pwc-archive/papers-with-abstracts huggingface dataset:
Representativity Fairness in Clustering
Authors: Deepak P, Savitha Sam Abraham
Date: 2020-10-11 00:00:00
Tasks: Clustering, Decision Making, Fairness

Incorporating fairness constructs into machine learning algorithms is a topic of much societal importance and recent interest. Clustering, a fundamental task in unsupervised learning that manifests across a number of web data scenarios, has also been subject of attention within fair ML research. In this paper, we develop a novel notion of fairness in clustering, called representativity fairness. Representativity fairness is motivated by the need to alleviate disparity across objects' proximity to their assigned cluster representatives, to aid fairer decision making. We illustrate the importance of representativity fairness in real-world decision making scenarios involving clustering and provide ways of quantifying objects' representativity and fairness over it. We develop a new clustering formulation, RFKM, that targets to optimize for representativity fairness along with clustering quality. Inspired by the $K$-Means framework, RFKM incorporates novel loss terms to formulate an objective function. The RFKM objective and optimization approach guides it towards clustering configurations that yield higher representativity fairness. Through an empirical evaluation over a variety of public datasets, we establish the effectiveness of our method. We illustrate that we are able to significantly improve representativity fairness at only marginal impact to clustering quality.
A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data
Authors: Qi Zhao
Date: 2020-10-11 00:00:00

This paper presents a deep learning framework based on Long Short-term Memory Network(LSTM) that predicts price movement of cryptocurrencies from trade-by-trade data. The main focus of this study is on predicting short-term price changes in a fixed time horizon from a looking back period. By carefully designing features and detailed searching for best hyper-parameters, the model is trained to achieve high performance on nearly a year of trade-by-trade data. The optimal model delivers stable high performance(over 60% accuracy) on out-of-sample test periods. In a realistic trading simulation setting, the prediction made by the model could be easily monetized. Moreover, this study shows that the LSTM model could extract universal features from trade-by-trade data, as the learned parameters well maintain their high performance on other cryptocurrency instruments that were not included in training data. This study exceeds existing researches in term of the scale and precision of data used, as well as the high prediction accuracy achieved.
GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music
Authors: Qiuqiang Kong, Bochen Li, Jitong Chen, Yuxuan Wang
Date: 2020-10-11 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval

Symbolic music datasets are important for music information retrieval and musical analysis. However, there is a lack of large-scale symbolic datasets for classical piano music. In this article, we create a GiantMIDI-Piano (GP) dataset containing 38,700,838 transcribed notes and 10,855 unique solo piano works composed by 2,786 composers. We extract the names of music works and the names of composers from the International Music Score Library Project (IMSLP). We search and download their corresponding audio recordings from the internet. We further create a curated subset containing 7,236 works composed by 1,787 composers by constraining the titles of downloaded audio recordings containing the surnames of composers. We apply a convolutional neural network to detect solo piano works. Then, we transcribe those solo piano recordings into Musical Instrument Digital Interface (MIDI) files using a high-resolution piano transcription system. Each transcribed MIDI file contains the onset, offset, pitch, and velocity attributes of piano notes and pedals. GiantMIDI-Piano includes 90% live performance MIDI files and 10\% sequence input MIDI files. We analyse the statistics of GiantMIDI-Piano and show pitch class, interval, trichord, and tetrachord frequencies of six composers from different eras to show that GiantMIDI-Piano can be used for musical analysis. We evaluate the quality of GiantMIDI-Piano in terms of solo piano detection F1 scores, metadata accuracy, and transcription error rates. We release the source code for acquiring the GiantMIDI-Piano dataset at https://github.com/bytedance/GiantMIDI-Piano
Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate
Authors: Keval Doshi, Yasin Yilmaz
Date: 2020-10-10 00:00:00
Tasks: Anomaly Detection, Anomaly Detection In Surveillance Videos, Decision Making, Video Anomaly Detection

Anomaly detection in surveillance videos is attracting an increasing amount of attention. Despite the competitive performance of recent methods, they lack theoretical performance analysis, particularly due to the complex deep neural network architectures used in decision making. Additionally, online decision making is an important but mostly neglected factor in this domain. Much of the existing methods that claim to be online, depend on batch or offline processing in practice. Motivated by these research gaps, we propose an online anomaly detection method in surveillance videos with asymptotic bounds on the false alarm rate, which in turn provides a clear procedure for selecting a proper decision threshold that satisfies the desired false alarm rate. Our proposed algorithm consists of a multi-objective deep learning module along with a statistical anomaly detection module, and its effectiveness is demonstrated on several publicly available data sets where we outperform the state-of-the-art algorithms. All codes are available at https://github.com/kevaldoshi17/Prediction-based-Video-Anomaly-Detection-.
Product risk assessment: a Bayesian network approach
Authors: Joshua Hunte, Martin Neil, Norman Fenton
Date: 2020-10-09 00:00:00

Product risk assessment is the overall process of determining whether a product, which could be anything from a type of washing machine to a type of teddy bear, is judged safe for consumers to use. There are several methods used for product risk assessment, including RAPEX, which is the primary method used by regulators in the UK and EU. However, despite its widespread use, we identify several limitations of RAPEX including a limited approach to handling uncertainty and the inability to incorporate causal explanations for using and interpreting test data. In contrast, Bayesian Networks (BNs) are a rigorous, normative method for modelling uncertainty and causality which are already used for risk assessment in domains such as medicine and finance, as well as critical systems generally. This article proposes a BN model that provides an improved systematic method for product risk assessment that resolves the identified limitations with RAPEX. We use our proposed method to demonstrate risk assessments for a teddy bear and a new uncertified kettle for which there is no testing data and the number of product instances is unknown. We show that, while we can replicate the results of the RAPEX method, the BN approach is more powerful and flexible.
Handwriting Quality Analysis using Online-Offline Models
Authors: Yahia Hamdi, Hanen Akouaydi, Houcine Boubaker, Adel M. ALIMI
Date: 2020-10-09 00:00:00
Tasks: Specificity

This work is part of an innovative e-learning project allowing the development of an advanced digital educational tool that provides feedback during the process of learning handwriting for young school children (three to eight years old). In this paper, we describe a new method for children handwriting quality analysis. It automatically detects mistakes, gives real-time on-line feedback for children's writing, and helps teachers comprehend and evaluate children's writing skills. The proposed method adjudges five main criteria shape, direction, stroke order, position respect to the reference lines, and kinematics of the trace. It analyzes the handwriting quality and automatically gives feedback based on the combination of three extracted models: Beta-Elliptic Model (BEM) using similarity detection (SD) and dissimilarity distance (DD) measure, Fourier Descriptor Model (FDM), and perceptive Convolutional Neural Network (CNN) with Support Vector Machine (SVM) comparison engine. The originality of our work lies partly in the system architecture which apprehends complementary dynamic, geometric, and visual representation of the examined handwritten scripts and in the efficient selected features adapted to various handwriting styles and multiple script languages such as Arabic, Latin, digits, and symbol drawing. The application offers two interactive interfaces respectively dedicated to learners, educators, experts or teachers and allows them to adapt it easily to the specificity of their disciples. The evaluation of our framework is enhanced by a database collected in Tunisia primary school with 400 children. Experimental results show the efficiency and robustness of our suggested framework that helps teachers and children by offering positive feedback throughout the handwriting learning process using tactile digital devices.
Optimization of loading factor preventing target cancellation
Authors: Boris N. Oreshkin, Peter A. Bakulev
Date: 2020-10-09 00:00:00

Adaptive algorithms based on sample matrix inversion belong to an important class of algorithms used in radar target detection to overcome prior uncertainty of interference covariance. Sample matrix inversion problem is generally ill conditioned. Moreover, the contamination of the empirical covariance matrix by the useful signal leads to significant degradation of performance of this class of adaptive algorithms. Regularization, also known in radar literature as sample covariance loading, can be used to combat both ill conditioning of the original problem and contamination of the empirical covariance by the desired signal. However, the optimum value of loading factor cannot be derived unless strong assumptions are made regarding the structure of covariance matrix and useful signal penetration model. In this paper an iterative algorithm for loading factor optimization based on the maximization of empirical signal to interference plus noise ratio (SINR) is proposed. The proposed solution does not rely on any assumptions regarding the structure of empirical covariance matrix and signal penetration model. The paper also presents simulation examples showing the effectiveness of the proposed solution.
Demonstration of a Cloud-based Software Framework for Video Analytics Application using Low-Cost IoT Devices
Authors: Bhavin Joshi, Tapan Pathak, Vatsal Patel, Sarth Kanani, Pankesh Patel, Muhammad Intizar Ali, John Breslin
Date: 2020-09-29 00:00:00

The design of products and services such as a Smart doorbell, demonstrating video analytics software/algorithm functionality, is expected to address a new kind of requirements such as designing a scalable solution while considering the trade-off between cost and accuracy; a flexible architecture to deploy new AI-based models or update existing models, as user requirements evolve; as well as seamlessly integrating different kinds of user interfaces and devices. To address these challenges, we propose a smart doorbell that orchestrates video analytics across Edge and Cloud resources. The proposal uses AWS as a base platform for implementation and leverages Commercially Available Off-The-Shelf(COTS) affordable devices such as Raspberry Pi in the form of an Edge device.
Recurrent convolutional neural network for the surrogate modeling of subsurface flow simulation
Authors: Hyung Jun Yang, Timothy Yeo, Jaewoo An
Date: 2020-10-08 00:00:00
Tasks: Time Series, Time Series Analysis

The quantification of uncertainty on fluid flow in porous media is often hampered by multi-scale heterogeneity and insufficient site characterization. Monte-Carlo simulation (MCS), which runs numerical simulations for a large number of realization of input parameters , becomes infeasible when simulation cost is expensive or the degree of uncertainty is large. Many deep-neural-network-based methods are developed in order to replace the numerical flow simulation, but previous studies focused only on generating several snapshots of outputs at the fixed time steps, and lack to reflect the time dependent property of simulation data. Recently, the convolutional long short term memory (ConvLSTM) is utilized to deal with time series image data. Here, we propose to combine SegNet with ConvLSTM layers for the surrogate modeling of numerical flow simulation. The results show that the proposed method improves the performance of SegNet based surrogate model remarkably when the output of the simulation is time series data.
On social welfare orders satisfying anonymity and asymptotic density-one Pareto
Authors: Ram Sewak Dubey, Giorgio Laguzzi, Francesco Ruscitti
Date: 2020-08-11 00:00:00

We study the nature (i.e., constructive as opposed to non-constructive) of social welfare orders on infinite utility streams, and their representability by means of real-valued functions. We assume finite anonymity and introduce a new efficiency concept we refer to as asymptotic density-one Pareto. We characterize the existence of representable and constructive social welfare orders (satisfying the above properties) in terms of easily verifiable conditions on the feasible set of one-period utilities.
Implementation of modified OLSR protocol in AANETs for UDP and TCP environment
Authors: Pardeep Kumar⇑, Seema Verma
Date: 2019-07-25 00:00:00

Airborne Ad-hoc Networks (AANETs) are becoming highly popular nowadays. Due to the fast movements
of aircraft, there are frequent topology updates which result into a link break between two communicat-
ing aircraft. The routing overhead increases multiple times in search of a new route continuously. To deal
with this issue of increased overhead, this paper presents a new routing scheme named as Airborne-OLSR
(AOLSR). The proposed scheme provides more optimization of Multi-point Relay (MPR) selection criteria
used in existing Optimized Link State Routing (OLSR) protocol. This decreases the amount of overhead by
selecting the MPR either on the right side or the left side of the source node which depends on the loca-
tion of the node to which data is to be sent. The decrease in overhead will also result into more band-
width availability which decreases the possibility of link break. The proposed routing scheme has been
compared with the existing OLSR protocol for UDP and TCP environment with varying node speed in
3-D Gauss Markov Mobility model using network simulator-3(ns-3). The simulation analysis shows that
the proposed scheme is better in terms of packet delivery ratio, End-to-End delay, routing overhead and
throughput as compared to OLSR. We have also compared the AOLSR with previously developed AANET
specific protocols like automatic dependent surveillance-broadcast system aided geographic routing
protocol(A-GR) and geographic routing protocol for aircraft ad hoc network (GRAA).
Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm
Authors: Alaa Tharwat
Date: 2018-04-12 00:00:00

Mobile crowdsensing is a recent model in which a group of mobile users uses their smart devices such
as smartphones or wearable devices to cooperatively perform a large-scale sensing task. In this paper,
a novel model will be introduced for recognizing/classifying human activities that were collected from
sensor units on the chest, legs, and arms. The proposed model employed the k-Nearest Neighbor (k-NN)
classifier which is one of the most common classifiers. k-NN has only one parameter, k, to determine
the number of selected nearest neighbors to the test or unknown samples for predicting the class labels of the unknown samples. Searching for the value of k which has a great impact on the classification
performance is difficult especially with high dimensional data. This paper employs the Particle Swarm
Optimization (PSO) algorithm to search for the optimal value of the k parameter in the k-NN Classifier.
This paper shows first experimentally how the PSO in the proposed algorithm searches for the optimal
value of k parameter to reduce the misclassification rate of the k-NN classifier. Then, in the second experiment, ten standard datasets are utilized to benchmark the performance of the proposed algorithm.
For verification, the results of the PSO-kNN algorithm are compared with two well-known algorithms:
Genetic Algorithm (GA) and Ant Bee Colony Optimization (ABCO). In the third experiment, the proposed
PSO-kNN algorithm was employed for recognizing human activities. The experimental results proved that
the PSO-kNN algorithm is able to find the optimal or near optimal value(s) of the k parameter which enhances the accuracy of k-NN classifier. The results also demonstrated lower error rates compared when
GA and ABCO algorithms.
Self-Replicating Machines in Continuous Space with Virtual Physics
Authors: Arnold Smith, Peter Turney, Robert Ewaschuk
Date: 2003-04-15 00:00:00
Tasks: Artificial Life

JohnnyVon is an implementation of self-replicating machines in continuous two-dimensional space. Two types of particles drift about in a virtual liquid. The particles are automata with discrete internal states but continuous external relationships. Their internal states are governed by finite state machines but their external relationships are governed by a simulated physics that includes Brownian motion, viscosity, and spring-like attractive and repulsive forces. The particles can be assembled into patterns that can encode arbitrary strings of bits. We demonstrate that, if an arbitrary "seed" pattern is put in a "soup" of separate individual particles, the pattern will replicate by assembling the individual particles into copies of itself. We also show that, given sufficient time, a soup of separate individual particles will eventually spontaneously form self-replicating patterns. We discuss the implications of JohnnyVon for research in nanotechnology, theoretical biology, and artificial life.
A Noncoherent Space-Time Code from Quantum Error Correction
Authors: S. Andrew Lanham, Travis C. Cuvelier, Corey Ostrove, Brian La Cour, Granville Ott, Robert Heath Jr
Date: 2018-12-18 00:00:00
Tasks: Decoder

In this work, we develop a space-time block code for noncoherent communication using techniques from the field of quantum error correction. We decompose the multiple-input multiple-output (MIMO) channel into operators from quantum mechanics, and design a non-coherent space time code using the quantum stabilizer formalism. We derive an optimal decoder, and analyze the former through a quantum mechanical lens. We compare our approach to a comparable coherent approach and a noncoherent differential approach, achieving comparable or better performance.
A Better Alternative to Piecewise Linear Time Series Segmentation
Authors: Daniel Lemire
Date: 2006-05-24 00:00:00
Tasks: Time Series, Time Series Analysis

Time series are difficult to monitor, summarize and predict. Segmentation organizes time series into few intervals having uniform characteristics (flatness, linearity, modality, monotonicity and so on). For scalability, we require fast linear time algorithms. The popular piecewise linear model can determine where the data goes up or down and at what rate. Unfortunately, when the data does not follow a linear model, the computation of the local slope creates overfitting. We propose an adaptive time series model where the polynomial degree of each interval vary (constant, linear and so on). Given a number of regressors, the cost of each interval is its polynomial degree: constant intervals cost 1 regressor, linear intervals cost 2 regressors, and so on. Our goal is to minimize the Euclidean (l_2) error for a given model complexity. Experimentally, we investigate the model where intervals can be either constant or linear. Over synthetic random walks, historical stock market prices, and electrocardiograms, the adaptive model provides a more accurate segmentation than the piecewise linear model without increasing the cross-validation error or the running time, while providing a richer vocabulary to applications. Implementation issues, such as numerical stability and real-world performance, are discussed.
When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation
Authors: Dietmar Jannach, Malte Ludewig
Date: 2017-08-01 00:00:00
Tasks: Recommendation Systems, Session-Based Recommendations

Deep learning methods have led to substantial progress in various application fields of AI, and in recent years a number of proposals were made to improve recommender systems with artificial neural networks. For the problem of making session-based recommendations, i.e., for recommending the next item in an anonymous session, Hidasi et al.~recently investigated the application of recurrent neural networks with Gated Recurrent Units (GRU4REC). Assessing the true effectiveness of such novel approaches based only on what is reported in the literature is however difficult when no standard evaluation protocols are applied and when the strength of the baselines used in the performance comparison is not clear. In this work we show based on a comprehensive empirical evaluation that a heuristics-based nearest neighbor (kNN) scheme for sessions outperforms GRU4REC in the large majority of the tested configurations and datasets. Neighborhood sampling and efficient in-memory data structures ensure the scalability of the kNN method. The best results in the end were often achieved when we combine the kNN approach with GRU4REC, which shows that RNNs can leverage sequential signals in the data that cannot be detected by the co-occurrence-based kNN method.
BTrDB: Optimizing Storage System Design for Timeseries Processing
Authors: Michael P Andersen, David E. Culler
Date: 2016-02-22 00:00:00

The increase in high-precision, high-sample-rate telemetry timeseries poses a problem for existing timeseries databases which can neither cope with the throughput demands of these streams nor provide the necessary primitives for effective analysis of them. We present a novel abstraction for telemetry timeseries data and a data structure for providing this abstraction: a timepartitioning version-annotated copy-on-write tree. An implementation in Go is shown to outperform existing solutions, demonstrating a throughput of 53 million inserted values per second and 119 million queried values per second on a four-node cluster. The system achieves a 2.9x compression ratio and satisfies statistical queries spanning a year of data in under 200ms, as demonstrated on a year-long production deployment storing 2.1 trillion data points. The principles and design of this database are generally applicable to a large variety of timeseries types and represent a significant advance in the development of technology for the Internet of Things.
Free Radical Scavenging and Cytotoxic Activities of Substituted Pyrimidines
Authors: Qurat-ul-Ain, Shafqat Hussain, M. Iqbal Coudhary, Khalid Mohammed Khan
Date: 2020-03-06 00:00:00

A library of substituted pyrimidines was synthesized and evaluated for free radical scavenging, and in vitro cytotoxic activity in 3T3 cells. All compounds showed good free radical scavenging activity with IC50 values in the range of 42.9 + 0.31 to 438.3 3.3 {\mu}M as compared to the standard butylated hydroxytoluene having IC50 value of 128.83 2. 1 {\mu}M. The structure activity-relationship was also established. Selected analogues 1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 15, 19, 20, 21, 24, 25, 26 and 28 were tested for cytotoxicity in mouse fibroblast 3T3 cell line using MTT assay, and most of the analogues showed cytotoxicity. This study has identified a number of cytotoxic novel substituted pyrimidines having free radical scavenging activities that can be used as inhibitory compounds for those cancer cells whose growth is mediated by reactive oxygen species.