Reviewerly/exHarmony huggingface dataset:
A Lightweight Fully Convolutional Network for Cardiac MRI Segmentation

Cardiac magnetic resonance imaging is a commonly used method to assess the function and structure of the cardiovascular system. Separating the heart structure from Cardiac MRI is an important step in calculating the cardiac function index. The automatic segmentation algorithm can significantly reduce the workload of the doctor. Most of the current automatic segmentation algorithm has a large calculation amount and size, has high requirements on the hardware. Based on the U-Net model, this paper proposes a lightweight full convolution model with separable convolution and residual connection. Experiments show that the model reduces the number of parameters while ensuring learning ability and balances speed and accuracy.
Frames Learned by Prime Convolution Layers in a Deep Learning Framework

This brief addresses understandability of modern machine learning networks with respect to the statistical properties of their convolution layers. It proposes a set of tools for categorizing a convolution layer in terms of kernel property (meanlet, differencelet, or distrotlet) or kernel sequence property (frame spectra and intralayer correlation matrix). These tools are expected to be relevant for determining the generalization capabilities of a convolutional neural network. In particular, this brief highlights that the less frequency penalizing network among AlexNet, GoogleNet, RESNET101, and VGG19 is the more relevant one in terms of solutions for low-level ice-sheet feature enhancement.
Optimizing Gaussian Measure of Lattices using Dimensionality Reduction

The Shortest Vector problem is an intractable mathematical basis for the design of Learning with error-based Lattice cryptography which is a cryptographic primitive that has the capability to secure data against quantum threats. Approaches to tackle this problem have been based on the sampling from a discrete Gaussian distribution that is close to a theoretical one by exploiting the orthogonality of a lattice basis to ensure the secrecy of the basis from a polynomial time algorithm especially with a large dimensional Euclidean space. This orthogonality is achieved by using LLL(Lenstra-Lenstra-Lovasz) reduction to decompose the basis into its Gram-Schmidt equivalent with drawbacks in complexity as well as reduction to worst case scenarios. We propose to apply Dimensionality reduction to decompose the basis into linear independent vectors by constructing a collapse function as an optimization problem which can be solved on the condition that a Projection of the basis vectors from the High dimensional space to low dimensional manifold would have nearly orthogonal constitution. From the result of the Monte Carlo simulation, our approach improves the BER performance over the LLL algorithm for about 1db and 4db in the 4 × 4 and 6 × 6 uncoded system using 4QAM constellation.
Matrix Fisher-Gaussian Distribution on SO(3) ×ℝ<sup>n</sup> for Attitude Estimation with a Gyro Bias

In this paper, we propose a new probability density function, referred to as the matrix Fisher-Gaussian (MFG) distribution, on the product of the special orthogonal group and the Euclidean space. MFG is constructed by conditioning a multivariate Gaussian distribution from the ambient Euclidean space, such that the correlation between attitudes and linear components is formulated at the tangent space of the mean attitude. The desirable feature is that MFG can globally represent large uncertainties in the attitude of a rigid body correlated with any variable in the Euclidean space, thereby eliminating singularities and complexities inherent to local coordinates. Several stochastic properties and an approximate maximum likelihood estimation are derived for MFG, and it is further utilized for unscented attitude estimation with a gyro bias. It is illustrated that the proposed attitude estimation scheme with MFG exhibits more accurate estimates than the multiplicative extended Kalman filter for a challenging case of large initial estimation errors.
Software Curriculum Transformation at the University Level

Many software-related degrees exist, and a diversity of programs makes it difficult for candidates to choose where they wish to study. Selecting the wrong program costs students time, money, and considerable effort. Though several institutions have created curriculum guidelines for data science related programs, an overall consensus on program content does not exist at either the undergraduate or graduate levels. This paper examines the most common course requirements, such as data mining, machine learning, mathematics, software engineering, data analysis, and data visualization. We then compare the requirement analysis against the specifics of data science related programs offered at the Universidad de Lima, the Universidad Pacifico, and the University of St. Thomas. The results show that all three universities have active programs worth consideration and give students a model of what to look for when selecting their programs.
Safe Motion Planning under Partial Observability with an Optimal Deterministic Planner

This paper derives a principled framework for efficiently and safely navigating partially observable multi-agent systems using an optimal deterministic planner. This is accomplished by decoupling the navigation problem into independent collision avoidance and guidance problems and by providing mechanisms for solving both efficiently. While the framework does forgo global optimality in order to compute solutions, we argue that such optimality is unattainable in practice due to intractability, so nothing is actually sacrificed. An example solution is demonstrated for a novel graph traversal problem using a deterministic, single-agent velocity profile planner in a partially observable, multi-agent setting.
Can Scientific Publication's Network Structural Features Predict its Citation?

The citation relationship between scientific publications constitutes a huge and complex citation network, which is of great significance for hotspot analysis and cutting-edge prediction in different fields. Nevertheless, how to evaluate the novelty and impact of a scientific publication in its early stages remains an open question. To address this issue, we apply a network representation learning approach (struc2vec) to represent the full complexity of citation network structure, explore the extent to which an emerging science publication has changed the network structure of existing knowledge, and explain the relationship between this change and the paper's cited numbers from both clustering and network visualization perspectives. We found that the structural features captured by struc2vec can predict future citations of scientific publications to some extent. The predictive effects can be interpreted by how a new publication connects to and alters the existing structure of scientific knowledge in our visual analytics.
Code Free Chatbot Development

Advancement in technology and innovation in teaching such as chatbot and extended reality can be daunting for teachers but as an educator, we need to leverage on these advancements to respond to the changes and challenges in the teaching and learning landscape. There are a number of tools available for teachers to use to overcome the challenges, and one of them is the application of artificial intelligence (AI) but creating a chatbot requires complex computer programming skills, and it is usually built from scratch to fit the intended educational purpose. This practice makes it difficult for teachers to adapt existing systems or to attempt in creating a similar version. In this workshop, we will be sharing our experiences gained from developing various chatbots for higher education using a commercial platform that can jumpstart your chatbot.
A Face Recognition Module For Video Content Analysis in Malaysian Parliament Sessions

Nowadays, real time feed video data are publicly available through various social network platforms. These data might be a live CCTV recording, news broadcast, parliament session, and so on. As the data usually contains many interesting events, various video semantic analysis tasks can be performed, such as video summarizing, motion detection, face recognition, video tracking and style detection, for the purpose of extracting these events from large video datasets.In this paper, we propose a face recognition module to be used for video content analysis of the Parliament of Malaysia sessions. The proposed system is expected to help Malaysian citizens in identifying their respective parliament representative's performance in quantitative and more objective manner.
Security Concepts in Smart Cities

Both the population growth in the world and the number of people living in the city are increasing day by day while the rural population decreases. However, lack of resources, limited sources or difficulties in city life also arise. In response to these situations, the Smart City concept emerges and thanks to this concept, which integrates information and communication technology, it optimizes the available resources and makes people's life more qualified, efficient and comfortable. Smart cities include the phases of transferring, storing and processing real-time data from sensors and manage variable subsystems. While the transactions are carried out successfully for the purposes, the security and privacy problems must be examined carefully. In this paper, we analyze security issues and challenges in two ways: IoT-based and cloud-based. And as a solution proposal, it is stated that we can use authentication, encrypted communication, and blockchain to ensure data privacy. It is difficult to provide security in smart city architectures due to its decentralized and distributed structure with the existing technology IoT. Blockchain offers a security approach with a decentralized and distributed structure.
Software Development Analytics in Practice: A Systematic Literature Review

Context:Software Development Analytics is a research area concerned with providing insights to improve product deliveries and processes. Many types of studies, data sources and mining methods have been used for that purpose. Objective:This systematic literature review aims at providing an aggregate view of the relevant studies on Software Development Analytics in the past decade, with an emphasis on its application in practical settings. Method:Definition and execution of a search string upon several digital libraries, followed by a quality assessment criteria to identify the most relevant papers. On those, we extracted a set of characteristics (study type, data source, study perspective, development life-cycle activities covered, stakeholders, mining methods, and analytics scope) and classified their impact against a taxonomy. Results:Source code repositories, experimental case studies, and developers are the most common data sources, study types, and stakeholders, respectively. Product and project managers are also often present, but less than expected. Mining methods are evolving rapidly and that is reflected in the long list identified. Descriptive statistics are the most usual method followed by correlation analysis. Being software development an important process in every organization, it was unexpected to find that process mining was present in only one study. Most contributions to the software development life cycle were given in the quality dimension. Time management and costs control were lightly debated. The analysis of security aspects suggests it is an increasing topic of concern for practitioners. Risk management contributions are scarce. Conclusions:There is a wide improvement margin for software development analytics in practice. For instance, mining and analyzing the activities performed by software developers in their actual workbench, the IDE.
Safer reinforcement learning through evolved instincts

An important goal in reinforcement learning is to create agents that can quickly adapt to new goals but at the same time avoid situations that might cause damage to themselves or their environments. One way agents learn is through exploration mechanisms, which are needed to discover new policies. However, in deep reinforcement learning, exploration is normally done by injecting noise in the action space. While performing well in many domains, this setup has the inherent risk that the noisy actions lead agents to unsafe environment states. In this paper, we introduce a novel approach called Meta-Learned Instinctual Networks (MLIN) that allows agents to perform lifetime learning while avoiding hazardous states. At the core of the approach is a plastic network trained through reinforcement learning and an evolved "instinctual" network, which does not change during the agent's lifetime but can modulate the noisy output of the plastic network. We test our idea on a simple 2D navigation task with hazard zones, in which the agent has to learn to approach new targets during deployment. While a standard meta-trained network performs poorly in these tasks, MLIN allows agents to learn to navigate to new targets while minimizing collisions with hazard zones. These results suggest that meta-learning augmented with an instinctual network is a promising approach for safe AI.
Challenges and Solutions of Identifying Similarities and Duplication in Digital Libraries

Large-scale digital libraries such as the HathiTrust, with access to over 17 million texts, include duplicative texts and metadata inconsistencies that impede information access and retrieval. At this scale, it is untenable to manually evaluate each text. The SaDDL (Similarities and Duplication in Digital Libraries) project has been developing content-based methods for identifying similar text relationships within a digital library. This framework allows us to quantify words, themes, and concepts in order to identify similarity on a massive scale currently unobtainable by human effort. Secondly, this method allows us to identify the most representative scan of a target work. This poster presents the way of reconstructing the same work relationships deriving from content comparison directly, rather than matched superficial metadata to obtain the most representative copy---defined as the most complete, correct, cleanest expression of work. The philosophy behind this approach is to study the collection emphasizing the content itself, which is closer to the essence of the text.
New Approaches for Performance Optimization and Analysis of Large-Scale Dynamic Social Network Analysis using Anytime Anywhere Algorithms

During the last decade, the availability of large amounts of social network information from various social and socio-technical networks has increased dramatically. These data sources are inherently dynamic with constantly evolving relationships and connections between entities. Research in this area must address the challenge of analyzing these dynamic datasets under potentially strict time constraints. In addition, due to the sheer size of these networks, they tend to be stored and analyzed on distributed platforms. In our previous work, we designed methodologies which are anytime and anywhere to design scalable parallel/distributed algorithms that incorporate different forms of network changes. In this work, we will investigate various schemas to balance the incorporation of dynamic network changes that will substantially reduce idleness and load imbalances among processors. We will show theoretically that in most cases our buffer-based methodology performs better than the more common way of handling changes as they come in.
Exploring Blockchain Mining Mechanism Limitations

Blockchain is a new technical revolution based on decentralized architecture to support peer-to-peer communication without a third party. All transactions occurring in the blockchain network are recorded in a distributed shared ledger and become immutable. Mining is the mechanism of confirming transactions and creating blocks in the chain. Miners are nodes representing the network computational power responsible for forming blocks in the network through running complex mathematical computations. This paper investigates the current blockchain mining mechanism limitations.
A wearable fitness tracking ecosystem for exercise therapy for traumatic brain injury patients

Previous research has shown that exercise can be an effective means of treating patients with mild traumatic brain injury. However, compliance with prescribed exercise regimen has been low. Design of an ecosystem for utilizing a wrist worn fitness tracking watch is described which is aimed at increasing patient compliance with the prescribed exercise. The hardware and software structure of the ecosystem are described, and examples of the system function are presented.
Evolving neural network agents to play atari games with compact state representations

Recent success in solving hard reinforcement learning problems can be partly credited to the use of deep neural networks, which can extract high-level features and learn compact state representations from high-dimensional inputs, such as images. However, the large networks required to learn both state representation and policy using this approach limit the effectiveness and benefits of neuroevolution methods that have proven effective at solving simpler problems in the past. One potential solution to this problem is to separate state representation and policy learning and only apply neuroevolution to the latter. We extend research following this approach by evolving small policy networks for Atari games using NEAT, that learn from compact state representations provided by the recently released Atari Annotated RAM Interface (Atari ARI). Our results show that it is possible to evolve agents that exceed expert human performance using these compact state representations, and that, for some games, successful policy networks can be evolved that contain only a few or even no hidden nodes.
Evolving genetic programming trees in a rule-based learning framework

Rule-based machine learning (RBML) algorithms such as learning classifier systems (LCS) are well suited to classification problems with complex interactions and heterogeneous associations. Alternatively, genetic programming (GP) has a complementary set of strengths and weaknesses best suited to regression problems and homogeneous associations. Both approaches yield largely interpretable solutions. An ideal ML algorithm would have the capacity to adapt and blend representation to best suit the problem at hand. In order to combine the strengths of these respective algorithm representations, a framework allowing coexistence and co-evolution of trees and rules is needed. In this work, we lay the empirical groundwork for such a framework by demonstrating the capability of GP trees to be evolved within an LCS-algorithm framework with comparable performance to a set of standard GP frameworks. We discuss how these results support the feasibility of a GP-LCS framework and next-step challenges to be addressed.
Feature engineering for improving robustness of crossover in symbolic regression

Isolating the fitness-contribution of substructures is typically a difficult task in Genetic Programming (GP). Hence, useful substructures are lost when the overall structure (model) performs poorly. Furthermore, while crossover is heavily used in GP, it typically produces offspring models with significantly lower fitness than that of the parents. In symbolic regression, this degradation also occurs because the coefficients of an evolving model lose utility after crossover. This paper proposes isolating the fitness-contribution of various substructures and reducing the negative impact of crossover by evolving a set of features instead of monolithic models. The method then leverages multiple linear regression (MLR) to optimise the coefficients of these features. Since adding new features cannot degrade the accuracy of an MLR produced model, MLR-aided GP models can bloat. To penalise such additions, we use Adjusted R2 as the fitness function. The paper compares the proposed method with standard GP and GP with linear scaling. Experimental results show that the proposed method matches the accuracy of the competing methods within only 1/10th of the number of generations. Also, the method significantly decreases the rate of post-crossover fitness degradation.
Activity Recognition using Multi-Class Classification inside an Educational Building

Activity Recognition can be referred to as the process of describing and classifying actions, pinpoint specific movements, and extract unique patterns from the dataset using heterogeneous sensing modalities. Activity Recognition approaches have garnered the attention of researchers in the energy management domain to enhance energy utilization in buildings. In our experiment, we define activities as a combination of different actions, which are detected using multiple sensors. To learn insights for the various activities, we used inexpensive Passive Infrared (PIR) sensors in the test-bed. This study aims at gaining high-level knowledge about activities from the low-resolution sensors deployed. For accurate occupancy counts, we have used 3D Stereo Vision Cameras at the entrance, and count lines are defined to capture the transitions of inflow and outflow of multiple occupants. Multi-class labels enable activity recognition on the collected dataset. The multi-class labels used are 1) Moving, 2) Stagnant, 3) Outside, 4) Both (Moving and Stagnant), 5) No activity inside. The labeling for the multiclass is done through an algorithm using supervised learning. The data acquisition gets carried out from 23 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> November to 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> December 2018, spanning over a period for 11 days. The results document that Gradient Boosting Classifier outperforms any other Machine Learning Classification (MLC) algorithm with an accuracy of 97.59% and an F1 score of 97.40% for activity recognition. This paper also explicitly highlights the challenges and limitations faced during the initial phase for the deployment, and it identifies the key research trends and directs towards the potential improvements in the field of occupancy sensing for energy-efficient buildings.