OpenRaiser/Intern-Atlas huggingface dataset:
Auction Based Clustered Federated Learning in Mobile Edge Computing System
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, mobile clients' computing ability and storage capacity have greatly improved, efficiently dealing with some applications locally. Federated learning is a promising distributed machine learning solution that uses local computing and local data to train the Artificial Intelligence (AI) model. Combining local computing and federated learning can train a powerful AI model under the premise of ensuring local data privacy while making full use of mobile clients' resources. However, the heterogeneity of local data, that is, Non-independent and identical distribution (Non-IID) and imbalance of local data size, may bring a bottleneck hindering the application of federated learning in mobile edge computing (MEC) system. Inspired by this, we propose a cluster-based clients selection method that can generate a federated virtual dataset that satisfies the global distribution to offset the impact of data heterogeneity and proved that the proposed scheme could converge to an approximate optimal solution. Based on the clustering method, we propose an auction-based clients selection scheme within each cluster that fully considers the system's energy heterogeneity and gives the Nash equilibrium solution of the proposed scheme for balance the energy consumption and improving the convergence rate. The simulation results show that our proposed selection methods and auction-based federated learning can achieve better performance with the Convolutional Neural Network model (CNN) under different data distributions.
Constrained Text Generation with Global Guidance -- Case Study on CommonGen
Venue: arXiv cs.CL 2021
Year: 2021

This paper studies constrained text generation, which is to generate sentences under certain pre-conditions. We focus on CommonGen, the task of generating text based on a set of concepts, as a representative task of constrained text generation. Traditional methods mainly rely on supervised training to maximize the likelihood of target sentences.However, global constraints such as common sense and coverage cannot be incorporated into the likelihood objective of the autoregressive decoding process. In this paper, we consider using reinforcement learning to address the limitation, measuring global constraints including fluency, common sense and concept coverage with a comprehensive score, which serves as the reward for reinforcement learning. Besides, we design a guided decoding method at the word, fragment and sentence levels. Experiments demonstrate that our method significantly increases the concept coverage and outperforms existing models in various automatic evaluations.
Development of An Assessment Benchmark for Synchronous Online Learning for Nigerian Universities
Venue: arXiv cs.CY 2021
Year: 2021

In recent times, as a result of COVID-19 pandemic, higher institutions in Nigeria have been shutdown and the leadership of Academic Staff Union of University (ASUU) said that Nigerian universities cannot afford to mount Online learning platforms let alone conduct such learning system in Nigeria due to lack of infrastructure, capacity and skill sets in the face of COVID-19 pandemic. In the light of this, this research undertook an online survey using University of Nigeria, Nsukka (UNN) as a case study to know which type of online learning system ASUU leadership is talking about - Asynchronous or Synchronous? How did ASUU come about their facts? Did ASUU base their assertion on facts, if YES, what are the benchmarks? Therefore, this research project is focused on providing benchmarks to assess if a Nigerian University has what it takes to run a synchronous Online Learning. It includes Infrastructure needed (Hardware, Software, Network connectivity), Skill sets from staff (Computer literacy level). In a bid to do this, an online survey was administered to the staff of Centre for Distance and E-learning of UNN and out of the 40 members of that section of the University, we had 32 respondents. The survey seeks to find whether UNN has the requisite infrastructure and the skill sets to mount synchronous online learning. The available results of the study reveal that UNN is deficit in both the requisite infrastructure and Skills sets to mount synchronous online learning.
An FPTAS for the $Δ$-modular multidimensional knapsack problem
Venue: arXiv cs.CC 2021
Year: 2021

It is known that there is no EPTAS for the $m$-dimensional knapsack problem unless $W[1] = FPT$. It is true already for the case, when $m = 2$. But, an FPTAS still can exist for some other particular cases of the problem. In this note, we show that the $m$-dimensional knapsack problem with a $Δ$-modular constraints matrix admits an FPTAS, whose complexity bound depends on $Δ$ linearly. More precisely, the proposed algorithm complexity is $$O(T_{LP} \cdot (1/\varepsilon)^{m+3} \cdot (2m)^{2m + 6} \cdot Δ),$$ where $T_{LP}$ is the linear programming complexity bound. In particular, for fixed $m$ the arithmetical complexity bound becomes $$ O(n \cdot (1/\varepsilon)^{m+3} \cdot Δ). $$ Our algorithm is actually a generalisation of the classical FPTAS for the $1$-dimensional case. Strictly speaking, the considered problem can be solved by an exact polynomial-time algorithm, when $m$ is fixed and $Δ$ grows as a polynomial on $n$. This fact can be observed combining previously known results. In this paper, we give a slightly more accurate analysis to present an exact algorithm with the complexity bound $$ O(n \cdot Δ^{m + 1}), \quad \text{ for $m$ being fixed}. $$ Note that the last bound is non-linear by $Δ$ with respect to the given FPTAS.
Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning
Venue: arXiv cs.SD 2021
Year: 2021

For centuries researchers have used sound to monitor and study wildlife. Traditionally, conservationists have identified species by ear; however, it is now common to deploy audio recording technology to monitor animal and ecosystem sounds. Animals use sound for communication, mating, navigation and territorial defence. Animal sounds provide valuable information and help conservationists to quantify biodiversity. Acoustic monitoring has grown in popularity due to the availability of diverse sensor types which include camera traps, portable acoustic sensors, passive acoustic sensors, and even smartphones. Passive acoustic sensors are easy to deploy and can be left running for long durations to provide insights on habitat and the sounds made by animals and illegal activity. While this technology brings enormous benefits, the amount of data that is generated makes processing a time-consuming process for conservationists. Consequently, there is interest among conservationists to automatically process acoustic data to help speed up biodiversity assessments. Processing these large data sources and extracting relevant sounds from background noise introduces significant challenges. In this paper we outline an approach for achieving this using state of the art in machine learning to automatically extract features from time-series audio signals and modelling deep learning models to classify different bird species based on the sounds they make. The acquired bird songs are processed using mel-frequency cepstrum (MFC) to extract features which are later classified using a multilayer perceptron (MLP). Our proposed method achieved promising results with 0.74 sensitivity, 0.92 specificity and an accuracy of 0.74.
A Simple Post-Processing Technique for Improving Readability Assessment of Texts using Word Mover's Distance
Venue: arXiv cs.CL 2021
Year: 2021

Assessing the proper difficulty levels of reading materials or texts in general is the first step towards effective comprehension and learning. In this study, we improve the conventional methodology of automatic readability assessment by incorporating the Word Mover's Distance (WMD) of ranked texts as an additional post-processing technique to further ground the difficulty level given by a model. Results of our experiments on three multilingual datasets in Filipino, German, and English show that the post-processing technique outperforms previous vanilla and ranking-based models using SVM.
Integration of Convolutional Neural Networks in Mobile Applications
Venue: arXiv cs.LG 2021
Year: 2021

When building Deep Learning (DL) models, data scientists and software engineers manage the trade-off between their accuracy, or any other suitable success criteria, and their complexity. In an environment with high computational power, a common practice is making the models go deeper by designing more sophisticated architectures. However, in the context of mobile devices, which possess less computational power, keeping complexity under control is a must. In this paper, we study the performance of a system that integrates a DL model as a trade-off between the accuracy and the complexity. At the same time, we relate the complexity to the efficiency of the system. With this, we present a practical study that aims to explore the challenges met when optimizing the performance of DL models becomes a requirement. Concretely, we aim to identify: (i) the most concerning challenges when deploying DL-based software in mobile applications; and (ii) the path for optimizing the performance trade-off. We obtain results that verify many of the identified challenges in the related work such as the availability of frameworks and the software-data dependency. We provide a documentation of our experience when facing the identified challenges together with the discussion of possible solutions to them. Additionally, we implement a solution to the sustainability of the DL models when deployed in order to reduce the severity of other identified challenges. Moreover, we relate the performance trade-off to a new defined challenge featuring the impact of the complexity in the obtained accuracy. Finally, we discuss and motivate future work that aims to provide solutions to the more open challenges found.
Empowering Visual Internet-of-Things Mashups with Self-Healing Capabilities
Venue: arXiv cs.SE 2021
Year: 2021

Internet-of-Things (IoT) systems have spread among different application domains, from home automation to industrial manufacturing processes. The rushed development by competing vendors to meet the market demand of IoT solutions, the lack of interoperability standards, and the overall lack of a defined set of best practices have resulted in a highly complex, heterogeneous, and frangible ecosystem. Several works have been pushing towards visual programming solutions to abstract the underlying complexity and help humans reason about it. As these solutions begin to meet widespread adoption, their building blocks usually do not consider reliability issues. Node-RED, being one of the most popular tools, also lacks such mechanisms, either built-in or via extensions. In this work we present SHEN (Self-Healing Extensions for Node-RED) which provides 17 nodes that collectively enable the implementation of self-healing strategies within this visual framework. We proceed to demonstrate the feasibility and effectiveness of the approach using real devices and fault injection techniques.
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Venue: arXiv cs.RO 2021
Year: 2021

Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems. We propose efficient mechanisms to both characterize and generate testing scenarios using a state-of-the-art driving simulator. For any scenario, our method generates a set of possible driving paths and identifies all the possible safe driving trajectories that can be taken starting at different times, to compute metrics that quantify the complexity of the scenario. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project, as well as adversarial scenarios generated in simulation. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident. We demonstrate a strong correlation between the proposed metrics and human intuition.
Photonic Computing to Accelerate Data Processing in Wireless Communications
Venue: arXiv cs.ET 2021
Year: 2021

Massive multiple-input multiple-output (MIMO) systems are considered as one of the leading technologies employed in the next generations of wireless communication networks (5G), which promise to provide higher spectral efficiency, lower latency, and more reliability. Due to the massive number of devices served by the base stations (BS) equipped with large antenna arrays, massive-MIMO systems need to perform high-dimensional signal processing in a considerably short amount of time. The computational complexity of such data processing, while satisfying the energy and latency requirements, is beyond the capabilities of the conventional widely-used digital electronics-based computing, i.e., Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs). In this paper, the speed and lossless propagation of light is exploited to introduce a photonic computing approach that addresses the high computational complexity required by massive-MIMO systems. The proposed computing approach is based on photonic implementation of multiply and accumulate (MAC) operation achieved by broadcast-and-weight (B&W) architecture. The B&W protocol is limited to real and positive values to perform MAC operations. In this work, preprocessing steps are developed to enable the proposed photonic computing architecture to accept any arbitrary values as the input. This is a requirement for wireless communication systems that typically deal with complex values. Numerical analysis shows that the performance of the wireless communication system is not degraded by the proposed photonic computing architecture, while it provides significant improvements in time and energy efficiency for massive-MIMO systems as compared to the most powerful Graphics Processing Units (GPUs).
Real-time Nonrigid Mosaicking of Laparoscopy Images
Venue: arXiv cs.CV 2021
Year: 2021

The ability to extend the field of view of laparoscopy images can help the surgeons to obtain a better understanding of the anatomical context. However, due to tissue deformation, complex camera motion and significant three-dimensional (3D) anatomical surface, image pixels may have non-rigid deformation and traditional mosaicking methods cannot work robustly for laparoscopy images in real-time. To solve this problem, a novel two-dimensional (2D) non-rigid simultaneous localization and mapping (SLAM) system is proposed in this paper, which is able to compensate for the deformation of pixels and perform image mosaicking in real-time. The key algorithm of this 2D non-rigid SLAM system is the expectation maximization and dual quaternion (EMDQ) algorithm, which can generate smooth and dense deformation field from sparse and noisy image feature matches in real-time. An uncertainty-based loop closing method has been proposed to reduce the accumulative errors. To achieve real-time performance, both CPU and GPU parallel computation technologies are used for dense mosaicking of all pixels. Experimental results on \textit{in vivo} and synthetic data demonstrate the feasibility and accuracy of our non-rigid mosaicking method.
TensorGP -- Genetic Programming Engine in TensorFlow
Venue: arXiv cs.AI 2021
Year: 2021

In this paper, we resort to the TensorFlow framework to investigate the benefits of applying data vectorization and fitness caching methods to domain evaluation in Genetic Programming. For this purpose, an independent engine was developed, TensorGP, along with a testing suite to extract comparative timing results across different architectures and amongst both iterative and vectorized approaches. Our performance benchmarks demonstrate that by exploiting the TensorFlow eager execution model, performance gains of up to two orders of magnitude can be achieved on a parallel approach running on dedicated hardware when compared to a standard iterative approach.
A comparison of low-cost behavioral observation software applications and recommendations for use
Venue: arXiv cs.SI 2021
Year: 2021

In the field of animal behavior and behavioral ecology, many standardized methods to observe animal behavior were established in the last decades. While the protocols remained similar, behavioral researchers can take advantage of technological advancements to enter observations directly onto a handheld computer (phone, tablet, etc.), saving time and potentially increasing fidelity of recordings. However, we now have the choice between many different platforms for recording behavioral observations. Our challenge is choosing the most appropriate platform that fits a particular study question, research design, budget, and desired amount of preparatory time. Here, we review six low-cost software applications for handheld computers that are available for real-time entry of behavioral observations: Animal Behaviour Pro, Animal Observer, BORIS, CyberTracker, Prim8, and ZooMonitor. We discuss the preliminary decisions that have to be made about the study design, and we assess the six applications by providing the advantages and disadvantages of each platform and an overall application comparison. Our goal is to help researchers make calculated decisions about what behavioral observation platform is best for their study system and question.
Conceptual capacity and effective complexity of neural networks
Venue: arXiv cs.LG 2021
Year: 2021

We propose a complexity measure of a neural network mapping function based on the diversity of the set of tangent spaces from different inputs. Treating each tangent space as a linear PAC concept we use an entropy-based measure of the bundle of concepts in order to estimate the conceptual capacity of the network. The theoretical maximal capacity of a ReLU network is equivalent to the number of its neurons. In practice however, due to correlations between neuron activities within the network, the actual capacity can be remarkably small, even for very big networks. Empirical evaluations show that this new measure is correlated with the complexity of the mapping function and thus the generalisation capabilities of the corresponding network. It captures the effective, as oppose to the theoretical, complexity of the network function. We also showcase some uses of the proposed measure for analysis and comparison of trained neural network models.
Privacy-Preserving Infection Exposure Notification without Trust in Third Parties
Venue: arXiv cs.CR 2021
Year: 2021

In response to the COVID-19 pandemic, Bluetooth-based contact tracing has been deployed in many countries with the help of the developers of smartphone operating systems that provide APIs for privacy-preserving exposure notification. However, it has been assumed by the design that the OS developers, smartphone vendors, or governments will not violate people's privacy. We propose a privacy-preserving exposure notification under situations where none of the middle entities can be trusted. We believe that it can be achieved with small changes to the existing mechanism: random numbers are generated on the application side instead of the OS, and the positive test results are reported to a public ledger (e.g. blockchain) rather than to a government server, with endorsements from the medical institutes with blind signatures. We also discuss how to incentivize the peer-to-peer maintenance of the public ledger if it should be newly built. We show that the level of verifiability is much higher with our proposed design if a consumer group were to verify the privacy protections of the deployed systems. We believe that this will allow for safer contact tracing, and contribute to healthier lifestyles for citizens who may want to or have to go out under pandemic situations.
uTHCD: A New Benchmarking for Tamil Handwritten OCR
Venue: arXiv cs.CV 2021
Year: 2021

Handwritten character recognition is a challenging research in the field of document image analysis over many decades due to numerous reasons such as large writing styles variation, inherent noise in data, expansive applications it offers, non-availability of benchmark databases etc. There has been considerable work reported in literature about creation of the database for several Indic scripts but the Tamil script is still in its infancy as it has been reported only in one database [5]. In this paper, we present the work done in the creation of an exhaustive and large unconstrained Tamil Handwritten Character Database (uTHCD). Database consists of around 91000 samples with nearly 600 samples in each of 156 classes. The database is a unified collection of both online and offline samples. Offline samples were collected by asking volunteers to write samples on a form inside a specified grid. For online samples, we made the volunteers write in a similar grid using a digital writing pad. The samples collected encompass a vast variety of writing styles, inherent distortions arising from offline scanning process viz stroke discontinuity, variable thickness of stroke, distortion etc. Algorithms which are resilient to such data can be practically deployed for real time applications. The samples were generated from around 650 native Tamil volunteers including school going kids, homemakers, university students and faculty. The isolated character database will be made publicly available as raw images and Hierarchical Data File (HDF) compressed file. With this database, we expect to set a new benchmark in Tamil handwritten character recognition and serve as a launchpad for many avenues in document image analysis domain. Paper also presents an ideal experimental set-up using the database on convolutional neural networks (CNN) with a baseline accuracy of 88% on test data.
A review of machine learning in processing remote sensing data for mineral exploration
Venue: arXiv cs.LG 2021
Year: 2021

The decline of the number of newly discovered mineral deposits and increase in demand for different minerals in recent years has led exploration geologists to look for more efficient and innovative methods for processing different data types at each stage of mineral exploration. As a primary step, various features, such as lithological units, alteration types, structures, and indicator minerals, are mapped to aid decision-making in targeting ore deposits. Different types of remote sensing datasets, such as satellite and airborne data, make it possible to overcome common problems associated with mapping geological features. The rapid increase in the volume of remote sensing data obtained from different platforms has encouraged scientists to develop advanced, innovative, and robust data processing methodologies. Machine learning methods can help process a wide range of remote sensing datasets and determine the relationship between components such as the reflectance continuum and features of interest. These methods are robust in processing spectral and ground truth measurements against noise and uncertainties. In recent years, many studies have been carried out by supplementing geological surveys with remote sensing datasets, which is now prominent in geoscience research. This paper provides a comprehensive review of the implementation and adaptation of some popular and recently established machine learning methods for processing different types of remote sensing data and investigates their applications for detecting various ore deposit types. We demonstrate the high capability of combining remote sensing data and machine learning methods for mapping different geological features that are critical for providing potential maps. Moreover, we find there is scope for advanced methods to process the new generation of remote sensing data for creating improved mineral prospectivity maps.