Reviewerly/exHarmony huggingface dataset:
Integrals over Gaussians under linear domain constraints

Integrals of linearly constrained multivariate Gaussian densities are a frequent problem in machine learning and statistics, arising in tasks like generalized linear models and Bayesian optimization. Yet they are notoriously hard to compute, and to further complicate matters, the numerical values of such integrals may be very small. We present an efficient black-box algorithm that exploits geometry for the estimation of integrals over a small, truncated Gaussian volume, and to simulate therefrom. Our algorithm uses the Holmes-Diaconis-Ross (HDR) method combined with an analytic version of elliptical slice sampling (ESS). Adapted to the linear setting, ESS allows for rejection-free sampling, because intersections of ellipses and domain boundaries have closed-form solutions. The key idea of HDR is to decompose the integral into easier-to-compute conditional probabilities by using a sequence of nested domains. Remarkably, it allows for direct computation of the logarithm of the integral value and thus enables the computation of extremely small probability masses. We demonstrate the effectiveness of our tailored combination of HDR and ESS on high-dimensional integrals and on entropy search for Bayesian optimization.
Explainability Design Patterns in Clinical Decision Support Systems

This paper reports on the ongoing PhD project in the field of explaining the clinical decision support systems (CDSSs) recommendations to medical practitioners. Recently, the explainability research in the medical domain has witnessed a surge of advances with a focus on two main methods: The first focuses on developing models that are explainable and transparent in its nature (e.g. rule-based algorithms). The second investigates the interpretability of the black-box models without looking at the mechanism behind it (e.g. LIME) as a post-hoc explanation. However, overlooking the human-factors and the usability aspect of the explanation introduced new risks following the system recommendations, e.g. over-trust and under-trust. Due to such limitation, there is a growing demand for usable explanations for CDSSs to enable the integration of trust calibration and informed decision-making in these systems by identifying when the recommendation is correct to follow. This research aims to develop explainability design patterns with the aim of calibrating medical practitioners trust in the CDSSs. This paper concludes the PhD methodology and literature around the research problem is also discussed.
Survey on Privacy Preserving Data Mining Techniques

Privacy preservation in Data Mining has become more prominent and popular because of its property of maintaining privacy of sensitive data for analysis purposes.In this decade, enormous volume of data is created by many sectors especially healthcare, and it is vital to analyze and extract the right information out of it.For instance, the integration of patient's medical records and health test data helps to identify the relation between atypical test result and disease.Incorporating association rule mining on this data aids in creating new information which contributes in disease prevention.During association rule mining procedure, it is crucial to maintain the privacy and security of data, the business's vital information should not be leaked.In this paper, we provide an effective solution of privacy preservation along with association rule mining.Our paper is focused on healthcare datasets; however, it can be extended and implemented in various areas
Feasibility of Utilizing Smart-phone Cameras for Seismic Structural Damage Detection

When major natural disasters hit dense metropolitan areas, inspection is typically conducted by teams of engineers tasked with labeling buildings according to their damage state: safe, needs further evaluation, or unsafe. The physical inspection process can take from several days to weeks to be completed. Automated assessment is an attractive alternative to manual inspection but requires deploying a dense network of sensors at the granularity of each structure. Such a network may seem impractical with respect to cost or deployment time. However, with the advent of the Internet of Things (IoT) era, a massive network of citizen-owned smart devices such as tablets and smart-phones that contain vibration and vision sensors is already readily available and deployed. While prior work focused on using smart-phones to providing early warning, we focus specifically on utilizing smart-phone video capture to directly assess the structural health of buildings post event, thus providing emergency personnel with immediate actionable information regarding the state of the building. The fact that smart phone cameras are already located inside a given building makes the proposed solution insensitive to weather conditions and visibility range and does not require an off-structure reference point. Experimental results using shake tables show that the proposed technique can achieve sub-millimeter accuracy demonstrating its suitability for structural health monitoring applications.
Conclusions: Media-related Educational Competencies of German and US Preservice Teachers

In the previous three main parts, the dimensions of modeling, measuring and advancing media-related educational competencies were analyzed in detail. Against this background, it has now become evident how closely interrelated the three dimensions are. The fourth research question addresses this issue: “What is the relationship between modeling, measuring and advancing media-related educational competencies in both countries?” Hence, the following final chapter will bring together and draw conclusions on the findings presented above and conclude the work with an overall summary and analysis of the relationships between modeling, measuring and advancing media-related educational competencies and with an outlook to further perspectives and research desiderata.
Exploring Lattice-based Post-Quantum Signature for JWT Authentication: Review and Case Study

Due to the scalability of the stateless and compact JSON Web Tokens (JWT), they are increasingly used in securing modern applications to support single-sign-on context and establish trust in microservices or connected-devices architectures. However, currently JWT relies on traditional encryption algorithms that are based on integer factorization or discrete logarithm problems; this exposes JWT signatures to vulnerability when quantum computers become available. Recently, NIST has announced new standards which are quantum safe. In this paper, we investigate the usage of two quantum-resistant algorithms, which are NIST candidates of round 2 standardization process in 2019, to create and verify JWT signatures. Namely, we consider lattice-based schemes (DILITHIUM and qTESLA) and compare their performance against RSA for digital signature for different security levels defined by NIST. The results show that lattice-based digital signature schemes have better performance than RSA in terms of request per seconds and average response time. DILITHIUM has demonstrated the best performance while also having smaller performance loss when scaling the security level.
Part of Speech Tagging for Javanese Language with Hidden Markov Model

Indonesia has many cultures and local language, one of the most is Javanese with the Javanese language. The Javanese language is used in the region of Central Java and East Java, the word structure of the Javanese language has a similar to the Indonesian word class. Part of Speech (POS) Tagging is a process for labeling word classes for each input word that corresponding. POS Tag for Indonesian Language has been done a lot and got very good accuracy with various method application. This study aims to provide the word class label for Javanese language and the datasets used was obtained from online news with Javanese Ngoko language. The method used in this study is the Hidden Markov Model (HMM) with use of the HMM method get the highest accuracy is 96.2 %.&#x0D; Keywords: POS Tagging, Javanese Ngoko, Labeling, Hidden Markov Model
A Similarity Inference Metric for RGB-Infrared Cross-Modality Person Re-identification

RGB-Infrared (IR) cross-modality person re-identification (re-ID), which aims to search an IR image in RGB gallery or vice versa, is a challenging task due to the large discrepancy between IR and RGB modalities. Existing methods address this challenge typically by aligning feature distributions or image styles across modalities, whereas the very useful similarities among gallery samples of the same modality (i.e. intra-modality sample similarities) is largely neglected. This paper presents a novel similarity inference metric (SIM) that exploits the intra-modality sample similarities to circumvent the cross-modality discrepancy targeting optimal cross-modality image matching. SIM works by successive similarity graph reasoning and mutual nearest-neighbor reasoning that mine cross-modality sample similarities by leveraging intra-modality sample similarities from two different perspectives. Extensive experiments over two cross-modality re-ID datasets (SYSU-MM01 and RegDB) show that SIM achieves significant accuracy improvement but with little extra training as compared with the state-of-the-art.
AN IMPLICATION STUDY OF SOCIAL MEDIA LITERACY AT SCHOOL

Internet has become a familiar instrument for the school aged children in Indonesia. The use of social media platforms – has changed the way of communication. In recent years, many students are drawn to social networking sites. This trend has caused the increasing reports of cyberbullying and potential cyber threats. The study took a qualitative approach and used the perimeter of the focus group discussion among 9-12 years old children in an international school in Jakarta. The thematic analysis grounded from observations, interviews and document reviews. The objective of the study is to seek the implication for schools to stand for a policy to promote constructive and safe school environment through social media literacy. It will deal with the aspects to empower students to understand that filtering and evaluating information are fundamental in daily internet browsing as well as understanding about cyberbullying and its consequences. The study concluded an implication towards the needs for schools to have a guideline for social media using among students. The guideline may serve as social media literacy to be implemented along with the digital literacy taught at schools, in expectance that students may become the responsible and ethical users of social media and technology.
Digitalization of the agro-industrial complex: analysis of existing vulnerabilities in quantum cryptography systems

This article deals with vulnerabilities of quantum cryptography systems and quantum key distribution. Solutions that exclude the possibility of quantum attacks on existing quantum key distribution systems are proposed.
EasyQuant: Post-training Quantization via Scale Optimization

The 8 bits quantization has been widely applied to accelerate network inference in various deep learning applications. There are two kinds of quantization methods, training-based quantization and post-training quantization. Training-based approach suffers from a cumbersome training process, while post-training quantization may lead to unacceptable accuracy drop. In this paper, we present an efficient and simple post-training method via scale optimization, named EasyQuant (EQ),that could obtain comparable accuracy with the training-based method.Specifically, we first alternately optimize scales of weights and activations for all layers target at convolutional outputs to further obtain the high quantization precision. Then, we lower down bit width to INT7 both for weights and activations, and adopt INT16 intermediate storage and integer Winograd convolution implementation to accelerate inference.Experimental results on various computer vision tasks show that EQ outperforms the TensorRT method and can achieve near INT8 accuracy in 7 bits width post-training.
Photonics based perfect secrecy cryptography: Toward fully classical implementations

Developing an unbreakable cryptography is a long-standing question and a global challenge in the internet era. Photonics technologies are at the frontline of research, aiming at providing the ultimate system with capability to end the cybercrime industry by changing the way information is treated and protected now and in the long run. Such a perspective discusses some of the current challenges as well as opportunities that classical and quantum systems open in the field of cryptography as both a field of science and engineering.
Exploring Automatic Short Answer Grading as a Tool to Assist in Human Rating

This project proposes using BERT (Bidirectional Encoder Representations from Transformers) as a tool to assist educators with automated short answer grading (ASAG) as opposed to replacing human judgement in high-stakes scenarios. Many educators are hesitant to give authority to an automated system, especially in assessment tasks such as grading constructed response items. However, evaluating free-response text can be time and labor costly for one rater, let alone multiple raters. In addition, some degree of inconsistency exists within and between raters for assessing a given task. Recent advances in Natural Language Processing have resulted in subsequent improvements for technologies that rely on artificial intelligence and human language. New, state-of-the-art models such as BERT, an open source, pre-trained language model, have decreased the amount of training data needed for specific tasks and in turn, have reduced the amount of human annotation necessary for producing a high-quality classification model. After training BERT on expert ratings of constructed responses, we use subsequent automated grading to calculate Cohen's Kappa as a measure of inter-rater reliability between the automated system and the human rater. For practical application, when the inter-rater reliability metric is unsatisfactory, we suggest that the human rater(s) use the automated model to call attention to ratings where a second opinion might be needed to confirm the rater's correctness and consistency of judgement.
An Efficient Classifier using Machine Learning Technique for Individual Action Identification

Human action recognition is an important branch of computer vision and is getting increasing attention from researchers.It has been applied in many areas including surveillance, healthcare, sports and computer games.This proposed work focuses on designing a human action recognition system for a human interaction dataset.Literature research is conducted to determine suitable algorithms for action recognition.In this proposed work, three machine learning models are implemented as the classifiers for human actions.An image processing method and a projection-based feature extraction algorithm are presented to generate training examples for the classifier.The action recognition task is divided into two parts: 4-class human posture recognition and 5-class human motion recognition.Classifiers are trained to classify input data into one of the posture or motion classes.Performance evaluations of the classifiers are carried out to assess validation accuracy and test accuracy for action recognition.The architecture designs for the centralized and distributed recognition systems are presented.Later these designed architectures are simulated on the sensor network to evaluate feasibility and recognition performance.Overall, the designed classifiers show a promising performance for action recognition.
Computer-supported Analysis of Positive Properties, Ultrafilters and Modal Collapse in Variants of Gödel's Ontological Argument

Three variants of Kurt G\"odel's ontological argument, proposed by Dana Scott, C. Anthony Anderson and Melvin Fitting, are encoded and rigorously assessed on the computer. In contrast to Scott's version of G\"odel's argument the two variants contributed by Anderson and Fitting avoid modal collapse. Although they appear quite different on a cursory reading they are in fact closely related. This has been revealed in the computer-supported formal analysis presented in this article. Key to our formal analysis is the utilization of suitably adapted notions of (modal) ultrafilters, and a careful distinction between extensions and intensions of positive properties.
Sequoia: A Playground for Logicians

Sequent calculus is a pervasive technique for studying logics and their properties due to the regularity of rules, proofs, and meta-property proofs across logics. However, even simple proofs can be large, and writing them by hand is often messy. Moreover, the combinatorial nature of the calculus makes it easy for humans to make mistakes or miss cases. Sequoia aims to alleviate these problems. Sequoia is a web-based application for specifying sequent calculi and performing basic reasoning about them. The goal is to be a user-friendly program, where logicians can specify and “play” with their calculi. For that purpose, we provide an intuitive interface where inference rules can be input in and are immediately rendered with the corresponding symbols. Users can then build proof trees in a streamlined and minimal-effort way, in whichever calculus they defined. In addition to that, we provide checks for some of the most important meta-theoretical properties, such as weakening admissibility and identity expansion, given that they proceed by the usual structural induction. In this sense, the logician is only left with the tricky and most interesting cases of each analysis.
Conversion of a Conventional Wheelchair into an Autonomous Personal Transportation Testbed

Personal transportation is the act of transporting an individual by using a small, low-speed vehicle. It is a very hot research topic both in industry and academia. There are many different types of personal transportation vehicles, and wheelchairs are one of them. Autonomous driving is another very popular subject that is applicable to the personal transportation vehicles. Autonomous personal transportation vehicles are good examples of service robotics applications. In this study, conversion procedure of a conventional electric wheelchair into an autonomous personal transportation testbed and the application of some basic autonomous driving algorithms on the developed testbed are explained. In literature, there are several studies providing information on wheelchairs’ autonomy but not deep information about the conversion itself. In this paper, the conversion process is investigated in detail, under two main sections. The first part is by-wire conversion, which allows the wheelchair to be controlled via computer commands. The second part includes the studies on sensors, computational system, and human interface. After making such modifications on wheelchair, fundamental algorithms required for autonomy, such as mapping and localization, are implemented successfully. The results are promising for the usage of the developed system as a testbed for examining new autonomous algorithms and evaluating the performance of the perceptional/computational components.
Quantum State Learning via Single-Shot Measurements

We experimentally implement a machine-learning method for accurately identifying unknown pure quantum states. The method, called single-shot measurement learning, achieves the theoretical optimal accuracy for $\epsilon = O(N^{-1})$ in state learning and reproduction, where $\epsilon$ and $N$ denote the infidelity and number of state copies, without employing computationally demanding tomographic methods. This merit results from the inclusion of weighted randomness in the learning rule governing the exploration of diverse learning routes. We experimentally verify the advantages of our scheme by using a linear-optics setup to prepare and measure single-photon polarization qubits. The experimental results show highly accurate state learning and reproduction exhibiting infidelity of $O(N^{-0.983})$ down to $10^{-5}$, without estimation of the experimental parameters.
Variance reduction for Riemannian non-convex optimization with batch size adaptation

Variance reduction techniques are popular in accelerating gradient descent and stochastic gradient descent for optimization problems defined on both Euclidean space and Riemannian manifold. In this paper, we further improve on existing variance reduction methods for non-convex Riemannian optimization, including R-SVRG and R-SRG/R-SPIDER with batch size adaptation. We show that this strategy can achieve lower total complexities for optimizing both general non-convex and gradient dominated functions under both finite-sum and online settings. As a result, we also provide simpler convergence analysis for R-SVRG and improve complexity bounds for R-SRG under finite-sum setting. Specifically, we prove that R-SRG achieves the same near-optimal complexity as R-SPIDER without requiring a small step size. Empirical experiments on a variety of tasks demonstrate effectiveness of proposed adaptive batch size scheme.
Kernel Stein Generative Modeling

We are interested in gradient-based Explicit Generative Modeling where samples can be derived from iterative gradient updates based on an estimate of the score function of the data distribution. Recent advances in Stochastic Gradient Langevin Dynamics (SGLD) demonstrates impressive results with energy-based models on high-dimensional and complex data distributions. Stein Variational Gradient Descent (SVGD) is a deterministic sampling algorithm that iteratively transports a set of particles to approximate a given distribution, based on functional gradient descent that decreases the KL divergence. SVGD has promising results on several Bayesian inference applications. However, applying SVGD on high dimensional problems is still under-explored. The goal of this work is to study high dimensional inference with SVGD. We first identify key challenges in practical kernel SVGD inference in high-dimension. We propose noise conditional kernel SVGD (NCK-SVGD), that works in tandem with the recently introduced Noise Conditional Score Network estimator. NCK is crucial for successful inference with SVGD in high dimension, as it adapts the kernel to the noise level of the score estimate. As we anneal the noise, NCK-SVGD targets the real data distribution. We then extend the annealed SVGD with an entropic regularization. We show that this offers a flexible control between sample quality and diversity, and verify it empirically by precision and recall evaluations. The NCK-SVGD produces samples comparable to GANs and annealed SGLD on computer vision benchmarks, including MNIST and CIFAR-10.
[Paper] The Effect of MMT AL-FEC on QoE of Error-Concealed Video Streaming

In this paper, we assess subjective QoE (Quality of Experience) and objective application-level QoS (Quality of Service) of video IP transmission with error concealment mechanisms of H.264/AVC and AL-FEC (Application-Level Forward Error Correction) of MMT (MPEG Media Transport), which is an application-level protocol for video transmission. The mutual effect is expected to realize more efficient video streaming from a QoE point of view. The experiment treats two contents, two types of the number of slices per picture frame, and two types of the total bitrate of video and its FEC code. We experiment with several load conditions. We then compare FEC schemes with three code rate values and no FEC scheme. We show from the assessment results that the appropriate code rate for QoE enhancement depends on not only network conditions but also contents.