Reviewerly/exHarmony huggingface dataset:
Differentially Private Clustering: Tight Approximation Ratios

We study the task of differentially private clustering. For several basic clustering problems, including Euclidean DensestBall, 1-Cluster, k-means, and k-median, we give efficient differentially private algorithms that achieve essentially the same approximation ratios as those that can be obtained by any non-private algorithm, while incurring only small additive errors. This improves upon existing efficient algorithms that only achieve some large constant approximation factors. Our results also imply an improved algorithm for the Sample and Aggregate privacy framework. Furthermore, we show that one of the tools used in our 1-Cluster algorithm can be employed to get a faster quantum algorithm for ClosestPair in a moderate number of dimensions.
ARBML: Democritizing Arabic Natural Language Processing Tools

Automating natural language understanding is a lifelong quest addressed for decades. With the help of advances in machine learning and particularly, deep learning, we are able to produce state of the art models that can imitate human interactions with languages. Unfortunately, these advances are controlled by the availability of language resources. Arabic advances in this field , although it has a great potential, are still limited. This is apparent in both research and development. In this paper, we showcase some NLP models we trained for Arabic. We also present our methodology and pipeline to build such models from data collection, data preprocessing, tokenization and model deployment. These tools help in the advancement of the field and provide a systematic approach for extending NLP tools to many languages.
A Simple and Effective Framework for Pairwise Deep Metric Learning

Deep metric learning (DML) has received much attention in deep learning due to its wide applications in computer vision. Previous studies have focused on designing complicated losses and hard example mining methods, which are mostly heuristic and lack of theoretical understanding. In this paper, we cast DML as a simple pairwise binary classification problem that classifies a pair of examples as similar or dissimilar. It identifies the most critical issue in this problem—imbalanced data pairs. To tackle this issue, we propose a simple and effective framework to sample pairs in a batch of data for updating the model. The key to this framework is to define a robust loss for all pairs over a mini-batch of data, which is formulated by distributionally robust optimization. The flexibility in constructing the uncertainty decision set of the dual variable allows us to recover state-of-the-art complicated losses and also to induce novel variants. Empirical studies on several benchmark data sets demonstrate that our simple and effective method outperforms the state-of-the-art results.
Path Planning Based on Improved Particle Swarm Optimization for AUVs

Xiong, Q.; Zhang, H., and Rong, Q., 2020. Path planning based on improved particle swarm optimization for AUVs. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 279–282. Coconut Creek (Florida), ISSN 0749-0208.Path planning plays an important role in the motion of autonomous underwater vehicles (AUVs). It has always been a hot research topic. Aiming at the problems of traditional particle swarm optimization (PSO) in path planning, an improved PSO algorithm is proposed in this paper. In the initialization stage, the particles are initialized by a chaotic sequence. During algorithm processing, the sine function is used to dynamically decrease the particle weight. In the region near the optimal solution selected in each iteration of the PSO algorithm, a chaos algorithm is used to avoid falling into the local optimal value. Simulation experiment results show that the improved PSO algorithm greatly increases the effectiveness of the initial particles and can get the optimal path more quickly and accurately.
Orientation and Scale Based Weights Initialization Scheme for Deep Convolutional Neural Networks

Image classification is generally about the understanding of information in the images concerned. The more the system able to understand the image contains, the more effective it will be in classifying desired images. Recent work has shown that the convolutional neural network (CNN) paradigm is useful for obtaining more accurate image classification results. A crucial component in the CNN is the convolution filters which consist of a series of predefined filter weight initialization values. The filter weights are then automatically learned by the neural network throughout the back- propagation training algorithm. However, most initialization schemes used in the deep convolutional neural networks are mainly to deal with vanishing gradient problems. Thus, selecting optimal weights are crucial to improve convergence and minimize the complexity which can enhance the generalization performance. One possible solution is to replace the standard weights with parameterized filters that proven to be efficient in extracting useful features such as Gabor filter bank. The Gabor filter bank is popular due to its ability in dealing with spatial transformation, especially on edges and texture information of different scales and directions. Thus, in this paper, we investigate the effect of utilizing Gabor and convolutional filters on small size kernels of deep VGG-16 architecture. The standard VGG-16 filter is replaced with the Gabor filter bank to obtain uniform distribution at all layers of the network. The result shows that the orientation and scale weights initialization scheme outperforms the standard filter weights on an image classification problem.
Moments of Confusion in Simulation-Based Learning Environments

Confusion is an important epistemic emotion because it can help students focus their attention and effort when solving complex learning tasks. However, unresolved confusion can be detrimental because it may result in students’ disengagement. This is especially concerning in simulation environments using discovery-based learning, which puts more of the onus for learning on the students. Thus, students with misconceptions may become confused. In this study, the possible moments of confusion in a simulation-based predict-observe-explain (POE) environment were investigated. Log-based interaction patterns of undergraduate students from a fully online course were analyzed. It was found that POE environments can offer a level of difficulty that potentially triggers some confusion, and a likely moment of students’ confusion was the observe task. It was also found that confidence in prior knowledge is an important factor that can contribute to students’ confusion. Students mostly struggled when they discovered a mismatch between the subjective and objective correctness of their responses. The effects of such a mismatch were more pronounced when confusion markers were analyzed than when students’ learning outcomes were observed. These findings may guide future works to bridge the knowledge gaps that lead to confusion in POE environments.
The Application Prospects of Blockchain Technology in Human Resource Management

&lt;p&gt;With the development of the times and the rapid progress of science and technology, blockchain technology has been widely used in various industries. With the intensification of social competitiveness, the employment situation in the job market in recent years is more severe. For employers, using blockchain technology to help human resource management can improve work efficiency while saving related resource costs. Therefore, blockchain technology has also been widely used in human resource management. This article will conduct a more in-depth analysis and research on the application prospects of blockchain technology in human resource management in the future.&lt;/p&gt;
AdelaideCyC at SemEval-2020 Task 12: Ensemble of Classifiers for Offensive Language Detection in Social Media

This paper describes the systems our team (AdelaideCyC) has developed for SemEval Task 12 (OffensEval 2020) to detect offensive language in social media.The challenge focuses on three subtasks -offensive language identification (subtask A), offense type identification (subtask B), and offense target identification (subtask C).Our team has participated in all the three subtasks.We have developed machine learning and deep learning-based ensembles of models.We have achieved F1-scores of 0.906, 0.552, and 0.623 in subtask A, B, and C respectively.While our performance scores are promising for subtask A, the results demonstrate that subtask B and C still remain challenging to classify.
Mechanized logical relations for termination-insensitive noninterference

We present an expressive information-flow control type system with recursive types, existential types, label polymorphism, and impredicative type polymorphism for a higher-order programming language with higher-order state. We give a novel semantic model of this type system and show that well-typed programs satisfy termination-insensitive noninterference. Our semantic approach supports compositional integration of syntactically well-typed and syntactically ill-typed---but semantically sound---components, which we demonstrate through several interesting examples. We define our model using logical relations on top of the Iris program logic framework; to capture termination-insensitivity, we develop a novel language-agnostic theory of Modal Weakest Preconditions. We formalize all of our theory and examples in the Coq proof assistant.
Abstracting gradual typing moving forward: precise and space-efficient

Abstracting Gradual Typing (AGT) is a systematic approach to designing gradually-typed languages. Languages developed using AGT automatically satisfy the formal semantic criteria for gradual languages identified by Siek et al. Nonetheless, vanilla AGT semantics can still have important shortcomings. First, a gradual language's runtime checks should preserve the space-efficiency guarantees inherent to the underlying static and dynamic languages. To the contrary, the default operational semantics of AGT break proper tail calls. Second, a gradual language's runtime checks should enforce basic modular type-based invariants expected from the static type discipline. To the contrary, the default operational semantics of AGT may fail to enforce some invariants in surprising ways. We demonstrate this in the GTFL ≲ language of Garcia et al. This paper addresses both problems at once by refining the theory underlying AGT's dynamic checks. Garcia et al. observe that AGT involves two abstractions of static types: one for the static semantics and one for the dynamic semantics. We recast the latter as an abstract interpretation of subtyping itself, while gradual types still abstract static types. Then we show how forward-completeness (Giacobazzi and Quintarelli) is key to supporting both space-efficient execution and reliable runtime type enforcement.
Improving Arabic Text Categorization Using Transformer Training Diversification

Automatic categorization of short texts, such as news headlines and social media posts, has many applications ranging from content analysis to recommendation systems. In this paper, we use such text categorization i.e., labeling the social media posts to categories like ‘sports’, ‘politics’, ‘human-rights’ among others, to showcase the efficacy of models across different sources and varieties of Arabic. In doing so, we show that diversifying the training data, whether by using diverse training data for the specific task (an increase of 21% macro F1) or using diverse data to pre-train a BERT model (26% macro F1), leads to overall improvements in classification effectiveness. In our work, we also introduce two new Arabic text categorization datasets, where the first is composed of social media posts from a popular Arabic news channel that cover Twitter, Facebook, and YouTube, and the second is composed of tweets from popular Arabic accounts. The posts in the former are nearly exclusively authored in modern standard Arabic (MSA), while the tweets in the latter contain both MSA and dialectal Arabic.
Compositional Embeddings for Multi-Label One-Shot Learning

We present a compositional embedding framework that infers not just a single class per input image, but a set of classes, in the setting of one-shot learning. Specifically, we propose and evaluate several novel models consisting of (1) an embedding function f trained jointly with a "composition" function g that computes set union operations between the classes encoded in two embedding vectors; and (2) embedding f trained jointly with a "query" function h that computes whether the classes encoded in one embedding subsume the classes encoded in another embedding. In contrast to prior work, these models must both perceive the classes associated with the input examples and encode the relationships between different class label sets, and they are trained using only weak one-shot supervision consisting of the label-set relationships among training examples. Experiments on the OmniGlot, Open Images, and COCO datasets show that the proposed compositional embedding models outperform existing embedding methods. Our compositional embedding models have applications to multi-label object recognition for both one-shot and supervised learning.
Infinite Families of Quantum-Classical Hybrid Codes

Hybrid codes simultaneously encode both quantum and classical information into physical qubits. We give several general results about hybrid codes, most notably that the quantum codes comprising a genuine hybrid code must be impure and that hybrid codes can always detect more errors than comparable quantum codes. We also introduce the weight enumerators for general hybrid codes, which we then use to derive linear programming bounds. Finally, inspired by the construction of some families of nonadditive codes, we construct several infinite families of genuine hybrid codes with minimum distance two and three.
Towards Fair Cross-Domain Adaptation via Generative Learning

Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions. Existing DA normally assumes the well-labeled source domain is class-wise balanced, which means the size per source class is relatively similar. However, in real-world applications, labeled samples for some categories in the source domain could be extremely few due to the difficulty of data collection and annotation, which leads to decreasing performance over target domain on those few-shot categories. To perform fair cross-domain adaptation and boost the performance on these minority categories, we develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification. Specifically, generative feature augmentation is explored to synthesize effective training data for few-shot source classes, while effective cross-domain alignment aims to adapt knowledge from source to facilitate the target learning. Experimental results on two large cross-domain visual datasets demonstrate the effectiveness of our proposed method on improving both few-shot and overall classification accuracy comparing with the state-of-the-art DA approaches.
Efficient-Frequency: a hybrid visual forensic framework for facial forgery detection

The recent years have witnessed the significant development of visual forgery techniques and their malicious applications such as spreading of fake news and rumours, defamation or blackmailing of politicians and celebrities, manipulation of election result in political warfare. The manipulated contents have reached to such sophisticated level that human cannot tell apart whether a given content is real or fake. To deal with this serious threat, a rich body of visual forensic techniques has been proposed for detecting forged video and images. However, existing techniques either rely solely on engineered features or require a complex deep learning model to extract the underlying patterns. In this paper, we propose a novel end-to-end visual forensic framework that can incorporate different modalities to efficiently classify real and forged contents. The model employs both the original content and its frequency domain analysis to fully exploit the richness of the image latent patterns. They are forwarded into two separated EfficientNet, a light yet efficient neural network architecture specialized for image classification, for pattern extraction. Then, we design a late-fusion mechanism to fuse the learnt features in original and frequency domain based on the importance of the underlying information. Our experimental results show that our proposed technique outperforms other state-of-the-art forensic approaches in many datasets and being robust to various visual forgery techniques.
The Financial Document Structure Extraction Shared task (FinToc 2020)

This paper presents the FinTOC-2020 Shared Task on structure extraction from financial documents, its participants results and their findings. This shared task was organized as part of The 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING’2020). This shared task aimed to stimulate research in systems for extracting table-of-contents (TOC) from investment documents (such as financial prospectuses) by detecting the document titles and organizing them hierarchically into a TOC. For the second edition of this shared task, two subtasks were presented to the participants: one with English documents and the other one with French documents.
Hybrid Bidirectional Rapidly Exploring Random Tree Path Planning Algorithm with Reinforcement Learning

The randomness of path generation and slow convergence to the optimal path are two major problems in the current rapidly exploring random tree (RRT) path planning algorithm. Herein, a novel reinforcement-learning-based hybrid bidirectional rapidly exploring random tree (H-BRRT) is presented to solve these problems. To model the random exploration process, a target gravitational strategy is introduced. Reinforcement learning is applied to the improved target gravitational strategy using two operations: random exploration and target gravitational exploration. The algorithm is controlled to switch operations adaptively according to the accumulated performance. It not only improves the search efficiency, but also shortens the generated path after the proposed strategy is applied to a bidirectional rapidly exploring random tree (BRRT). In addition, to solve the problem of the traditional RRT continuously falling into the local optimum, an improved exploration strategy with collision weight is applied to the BRRT. Experimental results implemented in a robot operating system indicate that the proposed H-BRRT significantly outperforms alternative approaches such as the RRT and BRRT. The proposed algorithm enhances the capability of identifying unknown spaces and avoiding local optima.
An empirical evaluation of cost-based federated SPARQL query processing engines

Finding a good query plan is key to the optimization of query runtime. This holds in particular for cost-based federation engines, which make use of cardinality estimations to achieve this goal. A number of studies compare SPARQL federation engines across different performance metrics, including query runtime, result set completeness and correctness, number of sources selected and number of requests sent. Albeit informative, these metrics are generic and unable to quantify and evaluate the accuracy of the cardinality estimators of cost-based federation engines. To thoroughly evaluate cost-based federation engines, the effect of estimated cardinality errors on the overall query runtime performance must be measured. In this paper, we address this challenge by presenting novel evaluation metrics targeted at a fine-grained benchmarking of cost-based federated SPARQL query engines. We evaluate five cost-based federated SPARQL query engines using existing as well as novel evaluation metrics by using LargeRDFBench queries. Our results provide a detailed analysis of the experimental outcomes that reveal novel insights, useful for the development of future cost-based federated SPARQL query processing engines.
Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

Providing provenance in scientific workflows is essential for reproducibility and auditability purposes. In this work, we propose a framework that verifies the correctness of the aggregate statistics obtained as a result of a genome-wide association study (GWAS) conducted by a researcher while protecting individuals' privacy in the researcher's dataset. In GWAS, the goal of the researcher is to identify highly associated point mutations (variants) with a given phenotype. The researcher publishes the workflow of the conducted study, its output, and associated metadata. They keep the research dataset private while providing, as part of the metadata, a partial noisy dataset (that achieves local differential privacy). To check the correctness of the workflow output, a verifier makes use of the workflow, its metadata, and results of another GWAS (conducted using publicly available datasets) to distinguish between correct statistics and incorrect ones. For evaluation, we use real genomic data and show that the correctness of the workflow output can be verified with high accuracy even when the aggregate statistics of a small number of variants are provided. We also quantify the privacy leakage due to the provided workflow and its associated metadata and show that the additional privacy risk due to the provided metadata does not increase the existing privacy risk due to sharing of the research results. Thus, our results show that the workflow output (i.e., research results) can be verified with high confidence in a privacy-preserving way. We believe that this work will be a valuable step towards providing provenance in a privacy-preserving way while providing guarantees to the users about the correctness of the results.
RODE: Learning Roles to Decompose Multi-Agent Tasks

Role-based learning holds the promise of achieving scalable multi-agent learning by decomposing complex tasks using roles. However, it is largely unclear how to efficiently discover such a set of roles. To solve this problem, we propose to first decompose joint action spaces into restricted role action spaces by clustering actions according to their effects on the environment and other agents. Learning a role selector based on action effects makes role discovery much easier because it forms a bi-level learning hierarchy -- the role selector searches in a smaller role space and at a lower temporal resolution, while role policies learn in significantly reduced primitive action-observation spaces. We further integrate information about action effects into the role policies to boost learning efficiency and policy generalization. By virtue of these advances, our method (1) outperforms the current state-of-the-art MARL algorithms on 10 of the 14 scenarios that comprise the challenging StarCraft II micromanagement benchmark and (2) achieves rapid transfer to new environments with three times the number of agents. Demonstrative videos are available at https://sites.google.com/view/rode-marl .