Reviewerly/exHarmony huggingface dataset:
Exploring Neural Entity Representations for Semantic Information

Neural methods for embedding entities are typically extrinsically evaluated on downstream tasks and, more recently, intrinsically using probing tasks.Downstream task-based comparisons are often difficult to interpret due to differences in task structure, while probing task evaluations often look at only a few attributes and models.We address both of these issues by evaluating a diverse set of eight neural entity embedding methods on a set of simple probing tasks, demonstrating which methods are able to remember words used to describe entities, learn type, relationship and factual information, and identify how frequently an entity is mentioned.We also compare these methods in a unified framework on two entity linking tasks and discuss how they generalize to different model architectures and datasets. ModelsWe compare eight different approaches to generating entity embeddings, organized along two dimensions: the training process of the underlying model, and the content used to inform the embeddings.Along the training dimension, the first method 1 https://github.com/AJRunge523/entitylens
Value-driven Hindsight Modelling

Value estimation is a critical component of the reinforcement learning (RL) paradigm. The question of how to effectively learn predictors for value from data is one of the major problems studied by the RL community, and different approaches exploit structure in the problem domain in different ways. Model learning can make use of the rich transition structure present in sequences of observations, but this approach is usually not sensitive to the reward function. In contrast, model-free methods directly leverage the quantity of interest from the future but have to compose with a potentially weak scalar signal (an estimate of the return). In this paper we develop an approach for representation learning in RL that sits in between these two extremes: we propose to learn what to model in a way that can directly help value prediction. To this end we determine which features of the future trajectory provide useful information to predict the associated return. This provides us with tractable prediction targets that are directly relevant for a task, and can thus accelerate learning of the value function. The idea can be understood as reasoning, in hindsight, about which aspects of the future observations could help past value prediction. We show how this can help dramatically even in simple policy evaluation settings. We then test our approach at scale in challenging domains, including on 57 Atari 2600 games.
Simple communication complexity separation from quantum state antidistinguishability

A set of $n$ pure quantum states is called antidististinguishable if there exists an $n$-outcome measurement that never outputs the outcome `$k$' on the $k$-th quantum state. We describe sets of quantum states for which any subset of three states is antidistinguishable and use this to produce a two-player communication task that can be solved with $\log d$ qubits, but requires one-way communication of at least $\log (4/3) (d-1) - 1 \approx 0.415 (d-1) - 1$ classical bits. The advantages of the approach are that the proof is simple and self-contained -- not needing, for example, to rely on hard-to-establish prior results in combinatorics -- and that with slight modifications, non-trivial bounds can be established in any dimension $\geq 3$. The task can be framed in terms of the separated parties solving a relation, and the separation is also robust to multiplicative error in the output probabilities. We show, however, that for this particular task, the separation disappears if two-way classical communication is allowed. Finally, we state a conjecture regarding antidistinguishability of sets of states, and provide some supporting numerical evidence. If the conjecture holds, then there is a two-player communication task that can be solved with $\log d$ qubits, but requires one-way communication of $\Omega (d \log d)$ classical bits.
Signal to Noise Ratio optimization for extended sources with a new kind of MURA masks

Using coded aperture, for localization of radioactive hot-spots, results in enhanced efficiency and under certain configurations wide Field of View (FOV). We present a coded aperture assembly technique which can be restructured easily, as well as the reduction of the intrinsic noise of coded apertures constructed with this technique, when they localize spatially extended {\gamma}-emitters. Specifically, Modified-Uniformly-Redundant-Array (MURA) coded apertures are structured by embedding lead spheres in a matrix of positions machined inside a transparent medium such as acrylic glass, resulting in an advantageous transparent to opaque area ratio and thus an improved detection efficiency. This configuration also induces a systematic, element-wise, noise on the Point-Spread-Function (PSF) of the correlation matrix. When imaging with these apertures extended hot-spots, a penumbra phenomenon occurrs and reduces this intrinsic noise, in the way a kernel filter does. Fast-Fourier-Transform (FFT) is used to analyze the effect of this phenomenon on the correlation matrix and to explain the maximization of its Signal-to-Noise Ratio (SNR) for certain extent of the hot-spots. Simulations have been used for the detailed study of the SNR dependence on the dimensions of the hot-spot, while experiments with two 99mTc cylindrical sources with 11mm and 24mm diameter, respectively and 1.5 MBq activity each, confirm the reduction of the intrinsic noise. The results define the way of optimization of the imaging setup for the detection of extended hot-spots. Such an optimization could be useful for example in the case of lymph nodes or thyroid remnant imaging in nuclear medicine. Finally, we propose a kernel filter, derived by the Auto-Correlation-Function (ACF), to be applied on PSFs with high intrinsic noise, in order to eliminate it.
Perceptual Image Super Resolution Using Deep Learning and Super Resolution Convolution Neural Networks (SRCNN)

The main objective of Perceptual Image Super Resolution is to obtain a high resoluted image from a normal low resolution image. The task is very simple that we just want to make a Low firmness appearance into a extraordinary resolution image. To perform this task we have various methods like Classical Approach in which we try to maximize the mean squared error, evaluate by PSNR(Peak-Signal-to-Noise-Ratio). The first method used to perform this operation was SRCNN (Super Resolution Convolution Neural Network) and these days many of them use DRCN and VDSR which are slightly upgraded methods. Another technique used for the purpose of upscaling to get a high resoluted image from normal little resolution image is the state of art by PSNR. This method was a quite simple one in which we take a low determination image as input and place in a convolution neural network(CNN) and produce a high resolution image as the output. In this technique the edges will be clearly defined, but the whole image will be blurred. This method is unable to produce good-looking textures.
The Comparative Study for Predicting Disease Outbreak

To know the prediction of disease outbreak, proper predictive modeling is required to represent the dataset. This study presents the comparative predictive modeling for predicting disease outbreak using two models i.e., optimizable support vector machine (SVM) and optimizable gaussian process regression (GPR). The dataset used in this study contains three cases i.e., positive cases, recovered cases, and death cases. The dataset at each case is divided into training dataset for the training process and external validation dataset for the validation process. Based on the training process and validation process, the root mean square error (RMSE) at positive cases, recovered cases, and death cases using optimizable GPR is substantially more effective for prediction than the optimizable SVM. According to the result performance, by applying optimizable GPR, the training process has the average RMSE of 19.54 and the validation process has the average RMSE of 15.85.
FPGA Implementation of Some Second Round NIST Lightweight Cryptography Candidates

For almost one decade, the academic community has been working in the design and analysis of new lightweight primitives. This cryptography development aims to provide solutions tailored for resource-constrained devices. The U.S. National Institute of Standards and Technology (NIST) started an open process to create a Lightweight Cryptography Standardization portfolio. As a part of the process, the candidates must demonstrate their suitability for hardware implementation. Cost and performance are two of the criteria to be evaluated. In this work, we present the analysis of costs and performance in hardware implementations over five NIST LWC Round 2 candidates, COMET, ESTATE-AES/Gift, LOCUS, LOTUS, and Oribatida. Each candidate’s implementation was adapted to the Hardware API for Lightweight Cryptography for fair benchmarking of hardware cores. The results were generated for Xilinx Artix-7 xc7a12tcsg325-3. The results indicate that it is feasible to achieve the reduction of each solution below 2000 LUTs and 2000 slices where some of them (the variants of ESTATE-AES/Gift) are below 850 LUTs and 600 FF when they are included in the LWC CryptoCore.
Software engineering principles: A systematic mapping study and a quantitative literature review

Software engineering, a fairly recent engineering discipline, is still evolving without a wide consensus on a body of fundamental principles as in traditional engineering fields with their own long-established principles originating from physics, chemistry and mathematics. This paper reports on a systematic mapping study (SMS) that identified 30 papers and books from 1969 to January 2020, each proposing their own sets of software engineering principles (SEP). Within these studies a total of 592 SEP were proposed, these studies were reviewed and classified on the basis of four mapping questions examining publication trends, use of explicit criteria for the proposed SEP, whether authors clearly described a methodology to come up with the proposed SEP, and the applicability of SEP throughout the development process. The key finding in this study are: a) the majority of the studies were published over two decades from 1989 to 2009, and then the publication rate slowed in the following decade; b) the largest number of SEP, by far, was proposed by Davis; c) only six studies used explicit criteria to identify their proposed SEP, and the other 24 studies identified their principles based on their own analysis without traceability to a methodology or selection criteria; d) most authors did not identify which principles to use in specific contexts of the software engineering domain; e) only two studies used some of the proposed SEP throughout the software development process.
Optimization of Genomic Classifiers for Clinical Deployment: Evaluation of Bayesian Optimization to Select Predictive Models of Acute Infection and In-Hospital Mortality.

Acute infection, if not rapidly and accurately detected, can lead to sepsis, organ failure and even death. Current detection of acute infection as well as assessment of a patient's severity of illness are imperfect. Characterization of a patient's immune response by quantifying expression levels of specific genes from blood represents a potentially more timely and precise means of accomplishing both tasks. Machine learning methods provide a platform to leverage this host response for development of deployment-ready classification models. Prioritization of promising classifiers is dependent, in part, on hyperparameter optimization for which a number of approaches including grid search, random sampling and Bayesian optimization have been shown to be effective. We compare HO approaches for the development of diagnostic classifiers of acute infection and in-hospital mortality from gene expression of 29 diagnostic markers. We take a deployment-centered approach to our comprehensive analysis, accounting for heterogeneity in our multi-study patient cohort with our choices of dataset partitioning and hyperparameter optimization objective as well as assessing selected classifiers in external (as well as internal) validation. We find that classifiers selected by Bayesian optimization for in-hospital mortality can outperform those selected by grid search or random sampling. However, in contrast to previous research: 1) Bayesian optimization is not more efficient in selecting classifiers in all instances compared to grid search or random sampling-based methods and 2) we note marginal gains in classifier performance in only specific circumstances when using a common variant of Bayesian optimization (i.e. automatic relevance determination). Our analysis highlights the need for further practical, deployment-centered benchmarking of HO approaches in the healthcare context.
Spam detection and high-quality features to analyse question –answer pairs

Purpose In community question and answer (CQA) services, because of user subjectivity and the limits of knowledge, the distribution of answer quality can vary drastically – from highly related to irrelevant or even spam answers. Previous studies of CQA portals have faced two important issues: answer quality analysis and spam answer filtering. Therefore, the purposes of this study are to filter spam answers in advance using two-phase identification methods and then automatically classify the different types of question and answer (QA) pairs by deep learning. Finally, this study proposes a comprehensive study of answer quality prediction for different types of QA pairs. Design/methodology/approach This study proposes an integrated model with a two-phase identification method that filters spam answers in advance and uses a deep learning method [recurrent convolutional neural network (R-CNN)] to automatically classify various types of questions. Logistic regression (LR) is further applied to examine which answer quality features significantly indicate high-quality answers to different types of questions. Findings There are four prominent findings. (1) This study confirms that conducting spam filtering before an answer quality analysis can reduce the proportion of high-quality answers that are misjudged as spam answers. (2) The experimental results show that answer quality is better when question types are included. (3) The analysis results for different classifiers show that the R-CNN achieves the best macro-F1 scores (74.8%) in the question type classification module. (4) Finally, the experimental results by LR show that author ranking, answer length and common words could significantly impact answer quality for different types of questions. Originality/value The proposed system is simultaneously able to detect spam answers and provide users with quick and efficient retrieval mechanisms for high-quality answers to different types of questions in CQA. Moreover, this study further validates that crucial features exist among the different types of questions that can impact answer quality. Overall, an identification system automatically summarises high-quality answers for each different type of questions from the pool of messy answers in CQA, which can be very useful in helping users make decisions.
DESIGN OF MOBILE HEART DISEASE APPLICATION BASED ON USERS PERSPECTIVE USING DESIGN SCIENCE RESEARCH APPROACH

In the era of the Fourth Industrial Revolution (4IR), many mobile heart disease applications are available on Apple App Store and Google Play and these applications enable heart patients to carry out self-monitoring of their heart conditions practically easily. However, when the user feels dissatisfied with the usability and affordances of the mobile heart disease application, it impacts the user satisfaction level to the extent that the users do not use it anymore. The user satisfaction level is a very important factor in the usability of any mobile application including mobile heart disease application which is comfortable and pleasant for continuous use. This paper uses the User-Centered Design (UCD) method towards understanding users’ perspective as the problem of lack of user satisfaction affects the usability and affordances of mobile heart disease applications. This study recruits twenty heart patients and four cardiologists in a hospital in Palembang, Indonesia for designing a mobile heart disease prototype called HeartM. The prototype has several features including measuring the patient’s heart rate, recording heart rate, chatting, sending reminders, and configuring system settings. The focus of this paper is on the design of the mobile heart disease application on its usability, affordances, and user satisfaction aspects based on the user's perspective. The overall study adopts the Design Science Research (DSR) approach. DSR approach involves several iterations to ensure the final prototype produces a better design compared to its previous iterations. This paper contributes to design knowledge and offers recommendations particularly for the design of mobile heart disease applications.
A Scrum-Based Development Process to Support Co-creation with Elders in the eHealth Domain

Since the publication of agile manifesto in 2001, agile methodologies has been gaining significant interest in both software industry and research community. Agile User-Centered Design (AUCD) assesses the challenge of integrating agile development with user experience and usability techniques. Although both methodologies have similarities, their scope and perspective are different and difficult to integrate. The eHealth domain implies additional challenges in terms of usability, due to the differences between healthcare professionals, and lack of knowledge of the day-to-day work carried out in different care tiers. Therefore, the challenge is twofold: to achieve an adequate symbiosis of work between teams, and design a solution adapted to the needs of diverse stakeholders with vast differences in their context of use. We designed a lightweight AUCD process adapted to such situation, and we present our experience in the design and implementation of such development process for the development of a system to monitor frailty in elder patients with support for both community and specialized care. As a result, our UCD process has achieved both iterative and incremental value generation, maintaining a good coordination between developers and UX designers, and resulting on a usable solution with regard to target users.
Topological photonics and beyond: introduction

Topological photonics has been opening exciting opportunities in recent optics research. In this feature issue, we present a collection of papers outlining state-of-the-art and application perspectives for this thriving field of research.
Substituto - A Synchronous Educational Language Game for Simultaneous Teaching and Crowdsourcing

This paper investigates a general framework for synchronous educational language games that simultaneously allows researchers to crowdsource learner answers in a controlled environment.Our prototype Substituto 1 allows teachers and students to interact in realtime while undergoing language learning exercises; ensuring that the learner's progress is not hurt by the introduction of crowdsourcing elements.We evaluate Substituto with a small-scale user study that focuses on training the use of English verb-particle constructions (VPCs), such as break down or take over, and test their use with second language learners of English of different proficiency levels over five pilot sessions.With the study we aim to ensure that our prototypical implementation behaves as expected and to identify any major design flaws that should be addressed.The preliminary results we achieved in order to evaluate the educational value, the user experience and the crowdsourcing capacity of Substituto confirm that it has the potential to become a valuable asset for language learning, a pleasant learning instrument and a crowdsourcing tool for collecting linguistic knowledge.
Unreined Students or Not: Modes of Freedom in a Project-Based Software Engineering Course

Software engineering courses include practical and theoretical elements that give many options for pedagogical combinations among them. In this paper, we report on two different pedagogical approaches for an undergraduate, introductory project-based software engineering course with more than 500 students working in collaborative scrum teams. We call one approach `Every Student is an Innovator', and the other `No Student Left Behind'. This SE course has been long-running, with stable learning objectives and content. However, from one year to another, we radically changed the pedagogical approach of the course along several dimensions, among them the technical framework, software tools, project topic, mentor roles, assessment form and frequency, feedback and degree of student innovativeness. We report on the perceived challenges, detailed changes, the anticipated effects on the course learning outcomes. The results showed that innovativeness and fun need freedom and flexibility with processes and technology. However, strict design requirements and systematic guidance ensure fulfillment of learning objectives. Analyzing student and staff feedback, we find that both approaches lead to students using more time than intended and worrying about unknown assessment criteria.
Cybersecurity research for the future

Considering the wide range of technological and societal trade-offs associated with cybersecurity.
On the Feasibility of Automating Stock Market Manipulation

This work presents the first findings on the feasibility of using botnets to automate stock market manipulation. Our analysis incorporates data gathered from SEC case files, security surveys of online brokerages, and dark web marketplace data. We address several technical challenges, including how to adapt existing techniques for automation, the cost of hijacking brokerage accounts, avoiding detection, and more. We consolidate our findings into a working proof-of-concept, man-in-the-browser malware, Bot2Stock, capable of controlling victim email and brokerage accounts to commit fraud. We evaluate our bots and protocol using agent-based market simulations, where we find that a 1.5% ratio of bots to benign traders yields a 2.8% return on investment (ROI) per attack. Given the short duration of each attack (< 1 minute), achieving this ratio is trivial, requiring only 4 bots to target stocks like IBM. 1,000 bots, cumulatively gathered over 1 year, can turn $100,000 into $1,022,000, placing Bot2Stock on par with existing botnet scams.
Cryptographic reverse firewalls for interactive proof systems

We study interactive proof systems (IPSes) in a strong adversarial setting where the machines of honest parties might be corrupted and under control of the adversary. Our aim is to answer the following, seemingly paradoxical, questions: Can Peggy convince Vic of the veracity of an NP statement, without leaking any information about the witness even in case Vic is malicious and Peggy does not trust her computer? Can we avoid that Peggy fools Vic into accepting false statements, even if Peggy is malicious and Vic does not trust her computer? In this paper, we put forward several natural security properties for RFs in the concrete setting of IPSes. As our main contribution, we construct efficient RFs for different IPSes derived from a large class of Sigma protocols that we call malleable. A nice feature of our design is that it is completely transparent, in the sense that our RFs can be directly applied to already deployed IPSes, without the need to re-implement them.