Reviewerly/exHarmony huggingface dataset:
Towards knowledge-based virtual assistant development with the aid of ontology transformations

One of the challenges for the popularization of knowledge-based systems (KBS) is to be more user-friendly and involved in solving everyday tasks. A way to encourage users to apply KBSs more actively is the use of up-to-date achievements in the field of human-machine communication, in particular a special type of intelligent systems – virtual assistants. In this paper we present the roadmap ideas for proper upgradation of the Prototyping Expert Systems based On Transformations (PESoT) technique for the virtual assistant facilities. This technique involves the use and transformation of conceptual models and ontologies for creating software and source codes. In this paper, we present computation- and platform-independent models (ontology of the dialog model, ontology for the Aimylogic tool) as well as correspondence examples for transformation implementation.
Glyph-based visualization of health trajectories

Whenever a diagnosis is given, a procedure is performed, or a drug is prescribed, it leads to an entry into an electronic health record (EHR) system. Previously, this data was difficult to utilize because of rules regarding confidentiality, but new security approaches and pseudonymization have enabled us to work with this data. Health-related data is voluminous and complex, and it can be difficult to abstract a meaningful overview. One of the complexities is its longitudinality. Often medical research is cross-sectional - we often take a point in time for analysis, when instead, it might be more beneficial to see the trajectory that led to the point in time. We are currently developing a trajectory visualization tool for longitudinal electronic health data. It is a web-based tool that interfaces with the OHDSI data infrastructure and visualizes the cohorts and concept sets (groups of medical codes) defined via the OHDSI Atlas GUI. Currently, our tool is in user testing and it will be deployed to a wider user group during the spring. The user feedback has been positive. Users find the tool especially useful in understanding and debugging their OHDSI Atlas cohort definitions.
Visualization overview: Using modern text mining techniques to provide insight into visualization research practice

We take a new approach to analyze what the general focus of Visualization research is, among the panoply of different topics and approaches. Due to the intrinsic characteristics of this kind of in-depth research, being often too time-consuming and difficult to carry, we resort to text mining techniques to streamline this analysis. This study was carried out by applying topic modeling for discovering the abstract topics that occur in visualization papers. The study used the vispubdata.org dataset as the reference to gather almost every paper presented, from 1990 to 2018, at the IEEE Visualization (VIS) set of conferences: InfoVis, SciVis, VAST, and Vis. We requested ten topics and assigned each one its ten most important and representative terms. With this analysis, we intend to envelop the current practices in the visualization research community.
Interpretable Machine Learning for Self-Service High-Risk Decision-Making

This paper contributes to interpretable machine learning via visual knowledge discovery in general line coordinates (GLC). The concepts of hyperblocks as interpretable dataset units and general line coordinates are combined to create a visual self-service machine learning model. Dynamic Scaffolding Coordinates as lossless multidimensional coordinate systems are proposed, and their applications as visual models is shown. DSC1 and DSC2 can map multiple dataset attributes to a single two-dimensional (X, Y) Cartesian plane using a graph construction algorithm. The hyperblock analysis was used to determine visually appealing dataset attribute orders and to reduce line occlusion. It is shown that hyperblocks can generalize decision tree rules and a series of DSC1 or DSC2 plots can visualize a decision tree. The DSC1 and DSC2 plots were tested on benchmark datasets from the UCI ML repository. They allowed for visual classification of data. Additionally, areas of hyperblock impurity were discovered and used to establish dataset splits that highlight the upper estimate of worst-case model accuracy to guide model selection for high-risk decision-making. Major benefits of DSC1 and DSC2 is their highly interpretable nature. They allow domain experts to control or establish new machine learning models through visual pattern discovery.
A Deep Learning Approach to Concept Maps Similarity

Concept maps are graphic tools to organize, represent and share knowledge. In particular, a concept map can explicitly express the knowledge of a person or group, about a given domain of interest. Concept maps are used effectively to support learning of any topic, at any level: from Primary School to University, and to professional/vocational training, it can stimulate and unveil the occurrence of meaningful learning. In an educational context, having the possibility to compare Concept Maps coming from different students, also by means of an automated computation of map similarity, can reveal to be a great asset for a teacher. And this is so much more true when the number of students is very high, like in Massive Open Online Course. Here we propose a similarity measure based on two deep learning techniques that produce embeddings of the single structures that make up a concept map. We also report about a preliminary experiment, having encouraging results.
Glyph-Based Visual Analysis of Q-Leaning Based Action Policy Ensembles on Racetrack

Recently, deep reinforcement learning has become very successful in making complex decisions, achieving super-human performance in Go, chess, and challenging video games. When applied to safety-critical applications, however, like the control of cyber-physical systems with a learned action policy, the need for certification arises. To empower domain experts to decide whether to trust a learned action policy, we propose visualization methods for a detailed assessment of action policies implemented as neural networks trained with Q-learning. We propose a highly responsive visual analysis tool that fosters efficient analysis of Q-learning based action policies over the complete state space of the system, which is essential for verification and gaining detailed insights on policy quality. For efficient visual inspection of the per-action Q-value rating over the state space, we designed three glyphs that provide different levels of detail. In particular, we introduce the two-dimensional Q-Glyph that visually encodes Q-values in a compact manner while preserving directional information of the actions. Placing glyphs in ordered stacks allows for simultaneous inspection of policy ensembles, that for example result from Q-learning meta parameter studies. Further analysis of the policy is supported by enabling inspection of individual traces generated from a chosen start state. A user study was conducted to evaluate the effectiveness of our tool applied to the Racetrack case study, which is a commonly used benchmark in the AI community abstracting driving control.
Methodology for Evaluating the Potential of Disaggregated Memory Systems

Tightly-coupled HPC systems have rigid memory allocation and can result in expensive memory resource underutilization. As novel memory and network technologies mature, disaggregated memory systems are becoming a promising solution for future HPC systems. It allows workloads to use the available memory of the entire system. In this paper, we propose a design framework to explore the disaggregated memory system design space. The framework incorporates memory capacity, network bandwidth, and local and remote memory access ratio, and provides an intuitive approach to guide machine configurations based on technology trends and workload characteristics. We apply our framework to analyze eleven workloads from five computational scenarios, including AI training, data analysis, genomics, protein, and traditional HPC. We demonstrate the ability of our methodology to understand the potential and pitfalls on a disaggregated memory system and motivate machine configurations. Our methodology shows that the 10 out of our 11 applications/workflows can leverage disaggregated memory without affecting performance.
Keyframe Selection from Colonoscopy Videos to Enhance Visualization for Polyp Detection

Colonoscopy video acquisition and recording have been increasingly performed for comprehensive diagnosis and retrospective analysis of colorectal cancer (CRC). Reviewing video streams helps detect and inspect polyps, the precursor to CRC. However, visualizing these streams in their raw form puts a considerable burden on clinicians as most of the frames are clinically insignificant and are not useful for pathological interpretation. For improved visualization of diagnostically significant information, we have proposed an automated framework that discards the uninformative frames from raw videos. Our approach initially extracts high-quality colonoscopy frames using a deep learning model to assist clinicians in visualizing data in a refined form. Subsequently, our work validates the effectiveness of keyframe selection by employing polyp detection models. All the evaluations are performed either patient-wise or cross-dataset to suffice the real-time requirements. Experimental results show that the keyframe extraction saves reviewing time and enhances the detection performances. The proposed approach achieves a polyp detection F1-score of 79.78% (patient-wise) and 89.22% (cross-dataset) on the SUN and CVC-VideoClinicDB databases, respectively.
Equivariant quantum graph circuits: constructions for universal approximation over graphs

Abstract We investigate quantum circuits for graph representation learning, and propose equivariant quantum graph circuits (EQGCs) , as a class of parameterized quantum circuits with strong relational inductive bias for learning over graph-structured data. Conceptually, EQGCs serve as a unifying framework for quantum graph representation learning, allowing us to define several interesting subclasses subsuming existing proposals. In terms of the representation power, we prove that the subclasses of interest are universal approximators for functions over the bounded graph domain. This theoretical perspective on quantum graph machine learning methods opens many directions for further work, and could lead to models with capabilities beyond those of classical approaches. We also provide experimental evidence, and observe that the performance of EQGCs scales well with the depth of the model.
Defending against adversarial attacks on graph neural networks via similarity property

Graph Neural Networks (GNNs) are powerful tools in graph application areas. However, recent studies indicate that GNNs are vulnerable to adversarial attacks, which can lead GNNs to easily make wrong predictions for downstream tasks. A number of works aim to solve this problem but what criteria we should follow to clean the perturbed graph is still a challenge. In this paper, we propose GSP-GNN, a general framework to defend against massive poisoning attacks that can perturb graphs. The vital principle of GSP-GNN is to explore the similarity property to mitigate negative effects on graphs. Specifically, this method prunes adversarial edges by the similarity of node feature and graph structure to eliminate adversarial perturbations. In order to stabilize and enhance GNNs training process, previous layer information is adopted in case a large number of edges are pruned in one layer. Extensive experiments on three real-world graphs demonstrate that GSP-GNN achieves significantly better performance compared with the representative baselines and has favorable generalization ability simultaneously.
Opacity Enforcement in Discrete Event Systems Using Extended Insertion Functions Under Inserted Language Constraints

Opacity is a confidentiality property capturing the fact that certain secret behavior of a system cannot be revealed under any system evolution. Current-state opacity can be enforced by using an extended insertion mechanism, which is capable of inserting fake symbols before and after an actual output, in real time as the system evolves. This article studies the enforcement of current-state opacity for systems modeled by finite state automata using an extended insertion strategy under constraints on the way symbols can be inserted before and after an actual symbol generated by the system (e.g., constraints on the type, order, and number of inserted symbols). More specifically, we consider inserted language constraints captured by the notion of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(L_{b},L_{a})$</tex-math></inline-formula> -enforceability, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{b}$</tex-math></inline-formula> is the set of strings that can be inserted before, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{a}$</tex-math></inline-formula> is the set of strings that can be inserted after an observed event. If <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{b}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{a}$</tex-math></inline-formula> are regular languages, a verifier is constructed to derive a necessary and sufficient condition for opacity enforceability, and also to formulate an extended insertion strategy (if viable).
Classification of the DLT Consensus Algorithms with Focus on Blockchain

The blockchain technology attracted great scientific and applied interest in the last six–seven years. It successfully applies in different areas like fintech, smart cities, IoT, health care, banking, retail, oil industries, and many others. Regardless of the blockchain type (permissioned or permissionless), the data that must be stored in the blockchain network. The algorithm, which is used to “decide” whether to approve (confirm) or not storing the data in the network is the consensus algorithm. It is very important component of the proper blockchain functioning. The author is pursuing two goals with this research. The first one is to make a thorough retrieval of articles from different publishers, based on few keywords, and check if there are any specific trends of research interests in the recent years around these keywords. The second goal is to identify some classifications criteria—like types (public, private, hybrid, and federated), like layers (two layers and multi-layer), etc., and finally, to classify the existing Distributed Ledger Technology (DLT) consensus algorithms with focus on blockchain, based on the architecture and on the access type.
Human-inspired Video Imitation Learning on Humanoid Model

Generating good and human-like locomotion or other legged motions for bipedal robots has always been challenging. One of the emerging solutions to this challenge is to use imitation learning. The sources for imitation are mostly state-only demonstrations, so using state-of-the-art Generative Adversarial Imitation Learning (GAIL) with Imitation from Observation (IfO) ability will be an ideal frameworks to use in solving this problem. However, it is often difficult to allow new or complicated movements as the common sources for these frameworks are either expensive to set up or hard to produce satisfactory results without computationally expensive preprocessing, due to accuracy problems. Inspired by how people learn advanced knowledge after acquiring basic understandings of specific subjects, this paper proposes a Motion capture-aided Video Imitation (MoVI) learning framework based on Adversarial Motion Priors (AMP) by combining motion capture data of primary actions like walking with video clips of target motion like running, aiming to create smooth and natural imitation results of the target motion. This framework is able to produce various human-like locomotion by taking the most common and abundant motion capture data with any video clips of motion without the need for expensive datasets or sophisticated preprocessing.
Measuring Streaming System Robustness Using Non-parametric Goodness-of-Fit Tests

Due to unpredictable disturbances in the operating environment, stream processing systems may experience performance degradation and even catastrophic failure. Streaming systems must be robust in the face of such uncertainty in order to be deemed fit for purpose. Measuring and quantifying a system’s level of robustness is a non-trivial task. We present, compare and contrast a range of non-parametric goodness-of-fit tests which can act as quantifiers of a system’s level of robustness. We show that different tests produce differing relative measures of system robustness, affected by not only the test statistics inherent characteristics, but also by the particular latency percentile under scrutiny.
Identification of Crack Shapes by Digital Image Correlation Using Joint Estimation Maximum a Posteriori Method

Abstract A method that estimates invisible cracks from the surface based on the surface deformation measured by digital image correlation (DIC) is being developed. An inverse problem is setup to estimate such invisible cracks from the surface deformation. Surface deformation, measured by the DIC method, contains noise. Inverse problems have ill-conditions. The regularization method applied in this study is an extension of the joint estimation maximum a posteriori (JE-MAP) method. The JE-MAP algorithm alternates between MAP method estimation and the grab-cut (GC) method to avoid ill-conditions. The physical constraints on displacement and the forces at the cracks and the crack perimeters (ligaments) are added to the MAP method. The displacement and load at the cracks and the ligaments have a cross-sparse relationship. The MAP method estimates the displacement or the load at the cracks and the ligaments. The estimated result varies greatly at the boundary between the cracks and the ligaments. This boundary is determined by the GC method based on the estimated result. This study amplified the changes at the boundary between the cracks and the ligaments in the estimated results. The amplified results were input into the GC method to improve the boundary-determination accuracy. The regularization method developed from the JE-MAP method was combined with DIC method to estimate the cracks in invisible locations. The method proposed in this study estimated cracks more accurately than L1-norm regularization in inverse problems where the observed data were strain distributions measured by the DIC method.
Edge Video Analytics With Adaptive Information Gathering: A Deep Reinforcement Learning Approach

With growing popularity of enormous public safety and transportation infrastructure cameras, there are increasing demands for automatic mobile video analytics. The emerging multi-access edge computing (MEC) technology has been recently applied to improve the accuracy-latency tradeoff of mobile video analytics. In this paper, we study an MEC-enabled multi-device video analytics system and formulate the problem as a Markov decision process (MDP) to meet two practical challenges: i) the absence of ground truth in real-time and ii) the content-varying degradation-accuracy relation. In particular, we aim to design an online joint frame degradation and bandwidth allocation algorithm with the time-varying function and limited feedback from each device. Thanks to the MDP formulation and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -step return technique, the long-term goal offers adaptive information gathering and thus improves the average accuracy and latency. For sample efficiency, we decompose the MDP problem into discrete degradation adaptation subproblems and continuous bandwidth allocation subproblems. Based on the decomposition, we propose a deep reinforcement learning (DRL) based framework, referred to as DBAG, to solve the decomposed subproblems. DBAG integrates model-based optimization and model-free DRL to solve the MDP problem with a discrete-continuous hybrid action space. Under various network setups and public datasets, DBAG greatly improves the accuracy-latency tradeoff.
An Efficient Hardware Accelerator For Lossless Data Compression

Data compression is a trending field that is used in data storage and data transmission systems. Lossy compression means that data cannot be completely retrieved while in lossless compression the compressed data must be reconstructed exactly. Lossless data compression is used in compressing binary files, telemetry data and high-fidelity medical and scientific images where details are crucial. There is no generic compression algorithm that gives best compression ratio on all data pattern. In this paper, we propose a hybrid lossless hardware architecture that compresses most of data patterns such as repeated data, Gaussian distribution data and images. A profiling-before-compressing and then choosing the right compression hardware is proposed. The proposed design is a highly parallelized architecture that can compress/decompress 64 bytes/cycle with minor overhead. Moreover, it provides high compression ratio on small block sizes as well as large ones.
A Generic Real Time Autoencoder-Based Lossy Image Compression

Multimedia compression is a fundamental and significant research topic in the industrial field in the past several decades attempting to improve compression techniques. It is always a trade-off between size and quality where the growth rate of image, audio and video data is far beyond the improvement of the compression ratios achieved so far. Here, we are aiming to explore the potential of neural networks to achieve data compression, making use of multilayer neural networks providing a more efficient solution. In this paper, we present a lossy compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to replace the conventional transforms. Experimental results demonstrate that our method outperforms traditional coding algorithms, by achieving better compression ratios over the related work.
Leveraging Semi-Connected Devices To Enhance Federated Learning

Federated Learning (FL) was introduced to over-come traditional Machine Learning data privacy concerns, and thus, enable us to gain access to more data. Data owners, clients, are orchestrated by a central FL-server to train data locally and only share their model weights. FL approaches have mainly relied on Cloud and/or Edge to aggregate these model weights and propagate training knowledge across clients. However, several issues hinder the scalability of these approaches, especially in communication-challenged environments. In this paper, we propose a novel semi-distributed system to improve FL training accuracy and time, as well as resource-efficiency at the clients. We leverage co-located clusters of high-end IoT devices, known as FemtoClouds, to propagate training knowledge beyond the Edge. We only leverage Edge/Cloud opportunistically to prop-agate knowledge across FemtoCloud pools. Our evaluation shows that our semi-distributed FemtoClouds system achieves competitive accuracy to state-of-the-art FL approaches, with up to 95% time savings and up to 84% energy savings.