Reviewerly/exHarmony huggingface dataset:
Metaheuristics for data mining

In the context of big data, many scientific communities aim to provide efficient approaches to accommodate large-scale datasets. This is the case of the machine-learning community, and more generally, the artificial intelligence community. The aim of this article is to explain how data mining problems can be considered as combinatorial optimization problems, and how metaheuristics can be used to address them. Four primary data mining tasks are presented: clustering, association rules, classification, and feature selection. This article follows the publication of a book in 2016 concerning this subject (Dhaenens and Jourdan, Metaheuristics for big data, Wiley, New York, 2016); additionally, updated references and an analysis of the current trends are presented.
Human-Computer Interaction for Development (HCI4D): The Southern African Landscape

Human-Computer Interaction for Development (HCI4D) research aims to maximise the usability of interfaces for interacting with technologies designed specifically for under-served, under-resourced, and under-represented populations. In this paper, we provide a snapshot of Southern African HCI4D research against the background of the global HCI4D research landscape. We commenced with a systematic literature review of HCI4D (2010–2017) then surveyed Southern African researchers working in the area. The contribution is to highlight the context-specific themes and challenges that emerged from our investigation.
A Rapid Accurate Recognition System for Radar Emitter Signals

Radar signal recognition is an indispensable part of an electronic countermeasure system. In order to solve the problem that the current techniques have, which is a low recognition rate and a slow recognition speed for radar signals, a rapid accurate recognition system is proposed, especially for when multiple signals arrive at the receiver. The proposed system can recognize eight types of radar signals while separating signals: binary phase shift keying (BPSK), linear frequency modulation (LFM), Costas, Frank code, and P1–P4 codes. Regression variational mode decomposition (RVMD) is explored to separate the received signals, which saves time for parameter optimization of variational mode decomposition (VMD). Furthermore, signal separation and a noise removal technique based on VMD and the first component recognition technique based on a deep belief network (DBN) are proposed. In addition, in order to overcome the loss of the secondary component caused by signal separation, a fusion network is explored to increase the recognition rate of the secondary component in a short time. The simulation results show that the recognition system achieves an overall recognition rate of 99.5% and 94% at a signal-to-noise ratio (SNR) of 0 dB when receiving single signals and double signals, while spending 0.8 s and 2.23 s, respectively. The proposed system can also be used to recognize medical and mechanical signals.
An ambient assisted living system for dementia patients

Dementia is a major health and social care challenge of today and the near future as a result of increased human lifespan. Currently, there is no therapeutic solution for dementia, but a solution for managing the wandering behavior of dementia patients can be provided by an ambient assisted living system. In this paper, the design and implementation of iCarus, which is an intelligent ambient assisted living system for dealing with wandering behavior in early stages of dementia, is described. The aim of iCarus is to provide independent living for elderly people and a cost-effective way of monitoring them. iCarus is a zone-based system that forms a safety net. When a wandering episode occurs, rule-based context reasoning is employed to determine the actions that are to be executed. These actions include warning the patient, navigating the patient to his home, sending notifications to the caregiver(s), and initiating a real-time tracking session for the caregiver and the emergency service. Also, caregivers are able to construct their own rules and extend the functionality of the system according to their own needs. Constructing new rules is done by an innovative user interface. As a case study, iCarus is described and evaluated with a scenario. In order to evaluate the usability of iCarus, a questionnaire was administered after the users tried the system. The results were then statistically analyzed and reported.
On Knowledge distillation from complex networks for response prediction

Siddhartha Arora, Mitesh M. Khapra, Harish G. Ramaswamy. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach

Negation scope detection is widely performed as a supervised learning task which relies upon negation labels at word level.This suffers from two key drawbacks: (1) such granular annotations are costly and (2) highly subjective, since, due to the absence of explicit linguistic resolution rules, human annotators often disagree in the perceived negation scopes.To the best of our knowledge, our work presents the first approach that eliminates the need for word-level negation labels, replacing it instead with document-level sentiment annotations.For this, we present a novel strategy for learning fully interpretable negation rules via weak supervision: we apply reinforcement learning to find a policy that reconstructs negation rules from sentiment predictions at document level.Our experiments demonstrate that our approach for weak supervision can effectively learn negation rules.Furthermore, an out-of-sample evaluation via sentiment analysis reveals consistent improvements (of up to 4.66 %) over both a sentiment analysis with (i) no negation handling and (ii) the use of word-level annotations from humans.Moreover, the inferred negation rules are fully interpretable.
Goal Recognition for Rational and Irrational Agents

Contemporary cost-based goal-recognition assumes rationality: that observed behaviour is more or less optimal. Probabilistic systems, however, generate probability distributions on the basis of suboptimality. We show that, when an observed agent is only slightly irrational (suboptimal), state-of-the-art systems produce counter-intuitive results. We present a definition of rationality appropriate to situations where the ground truth is unknown, define a rationality measure (RM) that quantifies an agent's expected degree of suboptimality, and present a novel self-modulating probability distribution formula for goal recognition. Our formula recognises suboptimality and adjusts its level of confidence accordingly, thereby handling irrationality---and rationality---in an intuitive, principled manner.
Quantum Identity Authentication in the Counterfactual Quantum Key Distribution Protocol

In this paper, a quantum identity authentication protocol is presented based on the counterfactual quantum key distribution system. Utilizing the proposed protocol, two participants can verify each other’s identity through the counterfactual quantum communication system. The security of the protocol is proved against individual attacks. Furthermore, according to the characteristics of the counterfactual quantum key distribution system, we propose an authenticated counterfactual quantum key distribution protocol based on a novel strategy of mixing the two types of quantum cryptographic protocols randomly. The authenticated quantum key distribution can also be used to update the extent of the authentication keys.
Polynomial Based Progressive Secret Image Sharing Scheme With Smaller Shadow Size

Progressive secret image sharing (PSIS) scheme attracts the interests of researchers in recent years.Many approaches have been proposed to construct PSIS schemes.In most of these schemes, the size of the shadow is expanded from the original image.On the contrary, polynomial-based PSIS can reduce shadow size from the original image.Recently, Yang and Huang proposed a polynomial-based (k, n) PSIS, where the image can be progressively reconstructed from k to n shadows.However, the problem of Yang-Huang's scheme is that the percentage of the recovered partial image from t shadows is extremely low when t is close to k.Later, Yang and Chu constructed another polynomial-based (k, n) PSIS with smooth property to solve this problem, but the size of the shadow is expanded greatly from Yang-Huang' scheme.In this paper, we propose a new (k, n) PSIS based on polynomial to overcome the drawbacks of these two schemes.In our scheme, t shadows (t is close to k) can recover more percentage partial image than Yang-Huang's scheme with a little shadow size expansion; comparing with Yang-Chu's scheme, our scheme achieves almost the same smooth property with much smaller shadow size.
Calibration of the Relative Orientation between Multiple Depth Cameras Based on a Three-Dimensional Target

Depth cameras play a vital role in three-dimensional (3D) shape reconstruction, machine vision, augmented/virtual reality and other visual information-related fields. However, a single depth camera cannot obtain complete information about an object by itself due to the limitation of the camera's field of view. Multiple depth cameras can solve this problem by acquiring depth information from different viewpoints. In order to do so, they need to be calibrated to be able to accurately obtain the complete 3D information. However, traditional chessboard-based planar targets are not well suited for calibrating the relative orientations between multiple depth cameras, because the coordinates of different depth cameras need to be unified into a single coordinate system, and the multiple camera systems with a specific angle have a very small overlapping field of view. In this paper, we propose a 3D target-based multiple depth camera calibration method. Each plane of the 3D target is used to calibrate an independent depth camera. All planes of the 3D target are unified into a single coordinate system, which means the feature points on the calibration plane are also in one unified coordinate system. Using this 3D target, multiple depth cameras can be calibrated simultaneously. In this paper, a method of precise calibration using lidar is proposed. This method is not only applicable to the 3D target designed for the purposes of this paper, but it can also be applied to all 3D calibration objects consisting of planar chessboards. This method can significantly reduce the calibration error compared with traditional camera calibration methods. In addition, in order to reduce the influence of the infrared transmitter of the depth camera and improve its calibration accuracy, the calibration process of the depth camera is optimized. A series of calibration experiments were carried out, and the experimental results demonstrated the reliability and effectiveness of the proposed method.
Adaptive contrast enhancement based on histogram modification framework

An adaptive contrast enhancement method is proposed in this paper. The proposed method is based on the contrast limited adaptive histogram equalization (CLAHE) and the histogram modification framework. The predefined clip point for the clipped histogram of each block in original CLAHE may still result in excessive contrast enhancement in homogeneous regions, which gives the enhanced image an unnatural look and creates visual artifacts. By replacing the clipped histogram with a modified histogram, the proposed method achieves success in adaptively enhancing contrast in each block based on its content. In addition to this, a novel mapping function is introduced to further improve the enhanced result of histogram equalization (HE). Experiments are conducted with both visible images and infrared images to evaluate the performance of the proposed method. The results show that the proposed method gets better performance on contrast enhancement and visual quality of the enhanced results.
Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation

Sequential recommendation systems have become a research hotpot recently to suggest users with the next item of interest (to interact with). However, existing approaches suffer from two limitations: (1) The representation of an item is relatively static and fixed for all users. We argue that even a same item should be represented distinctively with respect to different users and time steps. (2) The generation of a prediction for a user over an item is computed in a single scale (e.g., by their inner product), ignoring the nature of multi-scale user preferences. To resolve these issues, in this paper we propose two enhancing building blocks for sequential recommendation. Specifically, we devise a Dynamic Item Block (DIB) to learn dynamic item representation by aggregating the embeddings of those who rated the same item before that time step. Then, we come up with a Prediction Enhancing Block (PEB) to project user representation into multiple scales, based on which many predictions can be made and attentively aggregated for enhanced learning. Each prediction is generated by a softmax over a sampled itemset rather than the whole item space for efficiency. We conduct a series of experiments on four real datasets, and show that even a basic model can be greatly enhanced with the involvement of DIB and PEB in terms of ranking accuracy. The code and datasets can be obtained from https://github.com/ouououououou/DIB-PEB-Sequential-RS
Algorithms and Hardness for Diameter in Dynamic Graphs.

The diameter, radius and eccentricities are natural graph parameters. While these problems have been studied extensively, there are no known dynamic algorithms for them beyond the ones that follow from trivial recomputation after each update or from solving dynamic All-Pairs Shortest Paths (APSP), which is very computationally intensive. This is the situation for dynamic approximation algorithms as well, and even if only edge insertions or edge deletions need to be supported.This paper provides a comprehensive study of the dynamic approximation of Diameter, Radius and Eccentricities, providing both conditional lower bounds, and new algorithms whose bounds are optimal under popular hypotheses in fine-grained complexity. Some of the highlights include: - Under popular hardness hypotheses, there can be no significantly better fully dynamic approximation algorithms than recomputing the answer after each update, or maintaining full APSP. - Nearly optimal partially dynamic (incremental/decremental) algorithms can be achieved via efficient reductions to (incremental/decremental) maintenance of Single-Source Shortest Paths. For instance, a nearly (3/2+epsilon)-approximation to Diameter in directed or undirected n-vertex, m-edge graphs can be maintained decrementally in total time m^{1+o(1)}sqrt{n}/epsilon^2. This nearly matches the static 3/2-approximation algorithm for the problem that is known to be conditionally optimal.
From Innocent Irene to Parental Patrick: Framing User Characteristics and Personas to Design for Cybersecurity

Abstract With the surging number of digital devices penetrating our daily routines, the risks inherent to cybersecurity—the protection of data on digital products connected to the Internet—have also increased since these devices (e.g., connected home devices, personal monitoring) collect, process, analyze and store users’ sensitive personal information. Thus, there is a pressing need to assist users in being aware of and dealing with potential cybersecurity threats. With the proposition that fulfilling the need starts with developing an in-depth understanding of the user behaviors in the context of cybersecurity, an exploratory study was conducted that employed three mixed qualitative and quantitative research methods—a trend analysis, an interview study, and an online survey study. The paper reports the user characteristics on (1) awareness levels of cybersecurity issues, (2) uses of digital devices, and (3) means of dealing with the privacy issues in product use. The results of the studies were translated into eight personas that systematically reflect distinct characteristics of users, which can help designers empathize with their potential users vulnerable to cybersecurity risks.
Almost optimal distribution-free junta testing

We consider the problem of testing whether an unknown n-variable Boolean function is a k-junta in the distribution-free property testing model, where the distance between functions is measured with respect to an arbitrary and unknown probability distribution over {0, 1}n. Chen, Liu, Servedio, Sheng and Xie [35] showed that the distribution-free k-junta testing can be performed, with one-sided error, by an adaptive algorithm that makes O(k2)/ϵ queries. In this paper, we give a simple two-sided error adaptive algorithm that makes O{k/ϵ) queries.
The Document Similarity Index based on the Jaccard Distance for Mail Filtering

We propose a new index of similarity for classification of emails into ham and spam ones with the Jaccard index. It takes advantage of co-occurrence value of all pairs of two words in emails. The co-occurrence of words represents a sort of context in documents because a word is often in use with another word in the same context. Our proposed method classified emails into hams or spams with high accuracy rate than the present filtering system using appearance frequency of word. Our method could extract patterns of word usage reflecting the context of emails.
Broadcast and Trace with $$N^{\varepsilon }$$ Ciphertext Size from Standard Assumptions

We construct a broadcast and trace scheme (also known as trace and revoke or broadcast, trace and revoke) with N users, where the ciphertext size can be made as low as $$O(N^\varepsilon )$$ , for any arbitrarily small constant $$\varepsilon >0$$ . This improves on the prior best construction of broadcast and trace under standard assumptions by Boneh and Waters (CCS ‘06), which had ciphertext size $$O(N^{1/2})$$ . While that construction relied on bilinear maps, ours uses a combination of the learning with errors (LWE) assumption and bilinear maps. Recall that, in both broadcast encryption and traitor-tracing schemes, there is a collection of N users, each of which gets a different secret key $${\mathsf {sk}}_i$$ . In broadcast encryption, it is possible to create ciphertexts targeted to a subset $$S \subseteq [N]$$ of the users such that only those users can decrypt it correctly. In a traitor tracing scheme, if a subset of users gets together and creates a decoder box D that is capable of decrypting ciphertexts, then it is possible to trace at least one of the users responsible for creating D. A broadcast and trace scheme intertwines the two properties, in a way that results in more than just their union. In particular, it ensures that if a decoder D is able to decrypt ciphertexts targeted toward a set S of users, then it should be possible to trace one of the users in the set S responsible for creating D, even if other users outside of S also participated. As of recently, we have essentially optimal broadcast encryption (Boneh, Gentry, Waters CRYPTO ’05) under bilinear maps and traitor tracing (Goyal, Koppula, Waters STOC ’18) under LWE, where the ciphertext size is at most poly-logarithmic in N. The main contribution of our paper is to carefully combine LWE and bilinear-map based components, and get them to interact with each other, to achieve broadcast and trace.
A deep-learning framework to detect sarcasm targets

In this paper we propose a deep learning framework for sarcasm target detection in predefined sarcastic texts.Identification of sarcasm targets can help in many core natural language processing tasks such as aspect based sentiment analysis, opinion mining etc.To begin with, we perform an empirical study of the socio-linguistic features and identify those that are statistically significant in indicating sarcasm targets (p-values in the range (0.05, 0.001)).Finally, we present a deeplearning framework augmented with sociolinguistic features to detect sarcasm targets in sarcastic book-snippets and tweets.We achieve a huge improvement in the performance in terms of exact match and dice score as compared to the current state-of-the-art baseline.
Variational Temporal Abstraction

We introduce a variational approach to learning and inference of temporally hierarchical structure and representation for sequential data. We propose the Variational Temporal Abstraction (VTA), a hierarchical recurrent state space model that can infer the latent temporal structure and thus perform the stochastic state transition hierarchically. We also propose to apply this model to implement the jumpy imagination ability in imagination-augmented agent-learning in order to improve the efficiency of the imagination. In experiments, we demonstrate that our proposed method can model 2D and 3D visual sequence datasets with interpretable temporal structure discovery and that its application to jumpy imagination enables more efficient agent-learning in a 3D navigation task.
What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition

Contextualized word embeddings have boosted many NLP tasks compared with traditional static word embeddings.However, the word with a specific sense may have different contextualized embeddings due to its various contexts.To further investigate what contextualized word embeddings capture, this paper analyzes whether they can indicate the corresponding sense definitions and proposes a general framework that is capable of explaining word meanings given contextualized word embeddings for better interpretation.The experiments show that both ELMo and BERT embeddings can be well interpreted via a readable textual form, and the findings may benefit the research community for a better understanding of what the embeddings capture 1 .
A Distance Regularized Level-Set Evolution Model Based MRI Dataset Segmentation of Brain’s Caudate Nucleus

The caudate nucleus of the brain is highly correlated to the emotional decision-making of pessimism, which is an important process for improving the understanding and treatment of depression; and the segmentation of the caudate nucleus is the most basic step in the process of analysis and research concerning this region. In this paper, Level Set Method (LSM) is applied for caudate nucleus segmentation. Firstly, Distance Regularized Level Set Evolution (DRLSE), Region-Scalable Fitting (RSF) and Local Image Fitting (LIF) models are proposed for segmentation of the caudate nucleus of Magnetic Resonance Imaging (MRI) images of the brain, and the segmentation results are compared by using selected evaluation indices. The average Dice Similarity Coefficient (DSC) values of the proposed three methods all exceed 85%, and the average Jaccard Similarity (JS) values are over 77%, respectively. The results indicate that all these three models can have good segmentation results for medical images with intensity inhomogeneity and meet the general segmentation requirements, while the proposed DRLSE model performs better in segmentation.