Reviewerly/exHarmony huggingface dataset:
On the Expected Dynamics of Nonlinear TD Learning.

While there are convergence guarantees for temporal difference (TD) learning when using linear function approximators, the situation for nonlinear models is far less understood, and divergent examples are known. Here we take a first step towards extending theoretical convergence guarantees to TD learning with nonlinear function approximation. More precisely, we consider the expected learning dynamics of the TD(0) algorithm for value estimation. As the step-size converges to zero, these dynamics are defined by a nonlinear ODE which depends on the geometry of the space of function approximators, the structure of the underlying Markov chain, and their interaction. We find a set of function approximators that includes ReLU networks and has geometry amenable to TD learning regardless of environment, so that the solution performs about as well as linear TD in the worst case. Then, we show how environments that are more reversible induce dynamics that are better for TD learning and prove global convergence to the true value function for well-conditioned function approximators. Finally, we generalize a divergent counterexample to a family of divergent problems to demonstrate how the interaction between approximator and environment can go wrong and to motivate the assumptions needed to prove convergence.
ITEM DIMENSIONALITY EXPLORATION BY MEANS OF CONSTRUCT MAP AND CATEGORICAL PRINCIPAL COMPONENTS ANALYSIS

The aim of this research is to study the merits and complementarity of Construct Mapping and Categorical Principal Components Analysis as two approaches that explore the dimensionality of multiple-choice items in achievement tests. Data from the two forms of the Romanian National Assessment Tests on Science were used to explore the dimensionality of items and to identify potentially problematic items that affect the equivalence of the two parallel forms. The findings confirm that the two tests have at best partial equivalence, but while the two methods both agree on test unidimensionality, they flag in part different items as potentially problematic. The results enable researchers and practitioners to make coherent data-driven decision regarding the use of unidimensional vs multidimensional IRT models. Keywords: categorical principal components analysis, construct map, item response theory, unidimensionality.
Targeted Attacks on Deep Reinforcement Learning Agents through Adversarial Observations.

We propose a new perspective on adversarial attacks against deep reinforcement learning agents. Our main contribution is CopyCAT, a targeted attack able to consistently lure an agent into following an outsider's policy. It is pre-computed, therefore fast inferred, and could thus be usable in a real-time scenario. We show its effectiveness on Atari 2600 games in the novel read-only setting. In this setting, the adversary cannot directly modify the agent's state -- its representation of the environment -- but can only attack the agent's observation -- its perception of the environment. Directly modifying the agent's state would require a write-access to the agent's inner workings and we argue that this assumption is too strong in realistic settings.
Analyzing the Group Formation Process in Intelligent Tutoring Systems

Due to the increasing use of active learning methodologies such as the Flipped Classroom, which usually use group activities, Intelligent Tutoring Systems (ITSs) have the opportunity of supporting them. In this work, we present a group formation tool that allows teachers to create easily and efficiently groups of students based on the K-means algorithm. Our tool allows creating the groups using videos' and exercises' information. Moreover, we have validated the suitability of our group formation process and tool through the students' scores. In order to gain insights into the group formation in learning systems which only use exercises or use mainly videos, we also compare the groups through the Jaccard similarity measurement. Results show that the groups formed using only the videos' information are quite similar to the groups created using just the exercises' information. Therefore systems that only use exercise interactions for group formation might be replaced by others that only use video interactions and the other way around.
Progressive Deep Web Crawling Through Keyword Queries For Data Enrichment

Data enrichment is the act of extending a local database with new attributes from external data sources. In this paper, we study a novel problem-how to progressively crawl the deep web (i.e., a hidden database) through a keyword-search API to enrich a local database in an e ective way. This is chal- lenging because these interfaces often limit the data access by enforcing the top-k constraint or limiting the number of queries that can be issued within a time window. In response, we propose SmartCrawl, a new framework to collect re- sults e ectively. Given a query budget b, SmartCrawl rst constructs a query pool based on the local database, and then iteratively issues a set of most bene cial queries to the hidden database such that the union of the query results can cover the maximum number of local records. The key technical challenge is how to estimate query bene t, i.e., the number of local records that can be covered by a given query. A simple approach is to estimate it as the query frequency in the local database. We nd that this is ine ective due to i) the impact of |ΔD|, where |ΔD| represents the number of local records that cannot be found in the hidden database, and ii) the top-k constraint enforced by the hidden database. We study how to mitigate the negative impacts of the two factors and propose e ective optimization techniques to improve performance. The experimental results show that on both simulated and real-world hidden databases, SmartCrawl signi cantly increases coverage over the local database as compared to the baselines.
Establishing an Open Access Repository for Doctor of Nursing Practice Projects

The Doctor of Nursing Practice (DNP) faculty at the George Washington University (GW) decided to create a digital DNP Project Repository for our students in 2016 based on the American Association of Colleges of Nursing (AACN) 2015 recommendations to do so. We describe the two-year process during which the DNP faculty and the GW librarians at our Health Sciences Library collaborated to create the DNP project repository. This article contains important information that was learned about digital institutional repositories, the criteria used in deciding to make the GW library's Health Sciences Research Commons the home for the repository, along with questions and concerns that arose during the process. Our library internally collaborated to customize the digital collection to meet the needs of DNP students and their project teams. Development included creating the URL for the collection, customizing digital metadata fields, developing submission forms and guidelines, and adding embargo options for projects. A pilot collection of six DNP projects was completed in 2017; and in 2018, the first cohort of 34 students submitted their projects to our repository. Outcomes are reported of the repository launch, lessons learned, and repository submission experiences of two DNP students. We believe our journey may guide others.
Neural Network-Based Diagnostic Tool for Identifying the Factors Responsible for Depression

The paper aims at establishing the output-to-input relationship of the real-life adult depression data using a neural network (NN) model. The said model has been developed to diagnose and detect the associated severity (grade) of the illness. An intelligent NN-based reverse model has been trained through batch mode and put to test on another set of real-life data. Reverse mapping of this model has been developed to isolate significantly contributing input components (factors) for any given case to expedite the preventive procedure for further deterioration as well as the start of treatment.
Pannel: Digital Transformation Impact on Society

In this panel, we are going to discuss the impact of digital transformation will have on society.Digital technologies are rapidly and profoundly changing the world in promising and threatening ways.Technologies such as Big data, artificial intelligence, machine learning, blockchain, 3D promise to have a profoundly disruptive effect on the industry, business models, governance models and on the way we interact with each other in society.These unprecedented speed, scale and scope of change can provoke inequalities favoring world's richest people become wealthier than ever and a social shock where people lose their old references and competences.Which influence the globalization, circular economy, sharing economy and climate change will have on this digital transformation -How they will shape it?(1) Left this transformation unmanaged, the technologies will shape us and in the age of artificial intelligence and biotechnologies, this can be very dangerous -How we can counteract it?(2) Norms and rules are needed on different domains and levels, strategies to respond to technologies dark side and risks, leaders must be more conscious of their acts and consequences on the environment and social stability -Can these measures help to achieve sustainable development?(3) The changes provoked by this digital transformation will affect governments, industry, and society as a whole -How governments shall design their e-Government and Smart Cities strategies to be better prepared?(4) How industry shall design its Industry 4.0 strategy to be better prepared?(5)
Double-robot obstacle avoidance path optimization for welding process

For path planning of two welding robots, intelligent robot path optimization with obstacle avoidance is introduced first, where the optimization objective is the shortest time. In the optimization process, grid method is used for modeling. Then, ant colony algorithm is applied as search strategy to realize obstacle avoidance between welding gun and workpiece. For obstacle avoidance of robot joints, the robot is modeled using the sphere and the capsule. Besides, two-level collision detection and geometrical collision avoidance are used to obtain collision free robots' path. At last, an improved particle swarm optimization algorithm is used to realize global path planning. Simulation results show that the proposed strategy could improve the effectiveness of the path planning. It can be used to shorten the teaching time and strengthen offline programming ability.
Limiting processes with dependent increments for measures on symmetric group of permutations

<!-- *** Custom HTML *** --> A family of measures on the set of permutations of the first $n$ integers, known as <i>Ewens sampling formula</i>, arises in population genetics. In a series of papers, the first two authors have developed necessary and sufficient conditions for the weak convergence of a partial sum process based on these measures to a process with independent increments. Under very general conditions, it has been shown that a partial sum process converges weakly in a function space if and only if a related process defined through sums of independent random variables converges. In this paper, a functional limit theory is developed where the limiting processes need not be processes with independent increments. Thus, under Ewens sampling formula, the limiting process of the partial sums of dependent variables differs from that of the associated process defined through the partial sums of independent random variables.
Interacting with Smart Consumer Cameras: Exploring Gesture, Voice, and AI Control in Video Streaming

Livestreaming and video calls have grown in popularity due to the increased connectivity and advancements in mobile devices. Our interactions with these cameras are limited as the cameras are either fixed or manually remote controlled. Here we present a Wizard-of-Oz elicitation study to inform the design of interactions with smart 360° cameras or robotic mobile desk cameras for use in video-conferences and live-streaming situations. There was an overall preference for devices that can minimize distraction, as well as, preferences for devices that demonstrate an understanding of video-meeting context. We find participants dynamically grow with regards to the complexity of interactions which illustrate the need for deeper event semantics within the Camera AI. Finally, we detail interaction techniques and design insights to inform the future personal video cameras for streaming and collaboration.
Interpreting and semantically describing Chinese traditional brocade: Xilankapu

Purpose The purpose of this paper is to introduce an interpreting schema and semantic description framework for a collection of images of Xilankapu, a traditional Chinese form of embroidered fabric and brocade artwork. Design/methodology/approach First, the authors interpret the artwork of Xilankapu through Gillian Rose’s “four site” theory by presenting how the brocades were made, how the patterns of Xilankapu are classified and the geometrical abstraction of visual images. To further describe the images of this type of brocade, this paper presents semantic descriptions that include objective–non-objective relations and a multi-layered semantic framework. Furthermore, the authors developed corresponding methods for scanning, storage and indexing images for retrieval. Findings As exploratory research on describing, preserving and indexing images of Xilankapu in the context of the preservation of cultural heritage, the authors collected 1,000+ images of traditional Xilankapu, classifying and storing some of the images in a database. They developed an index schema that combines concept- and content-based approaches according to the proposed semantic description framework. They found that the framework can describe, store and preserve semantic and non-semantic information of the same image. They relate the findings of this paper to future research directions for the digital preservation of traditional cultural heritages. Research limitations/implications The framework has been designed especially for brocade, and it needs to be extended to other types of cultural image. Originality/value The semantic description framework can describe connotative semantic information on Xilankapu. It can also assist the later information retrieval work in organizing implicit information about culturally related visual materials.
Using Description Logic and Abox Abduction to Capture Medical Diagnosis

Medical diagnosis can be defined as the detection of a disease by examining a patient’s signs, symptoms and history. Diagnostic reasoning can be viewed as a process of testing hypotheses guided by symptoms and signs. Solutions to diagnostic problems can be found by generating a limited number of hypotheses early in the diagnostic process and using them to guide subsequent collection of data. Each hypothesis, if correct, can be used to pre-dict what additional findings must be present, and the diagnostic process would then be a guided search for these findings. The process depends on the medical knowledge available. Description Logic-based ontologies provide class definitions (i.e., the necessary and sufficient conditions for defining class membership). In medicine, these definitions correspond to diagnostic criteria, i.e., the particular form of diseases should be associated with the relevant disease categories. In this paper, we model medical diagnosis as an (iterative) abductive reasoning process using ALC. ALC is employed to take advantage of its inference services. However, the inference capabilities provided by DL are not sufficient for diagnosis purposes. The contributions of the paper include: (1) arguing for the need for a disease-symptoms ontology, (2) proposing an ontological representation which, beside facilitating abductive reasoning, takes into account the diagnostic criteria such that specific patient conditions can be classified under a specific disease, and (3) employing Abox abduction to capture the process of medical diagnosis (the process of generating and testing hypotheses) on this proposed representation.
SINAI-DL at SemEval-2019 Task 5: Recurrent networks and data augmentation by paraphrasing

This paper describes the participation of the SINAI-DL team at Task 5 in SemEval 2019, called HatEval.We have applied some classic neural network layers, like word embeddings and LSTM, to build a neural classifier for both proposed tasks.Due to the small amount of training data provided compared to what is expected for an adequate learning stage in deep architectures, we explore the use of paraphrasing tools as source for data augmentation.Our results show that this method is promising, as some improvement has been found over nonaugmented training sets.
Automated Detection of Urban Flooding from News

Automated Detection of Urban Flooding from News Farzaneh Zarei and Mazdak Nik-Bakht Pages 515-520 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Although minor overflows do not cause a huge amount of loss; as the number of such overflows increases, the amount of water wasted, and the compound consequent challenges become considerable. Therefore, detecting overflows, investigating their cause root and resolving them in a timely manner are among new needs for infrastructure managers. This paper suggests a new method for detecting distributed water overflows by extracting the flood information (such as the date and location of the incident) from the news. As a case study, we crawled Montreal newspaper and news websites to detect the related news to urban flooding and their detailed information. We trained several classifiers to identify news relevant to flood. Our experiments illustrate that by applying mutual information method for feature selection and employing support vector machine (SVM) architecture as the classifier, relevant news can be detected with an accuracy (F-measure) of above 80%. Such actionable information can help infrastructure managers with a wide range of decisions from repair and maintenance of existing systems, to capacity evaluation for new designs. Keywords: Classification; Montreal newspaper; Urban Flooding; Water overflows DOI: https://doi.org/10.22260/ISARC2019/0069 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
A multimodal perspective on data visualization

This chapter discusses the multimodality of data visualizations, that is, how they combine multiple modes of expression, such as written language, photographs, diagrammatic elements, and illustrations, in various printed and digital media. Because the medium in which a data visualization is presented determines the modes of expression available, the chapter shows how different media can be pulled apart for multimodal analysis. The proposed approach is illustrated by analysing static information graphics, non-interactive, and interactive dynamic data visualizations.
Understanding Test-to-Code Traceability Links: The Need for a Better Visualizing Model

Visualization of test-to-code traceability links is a great approach to understand test-to-code relations. It efficiently supports software developers in various software development activities throughout the software development life cycle (SDLC) by browsing, recovering and maintaining, links between various software artifacts. However, only a small portion of research has been done on visualization of test-code relations and its importance in maintenance, comprehension, evolution, and refactoring of a software system. This paper extensively draws attention of the reader/researcher to the usefulness of visualizing test-to-code traceability links and opens up several research questions or research paths for further advanced exploration.
Approximate Degree, Secret Sharing, and Concentration Phenomena

The epsilon-approximate degree deg~_epsilon(f) of a Boolean function f is the least degree of a real-valued polynomial that approximates f pointwise to within epsilon. A sound and complete certificate for approximate degree being at least k is a pair of probability distributions, also known as a dual polynomial, that are perfectly k-wise indistinguishable, but are distinguishable by f with advantage 1 - epsilon. Our contributions are:- We give a simple, explicit new construction of a dual polynomial for the AND function on n bits, certifying that its epsilon-approximate degree is Omega (sqrt{n log 1/epsilon}). This construction is the first to extend to the notion of weighted degree, and yields the first explicit certificate that the 1/3-approximate degree of any (possibly unbalanced) read-once DNF is Omega(sqrt{n}). It draws a novel connection between the approximate degree of AND and anti-concentration of the Binomial distribution.- We show that any pair of symmetric distributions on n-bit strings that are perfectly k-wise indistinguishable are also statistically K-wise indistinguishable with at most K^{3/2} * exp (-Omega (k^2/K)) error for all k < K <= n/64. This bound is essentially tight, and implies that any symmetric function f is a reconstruction function with constant advantage for a ramp secret sharing scheme that is secure against size-K coalitions with statistical error K^{3/2} * exp (-Omega (deg~_{1/3}(f)^2/K)) for all values of K up to n/64 simultaneously. Previous secret sharing schemes required that K be determined in advance, and only worked for f=AND. Our analysis draws another new connection between approximate degree and concentration phenomena. As a corollary of this result, we show that for any d deg~_{1/3}(f). These upper and lower bounds were also previously only known in the case f=AND.
ShonkhaNet: A Dynamic Routing for Bangla Handwritten Digit Recognition Using Capsule Network

In the present world, one of the most interesting topics is Handwritten Recognition due to its academic and commercial interest in different research fields. But deal with it a little bit tough because of different size and style. There are many works have been accomplished base in handwritten recognition including Bangla. Here proposed a model which is classified Bangla handwritten numeral using capsule net (a new type of neural network represents activity vector as parameters). The Model is trained and valid with ISI handwritten database [1], BanglaLekha Isolated [2], CMATERdb 3.1.1 [3] and all database together that was achieved 99.28% validation accuracy on ISI handwritten character database, 97.62% validation accuracy on BanglaLekha Isolated, 98.33% validation accuracy on CMATERdb 3.1.1 dataset and 98.90% validation accuracy combination mixed dataset. This model gives satisfactory recognition accuracy compared to other existing models.