Reviewerly/exHarmony huggingface dataset:
Comparison of Binary Class and Multi-Class Classifier Using Different Data Mining Classification Techniques

Data mining (DM) is the process of retrieving information from huge data-sets and transforming them into meaningful decision. Classification technique is considered to be the most important data mining techniques as it becoming an enthralling topic to the scholars that precisely and effectively describes data for the knowledge-discovery. It is used to describe and distinguish data classes or concepts. There are two major classes of classification problems: Binary-class and Multi-class. In Binary-class classifications, the given data-set is categorized into two classes whereas in Multi-class classification, the given data-set is categorized into several classes based on the classification rules. This paper explores several DM classification approaches such as Decision tree like Classification and Regression Tree(CART) and Conditional Inference Tree(CTREE), Random Forest(RF), Support Vector Machine(SVM) and k-Nearest-Neighbour(KNN) to enhance the result of binary class and multi-class classifiers using the powerful Big data mining analytical tool R and RStudio. Various measures such as Accuracy, F-Score, Sensitivity etc. are used to evaluate the classifier’s performance and also predict which classifier will perform better when the training-testing data-sets are analysed with multiple partitions (%).
Combating Ambiguity for Hash-Code Learning in Medical Instance Retrieval

When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a query case from a large image database. The similarity between the query case and retrieved similar cases is determined by visual features extracted from pathologically abnormal regions. However, the manifestation of these regions often lacks specificity, i.e., different diseases can have the same manifestation, and different manifestations may occur at different stages of the same disease. To combat the manifestation ambiguity in medical instance retrieval, we propose a novel deep framework called Y-Net, encoding images into compact hash-codes generated from convolutional features by feature aggregation. Y-Net can learn highly discriminative convolutional features by unifying the pixel-wise segmentation loss and classification loss. The segmentation loss allows exploring subtle spatial differences for good spatial-discriminability while the classification loss utilizes class-aware semantic information for good semantic-separability. As a result, Y-Net can enhance the visual features in pathologically abnormal regions and suppress the disturbing of the background during model training, which could effectively embed discriminative features into the hash-codes in the retrieval stage. Extensive experiments on two medical image datasets demonstrate that Y-Net can alleviate the ambiguity of pathologically abnormal regions and its retrieval performance outperforms the state-of-the-art method by an average of 9.27% on the returned list of 10.
Usability Questionnaires to Evaluate Voice User Interfaces

Voice user interfaces (VUI) have been increasingly used in everyday settings and they are growing in popularity. These interfaces have predominantly eyes-free and hands-free interactions. This kind of experiences continues to be an inceptive field compared to other input methods such as touch or using the keyboard/mouse. Thus, it is important to identify tools used to evaluate the usability of VUIs. This article presents a systematic review, in which we analyzed 57 articles and describes nine questionnaires used for evaluating the usability of VUIs, assessing the potential suitability of these questionnaires to measure different types of interactions and various usability dimensions. We found that these questionnaires were used to evaluate the usability of voice-only and voice-added VUIs: AttrakDiff, ICF-US, MOS-X, SUISQ-R, SUS, SASSI, UEQ, PARADISE and USE, where the SUS questionnaire is the most commonly used. However, its items do not directly assess voice quality, although it evaluates the general user interaction with a system. All the questionnaires include items related to three usability dimensions (effectiveness, efficiency, and satisfaction). The questionnaire with the most homogeneous coverage regarding the number of items in each aspect of usability is the SASSI questionnaire. It is a normal practice to use multiple questionnaires to obtain a more complete measurement of usability. We perceive the necessity to increase usability research about the differences between the voice interaction with diverse display types (voice-first, voice-only, voice-added) and the dialog types (command-based and conversational), and how usability affects the user expectations about the VUIs.
Parallel sentences mining with transfer learning in an unsupervised setting

The quality and quantity of parallel sentences are known as very important training data for constructing neural machine translation (N-MT) systems.However, these resources are not available for many low-resource language pairs.Many existing methods need strong supervision and hence are not suitable.Although there have been several attempts at developing unsupervised models, they ignore the language-invariant between languages.In this paper, we propose an approach based on transfer learning to mine parallel sentences in an unsupervised setting.With the help of bilingual corpora of rich-resource language pairs, we can mine parallel sentences without bilingual supervision of low-resource language pairs.Experiments show that our approach improves the performance of mined parallel sentences compared with previous methods.In particular, we achieve good results at two real-world low-resource language pairs.
Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data

Prerequisite relations among concepts are crucial for educational applications, such as curriculum planning and intelligent tutoring.In this paper, we propose a novel concept prerequisite relation learning approach, named CPRL, which combines both concept representation learned from a heterogeneous graph and concept pairwise features.Furthermore, we extend CPRL under weakly supervised settings to make our method more practical, including learning prerequisite relations from learning object dependencies and generating training data with data programming.Our experiments on four datasets show that the proposed approach achieves the state-of-the-art results comparing with existing methods.
SeReNe: Sensitivity-Based Regularization of Neurons for Structured Sparsity in Neural Networks

Deep neural networks include millions of learnable parameters, making their deployment over resource-constrained devices problematic. Sensitivity-based regularization of neurons (SeReNe) is a method for learning sparse topologies with a structure, exploiting neural sensitivity as a regularizer. We define the sensitivity of a neuron as the variation of the network output with respect to the variation of the activity of the neuron. The lower the sensitivity of a neuron, the less the network output is perturbed if the neuron output changes. By including the neuron sensitivity in the cost function as a regularization term, we are able to prune neurons with low sensitivity. As entire neurons are pruned rather than single parameters, practical network footprint reduction becomes possible. Our experimental results on multiple network architectures and datasets yield competitive compression ratios with respect to state-of-the-art references.
Contextualized and Generalized Sentence Representations by Contrastive Self-Supervised Learning: A Case Study on Discourse Relation Analysis

We propose a method to learn contextualized and generalized sentence representations using contrastive self-supervised learning.In the proposed method, a model is given a text consisting of multiple sentences.One sentence is randomly selected as a target sentence.The model is trained to maximize the similarity between the representation of the target sentence with its context and that of the masked target sentence with the same context.Simultaneously, the model minimize the similarity between the latter representation and the representation of a random sentence with the same context.We apply our method to discourse relation analysis in English and Japanese and show that it outperforms strong baseline methods based on BERT, XLNet, and RoBERTa.
Robust Isometric Non-Rigid Structure-From-Motion

Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from keypoint correspondences established between monocular 2D images. Current NRSfM methods lack statistical robustness, which is the ability to cope with correspondence errors. This prevents one to use automatically established correspondences, which are prone to errors, thereby strongly limiting the scope of NRSfM. We propose a three-step automatic pipeline to solve NRSfM robustly by exploiting isometry. Step (i) computes the optical flow from correspondences, step (ii) reconstructs each 3D point's normal vector using multiple reference images and integrates them to form surfaces with the best reference and step (iii) rejects the 3D points that break isometry in their local neighborhood. Importantly, each step is designed to discard or flag erroneous correspondences. Our contributions include the robustification of optical flow by warp estimation, new fast analytic solutions to local normal reconstruction and their robustification, and a new scale-independent measure of 3D local isometric coherence. Experimental results show that our robust NRSfM method consistently outperforms existing methods on both synthetic and real datasets.
A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications

This paper starts by revealing a surprising finding: without any learning, a randomly initialized CNN can localize objects surprisingly well. That is, a CNN has an inductive bias to naturally focus on objects, named as Tobias ("The object is at sight") in this paper. This empirical inductive bias is further analyzed and successfully applied to self-supervised learning (SSL). A CNN is encouraged to learn representations that focus on the foreground object, by transforming every image into various versions with different backgrounds, where the foreground and background separation is guided by Tobias. Experimental results show that the proposed Tobias significantly improves downstream tasks, especially for object detection. This paper also shows that Tobias has consistent improvements on training sets of different sizes, and is more resilient to changes in image augmentations.
Predicting Co-occurring Emotions from Eye-Tracking and Interaction Data in MetaTutor

Emotions in Intelligent Tutoring Systems (ITS) are often modeled as single affective states, however there is evidence that emotions co-occur during learning, with implications for affect-aware ITS that need to have a comprehensive understanding of a student's affective state to react accordingly. In this paper we broaden the evidence that emotions co-occur in an educational context, and present a first attempt to predict these co-occurrences from data, using the MetaTutor ITS as a test-bed. We show that boredom+frustration, as well as curiosity+anxiety, frequently co-occur in MetaTutor, and that we can predict when these emotions co-occur significantly better than a baseline using eye-tracking and interaction data. These findings provide a first step toward building affect-aware ITS that can adapt to these complex co-occurring affective states.
Detecting and Analyzing Politically-Themed Stocks Using Text Mining Techniques and Transfer Entropy—Focus on the Republic of Korea’s Case

Politically-themed stocks mainly refer to stocks that benefit from the policies of politicians. This study gave the empirical analysis of the politically-themed stocks in the Republic of Korea and constructed politically-themed stock networks based on the Republic of Korea’s politically-themed stocks, derived mainly from politicians. To select politically-themed stocks, we calculated the daily politician sentiment index (PSI), which means politicians’ daily reputation using politicians’ search volume data and sentiment analysis results from politician-related text data. Additionally, we selected politically-themed stock candidates from politician-related search volume data. To measure causal relationships, we adopted entropy-based measures. We determined politically-themed stocks based on causal relationships from the rates of change of the PSI to their abnormal returns. To illustrate causal relationships between politically-themed stocks, we constructed politically-themed stock networks based on causal relationships using entropy-based approaches. Moreover, we experimented using politically-themed stocks in real-world situations from the schematized networks, focusing on politically-themed stock networks’ dynamic changes. We verified that the investment strategy using the PSI and politically-themed stocks that we selected could benchmark the main stock market indices such as the KOSPI and KOSDAQ around political events.
In the context of forensic casework, are there meaningful metrics of the degree of calibration?

Forensic-evaluation systems should output likelihood-ratio values that are well calibrated. If they do not, their output will be misleading. Unless a forensic-evaluation system is intrinsically well-calibrated, it should be calibrated using a parsimonious parametric model that is trained using calibration data. The system should then be tested using validation data. Metrics of degree of calibration that are based on the pool-adjacent-violators (PAV) algorithm recalibrate the likelihood-ratio values calculated from the validation data. The PAV algorithm overfits on the validation data because it is both trained and tested on the validation data, and because it is a non-parametric model with weak constraints. For already-calibrated systems, PAV-based ostensive metrics of degree of calibration do not actually measure degree of calibration; they measure sampling variability between the calibration data and the validation data, and overfitting on the validation data. Monte Carlo simulations are used to demonstrate that this is the case. We therefore argue that, in the context of casework, PAV-based metrics are not meaningful metrics of degree of calibration; however, we also argue that, in the context of casework, a metric of degree of calibration is not required.
Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas

In entity linking, mentions of named entities in raw text are disambiguated against a knowledge base (KB). This work focuses on linking to unseen KBs that do not have training data and whose schema is unknown during training. Our approach relies on methods to flexibly convert entities with several attribute-value pairs from arbitrary KBs into flat strings, which we use in conjunction with state-of-the-art models for zero-shot linking. We further improve the generalization of our model using two regularization schemes based on shuffling of entity attributes and handling of unseen attributes. Experiments on English datasets where models are trained on the CoNLL dataset, and tested on the TAC-KBP 2010 dataset show that our models are 12% (absolute) more accurate than baseline models that simply flatten entities from the target KB. Unlike prior work, our approach also allows for seamlessly combining multiple training datasets. We test this ability by adding both a completely different dataset (Wikia), as well as increasing amount of training data from the TAC-KBP 2010 training set. Our models are more accurate across the board compared to baselines.
Novel Random Key Encoding Schemes for the Differential Evolution of Permutation Problems

Differential evolution is a powerful nature-inspired real-parameter optimization algorithm that has been successfully used to solve a number of hard optimization problems. It has been used to tackle both continuous and discrete optimization problems. The application of a continuous method to discrete problems involves several challenges, including solution representation and search space–solution space mapping. In this work, we study random key encoding, a popular encoding scheme that is used to represent permutations in high-dimensional continuous spaces. We analyze the search space it constitutes, study its structure and properties, and introduce two novel modifications of the encoding. We investigate the proposed encoding strategies in the context of four variants of the differential evolution algorithm and demonstrate their usefulness for two widespread permutation problems: 1) the linear ordering problem and 2) the traveling salesman problem.
emrKBQA: A Clinical Knowledge-Base Question Answering Dataset

We present emrKBQA, a dataset for answering physician questions from a structured patient record.It consists of questions, logical forms and answers.The questions and logical forms are generated based on real-world physician questions and are slot-filled and answered from patients in the MIMIC-III KB (Johnson et al., 2016) through a semi-automated process.This community-shared release consists of over 940000 question, logical form and answer triplets with 389 types of questions and ≈7.5 paraphrases per question type.We perform experiments to validate the quality of the dataset and set benchmarks for question to logical form learning that helps answer questions on this dataset.
CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems

Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI.To advance the development of practical negotiation systems, we present CaSiNo: a novel corpus of over a thousand negotiation dialogues in English.Participants take the role of campsite neighbors and negotiate for food, water, and firewood packages for their upcoming trip.Our design results in diverse and linguistically rich negotiations while maintaining a tractable, closeddomain environment.Inspired by the literature in human-human negotiations, we annotate persuasion strategies and perform correlation analysis to understand how the dialogue behaviors are associated with the negotiation performance.We further propose and evaluate a multi-task framework to recognize these strategies in a given utterance.We find that multi-task learning substantially improves the performance for all strategy labels, especially for the ones that are the most skewed.We release the dataset, annotations, and the code to propel future work in human-machine negotiations:
Vector co‐occurrence morphological edge detection for colour image

Morphological edge detection is a principal component in pattern recognition and machine vision. Traditional edge detection operators only take pixel mutual into consideration. However, the edges are influenced not only by pixel mutual but also by the boundary characteristics. Here, the vector co-occurrence morphological edge detection operator is proposed, which takes the pixel and boundary information both into consideration. The vector co-occurrence algorithm is exploited to resist the influence of the noise points and detect the edges from the colour image rather than the grey image. And, we lead to define a precise definition of the manner of sorting high-dimensional data for the colour image. The experiment results always illustrate the advancement and practicability of our methods against the baseline method. In terms of experiments, the BSDS500 dataset is introduced to compare and analyse with other algorithms. Based on the standard benchmark index evaluation in the BSDS500 dataset, the ODS and AP of various algorithms are compared and analysed.
PASTLE: Pivot-aided space transformation for local explanations

During the last decade, more and more Artificial Intelligence systems have been designed using complex and sophisticated architectures to reach unprecedented predictive performance. The side effect is an increase in opacity of their inner workings which is inadmissible when such systems are applied in critical domains (healthcare, finance and so on). The eXplainable AI (XAI) research field aims to overcome this limitation thus helping humans to understand black-box decisions. In this paper we propose a novel model-agnostic XAI technique, named Pivot-Aided Space Transformation for Local Explanations (PASTLE), which exploits an instance-space transformation to explain any model's predictions, aiming to enhance human trust towards the AI decisions. We experimentally evaluate the effects of the introduced space transformation on various real-world data sets and our user study reveals promising results in terms of effective explainability.
Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation

Machine Learning (ML) algorithms, especially deep neural networks (DNN), have proven themselves to be extremely useful tools for data analysis, and are increasingly being deployed in systems operating on sensitive data, such as recommendation systems, banking fraud detection, and healthcare systems. This underscores the need for privacy-preserving ML (PPML) systems, and has inspired a line of research into how such systems can be constructed efficiently. However, most prior works on PPML achieve efficiency by requiring advanced ML algorithms to be simplified or substituted with approximated variants that are “MPC-friendly” before multi-party computation (MPC) techniques are applied to obtain a PPML systems. A drawback of this approach is that it requires careful fine-tuning of the combined ML and MPC algorithms, and might lead to less efficient algorithms or inferior quality ML (such as lower prediction accuracy). This is an issue for secure training of DNNs in particular, as this involves several arithmetic algorithms that are thought to be “MPCunfriendly”, namely, integer division, exponentiation, inversion, and square root extraction. In this work, we take a structurally different approach and propose a framework that allows efficient and secure evaluation of full-fledged state-of-the-art ML algorithms via secure multi-party computation. Specifically, we propose secure and efficient protocols for the above seemingly MPC-unfriendly computations (but which are essential to DNN). Our protocols are three-party protocols in the honest-majority setting, and we propose both passively secure and actively secure with abort variants. A notable feature of our protocols is that they simultaneously provide high accuracy and efficiency. This framework enables us to efficiently and securely compute modern ML algorithms such as Adam (Adaptive moment estimation) and the softmax function “as is”, without resorting to approximations. As a result, we obtain secure DNN training that outperforms state-of-the-art threeparty systems; our full training is up to 6.7 times faster than just the online phase of FALCON (Wagh et al. at PETS’21) and up to 4.2 times faster than Dalskov et al. (USENIX’21) on the standard benchmark network for secure training of DNNs. The potential advantage of our approach is even greater when considering more complex realistic networks. To demonstrate this, we perform measurements on real-world DNNs, AlexNet and VGG16, which are large networks containing millions of parameters. The performance of our framework for these networks is up to a factor of 26 ∼ 33 faster for AlexNet and 48 ∼ 51 faster for VGG16 to achieve an accuracy of 60% and 70%, respectively, when compared to FALCON. Even compared to CRYPTGPU (Tan et al. IEEE S&amp;P’21), which is optimized for and runs on powerful GPUs, our framework achieves a factor of 2.1 and 4.1 faster performance, respectively, on these networks.