Reviewerly/exHarmony huggingface dataset:
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms

We study the task of replicating the functionality of black-box neural models, for which we only know the output class probabilities provided for a set of input images. We assume back-propagation through the black-box model is not possible and its training images are not available, e.g. the model could be exposed only through an API. In this context, we present a teacher-student framework that can distill the black-box (teacher) model into a student model with minimal accuracy loss. To generate useful data samples for training the student, our framework (i) learns to generate images on a proxy data set (with images and classes different from those used to train the black-box) and (ii) applies an evolutionary strategy to make sure that each generated data sample exhibits a high response for a specific class when given as input to the black box. Our framework is compared with several baseline and state-of-the-art methods on three benchmark data sets. The empirical evidence indicates that our model is superior to the considered baselines. Although our method does not back-propagate through the black-box network, it generally surpasses state-of-the-art methods that regard the teacher as a glass-box model. Our code is available at: this https URL.
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features.

Deep generative networks trained via maximum likelihood on a natural image dataset like CIFAR10 often assign high likelihoods to images from datasets with different objects (e.g., SVHN). We refine previous investigations of this failure at anomaly detection for invertible generative networks and provide a clear explanation of it as a combination of model bias and domain prior: Convolutional networks learn similar low-level feature distributions when trained on any natural image dataset and these low-level features dominate the likelihood. Hence, when the discriminative features between inliers and outliers are on a high-level, e.g., object shapes, anomaly detection becomes particularly challenging. To remove the negative impact of model bias and domain prior on detecting high-level differences, we propose two methods, first, using the log likelihood ratios of two identical models, one trained on the in-distribution data (e.g., CIFAR10) and the other one on a more general distribution of images (e.g., 80 Million Tiny Images). We also derive a novel outlier loss for the in-distribution network on samples from the more general distribution to further improve the performance. Secondly, using a multi-scale model like Glow, we show that low-level features are mainly captured at early scales. Therefore, using only the likelihood contribution of the final scale performs remarkably well for detecting high-level feature differences of the out-of-distribution and the in-distribution. This method is especially useful if one does not have access to a suitable general distribution. Overall, our methods achieve strong anomaly detection performance in the unsupervised setting, and only slightly underperform state-of-the-art classifier-based methods in the supervised setting. Code can be found at this https URL.
Research on The Developmental Aspects of The Steam Education Program Development in Korea

This study examines the types of STEAM education programs through its development in Korea and the direction in which Korean creative education research focuses based on ICT in the fourth industrial era. The preliminary data of this study were investigated based on KOFAC's proposal request, and the development pattern by year and type from 2012 to 2020 is analyzed. The STEAM education program development maintains three basic types, and the other types are subtle but attempt to change in education development by presenting new fields every year. The three basic types are: Interdisciplinary Subject Integration Advanced Products Utilization and Science-Arts Convergence. Based on the development process in the subject area according to each type, the characteristics and relationships of the type are examined, and the link between the development of science and technology and social phenomenon is confirmed. As a researcher who continuously participated in the STEAM education project, the purpose is to analyze the linkage between the types and to suggest the direction of STEAM education development in Korean education in the future.
A Realist Model of Communication Applications for Informational Technology and Artificial Cognitive Systems

The structure of the Shannon-Weaver model of communication (1948) has been the point of departure for the coming communication models that ever since have functioned as a base for developing systems and strategies within all the fields that depend on informational transfer. Attempts to develop the model have not affected its basic structure, or the use of the central concept of information. As it will be argued in this paper, this presents a number of limitations to the development of informational technology and more functional artificial cognitive systems. After contextualizing and adapting a number of central concepts for any theory of communication, the author presents a model of communication that is viable as a foundation for the development of artificial cognitive systems used for functions like natural language-processing, object-recognition and machine-learning.
Self-Sovereign Identity and Blockchain-Based Content Management

The concept that a person aims to control my own identity without the intervention of a controlling entity is called self-sovereign identity (SSI). The SSI is an antithesis to the phenomenon that certain companies and organizations, called digital platformers, collect data centrally. This paper expands the concept of SSI and describes the possibilities and problems when applying it to self-content management using the concept of Self-Content Management (SCM). In particular, I discuss the possibility of self-sovereign management of digital content, and discuss DRM using blockchain technology as a means. This paper is assumed that SSI can be applied to content created by myself, but by properly managing content using blockchain technology, it is possible to complete licenses and levy fees. I also clarified that it could be a substitute for the resale right. As a result, SCM promoted the problem of “orphans’ works” and facilitated the processing of rights, and revealed that it could contribute to the further development of culture. However, I also address that there are two issues. One is the lack of institutional trust, rather than the technical credibility of the blockchain. The second is consistency with the Attorney law of Japan.
High-Dimensional Polynomial Approximation with Applications in Imaging and Recognition

Author(s): Rajagopal, Abhejit | Advisor(s): Chandrasekaran, Shivkumar | Abstract: Deep learning has demonstrated unreasonable effectiveness on several high dimensional regression and classification problems, far exceeding theoretical expectations. In this thesis, we analyze this phenomena from the perspective of approximation theory. Utilizing recent theoretical advances, we investigate whether and under what conditions deep networks can escape the curse of dimensionality, providing experimental evidence where the theory falls short. We use these insights to suggest new approaches to network design that is more in accordance with this theory, and give several examples of where such designs succeed.
Reconfigurable frequency coding of triggered single photons in the telecom C–band

In this work, we demonstrate reconfigurable frequency manipulation of quantum states of light in the telecom C-band.Triggered single photons are encoded in a superposition state of three channels using sidebands up to 53 GHz created by an off-the-shelf phase modulator.The single photons are emitted by an InAs/GaAs quantum dot grown by metal-organic vaporphase epitaxy within the transparency window of the backbone fiber optical network.A cross-correlation measurement of the sidebands demonstrates the preservation of the single photon nature; an important prerequisite for future quantum technology applications using the existing telecommunication fiber network.
Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback

The ubiquitous nature of chatbots and their interaction with users generate an enormous amount of data.Can we improve chatbots using this data?A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample.However, user feedback in most cases contains extraneous sequences hindering their usefulness as a training sample.In this work, we propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation.The generator's goal is to convert the feedback into a response that answers the user's previous utterance and to fool the discriminator which distinguishes feedback from natural responses.We show that augmenting original training data with these modified feedback responses improves the original chatbot performance from 69.94% to 75.96% in ranking correct responses on the PERSONACHAT dataset, a large improvement given that the original model is already trained on 131k samples. 1
Responsible Design by Philosophical Tools in Parallel Trajectory: Practicing the Practical Turn

Philosophy of Technology has produced a substantive amount of theories and reflections about the impacts of technology and innovations on our daily lives and social behaviours. Combining this conceptual toolkit with design, with its capability of actually changing things, promises a powerful approach to developing critical future-making practices. In this paper we explore some opportunities of the approach of the Practical Turn for Responsible Design, based on the experiences with a one-week Industrial Design Workshop at the University of Antwerp. Here, 18 students executed a conceptual design project for the improvement of public space. During the process they experienced and applied tools and theories from respectively Utopian Technology, the Product Impact Tool, and Open Script Design. The results ranged from an open electric bike-sharing system to a bus-stop that fosters ethical discussion. Apart from having interesting design results, the project shows how design can make tangible the inherent dilemmas and conflicts in designing for public space, where collective responsibilities and individual concerns often collide. This makes a strong case for the Practical Turn, that shows an inherent reciprocal effect; the materialization and visualization of ethical and social issues through design -based on the ethical reflection from the philosophical tools and theories- in its turn explicates the ethical reflection. The accompaniment of design projects with philosophical tools for reflection has shown to be usable for enhancing responsibility in design. We therefore propose the concept of a parallel trajectory, with carefully timed philosophical exercises.
A Parametric Version of LLL and Some Consequences: Parametric Shortest and Closest Vector Problems

Given a parametric lattice with a basis given by polynomials in $\Bbb{Z}[t]$, we give an algorithm to construct an LLL-reduced basis whose elements are eventually quasi-polynomial in $t$: that is, they are given by formulas that are piecewise polynomial in $t$ (for sufficiently large $t$), such that each piece is given by a congruence class modulo a period. As a consequence, we show that there are parametric solutions of the shortest vector problem and closest vector problem that are also eventually quasi-polynomial in $t$.
Dynamic Gas Estimation of Loops Using Machine Learning

Smart contracts on Ethereum can encode business logic and have been applied to many areas, such as token exchange, games, and others. Unlike general programs, the computations of contracts on Ethereum are restricted by the gas limit. If a transaction runs out of gas limit before execution finishes, EVM throws an out-of-gas (OG) exception, and the entire transaction fails, which reverts state before the transaction starts, but transaction fee is still deducted. It is essential to do gas estimation before sending transactions. Existing works mostly fail in estimating gas for loop functions because the iteration times of loops can not be statically decided. But we found that a quarter of all contracts have loop functions, and gas cost for loops is higher than for other functions. So it is necessary to do gas estimation for loop functions. In this work, we propose a gas estimation approach based on transaction trace to estimate gas for loop functions dynamically. Our main idea is that we can learn the relationship between historical transactions traces and their gas to estimate gas for new transactions. We implement our approach in machine learning algorithms. The results show that random forest and K-nearest neighbors can achieve a better estimation accuracy rate than SVR and LSTM.
Dense feature pyramid network for ship detection in SAR images

Synthetic aperture radar (SAR) is an active microwave imaging sensor, which can provide images in all-weather and all-day conditions. The various scales and irregular distribution of different ships in SAR images, is a heated and challenging problem. As a basic component in the object detection frameworks, Feature Pyramid Networks (FPNs) improve feature representations for detecting objects at different scales. However, FPN adopts the same convolution operation at different layers, which does not consider the differences between different levels. In this paper, we present Dense Feature Pyramid Network (DenseFPN). Based on the hierarchy of backbone network, the cross-scale connections and lateral connections, the shallow features and deep features are processed differently in DenseFPN. Compared with conventional FPN, we integrate DenseFPN into Faster R-CNN framework and thus form a novel detector. Experiments on high-resolution SAR images dataset (HRSID) have verified the effectiveness of the enhanced hierarchical feature in the proposed method compared with other typical CNN based methods.
A Secure and Verifiable Outsourcing Scheme for Assisting Mobile Device Training Machine Learning Model

In smart applications such as smart medical equipment, more data needs to be processed and trained locally and near the local end to prevent privacy leaks. However, the storage and computing capabilities of smart devices are limited, so some computing tasks need to be outsourced; concurrently, the prevention of malicious nodes from accessing user data during outsourcing computing is required. Therefore, this paper proposes EVPP (efficient, verifiable, and privacy-preserving), which is a computing outsourcing scheme used in the training process of machine learning models. The edge nodes outsource the complex computing process to the edge service node. First, we conducted a certain amount of testing to confirm the parts that need to be outsourced. In this solution, the computationally intensive part of the model training process is outsourced. Meanwhile, a random encryption perturbation is performed on the outsourced training matrix, and verification factors are introduced to ensure the verifiability of the results. In addition, the system can generate verifiable evidence that can be generated to build a trust mechanism when a malicious service node is found. At the same time, this paper also discusses the application of the scheme in other algorithms in order to be better applied. Through the analysis of theoretical and experimental data, it can be shown that the scheme proposed in this paper can effectively use the computing power of the equipment.
Prioritization of IT Product Backlog Items Using Decision Support Systems

The article focuses on the application of decision support systems for prioritization of product backlog items in IT projects implemented using the Scrum methodology. The study identified the features of prioritization of different types of the product backlog items — user stories, epics and themes. It is justified that high-level product backlog items (epics and themes) require comprehensive prioritization, due to the following reasons. First, high-level product backlog items are particularly important because they determine the planning and implementation of detailed user stories within individual sprints. Second, any high-level item can be considered in terms of different criteria. Third, the implementation of epics and themes takes longer time compared to the implementation of user stories, so it is necessary to take into account possible future states of the project's environment. Fourth, prioritizing epics and themes requires increased objectivity and validity, so group decision making with participation of several experts seems reasonable. Taking into consideration the aforementioned features the conclusion regarding limitations of existing methods of prioritization is made. It is argued that prioritization of high-level product backlog items (epics and themes) may be performed using multi-criteria decision making methods with availability of several problem situations (possible future states of the environment), as well as involvement of several experts. The idea of applying decision support methods and systems is illustrated on the appropriate example. It is also argued that increased consumption of time and resources related with setting and solving decision support tasks may be considered as acceptable for high-level product backlog items.
Speaker verification with TIMIT corpus - some remarks on classical methods

The aim of this paper is to present some research on speaker verification system based on Gaussian Mixture Model-Universal Background Model (GMM-UBM) approach. All tests were done for the TIMIT corpus. Performance for the standard Mel-Frequency Cepstral Coefficients (MFCC) and dynamic delta features is shown. Influence of feature dimensionality and model complexity on Equal Error Rate (EER) is presented. Additionally, an impact of Voice Activity Detection (VAD) and normalization techniques like Cepstral Mean and Variance Normalization (CMVN) and RelAtive SpecTrA (RASTA) filtering is covered. Each combination of factors was examined. It is shown that careful selection of traditional techniques may lead to very satisfying results when it comes to achieved EER values.
Data Analysis and Crawler Application Implementation Based on Python

The following topics are dealt with: learning (artificial intelligence); feature extraction; neural nets; object detection; convolutional neural nets; optimisation; Big Data; sensor fusion; and Internet of Things.
Light Textspotter: An Extreme Light Scene Text Spotter

Scene text spotting is a challenging open problem in computer vision community. Many insightful methods have been proposed, but most of them did not consider the enormous computational burden for better performance. In this work, an extreme light scene text spotter is proposed with a teacher-student (TS) structure. Specifically, light convolutional neural network (CNN) architecture, Shuffle Unit, is adopted with feature pyramid network (FPN) for feature extraction. Knowledge distillation and attention transfer are designed in the TS framework to boost text detection accuracy. Cascaded with a full convolution network (FCN) recognizer, our proposed method can be trained end-to-end. Because the resource consumption is halved, our method runs faster. The experimental results demonstrate that our method is more efficient and can achieve state-of-the-art detection performance comparing with other methods on benchmark datasets.
VarGrad: A Low-Variance Gradient Estimator for Variational Inference

We analyse the properties of an unbiased gradient estimator of the ELBO for variational inference, based on the score function method with leave-one-out control variates. We show that this gradient estimator can be obtained using a new loss, defined as the variance of the log-ratio between the exact posterior and the variational approximation, which we call the $\textit{log-variance loss}$. Under certain conditions, the gradient of the log-variance loss equals the gradient of the (negative) ELBO. We show theoretically that this gradient estimator, which we call $\textit{VarGrad}$ due to its connection to the log-variance loss, exhibits lower variance than the score function method in certain settings, and that the leave-one-out control variate coefficients are close to the optimal ones. We empirically demonstrate that VarGrad offers a favourable variance versus computation trade-off compared to other state-of-the-art estimators on a discrete VAE.
Peer Reviewing in Software Engineering

In this short article, we discuss perspectives on issues related to peer reviewing in software engineering journals and conferences. These perspectives are based on our experiences, which have been informed by being authors, reviewers, conference organisers, members of journal editorial boards, and also co-editors-in-chief of a journal, i.e., The Journal of Software Testing, Verification and Reliability (STVR).
EmbraceNet for activity: a deep multimodal fusion architecture for activity recognition

Human activity recognition using multiple sensors is a challenging but promising task in recent decades. In this paper, we propose a deep multimodal fusion model for activity recognition based on the recently proposed feature fusion architecture named EmbraceNet. Our model processes each sensor data independently, combines the features with the EmbraceNet architecture, and post-processes the fused feature to predict the activity. In addition, we propose additional processes to boost the performance of our model. We submit the results obtained from our proposed model to the SHL recognition challenge with the team name Yonsei-MCML.
Pendampingan Pembuatan Konten Virtual Tourism sebagai Inisiasi Living Lab Cagar Budaya Kampoeng Heritage Kayoetangan

The management of tourist destinations that not only have a function as a cultural heritage site but also residents' homes such as Kampoeng Wisata Heritage Kojoetangan, Malang City has a tendency to contain several problems that must be faced together because it is feared that if it is not immediately fixed it will only be a short-lived cultural euphoria. One solution that can be used is through the initiation of a living labbased collaboration in the form of co-creation of virtual tourism content creation based on virtual reality websites by considering the information and needs of stakeholders, namely, local government, destination managers, tourists, residents in Kampoeng Wisata Heritage Kojoetangan. .This community service is an implementation of action research in the form of assistance to destination managers, namely the Tourism Awareness Group
Ethical Testing in the Real World: Evaluating Physical Testing of Adversarial Machine Learning

This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems involving human subjects. Many papers that deploy such attacks characterize themselves as “real world.” Despite this framing, however, we found the physical or real-world testing conducted was minimal, provided few details about testing subjects and was often conducted as an afterthought or demonstration. Adversarial ML research without representative trials or testing is an ethical, scientific, and health/safety issue that can cause real harms. We introduce the problem and our methodology, and then critique the physical domain testing methodologies employed by papers in the field. We then explore various barriers to more inclusive physical testing in adversarial ML and offer recommendations to improve such testing notwithstanding these challenges