Reviewerly/exHarmony huggingface dataset:
DiiS: A Biomedical Data Access Framework for Aiding Data Driven Research Supporting FAIR Principles

Vast amounts of clinical and biomedical research data are produced daily. These data can help enable data driven healthcare through novel biomedical discoveries, improved diagnostics processes, epidemiology, and education. However, finding, and gaining access to these data and relevant metadata that are necessary to achieve these goals remains a challenge. Furthermore, data management and enabling widespread, albeit controlled, use poses a major challenge for data producers. These data sources are often geographically distributed, with diverse characteristics, and are controlled by a host of logistical and legal factors that require appropriate governance and access control guarantees. To overcome these obstacles, a set of guiding principles under the term FAIR has been previously introduced. The primary desirable dataset properties are thus that the data should be Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we introduce and describe an abstract framework that models these ideal goals, and could be a step toward supporting data driven research. We also develop a system instantiated on our framework called the Data integration and indexing System (DiiS). The system provides an integration model for making healthcare data available on a global scale. Our research work describes the challenges inhibiting data producers, data stewards, and data brokers in achieving FAIR goals for sharing biomedical data. We attempt to address some of the key challenges through the proposed system. We evaluated our framework using the software architecture testing technique and also looked at how different challenges in data integration are addressed by our system. Our evaluation shows that the DiiS framework is a user friendly data integration system that would greatly contribute to biomedical research.
Virtual Sensors for Optimal Integration of Human Activity Data

Sensors are becoming more and more ubiquitous as their price and availability continue to improve, and as they are the source of information for many important tasks. However, the use of sensors has to deal with noise and failures. The lack of reliability in the sensors has led to many forms of redundancy, but simple solutions are not always the best, and the precise way in which several sensors are combined has a big impact on the overall result. In this paper, we discuss how to deal with the combination of information coming from different sensors, acting thus as “virtual sensors”, in the context of human activity recognition, in a systematic way, aiming for optimality. To achieve this goal, we construct meta-datasets containing the “signatures” of individual datasets, and apply machine-learning methods in order to distinguish when each possible combination method could be actually the best. We present specific results based on experimentation, supporting our claims of optimality.
DECISION TREE CONSTRUCTION FOR THE CASE OF LOW-INFORMATIVE FEATURES

Context. The problem of automating the decision tree construction is addressed. The object of study is a decision tree. The subject of study is the methods of decision tree building.Objective. The purpose of the work is to create a method for constructing models based on decision trees for data samples that are characterizedby sets of individually low-informative features.Method. A method for decision tree constructing is proposed, which for a given sample determines the individual informativeness of features relatively to the output feature, and also evaluates the relationship of input features with each other as their individual informativity pairwise relatively to each other, at the step of forming the next node the method selects as a candidate feature the feature that gives the best partition in the whole set of features, after which it sequentially searches among all the features that are not selected for this node the one thatis individually most closely related with the selected candidate, then for the set of selected features, iterating through the available transformations from a given set, determines the quality of the partition for each transformation, selects the best transformation and adds it to the node. When forming the next node, the method tends to single out a group of the most closely interrelated features, the conversion of which into a scalar value will provide the best partitioning of a subsample of instances hit into this node. This makes possible to reduce the size of the model and the branching of the tree, speed up the calculations in recognizing instances based on the model, as well as improve the generalizing properties of the model and its interpretability. The proposed method allows using the constructed decision tree to assess the feature significance.Results. The developed method is implemented as software and investigated at signal represented by a set of individually lowinformative readings classification problem solving. Conclusions. The experiments have confirmed the efficiency of the proposed software and allow recommending it for use in practice in solving problems of diagnostics and automatic classification by features. The prospects for further research may consist in the creation of parallel methods for constructing decision trees based on the proposed method, optimization of its software implementations, and also in an experimental study of the proposed method on a wider set of practical problems
A Time-Series Data Analysis Methodology for Effective Monitoring of Partially Shaded Photovoltaic Systems

The majority of photovoltaic (PV) systems in the Netherlands are small scale, and installed on residential and commercial rooftops, where different objects in many cases may lead to the presence of shading and inevitable energy loss. Nevertheless, the energy loss due to expected shadow must be distinguished from the energy loss due to other malfunctions. In this study an algorithmic tool is presented that automates the process of analyzing monitoring data of partially shaded PV systems. The algorithm compares long-term and high-resolution yield data of a partially shaded PV system with the yield data of an unshaded PV system, as reference PV system, and automatically detects the energy loss due to the expected shadow, caused by any surrounding obstacles, and distinguishes it from any additional energy loss due to other malfunctions. This study focuses on PV systems with module-level power electronics (MLPE) since these are mostly used on PV systems on rooftops. Three different cases of shaded MLPE PV systems are presented to illustrate the versatility of the methodology. Furthermore, suggestions for further research are discussed at the end of the paper.
Community lexical access for an endangered polysynthetic language: An electronic dictionary for St. Lawrence Island Yupik

Benjamin Hunt, Emily Chen, Sylvia L.R. Schreiner, Lane Schwartz. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). 2019.
RLBOA: A Modular Reinforcement Learning Framework for Autonomous Negotiating Agents

Negotiation is a complex problem, in which the variety of settings and opponents that may be encountered prohibits the use of a single predefined negotiation strategy. Hence the agent should be able to learn such a strategy autonomously. To this end we propose RLBOA, a modular framework that facilitates the creation of autonomous negotiation agents using reinforcement learning. The framework allows for the creation of agents that are capable of negotiating effectively in many different scenarios. To be able to cope with the large size of the state and action spaces and diversity of settings, we leverage the modular BOA-framework. This decouples the negotiation strategy into a Bidding strategy, an Opponent model and an Acceptance condition. Furthermore, we map the multidimensional contract space onto the utility axis which enables a compact and generic state and action description. We demonstrate the value of the RLBOA framework by implementing an agent that uses tabular Q-learning on the compressed state and action space to learn a bidding strategy. We show that the resulting agent is able to learn well-performing bidding strategies in a range of negotiation settings and is able to generalize across opponents and domains.
Adaptive High-Dynamic Range Three-Dimensional Shape Measurement Using DMD Camera

The phase shifting profilometry for the object with high reflectivity variation is an important issue in equipment manufacturing, medical sciences, and entertainment industry. To address the problem, in this paper, an adaptive high dynamic range three-dimensional shape measurement using DMD camera is proposed. By analyzing the imaging characteristics affected by bidirectional reflectance distribution function and the crosstalk on CCD chip, the strategy that DMD modulation function is determined to avoid saturation prior to the acquisition of phase shifting images is applied. To verify the performance of the proposed method, the B/W flat checkerboard, the plastic button, and the metal slice with high-reflectivity variation are measured to reconstruct the 3D model. The experimental results show that our proposed method can correct the deformed phase which is caused by the image saturation. Finally, the measuring accuracy of the proposed method is given. Compared with two common high-dynamic range measurement methods, our proposed method has the advantages in the accuracy and the efficiency.
Go together: providing nonverbal awareness cues to enhance co-located sensation in remote communication

Abstract Nowadays, people are easy to have a remote conferencing in different scenarios. However, the potential of mobile video communication has yet to be fully exploited. In our research, we explore providing multiple nonverbal awareness cues to create deeper empathy or understanding between users in the telecommunication. We present our design for the use of remote communication between two geographically separated users. This system provides mutual nonverbal communication cues including independent viewpoint and gesture cue to support natural human-to-human interactions. Using our setups, two users are able to perform multiple joint activities including talking, looking and gesturing together. Finally, users can share co-located sensation and feel themselves “go together” side-by-side in the same place. We performed a evaluation to explore our system’s usability and how multiple awareness cues affect the remote communication. It consists of two experiments: an asymmetric work in which an indoor helper instructed a walking worker and a symmetric collaboration in a more realistic scenario. The positive results show that our design could significantly improve the human-to-human interactions and enhance co-located sensation.
Optimization of Genetic Algorithm for Implementation Designing and Modeling in Academic Scheduling

This research was investigated using the Genetic Algebra Method for scheduling lectures and practicums. Genetic algorithms are used to solve problems that are modeled according to evolutionary principles in biological sciences. The application of the Genetic Algebraic Method for Scheduling Lectures and Practices is expected to be further developed into a computerized system that can replace the manual lecture scheduling and practicum methods so that it can produce a more accurate schedule with existing rules in a shorter time. The results of the use of the genetic algorithm method are obtained from the results of the data which are fixed scheduling lecture data and can be recommended for lectures according to the lecture schedule obtained from the results of generating genetic algorithms.
Tensor Processing Units for Financial Monte Carlo

Monte Carlo methods are critical to many routines in quantitative finance such as derivatives pricing, hedging and risk metrics. Unfortunately, Monte Carlo methods are very computationally expensive when it comes to running simulations in high-dimensional state spaces where they are still a method of choice in the financial industry. Recently, Tensor Processing Units (TPUs) have provided considerable speedups and decreased the cost of running Stochastic Gradient Descent (SGD) in Deep Learning. After highlighting computational similarities between training neural networks with SGD and simulating stochastic processes, we ask in the present paper whether TPUs are accurate, fast and simple enough to use for financial Monte Carlo. Through a theoretical reminder of the key properties of such methods and thorough empirical experiments we examine the fitness of TPUs for option pricing, hedging and risk metrics computation. In particular we demonstrate that, in spite of the use of mixed precision, TPUs still provide accurate estimators which are fast to compute when compared to GPUs. We also show that the Tensorflow programming model for TPUs is elegant, expressive and simplifies automated differentiation.
User Interface Adaptation for Accessibility

In this chapter, we discuss methods and tools for adapting user interfaces to make them more accessible. We introduce the problem of user interface adaptation and characterize different techniques to be adapted to the user interface. We show that there is a broad range of methods and tools to transform existing interfaces to make them accessible. We describe such approaches by grouping them in two types of solutions: those that provide built-in adaptation mechanisms for the application and those which are external to the application.
Multi-level Similarity Perception Network for Person Re-identification

In this article, we propose a novel deep Siamese architecture based on a convolutional neural network (CNN) and multi-level similarity perception for the person re-identification (re-ID) problem. According to the distinct characteristics of diverse feature maps, we effectively apply different similarity constraints to both low-level and high-level feature maps during training stage. Due to the introduction of appropriate similarity comparison mechanisms at different levels, the proposed approach can adaptively learn discriminative local and global feature representations, respectively, while the former is more sensitive in localizing part-level prominent patterns relevant to re-identifying people across cameras. Meanwhile, a novel strong activation pooling strategy is utilized on the last convolutional layer for abstract local-feature aggregation to pursue more representative feature representations. Based on this, we propose final feature embedding by simultaneously encoding original global features and discriminative local features. In addition, our framework has two other benefits: First, classification constraints can be easily incorporated into the framework, forming a unified multi-task network with similarity constraints. Second, as similarity-comparable information has been encoded in the network’s learning parameters via back-propagation, pairwise input is not necessary at test time. That means we can extract features of each gallery image and build an index in an off-line manner, which is essential for large-scale real-world applications. Experimental results on multiple challenging benchmarks demonstrate that our method achieves splendid performance compared with the current state-of-the-art approaches.
Aiming beyond the Obvious: Identifying Non-Obvious Cases in Semantic Similarity Datasets

Existing datasets for scoring text pairs in terms of semantic similarity contain instances whose resolution differs according to the degree of difficulty.This paper proposes to distinguish obvious from non-obvious text pairs based on superficial lexical overlap and ground-truth labels.We characterise existing datasets in terms of containing difficult cases and find that recently proposed models struggle to capture the non-obvious cases of semantic similarity.We describe metrics that emphasise cases of similarity which require more complex inference and propose that these are used for evaluating systems for semantic similarity.
SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings

Lexical relations describe how meanings of terms relate to each other.Typical relations include hypernymy, synonymy, meronymy, etc. Automatic distinction of lexical relations is vital for NLP applications, and is also challenging due to the lack of contextual signals to discriminate between such relations.In this work, we present a neural representation learning model to distinguish lexical relations among term pairs based on Hyperspherical Relation Embeddings (SphereRE).Rather than learning embeddings for individual terms, the model learns representations of relation triples by mapping them to the hyperspherical embedding space, where relation triples of different lexical relations are well separated.We further introduce a Monte-Carlo based sampling and learning algorithm to train the model via transductive learning.Experiments over several benchmarks confirm SphereRE outperforms state-of-the-arts.
From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion

When debating a controversial topic, it is often desirable to expand the boundaries of discussion.For example, we may consider the pros and cons of possible alternatives to the debate topic, make generalizations, or give specific examples.We introduce the task of Debate Topic Expansion -finding such related topics for a given debate topic, along with a novel annotated dataset for the task.We focus on relations between Wikipedia concepts, and show that they differ from well-studied lexical-semantic relations such as hypernyms, hyponyms and antonyms.We present algorithms for finding both consistent and contrastive expansions and demonstrate their effectiveness empirically.We suggest that debate topic expansion may have various use cases in argumentation mining.
Hidden order in quantum many-body dynamics of driven-dissipative nonlinear photonic lattices

We study the dynamics of nonlinear photonic lattices driven by two-photon parametric processes. By means of matrix-product-state--based calculations, we show that a quantum many-body state with long-range hidden order can be generated from the vacuum. Although this order resembles that characterizing the Haldane insulator, our system is far from equilibrium due to the drive and photon loss. A possible explanation highlighting the role of the symmetry of the drive and the effect of photon loss is discussed. An implementation based on superconducting circuits is proposed and analyzed.
Data Sharing Practices among Researchers at South African Universities

Research data management practices have gained momentum the world over. This is due to increased demands by governments and other funding agencies to have research data archived and shared as widely as possible. This paper sought to establish the data sharing practices of researchers in South Africa. The study further sought to establish the level of collaboration among researchers in sharing research data at the university level. The outcomes of the survey will help the researchers to develop appropriate data literacy awareness programmes meant to stimulate growth in data sharing practices for the benefit of research, not only in South Africa, but the world at large. A survey research method was used to gather data from willing public universities in South Africa. A similar study was conducted in other countries such as the United Kingdom, France and Turkey but the Researchers believe that circumstances in the developed world may differ with the South African research environment, hence the current study. The major finding of this study was that most researchers preferred to use data produced by others but less keen on sharing their own data. This study is the first of its kind in South Africa which investigates data sharing practices of researchers from multi-disciplinary fields at the university level and will contribute immensely to the growing body of literature in the area of research data management.
Unstructured road detection via combining the model‐based and feature‐based methods

To achieve robust and accurate unstructured road detection, a model-based and feature-based combinatory method is proposed. This method obtains the road saliency map via the graph-based manifold ranking approach and accurately segments the road region by fitting the road model. First, a road graph is constructed with the superpixel nodes and the road distribution model. Then, the seed query nodes are robustly selected. Second, the manifold rankings with the foreground and background queries are conducted successively. To improve the robustness to false queries, the authors propose using two different ranking approaches to obtain the foreground and background saliency maps, and then they are combined into the road saliency map. In the end, they propose using a binomial model to fit the road with which the accuracy of the detected road region is improved. The performance of the proposed method is illustrated using two datasets in terms of the subjective visual quality and the objective quality measurements. The experimental results demonstrate that the proposed method achieves accurate and robust unstructured road detection even under the influence of water stains, kerbstones and hard light.
Improvement Feature Vector: Autoregressive Model of Median Filter Residual

To detect median filtering forensics, a four-feature ensemble including the median filter residual autoregressive (MFR AR) model, statistical properties, gradient-edge line, and HU invariant moments of an image were used to propose an improved feature vector. The defined novel feature vector was trained on a support vector machine (SVM) classifier for median filtering detection (MFD) of forgery images. The performance of the proposed MFD scheme was measured with several types of images: median filtered (window size: {3 × 3, 5 × 5, composite (3 × 3, 5 × 5)}), JPEG compressed (quality factor: 90) after median filtered, rotated (counterclockwise: 5%), and noise added (salt-pepper: 5%) which has been re-altered in various ways. Experimental results show high efficiency and performance of the MFD techniques. The area under the curve (AUC) by sensitivity (TP: true positive rate) and 1-specificity (FP: false positive rate) results of the proposed MFD scheme are 0.9 upper with the trained SVM classifier. Thus, the grade evaluation of the proposed scheme is ``Excellent (A)”.
Reasonably-Most-General Clients for JavaScript Library Analysis

A well-known approach to statically analyze libraries without having access to their client code is to model all possible clients abstractly using a most-general client. In dynamic languages, however, a most-general client would be too general: it may interact with the library in ways that are not intended by the library developer and are not realistic in actual clients, resulting in useless analysis results. In this work, we explore the concept of a reasonably-most-general client, in the context of a new static analysis tool REAGENT that aims to detect errors in TypeScript declaration files for JavaScript libraries. By incorporating different variations of reasonably-most-general clients into an existing static analyzer for JavaScript, we use REAGENT to study how different assumptions of client behavior affect the analysis results. We also show how REAGENT is able to find type errors in real-world TypeScript declaration files, and, once the errors have been corrected, to guarantee that no remaining errors exist relative to the selected assumptions.