Reviewerly/exHarmony huggingface dataset:
Learning Sparse Filters in Deep Convolutional Neural Networks with a  l1/l2 Pseudo-Norm

While deep neural networks (DNNs) have proven to be efficient for numerous tasks, they come at a high memory and computation cost, thus making them impractical on resource-limited devices. However, these networks are known to contain a large number of parameters. Recent research has shown that their structure can be more compact without compromising their performance. In this paper, we present a sparsity-inducing regularization term based on the ratio l1/l2 pseudo-norm defined on the filter coefficients. By defining this pseudo-norm appropriately for the different filter kernels, and removing irrelevant filters, the number of kernels in each layer can be drastically reduced leading to very compact Deep Convolutional Neural Networks (DCNN) structures. Unlike numerous existing methods, our approach does not require an iterative retraining process and, using this regularization term, directly produces a sparse model during the training process. Furthermore, our approach is also much easier and simpler to implement than existing methods. Experimental results on MNIST and CIFAR-10 show that our approach significantly reduces the number of filters of classical models such as LeNet and VGG while reaching the same or even better accuracy than the baseline models. Moreover, the trade-off between the sparsity and the accuracy is compared to other loss regularization terms based on the l1 or l2 norm as well as the SSL, NISP and GAL methods and shows that our approach is outperforming them.
ACOUSTIC-TURF: Acoustic-based Privacy-Preserving COVID-19 Contact  Tracing

In this paper, we propose a new privacy-preserving, automated contact tracing system, ACOUSTIC-TURF, to fight COVID-19 using acoustic signals sent from ubiquitous mobile devices. At a high level, ACOUSTIC-TURF adaptively broadcasts inaudible ultrasonic signals with randomly generated IDs in the vicinity. Simultaneously, the system receives other ultrasonic signals sent from nearby (e.g., 6 feet) users. In such a system, individual user IDs are not disclosed to others and the system can accurately detect encounters in physical proximity with 6-foot granularity. We have implemented a prototype of ACOUSTIC-TURF on Android and evaluated its performance in terms of acoustic-signal-based encounter detection accuracy and power consumption at different ranges and under various occlusion scenarios. Experimental results show that ACOUSTIC-TURF can detect multiple contacts within a 6-foot range for mobile phones placed in pockets and outside pockets. Furthermore, our acoustic-signal-based system achieves greater precision than wireless-signal-based approaches when contact tracing is performed through walls. ACOUSTIC-TURF correctly determines that people on opposite sides of a wall are not in contact with one another, whereas the Bluetooth-based approaches detect nonexistent contacts among them.
The resolution of Niho's last conjecture concerning sequences, codes,  and Boolean functions

A new method is used to resolve a long-standing conjecture of Niho concerning the crosscorrelation spectrum of a pair of maximum length linear recursive sequences of length $2^{2 m}-1$ with relative decimation $d=2^{m+2}-3$, where $m$ is even. The result indicates that there are at most five distinct crosscorrelation values. Equivalently, the result indicates that there are at most five distinct values in the Walsh spectrum of the power permutation $f(x)=x^d$ over a finite field of order $2^{2 m}$ and at most five distinct nonzero weights in the cyclic code of length $2^{2 m}-1$ with two primitive nonzeros $\alpha$ and $\alpha^d$. The method used to obtain this result proves constraints on the number of roots that certain seventh degree polynomials can have on the unit circle of a finite field. The method also works when $m$ is odd, in which case the associated crosscorrelation and Walsh spectra have at most six distinct values.
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks

Deep neural networks (DNNs) have achieved great success in image classification, but can be very vulnerable to adversarial attacks with small perturbations to images. To improve adversarial image generation for DNNs, we develop a novel method, called mFI-PSO, which utilizes a Manifold-based First-order Influence measure for vulnerable image and pixel selection and the Particle Swarm Optimization for various objective functions. Our mFI-PSO can thus effectively design adversarial images with flexible, customized options on the number of perturbed pixels, the misclassification probability, and the targeted incorrect class. Experiments demonstrate the flexibility and effectiveness of our mFI-PSO in adversarial attacks and its appealing advantages over some popular methods.
Class-Weighted Classification: Trade-offs and Robust Approaches

We address imbalanced classification, the problem in which a label may have low marginal probability relative to other labels, by weighting losses according to the correct class. First, we examine the convergence rates of the expected excess weighted risk of plug-in classifiers where the weighting for the plug-in classifier and the risk may be different. This leads to irreducible errors that do not converge to the weighted Bayes risk, which motivates our consideration of robust risks. We define a robust risk that minimizes risk over a set of weightings and show excess risk bounds for this problem. Finally, we show that particular choices of the weighting set leads to a special instance of conditional value at risk (CVaR) from stochastic programming, which we call label conditional value at risk (LCVaR). Additionally, we generalize this weighting to derive a new robust risk problem that we call label heterogeneous conditional value at risk (LHCVaR). Finally, we empirically demonstrate the efficacy of LCVaR and LHCVaR on improving class conditional risks.
What am I Searching for: Zero-shot Target Identity Inference in Visual  Search

Can we infer intentions from a person's actions? As an example problem, here we consider how to decipher what a person is searching for by decoding their eye movement behavior. We conducted two psychophysics experiments where we monitored eye movements while subjects searched for a target object. We defined the fixations falling on \textit{non-target} objects as "error fixations". Using those error fixations, we developed a model (InferNet) to infer what the target was. InferNet uses a pre-trained convolutional neural network to extract features from the error fixations and computes a similarity map between the error fixations and all locations across the search image. The model consolidates the similarity maps across layers and integrates these maps across all error fixations. InferNet successfully identifies the subject's goal and outperforms competitive null models, even without any object-specific training on the inference task.
An examination of applicability of face recognition sensors in public  facilities

This study aimed to explore the usability and applicability of face recognition sensors in public spaces to collect customer footfall data, which could then be analysed and evaluated for facility design and planning. Nine OMRON sensors were provided for the project and installed at five locations in a public facility for three months. The project was carried out by a local consortium with the cooperation of local technology-based Small Medium-sized Enterprises (SMEs), business organisations, and a local university. Collected data were analysed to develop a report with diagrams, and reveal issues and potential for practical application in the future.
Automatic Cross-Replica Sharding of Weight Update in Data-Parallel  Training

In data-parallel synchronous training of deep neural networks, different devices (replicas) run the same program with different partitions of the training batch, but weight update computation is repeated on all replicas, because the weights do not have a batch dimension to partition. This can be a bottleneck for performance and scalability in typical language models with large weights, and models with small per-replica batch size which is typical in large-scale training. This paper presents an approach to automatically shard the weight update computation across replicas with efficient communication primitives and data formatting, using static analysis and transformations on the training computation graph. We show this technique achieves substantial speedups on typical image and language models on Cloud TPUs, requiring no change to model code. This technique helps close the gap between traditionally expensive (ADAM) and cheap (SGD) optimizers, as they will only take a small part of training step time and have similar peak memory usage. It helped us to achieve state-of-the-art training performance in Google's MLPerf 0.6 submission.
Data Augmentation Imbalance For Imbalanced Attribute Classification

Pedestrian attribute recognition is an important multi-label classification problem. Although the convolutional neural networks are prominent in learning discriminative features from images, the data imbalance in multi-label setting for fine-grained tasks remains an open problem. In this paper, we propose a new re-sampling algorithm called: data augmentation imbalance (DAI) to explicitly enhance the ability to discriminate the fewer attributes via increasing the proportion of labels accounting for a small part. Fundamentally, by applying over-sampling and under-sampling on the multi-label dataset at the same time, the thought of robbing the rich attributes and helping the poor makes a significant contribution to DAI. Extensive empirical evidence shows that our DAI algorithm achieves state-of-the-art results, based on pedestrian attribute datasets, i.e. standard PA-100K and PETA datasets.
ON-OFF Privacy in the Presence of Correlation

We formulate and study the problem of ON-OFF privacy. ON-OFF privacy algorithms enable a user to continuously switch his privacy between ON and OFF. An obvious example is the incognito mode in internet browsers. But beyond internet browsing, ON-OFF privacy can be a desired feature in most online applications. The challenge is that the statistical correlation over time of user's online behavior can lead to leakage of information. We consider the setting in which a user is interested in retrieving the latest message generated by one of N sources. The user's privacy status can change between ON and OFF over time. When privacy is ON the user wants to hide his request. Moreover, since the user's requests depend on personal attributes such as age, gender, and political views, they are typically correlated over time. As a consequence, the user cannot simply ignore privacy when privacy is OFF. We model the correlation between user's requests by an N state Markov chain. The goal is to design query schemes with optimal download rate, that preserve privacy in an ON-OFF privacy setting. In this paper, we present inner and outer bounds on the achievable download rate for N sources. We also devise an efficient algorithm to construct an ON-OFF privacy scheme achieving the inner bound and prove its optimality in the case N = 2 sources. For N > 2, finding tighter outer bounds and efficient constructions of ON-OFF privacy schemes that would achieve them remains an open question.
Multi-Task Reinforcement Learning with Soft Modularization

Multi-task learning is a very challenging problem in reinforcement learning. While training multiple tasks jointly allow the policies to share parameters across different tasks, the optimization problem becomes non-trivial: It remains unclear what parameters in the network should be reused across tasks, and how the gradients from different tasks may interfere with each other. Thus, instead of naively sharing parameters across tasks, we introduce an explicit modularization technique on policy representation to alleviate this optimization issue. Given a base policy network, we design a routing network which estimates different routing strategies to reconfigure the base network for each task. Instead of directly selecting routes for each task, our task-specific policy uses a method called soft modularization to softly combine all the possible routes, which makes it suitable for sequential tasks. We experiment with various robotics manipulation tasks in simulation and show our method improves both sample efficiency and performance over strong baselines by a large margin.
Hierarchical Models: Intrinsic Separability in High Dimensions

It has long been noticed that high dimension data exhibits strange patterns. This has been variously interpreted as either a "blessing" or a "curse", causing uncomfortable inconsistencies in the literature. We propose that these patterns arise from an intrinsically hierarchical generative process. Modeling the process creates a web of constraints that reconcile many different theories and results. The model also implies high dimensional data posses an innate separability that can be exploited for machine learning. We demonstrate how this permits the open-set learning problem to be defined mathematically, leading to qualitative and quantitative improvements in performance.
A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable  Optimization Via Overparameterization From Depth

Training deep neural networks with stochastic gradient descent (SGD) can often achieve zero training loss on real-world tasks although the optimization landscape is known to be highly non-convex. To understand the success of SGD for training deep neural networks, this work presents a mean-field analysis of deep residual networks, based on a line of works that interpret the continuum limit of the deep residual network as an ordinary differential equation when the network capacity tends to infinity. Specifically, we propose a new continuum limit of deep residual networks, which enjoys a good landscape in the sense that every local minimizer is global. This characterization enables us to derive the first global convergence result for multilayer neural networks in the mean-field regime. Furthermore, without assuming the convexity of the loss landscape, our proof relies on a zero-loss assumption at the global minimizer that can be achieved when the model shares a universal approximation property. Key to our result is the observation that a deep residual network resembles a shallow network ensemble, i.e. a two-layer network. We bound the difference between the shallow network and our ResNet model via the adjoint sensitivity method, which enables us to apply existing mean-field analyses of two-layer networks to deep networks. Furthermore, we propose several novel training schemes based on the new continuous model, including one training procedure that switches the order of the residual blocks and results in strong empirical performance on the benchmark datasets.
Critical Review and Prospects on the Historical Narratives in Korean History Textbooks

History education is usually conducted through history textbooks, and the history narrative in history textbooks is the factor that has the greatest influence on students' understanding of history. In this study, by analyzing the achievement standards of the history curriculum, the types of historical narratives in Korean history textbooks were classified into 4 types: ①Narrative as a context of change, ②Nrrative as causality, ③Narrative a synthesis of individual facts [phenomena], ④Narrative as interpretation or mode of thought. Also, based on the hierarchy of historical narratives, they were classified into Macro Narratives, Meso Narratives, and Micro Narratives. Based on the latter three types, among the historical narratives of historical Korean history textbooks, as examples of macroscopic narratives, Nation narratives, Nationalist narratives, and Internal Development Narratives were selected. These Macro Narratives were mainly the ones that were criticized. In this study, apart from seeking alternatives such as reconstruction of macro narratives such as national and national narratives, macro narratives were deconstructed in a transitional situation and a history textbook composition was proposed focusing on micro narratives and intermediate narratives. It is to dismantle the form of history outlines taken by historical Korean history textbooks. It was also suggested that the sequence of history textbooks leading to elementary, middle, and high schools be secured in the form of reconstructing the four types of historical narratives that I have classified.
"A Critical Review on the Textbook Narratives of Goryeo Period -Dissonance between the Present Perspective and the Historical Context-"

As textbook narratives in which the dissonance between the 'current perspective' and the 'historical context' is prominent, this study reviewed the textbook narratives of the Goryeo family system & women and foreign relations in the early Goryeo period. Each of these topics is related to the women's human rights and the national independence, which are important value at present but at the same time has a different historical context. The current method of history education that deals with the above two themes does not aim to illuminate and understand the historical context of the revealed aspects in history and then look at the results from the present point of view. There is a tendency to see and interpret aspects of history in the context of the present. This not only has a problem in the light of the essence of history studies, but also has a problem in cultivating ‘an attitude that respects a way of life that is different from mine’, which is set as an educational goal of history education as a civic education that has been highlighted recently. It is possible to pursue the values that society needs through history education, whether it is self-identity(national consciousness) or civic consciousness. However, it is thought that this purpose can be more effectively pursued not by listing related aspects with the goal of educating such values themselves, but by otherizing history itself to understand the context and structure of the time.
YNU-HPCC at SemEval-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in English and Arabic

In this paper, we (a YNU-HPCC team) describe the system we built in the SemEval-2022 competition.As participants in Task 6 (titled "iSar-casmEval: Intended Sarcasm Detection In English and Arabic"), we implement the sentiment system for all three subtasks in English and Arabic.All subtasks involve the detection of sarcasm (binary and multilabel classification) and the determination of the sarcastic text location (sentence pair classification).Our system primarily applies the sequence classification model of a bidirectional encoder representation from a transformer (BERT).The BERT is used to extract sentence information from both directions for downstream classification tasks.A single basic model is used for single-sentence and sentence-pair binary classification tasks.For the multilabel task, the Label-Powerset method and binary cross-entropy loss function with weights are used.Our system exhibits competitive performance, obtaining 12/43 (21/32), 11/22, and 3/16 (8/13) rankings in the three official rankings for English (Arabic).
GisPy: A Tool for Measuring Gist Inference Score in Text

Decision making theories such as Fuzzy-Trace Theory (FTT) suggest that individuals tend to rely on gist, or bottom-line meaning, in the text when making decisions.In this work, we delineate the process of developing GisPy, an opensource tool in Python for measuring the Gist Inference Score (GIS) in text.Evaluation of GisPy on documents in three benchmarks from the news and scientific text domains demonstrates that scores generated by our tool significantly distinguish low vs. high gist documents.Our tool is publicly available to use at: https: //github.com/phosseini/GisPy.
CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Course

We introduce CS1QA, a dataset for code-based question answering in the programming education domain.CS1QA consists of 9,237 question-answer pairs gathered from chat logs in an introductory programming class using Python, and 17,698 unannotated chat data with code 1 .Each question is accompanied with the student's code, and the portion of the code relevant to answering the question.We carefully design the annotation process to construct CS1QA, and analyze the collected dataset in detail.The tasks for CS1QA are to predict the question type, the relevant code snippet given the question and the code and retrieving an answer from the annotated corpus.Results for the experiments on several baseline models are reported and thoroughly analyzed.The tasks for CS1QA challenge models to understand both the code and natural language.This unique dataset can be used as a benchmark for source code comprehension and question answering in the educational setting.
A Taxonomy of Bias-Causing Ambiguities in Machine Translation

This paper introduces a taxonomy of phenomena which cause bias in machine translation, covering gender bias (people being male and/or female), number bias (singular you versus plural you) and formality bias (informal you versus formal you).Our taxonomy is a formalism for describing situations in machine translation when the source text leaves some of these properties unspecified (eg.does not say whether doctor is male or female) but the target language requires the property to be specified (eg.because it does not have a gender-neutral word for doctor).The formalism described here is used internally by Fairslator 1 , a web-based tool for detecting and correcting bias in the output of any machine translator.
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification

Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services.Such systems should be able to enrol (E), verify (V), and identify (I) new and recurring users based on their personal information, e.g.postcode, name, and date of birth.In this work, we formalise the three authentication tasks and their evaluation protocols, and we present EVI, a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French.Our proposed models set the first competitive benchmarks, explore the challenges of multilingual natural language processing of spoken dialogue, and set directions for future research.
A Timestep aware Sentence Embedding and Acme Coverage for Brief but Informative Title Generation

The title generation task that summarizes article content in recapitulatory words relies heavily on utilizing the corresponding key context.To generate a title with appropriate information in the content and avoid repetition, we propose a title generation framework with two complementary components in this paper.First, we propose a Timestep aware Sentence Embedding (TSE) mechanism, which updates the sentences' representations by re-locating the critical words in the corresponding sentence for each decoding step.Then, we present an Acme Coverage (AC) mechanism to solve the repetition problem and preserve the remaining valuable keywords after each decoding step according to the final vocabulary distribution.We conduct comprehensive experiments on various title generation tasks with different backbones, the evaluation scores of ROUGE and METEOR in varying degrees are significantly outperforming most of the existing state-ofthe-art approaches.The experimental results demonstrate the effectiveness and generality of our novel generation framework TSE-AC.