Reviewerly/exHarmony huggingface dataset:
Evaluating the Magnitude Estimation Approach for Designing Sonification Mapping Topologies

A challenge in soniﬁcation design is mapping data param-eters onto acoustic parameters in a way that aligns with a listener’s mental model of how a given data parameter should sound. Studies have used the psychophysical scaling method of magnitude estimation to systematically evaluate how participants per-ceive mappings between data and sound parameters - giving data on perceived polarity and scale of the relationship between the data and sound parameters. As of yet, there has been little re-search investigating whether data-to-sound mappings that are de-signed based on results from these magnitude estimation experiments have any effect on users’ performance in an applied audi-tory display task. This paper presents an experiment that com-pares data-to-sound mappings in which the mapping’s polarity is based on results from a previous magnitude estimation experiment against mappings whose polarities are inverted. The experiment is based around a simple task in which participants need to rank WiFi networks based on how secure they are, where security is represented using an auditory display. Results suggest that for a simple auditory display like the one used here, whether or not the polarities of the data-to-sound mappings are based on magnitude estimation does not have a substantial effect on any objective per-formance measures gathered during the experiment. Finally, potential areas for future work are discussed that may continue to investigate the problems addressed by this paper.
What Does the Evidence Say? Models to Help Make Sense of the Biomedical Literature

Ideally decisions regarding medical treatments would be informed by the totality of the available evidence. The best evidence we currently have is in published natural language articles describing the conduct and results of clinical trials. Because these are unstructured, it is difficult for domain experts (e.g., physicians) to sort through and appraise the evidence pertaining to a given clinical question. Natural language technologies have the potential to improve access to the evidence via semi-automated processing of the biomedical literature. In this brief paper I highlight work on developing tasks, corpora, and models to support semi-automated evidence retrieval and extraction. The aim is to design models that can consume articles describing clinical trials and automatically extract from these key clinical variables and findings, and estimate their reliability. Completely automating `machine reading' of evidence remains a distant aim given current technologies; the more immediate hope is to use such technologies to help domain experts access and make sense of unstructured biomedical evidence more efficiently, with the ultimate aim of improving patient care. Aside from their practical importance, these tasks pose core NLP challenges that directly motivate methodological innovation.
Reproducibility in Machine Learning for Health

Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning. In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility. We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data and code accessibility. Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward.
Resolving Intra-Class Imbalance for GAN-Based Image Augmentation

Advanced machine learning and deep learning techniques have increasingly improved accuracy of image classification. Most existing studies have investigated the data imbalance problem among classes to further enhance classification accuracy. However, less attention has been paid to data imbalance within every single class. In this work, we present AC-GAN (Actor-Critic Generative Adversarial Network), a data augmentation framework that explicitly considers heterogeneity of intra-class data. AC-GAN exploits a novel loss function to weigh the impacts of different subclasses of data in a class on GAN training. It hence can effectively generate fake data of both majority and minority subclasses, which help train a more accurate classifier. We use defect detection as an example application to evaluate our design. The results demonstrate that the intra-class distribution of fake data generated by our AC-GAN can be more similar to that of raw data. With balanced training for various subclasses, AC-GAN enhances classification accuracy for no matter uniformly or non-uniformly distributed intra-class data.
Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning

Multi-view multi-task learning refers to dealing with dual-heterogeneous data,where each sample has multi-view features,and multiple tasks are correlated via common views.Existing methods do not sufficiently address three key challenges:(a) saving task correlation efficiently, (b) building a sparse model and (c) learning view-wise weights.In this paper, we propose a new method to directly handle these challenges based on multiplicative sparse feature decomposition.For (a), the weight matrix is decomposed into two components via low-rank constraint matrix factorization, which saves task correlation by learning a reduced number of model parameters.For (b) and (c), the first component is further decomposed into two sub-components,to select topic-specific features and learn view-wise importance, respectively. Theoretical analysis reveals its equivalence with a general form of joint regularization,and motivates us to develop a fast optimization algorithm in a linear complexity w.r.t. the data size.Extensive experiments on both simulated and real-world datasets validate its efficiency.
Computer Teaching Reform in Colleges and Universities Under the Background of Informatization

Colleges and universities should do computer education reform well based on informatization in order to improve the computer application ability of college students. This paper first analyzes the dilemma of computer education colleges and universities based on informatization, and then puts forward scientific countermeasures to optimize the contents of computer education in colleges and universities and improve the education level in order to achieve the goal of computer education reform in colleges and universities.
Accelerated Experimental Design for Pairwise Comparisons

Pairwise comparison labels are more informative and less variable than class labels, but generating them poses a challenge: their number grows quadratically in the dataset size. We study a natural experimental design objective, namely, D-optimality, that can be used to identify which K pairwise comparisons to generate. This objective is known to perform well in practice, and is submodular, making the selection approximable via the greedy algorithm. A naïve greedy implementation has O(N2 d2 K) complexity, where N is the dataset size, d is the feature space dimension, and K is the number of generated comparisons. We show that, by exploiting the inherent geometry of the dataset–namely, that it consists of pairwise comparisons–the greedy algorithm's complexity can be reduced to O(N2 (K + d) + N(dK + d2) + d2 K). We apply the same acceleration also to the so-called lazy greedy algorithm. When combined, the above improvements lead to an execution time of less than 1 hour for a dataset with 108 comparisons; the naïve greedy algorithm on the same dataset would require more than 10 days to terminate.
AspectSA: Unsupervised system for aspect based sentiment analysis in Spanish

This paper describes an unsupervised system for sentiment analysis is presented in Spanish. The system performs a complete fine grain analysis, where the most important characteristics or aspects of an opinion are identified in order to determine their sentiment associated. The unsupervised approach used allows to extract, identify and sentiment classify, from the analysis of opinions in Spanish in a specific domain, allowing to scale to another language and domain with great ease. For the validation of AspectSA, several experiments were carried out using corpus of opinions in the restaurant domain. The results obtained exceeded the majority of existing systems for the Spanish language.
Diarization of the Language Consulting Center Telephone Calls

In this paper, we describe a diarization of the archive data from the project “Access to a Linguistically Structured Database of Enquiries from the Language Consulting Center”. This project is attempting to provide improved access to the large archives of the Czech language of mainly telephone conversations collected continuously by The Language Consulting Center. One part of this archives contains mono recordings, where the data of the client and the language counsellor are mixed in one channel. In our proposed approach to a diarization, we used the information about the identity of the language counsellor acquired from the text transcription on the beginning of the conversation. For the initial stage of the diarization, our system based on clustering the x-vectors was adopted. The resegmentation step is used for refining the boundaries of speaker changes by the pre-trained Gaussian mixture model of the counsellor. Because of the uniqueness of our data, we compared our results with the Kaldi diarization as the baseline system.
Structure descriptor based on just noticeable difference for texture image classification

Local binary pattern (LBP) and its derivates have been widely used in texture classification. However, LBP-based methods are sensitive to noise, and some structure information represented by non-uniform patterns is lost due to the combination of these patterns. In this paper, a new local structure descriptor based on just noticeable difference (JND) for texture classification is proposed by exploring the spatial and relative intensity correlations among local neighborhood pixels. First, a JND map of the image is computed, and then we attempt to model the correlations among local neighborhood pixels by comparing the absolute differences in intensity between the central pixel and its neighbors with the corresponding JND threshold. A new visual pattern (JNDVP) is designed using modeled correlations to describe image structure. Next, considering that image contrast makes important contributions to structure description, contrast is employed as a weighting factor for JNDVP histogram creation to represent structural and contrast information in a single representation. Finally, the nearest neighborhood classifier is employed for texture classification. Results on two texture image databases demonstrate that the proposed structure descriptor is rotation invariant and more robust to noise than LBP. Moreover, texture classification based on JNDVP outperforms LBP-based methods.
Intent Aware Optimization for Content Based Lecture Video Retrieval Using Grey Wolf Optimizer

Nowadays, video recordings are widely used and ease to spread the knowledge among the students. Due to the rapid development of recording technologies and video based learning, the large number of videos is published in the Internet. The main challenge is to retrieve the appropriate video based on the user requirement. To achieve this objective, the newly designed intent aware optimization is proposed in this paper using grey wolf optimizer. The extraction of key frame is the initial step in the proposed system. Then, based on the input query, the keywords from the key frame are recognized by the optical character recognition and LVP. After the features are extracted, the PENN classifier is utilized to retrieve the relevant videos for the text or video query. Subsequently, the user selects one video which is used for the matching purpose based on the optimization. Thus, the intent aware optimization is newly designed using the grey wolf optimizer. The grey wolf optimizer is applied to the input database where the optimal solution is acquired by the clustering task. Finally, the user selected video is matched with the optimal solution to retrieve the more relevant video for the input query. The experimental results are validated and the performance is analysed by the parameters are F-measure, Recall and Precision. Then, the performance is also compared with the existing systems using MATLAB implementation. Thus, the higher precision value of 75% is attained which ensures the efficient retrieval of content based lecture video.
Seasonal Time Series Forecasting by F1-Fuzzy Transform

We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The time series’ trend is obtained via polynomial fitting: then, the dataset is partitioned in S seasonal subsets and the direct F1-transform components for each seasonal subset are calculated as well. The inverse F1-transforms are used to predict the value of the weather parameter in the future. We test our method on heat index datasets obtained from daily weather data measured from weather stations of the Campania Region (Italy) during the months of July and August from 2003 to 2017. We compare the results obtained with the statistics Autoregressive Integrated Moving Average (ARIMA), Automatic Design of Artificial Neural Networks (ADANN), and the seasonal F-transform methods, showing that the best results are just given by our approach.
Cooperative Fraud Detection Model With Privacy-Preserving in Real CDR Datasets

The researchers have shown broad concern about detection and recognition of fraudsters since telecommunication operators and the individual user are both suffering significant losses from fraud activities.Researchers have proposed various solutions to counter fraudulent activity.However, those methods may lose effectiveness in fraud detection because fraudsters always tend to cover their tracks by roaming among different telecommunication operators.What is more, due to the lack of real data, researchers have to do simulations in a virtual scenario, which makes their models and results less persuasive.In our previous paper, we proposed a novel strategy with high accuracy and security through cooperation among mobile telecommunication operators.In this manuscript, we will validate it in a real-world scenario using real Call Detail Records(CDR) data.We apply the Latent Dirichlet Allocation (LDA) model to profile users.Then we use a method based on Maximum Mean Discrepancy (MMD) to compare the distribution of samples to match roaming fraudsters.Cooperation between telecommunication operators may boost the accuracy of detection while the potential risk of privacy leakage exists.A strategy based on Differential Privacy(DP) is used to address this problem.We demonstrate that it can detect the fraudsters without revealing private data.Our model was validated using simulated dataset and showed its effectiveness.In this manuscript, experiments are performed on real CDRs data, and the result shows that our method has impressive performance in the real-world scenario as well.
Adequacy and Utilization of ICT Resources for Teaching Business Subjects in Senior Secondary Schools in Osun State, Nigeria

This study sought to determine the adequacy and utilization of ICT facilities for teaching and learning business subjects in public senior secondary schools in Osun State of Nigeria. Mixed method research design involving the distribution of questionnaires and the conduct of follow-up interviews was used for the study. The population consisted of business educators teaching in public secondary schools. No sample was drawn; the entire population was studied. The results show that ICT facilities are barely available, grossly inadequate and largely unutilized in teaching business subjects in Osun State public secondary schools in spite of the much-heralded introduction of ”Opon Imo” computer tablets supposedly made available to all senior secondary school students in the state. The study opined that government and other education stakeholders should provide functional ICT facilities and personnel in public secondary schools.
Stochastic Derivative-Free Optimization on Riemannian Manifolds

In this chapter, the main algorithm of the book, Extended RSDFO, is described. The chapter begins by formalizing a generalized framework, Riemannian Stochastic Derivative-Free Optimization (RSDFO) algorithms, for adapting Stochastic Derivative-Free Optimization (SDFO) algorithms from Euclidean spaces to Riemannian manifolds. RSDFO encompasses Riemannian adaptation of CMA-ES, and accentuates the main drawback of the Riemannian adaptation approach, i.e. the local restrictions. The chapter then describes Extended RSDFO, a principled population-based meta-algorithm that uses existing manifold optimization algorithms, such as RSDFO, as its local module. Extended RSDFO addresses the local restriction of RSDFO using both the intrinsic Riemannian geometry of the manifold M (search space) and the product statistical Riemannian geometry of families of mixture densities over M (decision spaces). The components of Extended RSDFO are constructed from a geometrical perspective, and its properties derived from first principles. In particular, we describe the geometry of the evolutionary steps of Extended RSDFO using a modified “regularized inverse" Fisher metric on the simplex of mixture coefficients, and derive the convergence behaviors of Extended RSDFO on compact connected Riemannian manifolds.
Conversational User Interfaces and Interactions

The four papers in this special section explore applications, systems, methodologies, and technologies that relate broad aspects to realize successful conversational user interfaces and interactions. Speech recognition has become a part of our daily lives. Today, we can enjoy a wide array of conversational user interfaces and speech-based interactive services, from speech-based personal assistants in our smartphones and smart speakers, e.g.,Amazon Echo, Alexa, Cortana, and Google Home, to chatbots in traditional messaging interfaces. These interfaces are becoming pervasive as reflected by the exponential growth in their adoption. Hence, the expectations are that computer based conversational systems will soon reach the level of human–human conversation.
Advancing Scholarship through the Research Data Program

Over the past five years, Baker Library at the Harvard Business School has developed a more formal program to facilitate the use of data throughout the lifecycle of business research.Driven by user needs and institutional data retention requirements, the Research Data Program (RDP) has established and brought together services in the realms of data acquisition and discovery, data curation and management, advice on research methods, and data sharing and archiving.These efforts are in line with a field-wide trend of librarian-led efforts to manage, share, and preserve institutional research data.One initiative of Baker's RDP, the Research Datasets Tool, is a searchable, secure discovery platform designed to enable the institution to log and find datasets purchased by individual faculty.Rollout of the Research Datasets Tool illustrates the benefits and challenges common to efforts to track institutional assets: increased ease of access and maximization of University resources are tempered by the burden of user education and the difficulty of integrating various information discovery systems.Over the next several years, Baker will continue to develop its existing research data tools and services, explore new areas of service, and work to better integrate its services with the larger university community.
Blocky Net: A New NeuroEvolution Method

A long-term goal in evolutionary machine learning is to evolve compact models, which can learn effectively in complex, high dimensional domains with many noisy and irrelevant features. NeuroEvolution, the process of using evolutionary computation to evolve the topology and weights of neural networks is an effective and general approach to this. To date there has been little work using methods such as NeuroEvolution of Augmenting Topologies (NEAT), on very large, noisy datasets with up to 100,000 variables and there have been few alternative methods proposed. This paper proposes a new network called Blocky Net with built-in feature selection, and a limited maximum parameter space and complexity. This method is compared against NEAT and FS-NEAT in a supervised classification setting, using 20 diverse datasets ranging from 10 up to 100,000 features. The results show the proposed method is a valuable alternative to NEAT and FS-NEAT with better performance on 13 of the 20 datasets tested versus 2 for FS-NEAT, and is better than NEAT in all cases. The analysis also shows this minimal method of NeuroEvolution allows for a number of promising future research directions.
Seasonality in Bitcoin Market

Bitcoin is an online communication protocol, which facilitates electronic transactions. It has grabbed the attention of investors and researchers in the recent past. The non-regulatory feature of Bitcoin makes it riskier and the element of speculation in its trading is higher than any other financial asset. The study provides an insight into the price dynamics of Bitcoin by examining the day of the week and month of the year effect for the period 2013 to 2017. The findings of the study indicate the existence of seasonality in the return behaviour of Bitcoin as returns for Monday is higher than any other day of the week. Likewise, the returns earned during the month of November are significantly different from other months of the year. The results of the study assert a violation of the assumption of weak-form market efficiency and imply that the Bitcoin market provides an opportunity for the investors to exploit the market from its predictable behavior and fetch abnormal gains.
Miniature Autonomy as One Important Testing Means in the Development of Machine Learning Methods for Autonomous Driving: How ML-based Autonomous Driving could be Realized on a 1:87 Scale

In the current state of autonomous driving machine learning methods are dominating, especially for the environment recognition. For such solutions, the reliability and the robustness is a critical question. A “miniature autonomy” with model vehicles at a small scale could be beneficial for different reasons. Examples are (1) the testability of dangerous and close-to-crash edge cases, (2) the possibility to test potentially dangerous concepts as end-to-end learning or combined inference and learning phases, (3) the need to optimize algorithms thoroughly, and (4) a potential reduction of test mile counts. Presented is the motivation for miniature autonomy and a discussion of testing of machine learning methods. Finally, two currently set up platforms including one with an FPGA-based TPU for ML acceleration are described.