Simulation results show that our RSL-MUD and SSL-MUD schemes significantly outperform their counterpart schemes in both reducing the transmission overhead and improving the error behavior of the system.
Classification of Alzheimer's Disease MRI Images with CNN Based Hybrid Method
Authors: Muhammed Yildirim *, Ahmet Cinar
Date: 2020-06-28 00:00:00
Tasks: General Classification

Alzheimer is a type of dementia disease that is common in older ages. This disease is a 
progressive form of neurological disease that causes the destruction of brain cells. Since 
Alzheimer's is a progressive disease, various problems increase over time. For this reason, 
it is very important to diagnose the disease early and start the treatment process. In this 
study, it was tried to determine at which stage the disease is or whether it is Alzheimer using 
brain images. CNN architectures are used to diagnose  the disease. In addition, a hybrid 
method we have developed has been proposed. With the architectures used, it is classified 
in 4 stages according to the disease progression level. In the proposed hybrid model, the 
Resnet50  method  is  used  as  the  basis.  The  results  are  obtained  separately  by  Alexnet, 
Resnet50, Densenet201, Vgg16, and the Hybrid method we developed. An accuracy of 90% 
has been achieved with the developed hybrid model. Consequently, when other scientific 
paper in the literature are investigated, it is finalized that the hybrid model developed to 
diagnose  Alzheimer’s  disease  has  achieved  the  success  achieved  by  other  CNN 
architectures and even offers better results
Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
Authors: Jinzhe Zeng, Liqun Cao, Mingyuan Xu, Tong Zhu, John Z. H. Zhang
Date: 2020-11-11 00:00:00

Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process. In this work, a neural network-based molecular dynamics (MD) simulation is carried out to simulate the benchmark combustion of methane. During MD simulation, detailed reaction processes leading to the creation of specific molecular species including various intermediate radicals and the products are intimately revealed and characterized. Overall, a total of 798 different chemical reactions were recorded and some new chemical reaction pathways were discovered. We believe that the present work heralds the dawn of a new era in which neural network-based reactive MD simulation can be practically applied to simulating important complex reaction systems at ab initio level, which provides atomic-level understanding of chemical reaction processes as well as discovery of new reaction pathways at an unprecedented level of detail beyond what laboratory experiments could accomplish.
An Efficient Approach For Malware Detection Using PE Header Specification
Authors: Tina Rezaei, Ali Hamzeh
Date: 2020-06-11 00:00:00
Tasks: BIG-bench Machine Learning, Malware Detection

Following  the dramatic  growth  of  malware  and the  essential  role  of  computer  systems  in  our  daily  lives,  the security  of  computer  systems  and  the  existence  of  malware detection   systems   become   critical.   In   recent   years,   many machine   learning   methods   have   been   used   to   learn   the behavioral  or  structural  patterns  of  malware.  Because  of  their high generalization capability, they have achieved great success in   detecting   malware.   In   this   paper,   to   identify   malware programs,  features  extracted  based  on  the  header  and  PE  file structure  are  used  to  train  several  machine  learning  models. The   proposed   method   identifies   malware   programs   with 95.59%  accuracy  using  only  nine  features,  the  values  of  which have a significant difference between malware and benign files. Due   to   the   high   speed   of   the   proposed   model   in   feature extraction and the low number of extracted features, which lead to  faster  model  training,  the  proposed  method  can  be  used  in real-time malware detection systems
Effort justification for fun activities?: The effect of location-based mobile coupons using games
Authors: Kim, H. J., & Song, H.
Date: 2019-12-20 00:00:00

Effort justification for fun activities?: The effect of location-based mobile coupons using games
A Tale of Two Desolvation Potentials: An Investigation of Protein Behavior Under High Hydrostatic Pressure
Authors: Andrei G. Gasic, Margaret S. Cheung
Date: 2019-11-15 00:00:00
Tasks: Protein Folding

Hydrostatic pressure is a common perturbation to probe the conformations of proteins. There are two common forms of pressure dependent potentials of mean force (PMFs) derived from hydrophobic molecules available for the coarse grained molecular simulations of protein folding and unfolding under hydrostatic pressure. Although both PMF includes a desolvation barrier separating the well of a direct contact and the well of a solvent mediated contact, how these features vary with hydrostatic pressure is still debated. There is a need of a systematic comparison of these two PMFs on a protein. We investigated the two different pressure dependencies on the desolvation potential in a structure based protein model using coarse grained molecular simulations. We compared them to the known behavior a real protein based on experimental evidence. We showed that the protein s folding transition curve on the pressure temperature phase diagram depends on the relationship between the potential well minima and pressure. For protein that reduces the total volume under pressure, it is essential for the PMF to carry the feature that the direct contact well is essential less stable than the water mediated contact well at high pressure. We also comment on the practicality and importance of structure based minimalist models for understanding the phenomenological behavior of a protein under a wide range of phase space.
Neural network for automatic farm control
Authors: Ildar Rakhmatulin
Date: 2020-02-01 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Prediction of metrological, botanical characteristics is extremely important for different directions in agriculture. The availability of these data allows us to adjust the process of growing crops, which has a huge impact on yield, speed of ripening and the presence of vitamins in the grown culture. Increasing yields due to changes in culture growing conditions without the use of gene mutations and herbicides are the most popular destination in the agriculture field. In this manuscript, a realisation of the neural network for the construct of an efficient autonomous farm was represented. The developed by farm creates the optimal conditions for growing a crop by controlling the following indicators: Illumination, PH of the ground, air temperature, the temperature of the ground, air humidity, CO2 concentration and humidity of the ground. Theoretical research and experimental research on the use of a neural network to predict vegetable growth were represented. The presented model can also be considered as a prototype device for testing various cultivated vegetables to identify the optimal characteristics for them growing.
Neural network for estimation of optical characteristics of optically active and turbid scattering media
Authors: Ali Alavi
Date: 2020-11-12 00:00:00

One native source of quality deterioration in medical imaging, and especially in our case optical coherence tomography (OCT), is the turbid biological media in which photon does not take a predictable path and many scattering events would influence the effective path length and change the polarization of polarized light. This inherent problem would cause imaging errors even in the case of high resolution of interferometric methods. To address this problem and considering the inherent random nature of this problem, in the last decades some methods including Monte Carlo simulation for OCT was proposed. In this approach simulation would give us a one on one comparison of underlying physical structure and its OCT imaging counterpart. Although its goal was to give the practitioners a better understanding of underlying structure, it lacks in providing a comprehensive approach to increase the accuracy and imaging quality of OCT imaging and would only provide a set of examples on how imaging method might falter. To mitigate this problem and to demonstrate a new approach to improve the medical imaging without changing any hardware, we introduce a new pipeline consisting of Monte Carlo simulation followed by a deep neural network.
Investigating Learning in Deep Neural Networks using Layer-Wise Weight Change
Authors: Ayush Manish Agrawal, Atharva Tendle, Harshvardhan Sikka, Sahib Singh, Amr Kayid
Date: 2020-11-13 00:00:00

Understanding the per-layer learning dynamics of deep neural networks is of significant interest as it may provide insights into how neural networks learn and the potential for better training regimens. We investigate learning in Deep Convolutional Neural Networks (CNNs) by measuring the relative weight change of layers while training. Several interesting trends emerge in a variety of CNN architectures across various computer vision classification tasks, including the overall increase in relative weight change of later layers as compared to earlier ones.
Testing the Genomic Bottleneck Hypothesis in Hebbian Meta-Learning
Authors: Rasmus Berg Palm, Elias Najarro, Sebastian Risi
Date: 2020-11-13 00:00:00
Tasks: Clustering, Meta-Learning

Hebbian meta-learning has recently shown promise to solve hard reinforcement learning problems, allowing agents to adapt to some degree to changes in the environment. However, because each synapse in these approaches can learn a very specific learning rule, the ability to generalize to very different situations is likely reduced. We hypothesize that limiting the number of Hebbian learning rules through a "genomic bottleneck" can act as a regularizer leading to better generalization across changes to the environment. We test this hypothesis by decoupling the number of Hebbian learning rules from the number of synapses and systematically varying the number of Hebbian learning rules. The results in this paper suggest that simultaneously learning the Hebbian learning rules and their assignment to synapses is a difficult optimization problem, leading to poor performance in the environments tested. However, parallel research to ours finds that it is indeed possible to reduce the number of learning rules by clustering similar rules together. How to best implement a "genomic bottleneck" algorithm is thus an important research direction that warrants further investigation.
Finding optimal Pulse Repetion Intervals with Many-objective Evolutionary Algorithms
Authors: Paul Dufossé, Cyrille Enderli
Date: 2020-11-13 00:00:00
Tasks: Evolutionary Algorithms

In this paper we consider the problem of finding Pulse Repetition Intervals allowing the best compromises mitigating range and Doppler ambiguities in a Pulsed-Doppler radar system. We revisit a problem that was proposed to the Evolutionary Computation community as a real-world case to test Many-objective Optimization algorithms. We use it as a baseline to compare several Evolutionary Algorithms for black-box optimization with different metrics. Resulting data is aggregated to build a reference set of Pareto optimal points and is the starting point for further analysis and operational use by the radar designer.
Symbolically Solving Partial Differential Equations using Deep Learning
Authors: Maysum Panju, Kourosh Parand, Ali Ghodsi
Date: 2020-11-12 00:00:00
Tasks: Deep Learning

We describe a neural-based method for generating exact or approximate solutions to differential equations in the form of mathematical expressions. Unlike other neural methods, our system returns symbolic expressions that can be interpreted directly. Our method uses a neural architecture for learning mathematical expressions to optimize a customizable objective, and is scalable, compact, and easily adaptable for a variety of tasks and configurations. The system has been shown to effectively find exact or approximate symbolic solutions to various differential equations with applications in natural sciences. In this work, we highlight how our method applies to partial differential equations over multiple variables and more complex boundary and initial value conditions.