Reviewerly/exHarmony huggingface dataset:
Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation

Mingyang Zhou, Josh Arnold, Zhou Yu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Combining Random Subspace Approach with smote Oversampling for Imbalanced Data Classification

Following work tries to utilize a hybrid approach of combining Random Subspace method and smote oversampling to solve a problem of imbalanced data classification. Paper contains a proposition of the ensemble diversified using Random Subspace approach, trained with a set oversampled in the context of each reduced subset of features. Algorithm was evaluated on the basis of the computer experiments carried out on the benchmark datasets and three different base classifiers.
DM2C: Deep Mixed-Modal Clustering

Data exhibited with multiple modalities are ubiquitous in real-world clustering tasks. Most existing methods, however, pose a strong assumption that the pairing information for modalities is available for all instances. In this paper, we consider a more challenging task where each instance is represented in only one modality, which we call mixed-modal data. Without any extra pairing supervision across modalities, it is difficult to find a universal semantic space for all of them. To tackle this problem, we present an adversarial learning framework for clustering with mixed-modal data. Instead of transforming all the samples into a joint modality-independent space, our framework learns the mappings across individual modal spaces by virtue of cycle-consistency. Through these mappings, we could easily unify all the samples into a single modal space and perform the clustering. Evaluations on several real-world mixed-modal datasets could demonstrate the superiority of our proposed framework.
Balancing Privity and Enforceability of BPM-Based Smart Contracts on Blockchains

Blockchains are a promising enabling technology for inter-organizational processes in untrusted environments and for the implementation of smart contracts in general. Smart contracts aim at three major objectives: observability, online enforceability and privity. Privity strives for limiting the sharing of information within a contract to those parties of a contract who have a contractual need to know. However, current BPM-based systems operating on blockchains do not address privity. The approaches deal with enforceability and privity as mutual exclusive properties. We show that the trade-offs between privity and enforceability can be considered in fine details and propose means to balance privity and enforceability in the design of smart contracts according to the application requirements. Besides this conceptual basis, we introduce patterns for encryption and key exchange allowing different levels of privity and for supporting proactive online enforceability in the presence of encrypted on-chain data.
A Primal-Dual link between GANs and Autoencoders

Since the introduction of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE), the literature on generative modelling has witnessed an overwhelming resurgence. The impressive, yet elusive empirical performance of GANs has lead to the rise of many GAN-VAE hybrids, with the hopes of GAN level performance and additional benefits of VAE, such as an encoder for feature reduction, which is not offered by GANs. Recently, the Wasserstein Autoencoder (WAE) was proposed, achieving performance similar to that of GANs, yet it is still unclear whether the two are fundamentally different or can be further improved into a unified model. In this work, we study the $f$-GAN and WAE models and make two main discoveries. First, we find that the $f$-GAN and WAE objectives partake in a primal-dual relationship and are equivalent under some assumptions, which then allows us to explicate the success of WAE. Second, the equivalence result allows us to, for the first time, prove generalization bounds for Autoencoder models, which is a pertinent problem when it comes to theoretical analyses of generative models. Furthermore, we show that the WAE objective is related to other statistical quantities such as the $f$-divergence and in particular, upper bounded by the Wasserstein distance, which then allows us to tap into existing efficient (regularized) optimal transport solvers. Our findings thus present the first primal-dual relationship between GANs and Autoencoder models, comment on generalization abilities and make a step towards unifying these models.
Some effective estimates for André–Oort in <i>Y</i>(1)<sup> <i>n</i> </sup>

Abstract Let <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>X</m:mi> <m:mo>⊂</m:mo> <m:mrow> <m:mi>Y</m:mi> <m:mo>⁢</m:mo> <m:msup> <m:mrow> <m:mo stretchy="false">(</m:mo> <m:mn>1</m:mn> <m:mo stretchy="false">)</m:mo> </m:mrow> <m:mi>n</m:mi> </m:msup> </m:mrow> </m:mrow> </m:math> {X\subset Y(1)^{n}} be a subvariety defined over a number field <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>𝔽</m:mi> </m:math> {{\mathbb{F}}} and let <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mrow> <m:mo stretchy="false">(</m:mo> <m:msub> <m:mi>P</m:mi> <m:mn>1</m:mn> </m:msub> <m:mo>,</m:mo> <m:mi mathvariant="normal">…</m:mi> <m:mo>,</m:mo> <m:msub> <m:mi>P</m:mi> <m:mi>n</m:mi> </m:msub> <m:mo stretchy="false">)</m:mo> </m:mrow> <m:mo>∈</m:mo> <m:mi>X</m:mi> </m:mrow> </m:math> {(P_{1},\ldots,P_{n})\in X} be a special point not contained in a positive-dimensional special subvariety of X . We show that if a coordinate <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mi>P</m:mi> <m:mi>i</m:mi> </m:msub> </m:math> {P_{i}} corresponds to an order not contained in a single exceptional Siegel–Tatuzawa imaginary quadratic field <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mi>K</m:mi> <m:mo>*</m:mo> </m:msub> </m:math> {K_{*}} , then the associated discriminant <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mo stretchy="false">|</m:mo> <m:mrow> <m:mi mathvariant="normal">Δ</m:mi> <m:mo>⁢</m:mo> <m:mrow> <m:mo stretchy="false">(</m:mo> <m:msub> <m:mi>P</m:mi> <m:mi>i</m:mi> </m:msub> <m:mo stretchy="false">)</m:mo> </m:mrow> </m:mrow> <m:mo stretchy="false">|</m:mo> </m:mrow> </m:math> {|\Delta(P_{i})|} is bounded by an effective constant depending only on <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>deg</m:mi> <m:mo>⁡</m:mo> <m:mi>X</m:mi> </m:mrow> </m:math> {\deg X} and <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mo stretchy="false">[</m:mo> <m:mrow> <m:mi>𝔽</m:mi> <m:mo>:</m:mo> <m:mi>ℚ</m:mi> </m:mrow> <m:mo stretchy="false">]</m:mo> </m:mrow> </m:math> {[{\mathbb{F}}:{\mathbb{Q}}]} . We derive analogous effective results for the positive-dimensional maximal special subvarieties. From the main theorem we deduce various effective results of André–Oort type. In particular, we define a genericity condition on the leading homogeneous part of a polynomial, and give a fully effective André–Oort statement for hypersurfaces defined by polynomials satisfying this condition.
Semiclassical analysis of dark-state transient dynamics in waveguide circuit QED

The interaction between superconducting qubits and one-dimensional microwave transmission lines has been studied experimentally and theoretically in the past two decades. In this work, we investigate the spontaneous emission of an initially excited artificial atom which is capacitively coupled to a semi-infinite transmission line, shorted at one end. This configuration can be viewed as an atom in front of a mirror. The distance between the atom and the mirror introduces a time delay in the system, which we take into account fully. When the delay time equals an integer number of atom oscillation periods, the atom converges into a dark state after an initial decay period. The dark state is an effect of destructive interference between the reflected part of the field and the part directly emitted by the atom. Based on circuit quantization, we derive linearized equations of motion for the system and use these for a semiclassical analysis of the transient dynamics. We also make a rigorous connection to the quantum optics system-reservoir approach and compare these two methods to describe the dynamics. We find that both approaches are equivalent for transmission lines with a low characteristic impedance, while they differ when this impedance is higher than the typical impedance of the superconducting artificial atom.
A novel music-based game with motion capture to support cognitive and motor function in the elderly

This paper presents a novel game prototype that uses music and motion detection as preventive medicine for the elderly. Given the aging populations around the globe, and the limited resources and staff able to care for these populations, eHealth solutions are becoming increasingly important, if not crucial, additions to modern healthcare and preventive medicine. Furthermore, because compliance rates for performing physical exercises are often quite low in the elderly, systems able to motivate and engage this population are a necessity. Our prototype uses music not only to engage listeners, but also to leverage the efficacy of music to improve mental and physical wellness. The game is based on a memory task to stimulate cognitive function, and requires users to perform physical gestures to mimic the playing of different musical instruments. To this end, the Microsoft Kinect sensor is used together with a newly developed gesture detection module in order to process users’ gestures. The resulting prototype system supports both cognitive functioning and physical strengthening in the elderly.
THE DOMINANT FACTOR FOR IMPROVING INFORMATION SECURITY AWARENESS

The advancement of science and technology especially in the field of Information Communication Technology (ICT) is characterized by the availability of information access faster, easier, convenient, but also vulnerable to try to steal (tap) and modify information. This study aims to determine the relationship between organizational support perception, competence, and motivation with information security awareness. The survey involved a population of 324 people affordable employees in local government that handling information systems in 33 provinces. The sample was taken 140 people by stratified proportional random sampling. The data were collected using a questionnaire and analyzed using multiple regression. The results of the research show: (1) Information security awareness is influenced positively and significantly by the organizational support perception, competence and motivation, (2) The competence is the dominant factor that influences the information security awareness compared to the organizational support perception and motivation, (3) Information security awareness can improve by competencies of knowledge, skill, attitude continuously and tiered, and the model instructional awareness training developed by ‘Schultz’ can be used to improve the attitude or character of information security awareness.
Endoscopic video defogging using luminance blending

Endoscopic video sequences provide surgeons with direct surgical field or visualisation on anatomical targets in the patient during robotic surgery. Unfortunately, these video images are unavoidably hazy or foggy to prevent surgeons from clear surgical vision due to typical surgical operations such as ablation and cauterisation during surgery. This Letter aims at removing fog or smoke on endoscopic video sequences to enhance and maintain a direct and clear visualisation of the operating field during robotic surgery. The authors propose a new luminance blending framework that integrates contrast enhancement with visibility restoration for foggy endoscopic video processing. The proposed method was validated on clinical endoscopic videos that were collected from robotic surgery. The experimental results demonstrate that their method provides a promising means to effectively remove fog or smoke on endoscopic video images. In particular, the visual quality of defogged endoscopic images was improved from 0.5088 to 0.6475.
Annotating temporal relations to determine the onset of psychosis symptoms

For patients with a diagnosis of schizophrenia, determining symptom onset is crucial for timely and successful intervention. In mental health records, information about early symptoms is often documented only in free text, and thus needs to be extracted to support clinical research. To achieve this, natural language processing (NLP) methods can be used. Development and evaluation of NLP systems requires manually annotated corpora. We present a corpus of mental health records annotated with temporal relations for psychosis symptoms. We propose a methodology for document selection and manual annotation to detect symptom onset information, and develop an annotated corpus. To assess the utility of the created corpus, we propose a pilot NLP system. To the best of our knowledge, this is the first temporally-annotated corpus tailored to a specific clinical use-case.
Silicon Quantum Photonics in the Short-Wave Infrared: A New Platform for Big Quantum Optics

Quantum photonic technology encodes information within single photons, and the entanglement between them. Quantum sensors accumulate information about their environment in single photons; quantum-secured communications distribute secret quantum key between parties on photons; and optical quantum computers wield huge entangled states of photons to arrive at their answers. For all these applications, photon loss is catastrophic.
Turkish Text Classification with Machine Learning and Transfer Learning

The problem of text classification is one of the most fundamental topics of study in the field of natural language processing, but when reviewing the literature, it is seen that there is an inadequate number of studies for the issue of Turkish text classification. Two different Turkish datasets were created for this aim. Word vectors were created on the first dataset of unlabeled texts. These word vectors were transferred to the second dataset created with data collected from various news sites by transfer learning. Text classification was applied with the machine learning algorithms on this dataset. The effects of transfer learning and transferring of word vectors on the accuracy rate and the performance of machine learning methods were analyzed in detail. When studying the experimental results, it was determined that Support Vector Machine model was performed more successful and It was seen that the accuracy rate was improved with transfer learning.
Generation of optical Fock and W states with single-atom-based bright quantum scissors

We introduce a multi-step protocol for optical quantum state engineering that performs as "bright quantum scissors" (BQS), namely truncates an arbitrary input quantum state to have at least a certain number of photons.The protocol exploits single-photon pulses and is based on the effect of single-photon Raman interaction, which is implemented with a single three-level Λ system (e.g. a single atom) Purcell-enhanced by a single-sided cavity.A single step of the protocol realises the inverse of the bosonic annihilation operator.Multiple iterations of the protocol can be used to deterministically generate a chain of single-photons in a W state.Alternatively, upon appropriate heralding, the protocol can be used to generate Fock-state optical pulses.This protocol could serve as a useful and versatile building block for the generation of advanced optical quantum states that are vital for quantum communication, distributed quantum information processing, and all-optical quantum computing.
Single Level Feature-to-Feature Forecasting with Deformable Convolutions

Future anticipation is of vital importance in autonomous driving and other decision-making systems. We present a method to anticipate semantic segmentation of future frames in driving scenarios based on feature-to-feature forecasting. Our method is based on a semantic segmentation model without lateral connections within the upsampling path. Such design ensures that the forecasting addresses only the most abstract features on a very coarse resolution. We further propose to express feature-to-feature forecasting with deformable convolutions. This increases the modelling power due to being able to represent different motion patterns within a single feature map. Experiments show that our models with deformable convolutions outperform their regular and dilated counterparts while minimally increasing the number of parameters. Our method achieves state of the art performance on the Cityscapes validation set when forecasting nine timesteps into the future.
Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification

Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform. The multi-layered perceptron neural network is employed as an image texture classifier. The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance.
JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

In 2019, around 57% of the population of the world has broadband access to the Internet.Moreover, there are 5.9 billion mobile broadband subscriptions, i.e., 1.3 subscriptions per user.So there is an enormous interconnected computational power held by users all around the world.Also, it is estimated that Internet users spend more than six and a half hours online every day.But in spite of being a great amount of time, those resources are idle most of the day.Therefore, taking advantage of them presents an interesting opportunity.In this study, we introduce JSDoop, a prototype implementation to profit from this opportunity.In particular, we propose a volunteer web browser-based high-performance computing library.JSdoop divides a problem into tasks and uses different queues to distribute the computation.Then, volunteers access the web page of the problem and start processing the tasks in their web browsers.We conducted a proof-of-concept using our proposal and TensorFlow.js to train a recurrent neural network that predicts text.We tested it in a computer cluster and with up to 32 volunteers.The experimental results show that training a neural network in distributed web browsers is feasible and accurate, has a high scalability, and it is an interesting area for research.
What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis

Xiaolei Huang, Jonathan May, Nanyun Peng. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
RDF Graph Anonymization Robust to Data Linkage

Privacy is a major concern when publishing new datasets in the context of Linked Open Data (LOD). A new dataset published in the LOD is indeed exposed to privacy breaches due to the linkage to objects already present in the other datasets of the LOD. In this paper, we focus on the problem of building safe anonymizations of an RDF graph to guarantee that linking the anonymized graph with any external RDF graph will not cause privacy breaches. Given a set of privacy queries as input, we study the data-independent safety problem and the sequence of anonymization operations necessary to enforce it. We provide sufficient conditions under which an anonymization instance is safe given a set of privacy queries. Additionally, we show that our algorithms for RDF data anonymization are robust in the presence of sameAs links that can be explicit or inferred by additional knowledge.
Building More Positive Online Communities through Improving Moderation and Strengthening Social Identity

In recent years, disruptive behaviors ranging from targeted harassment to increased prevalence of hate groups online have become a priority for researchers and practitioners to address. In my work I have analyzed moderation practices in online communities both qualitatively and quantitatively, studied the relationships between users and automated moderation agents, and developed tools to strengthen communities' identities and empower them to self-moderate more effectively. In my future work I will focus in more depth on the role of platforms and automated agents in shaping communities and their moderation practices with the goal of creating more positive, supportive spaces online.
Exponential Time Differencing Algorithm for Pulse-Coupled Hodgkin-Huxley Neural Networks

The exponential time differencing (ETD) method allows using a large time step to efficiently evolve the stiff systems such as Hodgkin-Huxley (HH) neural networks. For pulse-coupled HH networks, the synaptic spike times cannot be predetermined and are convoluted with neuron's trajectory itself. This presents a challenging issue for the design of an efficient numerical simulation algorithm. The stiffness in the HH equations are quite different between the spike and non-spike regions. Here, we design a second-order adaptive exponential time differencing algorithm (AETD2) for the numerical evolution of HH neural networks. Compared with the regular second-order Runge-Kutta method (RK2), our AETD2 method can use time steps one order of magnitude larger and improve computational efficiency more than ten times while excellently capturing accurate traces of membrane potentials of HH neurons. This high accuracy and efficiency can be robustly obtained and do not depend on the dynamical regimes, connectivity structure or the network size.
Human Values in Software: A New Paradigm for Requirements Engineering?

Requirements engineering (RE) has generally done a good job of helping to define software systems with the intended functionality and cost and that is safe, secure and reliable. However, there is a broader set of human values - such as transparency, integrity, diversity, compassion, social justice - that are largely ignored when we develop software systems. In this talk, I will argue that RE methods should place more emphasis on these human values so we do a better job of building software that aligns with our individual, corporate or societal values. Furthermore, drawing on recent evidence from case studies in industry, I will argue that dealing with human values in software systems is not just of interest to a small group of organisations; rather, all software projects should think about human values, build them in where appropriate, test for them, and use them to drive design decisions. When they are not dealt with in this way, there can be severe social and economic consequences.
Review of Visual Imitation Technologies in Simulation Systems.

The paper presents the overview of technologies and technical facilities used to create simulations and simulators, including for the space industry, as well as technologies for tracking the human body and visual simulation. An example of the realization on the virtual reality headset together with the virtual reality suit is given. This hybrid technology will allow solving not only the tasks of training and practicing operations in various situations, but also the tasks of remote controlling of objects of different complexity.