Reviewerly/exHarmony huggingface dataset:
k-Nearest Neighbor Curves in Imaging Data Classification

Background Lung disease quantification via medical images analysis is classically difficult. We propose a method based on normalized nearest neighborhood distance classifications for comparing individual CT scan air trapping distributions (representing 3D segmented parenchyma). Previously, between-image comparisons were precluded by the variation inherent to parenchyma segmentations, the dimensions of which are patient- and image-specific by nature. Method Nearest neighbor distance estimations are normalized by the theoretical distance according to the uniform distribution of the air-trapping. This normalization renders images of different size, shape and/or density comparable. The estimated distances for the k nearest neighbor describe the proximity of point patterns over the image. Our approach assumes a defined homogenous space so a completion pretreatment is used. Results Model robustness is characterized via simulation in order to verify that the required initial transformations do not bias uniformly-sampled results. Additional simulations were performed to assess the discriminant power of the method for different point pattern profiles. Simulation results demonstrate that the method robustly recognizes pattern dissimilarity. Finally the model is applied on data for illustrative purpose. Conclusion We demonstrate that a parenchyma-cuboid completion method provides the means of characterizing air trapping patterns in a chosen segmentation, and importantly, to compare such patterns between patients and/or images.
Quadrature-based interferometry using pulsed RGB illumination

This contribution introduces a novel image recording approach for phase retrieval in a RGB-interferometer setup with pulsed LED illumination and an oscillating reference mirror. The effective acquisition time of the interference images is below 100 μs with a repetition rate of 10 frames per second. The pulsed illumination is synchronized with the exposure gap of a Bayer-Pattern CMOS RGB camera to enable the recording of two π/2 phase shifted images with a short delay compared to the camera exposure time. The proposed quadrature method enables surface phase retrieval with a standard deviation of ≈ [3 nm, · · ·, 5 nm], depending on phase noise and actuator precision. The applicability of the reconstructed phase data to unambiguity range extension algorithms based on the exact fraction method is considered. Experimental results demonstrate the feasibility of the setup to measure the topography of samples in motion or oscillating by mechanical vibrations.
Optimization in Sanger sequencing

The main objective of this paper is to solve the optimization problem that is associated with the classification of DNA samples in PCR plates for Sanger sequencing. To achieve this goal, we design an integer linear programming model. Given that the real instances involve the classification of thousands of samples and the linear model can only be solved for small instances, the paper includes a heuristic to cope with bigger problems. The heuristic algorithm is based on the simulated annealing technique. This algorithm obtains satisfactory solutions to the problem in a short amount of time. It has been tested with real data and yields improved results compared to some commercial software typically used in (clinical) laboratories. Moreover, the algorithm has already been implemented in the laboratory and is being successfully used.
Panel: Knowledge Graph Industry Applications

This panel will focus on industry applications related to knowledge graph and showcase how knowledge graph transforms the conventional and unconventional industries to the new era of AI, ranging from innovations in medicine and healthcare, literature search, e-commerce, professional connections, to getting a ride.
KADAR KADMIUM PADA AIR SUMUR GALI DISEKITAR TEMPAT PEMROSESAN AKHIR SAMPAH (STUDI DI TEMPAT PEMROSESAN AKHIR SAMPAH X KABUPATEN JEMBER, INDONESIA)

The controlled landfill method can produce leachate water. Leachate water has high potential to contaminate ground water. The cadmium (Cd) can spread into the soil through a process of impregnation that follows the movement of groundwater flow. The purpose of this study was to analyze the relationship between the distance and well construction with cadmium content in the well water dug around the landfill. This research was an observational analytic research with cross sectional research design. The data collection method used in this research were observation, measurement, documentation and laboratory test. The result of laboratory test showed 80% of the wells exceed the quality standard (0,005 ppm). The result of bivariate analysis using chi square test showed that there was no correlation between the distance with cadmium content in dug well water around landfill (sig = 0.173) and there was a relation between well construction with cadmium content in dug well water around landfill (sig = 0.035). The good standart construction of dug well can inhibit the cadmium in the dug well water.
Self-Improving Generative Adversarial Reinforcement Learning

The lack of data efficiency and stability is one of the main challenges in end-to-end model free reinforcement learning (RL) methods. Recent researches solve the problem resort to supervised learning methods by utilizing human expert demonstrations, e.g. imitation learning. In this paper we present a novel framework which builds a self-improving process upon a policy improvement operator, which is used as a black box such that it has multiple implementation options for various applications. An agent is trained to iteratively imitate behaviors that are generated by the operator. Hence the agent can learn by itself without domain knowledge from human. We employ generative adversarial networks (GAN) to implement the imitation module in the new framework. We evaluate the framework performance over multiple application domains and provide comparison results in support.
BLOCK SYMMETRIC CIPHER WITH RANDOM S-BOXES

This paper describes a new 256-bit block symmetric substitution-permutation cipher, called managed substitution cipher. This is a cipher with single-layer permutated transformations in each cycle. The management of substituted transformations occur by including them in a chain so that the output value of the previous SL transform is fed to the input of the current fortified 32-bit substituted transformation (SL conversion) along with the current value of the input data block through the adder for modulo 2. This enables to activate almost all S-blocks of the second cycle and subsequent cycles and eventually improve the dynamic indicators of the arrival of the cipher to the state of random substitution. The results of the evaluation of randomness indicators and the possibility of using random S-blocks are given. It is shown that such construction of cycled function allows us to use random substitutions without any selection in a cipher without reducing its strength.
Clustering Regression Wavelet Analysis for Lossless Compression of Hyperspectral Imagery

Recently, Regression Wavelet Analysis (RWA) was proposed as a method for lossless compression of hyperspectral images. In RWA, a linear regression is performed after a spectral wavelet transform to generate predictors which estimate the detail coefficients from approximation coefficients at each scale of the spectral wavelet transform. In this work, we propose Clustering Regression Wavelet Analysis (RWA-C), an extension of the original 'Restricted' RWA model which may be used to improve compression performance while maintaining component scalability. We demonstrate that clustering may be used to group pixels with similar spectral profiles. These clusters may then be more efficiently processed to improve RWA prediction performance while only requiring a modest increase side-information and computational complexity.
LAMA

To satisfy increasing computing demands, heterogeneous computing platforms are gaining attention, especially CPU-FPGA platforms. Recently, emerging tightly coupled CPU-FPGA platforms with shared coherent caches (such as the Intel HARP and IBM POWER with CAPI) have been proposed to facilitate data communication and simplify the programming model. In this work, we propose LAMA, a static analysis and dynamic control combined framework for memory access management in such platforms, to further enhance the memory access efficiency and maintain the data consistency. Based on implementation results on the real Intel HARP2 platform, LAMA is shown to improve the performance by 34% on average with low overhead.
A Model-Driven Approach for Developing Responsive Web Apps

Nowadays users have multiple devices to access a myriad of web and mobile applications. This has increased the importance of developing such applications in a responsive way, i.e. with the ability to seamlessly display their contents on multiple devices. This paper proposes the XIS-Web technology as a model-driven approach focused in the development of responsive web applications. XIS-Web technology includes two main parts: the XIS-Web modeling language, implemented as a UML profile; and the XIS-Web framework, which is a set of integrated software tools. XIS-Web stands out in four key aspects: supports the modeling of web applications around six viewpoints, which ultimately promotes the separation of concerns that is key to managing complexity; generates user-interface models from extended use-case models, relieving this cumbersome and time consuming task from the user; employs latest generation web technologies (such as HTML5, JavaScript, CSS) that allow the required flexibility of developing responsive web applications; and allows the creation of platform-independent models without requiring a significant learning curve. This paper also presents an evaluation conducted in a controlled environment with a group of independent users, and briefly introduces simple case studies.
Interlinking SciGraph and DBpedia Datasets Using Link Discovery and Named Entity Recognition Techniques

In recent years we have seen a proliferation of Linked Open Data (LOD) compliant datasets becoming available on the web, leading to an increased number of opportunities for data consumers to build smarter applications which integrate data coming from disparate sources. However, often the integration is not easily achievable since it requires discovering and expressing associations across heterogeneous data sets. The goal of this work is to increase the discoverability and reusability of the scholarly data by integrating them to highly interlinked datasets in the LOD cloud. In order to do so we applied techniques that a) improve the identity resolution across these two sources using Link Discovery for the structured data (i.e. by annotating Springer Nature (SN) SciGraph entities with links to DBpedia entities), and b) enriching SN SciGraph unstructured text content (document abstracts) with links to DBpedia entities using Named Entity Recognition (NER). We published the results of this work using standard vocabularies and provided an interactive exploration tool which presents the discovered links w.r.t. the breadth and depth of the DBpedia classes.
EdDSA Over Galois Field GF(p^m) for Multimedia Data

The Edwards-curve Digital Signature Algorithm (EdDSA) was proposed to perform fast public-key digital signatures and thus replace the Elliptic-Curve Digital Signature Algorithm. Its key advantages over the latter include higher performance and straightforward, secure implementation for embedded devices. EdDSA algorithm is implemented over Galois Field. The operations like addition and multiplication in Galois field are different compared to normal addition and multiplication. Hence implementing EdDSA over Galois field provides more security compared to the conventional EdDSA signature. The basics of Galois Field and its application to store data is introduced. The finite field GF (pm) is an indispensable mathematical tool for some research fields such as information coding, cryptology, theory and application of network coding.
One-shot quantum state exchange

Quantum state exchange is a quantum communication task in which two users exchange their respective quantum information in the asymptotic setting. In this paper, we consider a one-shot version of the quantum state exchange task, in which the users hold a single copy of the initial state, and they exchange their parts of the initial state by means of entanglement-assisted local operations and classical communication. We first derive lower bounds on the least amount of entanglement required for carrying out this task, and provide conditions on the initial state such that the protocol succeeds with zero entanglement cost. Based on these results, we study how the users deal with their symmetric information in order to reduce the entanglement cost. Moreover, we show that it is possible for the users to gain extra shared entanglement after this task.
Towards a language independent Twitter bot detector.

This article describes our work in developing an application that recognizes automatically generated tweets. The objective of this machine learning application is to increase data accuracy in socio ...
Denoising of dynamic 3D meshes via low-rank spectral analysis

Recently, the new generation of different 3D scanner devices (e.g., conoscopic holography, structured light, photometric systems, etc.) has attracted a lot of attention due to their ability to provide more reliable results. The easiness of capturing real 3D objects has created revolutionary trends in many areas (e.g., gaming, prominence of heritage, military, medicine, etc.) and has significantly increased the interest for static and dynamic 3D models. However, despite the technological evolution of the 3D acquisition devices, there are still limitations, deteriorating the quality of the generated results (e.g., noise, outliers, and other abnormalities). These issues need to be addressed before the 3D models are used by other applications (such as segmentation, object recognition, tracking, etc.). In this paper, we introduce a novel method which exploits similarities at the spectral frequencies of individual meshes in soft or rigid body 3D animations. The noise is mainly distributed over high frequencies, while the spectrum of the graph Fourier transform of sequential meshes in a 3D animation, exhibits a low-rank which can be effectively exploited using robust principal component analysis (RPCA). Extensive evaluation studies, carried out using a variety of different arbitrarily complex 3D animations and noise patterns, verify that the proposed technique achieves plausible denoising results despite the constraints posed by arbitrarily motion scenarios.
Intrinsic Motivation in Virtual Assistant Interaction

Conversational virtual assistants have exploded in popularity in recent years. Developers have been working to create personality in virtual assistants, so that users treat them more than mere automation tools. Among other factors, motivation is considered playing an important role. However, previous researches focused mainly on extrinsic motivation, and ignored the fact that intrinsically motivated engagement cultivates better relationship of reliance. Intrinsic motivation is derived from inner urge to seek enjoyment in an action itself, while extrinsic motivation is oriented at consequences and rewards. In context of virtual assistant interaction, motivation is formed based on the user's prior expectation: whether the assistant can cope with his/her intention. When the expectation is contradicted, the user is subjected with disruption and is bounced back to learning process about the assistant's attributes. Related works in expectation effect found that the surprising amplitude of disruption is function of perception uncertainty and prediction error. The current research investigated effects of expectation towards the assistant's capability and uncertainty during perception on intrinsic motivation. We conducted laboratory environment experiments using Amazon Echo smart speakers and extracted subjective assessment as well as used free choice paradigm as objective measurement. The result showed a significant trend that small uncertainty encouraged more intrinsically motivated interaction while large uncertainty motivated the participant to interact in order to resolve uncertainty. Neither uncertainty nor expectation showed main effect on subjective assessment on intrinsic motivation. Our findings suggest that trading consistency for performance is a risky strategy in designing virtual personality.
Function approximation by deep networks

We show that deep networks are better than shallow networks at approximating functions that can be expressed as a composition of functions described by a directed acyclic graph, because the deep networks can be designed to have the same compositional structure, while a shallow network cannot exploit this knowledge. Thus, the blessing of compositionality mitigates the curse of dimensionality. On the other hand, a theorem called good propagation of errors allows to `lift' theorems about shallow networks to those about deep networks with an appropriate choice of norms, smoothness, etc. We illustrate this in three contexts where each channel in the deep network calculates a spherical polynomial, a non-smooth ReLU network, or another zonal function network related closely with the ReLU network.
An anti-forensic scheme on computer graphic images and natural images using generative adversarial networks

Computer graphic images (CGI) can be manufactured very similar to natural images (NI) by state-of-the-art algorithms in computer graphic filed. Thus, there are various identification algorithms proposed to detect CGI. However, the manipulation is complicated and difficult for an ultimate CGI against the forensic algorithms. Further, the forensics on CGI and NI made achievements in the different aspects with the encouragement of deep learning. Though the generated CGI can achieve high quality automatically by generative adversarial networks (GAN), CGI generation based on GAN is difficult to ensure that it cannot be detected by forensics. In this paper, we propose a brief and effective architecture based on GAN for preventing the generated images being detected under the forensics on CGI and NI. The adapted characteristics will make the CGI generated by GAN fools the detector and keep the end-to-end generation mode of GAN.
Differentiable Neural Architecture Search via Proximal Iterations.

Neural architecture search (NAS) recently attracts much research attention because of its ability to identify better architectures than handcrafted ones. However, many NAS methods, which optimize the search process in a discrete search space, need many GPU days for convergence. Recently, DARTS, which constructs a differentiable search space and then optimizes it by gradient descent, can obtain high-performance architecture and reduces the search time to several days. However, DARTS is still slow as it updates an ensemble of all operations and keeps only one after convergence. Besides, DARTS can converge to inferior architectures due to the strong correlation among operations. In this paper, we propose a new differentiable Neural Architecture Search method based on Proximal gradient descent (denoted as NASP). Different from DARTS, NASP reformulates the search process as an optimization problem with a constraint that only one operation is allowed to be updated during forward and backward propagation. Since the constraint is hard to deal with, we propose a new algorithm inspired by proximal iterations to solve it. Experiments on various tasks demonstrate that NASP can obtain high-performance architectures with 10 times of speedup on the computational time than DARTS.
FORMATION OF RISK-BALANCED STYLE OF PROFESSIONAL ACTIVITY AMONG UNIVERSITY STUDENTS

The basis of the sustainable development of society with ever-growing scientific and technical progress is noxology as a science about the dangers of the world, since the risk of being in modern society is higher than ever. In this regard, a scientific noxospheric picture of the world is being formed as an integrative knowledge system, which should be the basis for the formation of a new, noxological worldview, in which security is the main value norm of risk-oriented thinking. The article is aimed at the theoretical substantiation of the necessity of developing a risk-balanced style of professional activity among university students on the basis of risk-thinking as an objective property of the personality. The article also reveals the authors’ concept of the noxological consciousness and explains the term of a risk-balanced style of professional activity. The definition, structure and main constituent elements of this personal phenomenon are given. It is determined that noxological style of professional activity includes such components as risk-thinking, self-regulation, intellectual features and capabilities, “labor model”, individual biological rhythms, will, as well as habits and special techniques of activity. Various characteristics of risks in the professional activity of a specialist are considered. Based on the analysis of scientific and pedagogical literature on the problem and the method of observation, the modeling of the pedagogical system of training a specialist with a risk-balanced style of professional activity is presented, the effectiveness of which is confirmed by the formative experiment.
Towards a better description of visual exploration through temporal dynamic of ambient and focal modes

Human eye movements are far from being well described with current indicators. From the dataset provided by the ETRA 2019 challenge, we analyzed saccades and fixations during a free exploration of blank or natural scenes and during visual search. Based on the two modes of exploration, ambient and focal, we used the K coefficient [Krejtz et al. 2016]. We failed to find any differences between tasks but this indicator gives only the dominant mode over the entire recording. The stability of both modes, assesses with the switch frequency and the mode duration allowed to differentiate gaze behavior according to situations. Time course analyses of K coefficient and switch frequency corroborate that the latter is a useful indicator, describing a greater portion of the eye movement recording.
The iMet Collection 2019 Challenge Dataset

Existing computer vision technologies in artwork recognition focus mainly on instance retrieval or coarse-grained attribute classification. In this work, we present a novel dataset for fine-grained artwork attribute recognition. The images in the dataset are professional photographs of classic artworks from the Metropolitan Museum of Art, and annotations are curated and verified by world-class museum experts. In addition, we also present the iMet Collection 2019 Challenge as part of the FGVC6 workshop. Through the competition, we aim to spur the enthusiasm of the fine-grained visual recognition research community and advance the state-of-the-art in digital curation of museum collections.