Reviewerly/exHarmony huggingface dataset:
Fuzzy support vector machine for imbalanced data with borderline noise

This work is an extension of the Fuzzy Support Vector Machines for Class Imbalance Learning (FSVM-CIL) method proposed by Rukshan Batuwita and Vasile Palade. For FSVMs, a very important part is the fuzzy function transforming different distance measures to membership values between 0 and 1. The larger the membership value, the more important the corresponding training data point. Although various variants have been proposed recently, few have discussed proper fuzzy functions. This work first shows the limitations of fuzzy functions in original FSVM-CIL for imbalanced data with noise around the between-class borderline (noted as borderline noise in this paper), and then, a new fuzzy function, named the Gaussian fuzzy function, is proposed and explained in detail. Modifications are also made to the current distance measures. Experiments on several public imbalanced datasets show the effectiveness of the proposed methods through the comparison with FSVM-CIL and several other popular approaches for imbalanced data.
Sequential Weakly Labeled Multiactivity Localization and Recognition on Wearable Sensors Using Recurrent Attention Networks

With the popularity and development of the wearable devices such as smartphones, human activity recognition (HAR) based on sensors has become as a key research area in human computer interaction and ubiquitous computing. The emergence of deep learning leads to a recent shift in the research of HAR, which requires massive strictly labeled data in supervised learning scenario. In comparison with video data, activity data recorded from accelerometer or gyroscope are often more difficult to interpret and segment. Recently, several attention mechanisms are proposed to handle the weakly labeled human activity data, which do not require accurate data annotation. However, these attention-based models can only handle the weakly labeled dataset whose sample includes one target activity, as a result it limits efficiency and practicality. In the article, we propose a recurrent attention networks (RAN) to handle sequential weakly labeled multiactivity recognition and location tasks. The model can repeatedly perform steps of attention on multiple activities of one sample and each step is corresponding to the current focused activity. The effectiveness of the RAN model is validated on a collected sequential weakly labeled multiactivity dataset and the other two public datasets. The experiment results show that our RAN model can simultaneously infer multiactivity types from the coarse-grained sequential weak labels and determine specific locations of every target activity with only knowledge of which types of activities contained in the long sequence. It will greatly reduce the burden of manual labeling. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Spook: Sponge-Based Leakage-Resistant Authenticated Encryption with a Masked Tweakable Block Cipher

This paper defines Spook: a sponge-based authenticated encryption with associated data algorithm. It is primarily designed to provide security against side-channel attacks at a low energy cost. For this purpose, Spook is mixing a leakageresistant mode of operation with bitslice ciphers enabling efficient and low latency implementations. The leakage-resistant mode of operation leverages a re-keying function to prevent differential side-channel analysis, a duplex sponge construction to efficiently process the data, and a tag verification based on a Tweakable Block Cipher (TBC) providing strong data integrity guarantees in the presence of leakages. The underlying bitslice ciphers are optimized for the masking countermeasures against side-channel attacks. Spook is an efficient single-pass algorithm. It ensures state-of-the-art black box security with several prominent features: (i) nonce misuse-resilience, (ii) beyond-birthday security with respect to the TBC block size, and (iii) multiuser security at minimum cost with a public tweak. Besides the specifications and design rationale, we provide first software and hardware implementation results of (unprotected) Spook which confirm the limited overheads that the use of two primitives sharing internal components imply. We also show that the integrity of Spook with leakage, so far analyzed with unbounded leakages for the duplex sponge and a strongly protected TBC modeled as leak-free, can be proven with a much weaker unpredictability assumption for the TBC. We finally discuss external cryptanalysis results and tweaks to improve both the security margins and efficiency of Spook.
Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model

The recognition of human activities is usually considered to be a simple procedure. Problems occur in complex scenes involving high speeds. Activity prediction using Artificial Intelligence (AI) by numerical analysis has attracted the attention of several researchers. Human activities are an important challenge in various fields. There are many great applications in this area, including smart homes, assistive robotics, human–computer interactions, and improvements in protection in several areas such as security, transport, education, and medicine through the control of falling or aiding in medication consumption for elderly people. The advanced enhancement and success of deep learning techniques in various computer vision applications encourage the use of these methods in video processing. The human presentation is an important challenge in the analysis of human behavior through activity. A person in a video sequence can be described by their motion, skeleton, and/or spatial characteristics. In this paper, we present a novel approach to human activity recognition from videos using the Recurrent Neural Network (RNN) for activity classification and the Convolutional Neural Network (CNN) with a new structure of the human skeleton to carry out feature presentation. The aims of this work are to improve the human presentation through the collection of different features and the exploitation of the new RNN structure for activities. The performance of the proposed approach is evaluated by the RGB-D sensor dataset CAD-60. The experimental results show the performance of the proposed approach through the average error rate obtained (4.5%).
Walking on the Edge: Fast, Low-Distortion Adversarial Examples

Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input. This is natural given the increasing applications of deep neural networks in our everyday lives. When white-box attacks are almost always successful, it is typically only the distortion of the perturbations that matters in their evaluation. In this work, we argue that speed is important as well, especially when considering that fast attacks are required by adversarial training. Given more time, iterative methods can always find better solutions. We investigate this speed-distortion trade-off in some depth and introduce a new attack called boundary projection (BP) that improves upon existing methods by a large margin. Our key idea is that the classification boundary is a manifold in the image space: we therefore quickly reach the boundary and then optimize distortion on this manifold.
Single Image Haze Removal from Image Enhancement Perspective for Real-Time Vision-Based Systems

Vision-based systems operating outdoors are significantly affected by weather conditions, notably those related to atmospheric turbidity. Accordingly, haze removal algorithms, actively being researched over the last decade, have come into use as a pre-processing step. Although numerous approaches have existed previously, an efficient method coupled with fast implementation is still in great demand. This paper proposes a single image haze removal algorithm with a corresponding hardware implementation for facilitating real-time processing. Contrary to methods that invert the physical model describing the formation of hazy images, the proposed approach mainly exploits computationally efficient image processing techniques such as detail enhancement, multiple-exposure image fusion, and adaptive tone remapping. Therefore, it possesses low computational complexity while achieving good performance compared to other state-of-the-art methods. Moreover, the low computational cost also brings about a compact hardware implementation capable of handling high-quality videos at an acceptable rate, that is, greater than 25 frames per second, as verified with a Field Programmable Gate Array chip. The software source code and datasets are available online for public use.
Expand globally, shrink locally: Discriminant multi-label learning with missing labels

In multi-label learning, the issue of missing labels brings a major challenge. Many methods attempt to recovery missing labels by exploiting low-rank structure of label matrix. However, these methods just utilize global low-rank label structure, ignore both local low-rank label structures and label discriminant information to some extent, leaving room for further performance improvement. In this paper, we develop a simple yet effective discriminant multi-label learning (DM2L) method for multi-label learning with missing labels. Specifically, we impose the low-rank structures on all the predictions of instances from the same labels (local shrinking of rank), and a maximally separated structure (high-rank structure) on the predictions of instances from different labels (global expanding of rank). In this way, these imposed low-rank structures can help modeling both local and global low-rank label structures, while the imposed high-rank structure can help providing more underlying discriminability. Our subsequent theoretical analysis also supports these intuitions. In addition, we provide a nonlinear extension via using kernel trick to enhance DM2L and establish a concave-convex objective to learn these models. Compared to the other methods, our method involves the fewest assumptions and only one hyper-parameter. Even so, extensive experiments show that our method still outperforms the state-of-the-art methods.
Decentralized Control of Multi-Robot System in Cooperative Object Transportation Using Deep Reinforcement Learning

Object transportation could be a challenging problem for a single robot due to the oversize and/or overweight issues. A multi-robot system can take the advantage of increased driving power and more flexible configuration to solve such a problem. However, an increased number of individuals also changed the dynamics of the system which makes control of a multi-robot system more complicated. Even worse, if the whole system is sitting on a centralized decision making unit, the data flow could be easily overloaded due to the upscaling of the system. In this research, we propose a decentralized control scheme on a multi-robot system with each individual equipped with a deep Q-network (DQN) controller to perform an oversized object transportation task. DQN is a deep reinforcement learning algorithm, thus does not require the knowledge of system dynamics, instead, it enables the robots to learn appropriate control strategies through trial-and-error style interactions within the task environment. Since analogous controllers are distributed on the individuals, the computational bottleneck is avoided systematically. We demonstrate such a system in a scenario of carrying an oversized rod through a doorway by a two-robot team. The presented multi-robot system learns abstract features of the task and cooperative behaviors are observed. The decentralized DQN-style controller is showing strong robustness against uncertainties. In addition, We propose a universal metric to assess the cooperation quantitatively.
Personalized Bundle Recommendation in Online Games

In business domains, bundling is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers. Existing recommender systems mostly focus on recommending individual items that users may be interested in. In this paper, we target at a practical but less explored recommendation problem named bundle recommendation, which aims to offer a combination of items to users. To tackle this specific recommendation problem in the context of the virtual mall in online games, we formalize it as a link prediction problem on a user-item-bundle tripartite graph constructed from the historical interactions, and solve it with a neural network model that can learn directly on the graph-structure data. Extensive experiments on three public datasets and one industrial game dataset demonstrate the effectiveness of the proposed method. Further, the bundle recommendation model has been deployed in production for more than one year in a popular online game developed by Netease Games, and the launch of the model yields more than 60% improvement on conversion rate of bundles, and a relative improvement of more than 15% on gross merchandise volume (GMV).
Topic Modeling and Characterization of Hate Speech against Immigrants on Twitter around the Emergence of a Far-Right Party in Spain

In this paper, we sought to model and characterize hate speech against immigrants on Twitter in Spain around the appearance of the far-right party Vox. More than 240,000 tweets that included the term ‘Vox’ between November 2018 and April 2019 were automatically collected and analyzed. Only 1% of the sample included hate speech expressions. Within this subsample of 1977 messages, we found offenses (56%), incitements to hate (42%), and violent speech (2%). The most frequent terms used were classified into five categories: Spain, Immigration, Government, Islam, and Insults. The most common features were foul language, false or doubtful information, irony, distasteful expressions, humiliation or contempt, physical or psychological threats, and incitement to violence. Using unsupervised topic modeling, we found that the four underlying topics (control of illegal immigration, economic assistance for immigrants, consequences of illegal immigration, and Spain as an arrival point for African immigrants and Islamist terrorism) were similar to those in the discourse of Vox. We conclude that the hate speech against immigrants produced around Vox, and not necessarily by Vox, followed the general patterns of this type of speech detected in previous works, including Islamophobia, offensive language more often than violent language, and the refusal to offer public assistance to these collectives.
A novel chaos based optical cryptosystem for multiple images using DNA-blend and gyrator transform

In this paper, a novel optical encryption algorithm for multiple images based on chaos and DNA-blend in gyrator transform domain is proposed. Firstly, the multispectral image is divided into eight parts regularly. Subsequently the pixel positions of the blocked images are scrambled according to the pixel values of the chaotic sequences generated by 4D Lorenz system. Furthermore, eight kinds of deoxyribonucleic acid (DNA) matrices in binary form are also considered and utilized among the eight blocked fragments. Finally, the scrambled images are encoded and transformed in optical gyrator transform light field and the ciphertext is scrambled by Arnold transform to enhance the security. The proposed cryptosystem can be implemented by an electro-optical hybrid setup. Some parameters of the chaos system and gyrator transform serves as the extra keys of the encryption scheme. Some numerical simulations are implemented to testify the validity and robustness of the proposed cryptosystem for multiple images.
Fast Logic with Slow Qubits: Microwave-Activated Controlled-Z Gate on Low-Frequency Fluxoniums

We demonstrate a controlled-Z gate between capacitively coupled fluxonium qubits with transition frequencies $72.3~\textrm{MHz}$ and $136.3~\textrm{MHz}$. The gate is activated by a $61.6~\textrm{ns}$ long pulse at the frequency between non-computational transitions $|10\rangle - |20\rangle$ and $|11\rangle - |21\rangle$, during which the qubits complete only $4$ and $8$ Larmor periods, respectively. The measured gate error of $(8\pm1)\times 10^{-3}$ is limited by decoherence in the non-computational subspace, which will likely improve in the next generation devices. Although our qubits are about fifty times slower than transmons, the two-qubit gate is faster than microwave-activated gates on transmons, and the gate error is on par with the lowest reported. Architectural advantages of low-frequency fluxoniums include long qubit coherence time, weak hybridization in the computational subspace, suppressed residual $ZZ$-coupling rate (here $46~\mathrm{kHz}$), and absence of either excessive parameter matching or complex pulse shaping requirements.
Analyzing and generating multimode optical fields using self-configuring networks

Working with finite numbers of modes to describe, generate, and detect optical fields can be both mathematically economical and physically useful. Such a modal basis can map directly to various applications in communications, sensing, and processing. But, we need a way to generate and analyze such fields, including measurement and control of both the relative amplitudes and phases of the modal components. Ideally such an analysis scheme would operate directly on the field, without needing a separate, mutually coherent reference beam. Here, we show first how to measure all those relative amplitudes and phases automatically and simultaneously. The method repurposes a self-configuring network of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2</mml:mn> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> blocks, such as integrated Mach–Zehnder interferometers, that can automatically align itself to the optical field by a sequence of simple one-parameter power minimizations when network elements, such as phase shifters, are adjusted. The optical field is then directly deduced from the resulting settings of those elements. We show how the entire network can be calibrated for such measurements, automatically and with just two light beams. Then, using the same calibration and running the mesh backwards, we can also controllably generate an arbitrary multimode field. Explicit algorithms and formulas are given for operating this system.
PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction

Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction.For example, a female character in a story is often portrayed as passive and powerless ("She daydreams about being a doctor") while a man is portrayed as more proactive and powerful ("He pursues his dream of being a doctor").We formulate Controllable Debiasing, a new revision task that aims to rewrite a given text to correct the implicit and potentially undesirable bias in character portrayals.We then introduce POWERTRANSFORMER as an approach that debiases text through the lens of connotation frames (Sap et al., 2017), which encode pragmatic knowledge of implied power dynamics with respect to verb predicates.One key challenge of our task is the lack of parallel corpora.To address this challenge, we adopt an unsupervised approach using auxiliary supervision with related tasks such as paraphrasing and self-supervision based on a reconstruction loss, building on pretrained language models.Through comprehensive experiments based on automatic and human evaluations, we demonstrate that our approach outperforms ablations and existing methods from related tasks.Furthermore, we demonstrate the use of POWER-TRANSFORMER as a step toward mitigating the well-documented gender bias in character portrayal in movie scripts.
Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation

Literary tropes, from poetry to stories, are at the crux of human imagination and communication.Figurative language, such as a simile, goes beyond plain expressions to give readers new insights and inspirations.We tackle the problem of simile generation.Generating a simile requires proper understanding for effective mapping of properties between two concepts.To this end, we first propose a method to automatically construct a parallel corpus by transforming a large number of similes collected from Reddit to their literal counterpart using structured common sense knowledge.We then fine-tune a pretrained sequence to sequence model, BART (Lewis et al., 2019), on the literal-simile pairs to generate novel similes given a literal sentence.Experiments show that our approach generates 88% novel similes that do not share properties with the training data.Human evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts 37% 1 of the times, and three baseline systems including a recent metaphor generation model 71% 2 of the times when compared pairwise. 3We also show how replacing literal sentences with similes from our best model in machine generated stories improves evocativeness and leads to better acceptance by human judges.* The research was conducted when the author was at USC/ISI.1 We average 32.6% and 41.3% for 2 humans. 2 We average 82% ,63% and 68% for three baselines. 3The simile in the title is generated by our best model.Input: Generating similes effortlessly, output: Generating similes like a Pro.
Identifying Spurious Correlations for Robust Text Classification

The predictions of text classifiers are often driven by spurious correlations -e.g., the term Spielberg correlates with positively reviewed movies, even though the term itself does not semantically convey a positive sentiment.In this paper, we propose a method to distinguish spurious and genuine correlations in text classification.We treat this as a supervised classification problem, using features derived from treatment effect estimators to distinguish spurious correlations from "genuine" ones.Due to the generic nature of these features and their small dimensionality, we find that the approach works well even with limited training examples, and that it is possible to transport the word classifier to new domains.Experiments on four datasets (sentiment classification and toxicity detection) suggest that using this approach to inform feature selection also leads to more robust classification, as measured by improved worst-case accuracy on the samples affected by spurious correlations.