Reviewerly/exHarmony huggingface dataset:
A Comparative study of Hyper-Parameter Optimization Tools

Most of the machine learning models have associated hyper-parameters along with their parameters. While the algorithm gives the solution for parameters, its utility for model performance is highly dependent on the choice of hyperparameters. For a robust performance of a model, it is necessary to find out the right hyper-parameter combination. Hyper-parameter optimization (HPO) is a systematic process that helps in finding the right values for them. The conventional methods for this purpose are grid search and random search and both methods create issues in industrial-scale applications. Hence a set of strategies have been recently proposed based on Bayesian optimization and evolutionary algorithm principles that help in runtime issues in a production environment and robust performance. In this paper, we compare the performance of four python libraries, namely Optuna, Hyper-opt, Optunity, and sequential model-based algorithm configuration (SMAC) that has been proposed for hyper-parameter optimization. The performance of these tools is tested using two benchmarks. The first one is to solve a combined algorithm selection and hyper-parameter optimization (CASH) problem The second one is the NeurIPS black-box optimization challenge in which a multilayer perception (MLP) architecture has to be chosen from a set of related architecture constraints and hyper-parameters. The benchmarking is done with six real-world datasets. From the experiments, we found that Optuna has better performance for CASH problem and HyperOpt for MLP problem.
A Comparison of Online Hate on Reddit and 4chan: A Case Study of the 2020 US Election

The rapid integration of the Internet into our daily lives has led to many benefits but also to a number of new, wide-spread threats such as online hate, trolling, bullying, and generally aggressive behaviours. While research has traditionally explored online hate, in particular, on one platform, the reality is that such hate is a phenomenon that often makes use of multiple online networks. In this article, we seek to advance the discussion into online hate by harnessing a comparative approach, where we make use of various Natural Language Processing (NLP) techniques to computationally analyse hateful content from Reddit and 4chan relating to the 2020 US Presidential Elections. Our findings show how content and posting activity can differ depending on the platform being used. Through this, we provide initial comparison into the platform-specific behaviours of online hate, and how different platforms can serve specific purposes. We further provide several avenues for future research utilising a cross-platform approach so as to gain a more comprehensive understanding of the global hate ecosystem.
A Compilation Flow for the Generation of CNN Inference Accelerators on FPGAs

We present a compilation flow for the generation of CNN inference accelerators on FPGAs. The flow translates a frozen model into OpenCL kernels with the TVM compiler and uses the Intel OpenCL SDK to compile to an FPGA bitstream. We improve the quality of the generated hardware with optimizations applied to the base OpenCL kernels generated by TVM. These optimizations increase parallelism, reduce memory access latency, increase concurrency and save on-chip resources. We automate these optimizations in TVM and evaluate them by generating accelerators for LeNet-5, MobileNetV1 and ResNet-34 on an Intel Stratix~10SX. We show that the optimizations improve the performance of the generated accelerators by up to 846X over the base accelerators. The performance of the optimized accelerators is up to 4.57X better than TensorFlow on CPU, 3.83X better than single-threaded TVM and is only 0.34X compared to TVM with 56 threads. Our optimized kernels also outperform ones generated by a similar approach (that also uses high-level synthesis) while providing more functionality and flexibility. However, it underperforms an approach that utilizes hand-optimized designs. Thus, we view our approach as useful in pre-production environments that benefit from increased performance and fast prototyping, realizing the benefits of FPGAs without hardware design expertise.
A Context-Aware Feature Fusion Framework for Punctuation Restoration

To accomplish the punctuation restoration task, most existing approaches focused on leveraging extra information (e.g., part-of-speech tags) or addressing the class imbalance problem. Recent works have widely applied the transformer-based language models and significantly improved their effectiveness. To the best of our knowledge, an inherent issue has remained neglected: the attention of individual heads in the transformer will be diluted or powerless while feeding the long non-punctuation utterances. Since those previous contexts, not the followings, are comparatively more valuable to the current position, it's hard to achieve a good balance by independent attention. In this paper, we propose a novel Feature Fusion framework based on two-type Attentions (FFA) to alleviate the shortage. It introduces a two-stream architecture. One module involves interaction between attention heads to encourage the communication, and another masked attention module captures the dependent feature representation. Then, it aggregates two feature embeddings to fuse information and enhances context-awareness. The experiments on the popular benchmark dataset IWSLT demonstrate that our approach is effective. Without additional data, it obtains comparable performance to the current state-of-the-art models.
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP

In this paper we introduce an active learning method for symbolic regression using StackGP. The approach begins with a small number of data points for StackGP to model. To improve the model the system incrementally adds a data point such that the new point maximizes prediction uncertainty as measured by the model ensemble. Symbolic regression is re-run with the larger data set. This cycle continues until the system satisfies a termination criterion. We use the Feynman AI benchmark set of equations to examine the ability of our method to find appropriate models using fewer data points. The approach was found to successfully rediscover 72 of the 100 Feynman equations using as few data points as possible, and without use of domain expertise or data translation.
Actor-Critic Network for Q&amp;A in an Adversarial Environment

Significant work has been placed in the Q&A NLP space to build models that are more robust to adversarial attacks. Two key areas of focus are in generating adversarial data for the purposes of training against these situations or modifying existing architectures to build robustness within. This paper introduces an approach that joins these two ideas together to train a critic model for use in an almost reinforcement learning framework. Using the Adversarial SQuAD "Add One Sent" dataset we show that there are some promising signs for this method in protecting against Adversarial attacks.
Adaptive Background Matting Using Background Matching

Due to the difficulty of solving the matting problem, lots of methods use some kinds of assistance to acquire high quality alpha matte. Green screen matting methods rely on physical equipment. Trimap-based methods take manual interactions as external input. Background-based methods require a pre-captured, static background. The methods are not flexible and convenient enough to use widely. Trimap-free methods are flexible but not stable in complicated video applications. To be stable and flexible in real applications, we propose an adaptive background matting method. The user first captures their videos freely, moving the cameras. Then the user captures the background video afterwards, roughly covering the previous captured regions. We use dynamic background video instead of static background for accurate matting. The proposed method is convenient to use in any scenes as the static camera and background is no more the limitation. To achieve this goal, we use background matching network to find the best-matched background frame by frame from dynamic backgrounds. Then, robust semantic estimation network is used to estimate the coarse alpha matte. Finally, we crop and zoom the target region according to the coarse alpha matte, and estimate the final accurate alpha matte. In experiments, the proposed method is able to perform comparably against the state-of-the-art matting methods.
Adaptive n-ary Activation Functions for Probabilistic Boolean Logic

Balancing model complexity against the information contained in observed data is the central challenge to learning. In order for complexity-efficient models to exist and be discoverable in high dimensions, we require a computational framework that relates a credible notion of complexity to simple parameter representations. Further, this framework must allow excess complexity to be gradually removed via gradient-based optimization. Our n-ary, or n-argument, activation functions fill this gap by approximating belief functions (probabilistic Boolean logic) using logit representations of probability. Just as Boolean logic determines the truth of a consequent claim from relationships among a set of antecedent propositions, probabilistic formulations generalize predictions when antecedents, truth tables, and consequents all retain uncertainty. Our activation functions demonstrate the ability to learn arbitrary logic, such as the binary exclusive disjunction (p xor q) and ternary conditioned disjunction ( c ? p : q ), in a single layer using an activation function of matching or greater arity. Further, we represent belief tables using a basis that directly associates the number of nonzero parameters to the effective arity of the belief function, thus capturing a concrete relationship between logical complexity and efficient parameter representations. This opens optimization approaches to reduce logical complexity by inducing parameter sparsity.
A Dataset for Medical Instructional Video Classification and Question Answering

This paper introduces a new challenge and datasets to foster research toward designing systems that can understand medical videos and provide visual answers to natural language questions. We believe medical videos may provide the best possible answers to many first aids, medical emergency, and medical education questions. Toward this, we created the MedVidCL and MedVidQA datasets and introduce the tasks of Medical Video Classification (MVC) and Medical Visual Answer Localization (MVAL), two tasks that focus on cross-modal (medical language and medical video) understanding. The proposed tasks and datasets have the potential to support the development of sophisticated downstream applications that can benefit the public and medical practitioners. Our datasets consist of 6,117 annotated videos for the MVC task and 3,010 annotated questions and answers timestamps from 899 videos for the MVAL task. These datasets have been verified and corrected by medical informatics experts. We have also benchmarked each task with the created MedVidCL and MedVidQA datasets and proposed the multimodal learning methods that set competitive baselines for future research.
A Dual Weighting Label Assignment Scheme for Object Detection

Label assignment (LA), which aims to assign each training sample a positive (pos) and a negative (neg) loss weight, plays an important role in object detection. Existing LA methods mostly focus on the design of pos weighting function, while the neg weight is directly derived from the pos weight. Such a mechanism limits the learning capacity of detectors. In this paper, we explore a new weighting paradigm, termed dual weighting (DW), to specify pos and neg weights separately. We first identify the key influential factors of pos/neg weights by analyzing the evaluation metrics in object detection, and then design the pos and neg weighting functions based on them. Specifically, the pos weight of a sample is determined by the consistency degree between its classification and localization scores, while the neg weight is decomposed into two terms: the probability that it is a neg sample and its importance conditioned on being a neg sample. Such a weighting strategy offers greater flexibility to distinguish between important and less important samples, resulting in a more effective object detector. Equipped with the proposed DW method, a single FCOS-ResNet-50 detector can reach 41.5% mAP on COCO under 1x schedule, outperforming other existing LA methods. It consistently improves the baselines on COCO by a large margin under various backbones without bells and whistles. Code is available at https://github.com/strongwolf/DW.
Adversarial Parameter Attack on Deep Neural Networks

In this paper, a new parameter perturbation attack on DNNs, called adversarial parameter attack, is proposed, in which small perturbations to the parameters of the DNN are made such that the accuracy of the attacked DNN does not decrease much, but its robustness becomes much lower. The adversarial parameter attack is stronger than previous parameter perturbation attacks in that the attack is more difficult to be recognized by users and the attacked DNN gives a wrong label for any modified sample input with high probability. The existence of adversarial parameters is proved. For a DNN $F_{\Theta}$ with the parameter set $\Theta$ satisfying certain conditions, it is shown that if the depth of the DNN is sufficiently large, then there exists an adversarial parameter set $\Theta_a$ for $\Theta$ such that the accuracy of $F_{\Theta_a}$ is equal to that of $F_{\Theta}$, but the robustness measure of $F_{\Theta_a}$ is smaller than any given bound. An effective training algorithm is given to compute adversarial parameters and numerical experiments are used to demonstrate that the algorithms are effective to produce high quality adversarial parameters.
Affordance Learning from Play for Sample-Efficient Policy Learning

Robots operating in human-centered environments should have the ability to understand how objects function: what can be done with each object, where this interaction may occur, and how the object is used to achieve a goal. To this end, we propose a novel approach that extracts a self-supervised visual affordance model from human teleoperated play data and leverages it to enable efficient policy learning and motion planning. We combine model-based planning with model-free deep reinforcement learning (RL) to learn policies that favor the same object regions favored by people, while requiring minimal robot interactions with the environment. We evaluate our algorithm, Visual Affordance-guided Policy Optimization (VAPO), with both diverse simulation manipulation tasks and real world robot tidy-up experiments to demonstrate the effectiveness of our affordance-guided policies. We find that our policies train 4x faster than the baselines and generalize better to novel objects because our visual affordance model can anticipate their affordance regions.
A Game-theoretic Understanding of Repeated Explanations in ML Models

This paper formally models the strategic repeated interactions between a system, comprising of a machine learning (ML) model and associated explanation method, and an end-user who is seeking a prediction/label and its explanation for a query/input, by means of game theory. In this game, a malicious end-user must strategically decide when to stop querying and attempt to compromise the system, while the system must strategically decide how much information (in the form of noisy explanations) it should share with the end-user and when to stop sharing, all without knowing the type (honest/malicious) of the end-user. This paper formally models this trade-off using a continuous-time stochastic Signaling game framework and characterizes the Markov perfect equilibrium state within such a framework.
A gentle introduction to Quantum Natural Language Processing

The main goal of this master's thesis is to introduce Quantum Natural Language Processing (QNLP) in a way understandable by both the NLP engineer and the quantum computing practitioner. QNLP is a recent application of quantum computing that aims at representing sentences' meaning as vectors encoded into quantum computers. To achieve this, the distributional meaning of words is extended by the compositional meaning of sentences (DisCoCat model) : the vectors representing words' meanings are composed through the syntactic structure of the sentence. This is done using an algorithm based on tensor products. We see that this algorithm is inefficient on classical computers but scales well using quantum circuits. After exposing the practical details of its implementation, we go through three use-cases.
A Girl Has A Name, And It's ... Adversarial Authorship Attribution for Deobfuscation

Recent advances in natural language processing have enabled powerful privacy-invasive authorship attribution. To counter authorship attribution, researchers have proposed a variety of rule-based and learning-based text obfuscation approaches. However, existing authorship obfuscation approaches do not consider the adversarial threat model. Specifically, they are not evaluated against adversarially trained authorship attributors that are aware of potential obfuscation. To fill this gap, we investigate the problem of adversarial authorship attribution for deobfuscation. We show that adversarially trained authorship attributors are able to degrade the effectiveness of existing obfuscators from 20-30% to 5-10%. We also evaluate the effectiveness of adversarial training when the attributor makes incorrect assumptions about whether and which obfuscator was used. While there is a a clear degradation in attribution accuracy, it is noteworthy that this degradation is still at or above the attribution accuracy of the attributor that is not adversarially trained at all. Our results underline the need for stronger obfuscation approaches that are resistant to deobfuscation
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators

We propose and analyse a novel statistical procedure, coined AgraSSt, to assess the quality of graph generators that may not be available in explicit form. In particular, AgraSSt can be used to determine whether a learnt graph generating process is capable of generating graphs that resemble a given input graph. Inspired by Stein operators for random graphs, the key idea of AgraSSt is the construction of a kernel discrepancy based on an operator obtained from the graph generator. AgraSSt can provide interpretable criticisms for a graph generator training procedure and help identify reliable sample batches for downstream tasks. Using Stein`s method we give theoretical guarantees for a broad class of random graph models. We provide empirical results on both synthetic input graphs with known graph generation procedures, and real-world input graphs that the state-of-the-art (deep) generative models for graphs are trained on.
A Holistic View on Data Protection for Sharing, Communicating, and Computing Environments: Taxonomy and Future Directions

The data is an important asset of an organization and it is essential to keep this asset secure. It requires security in whatever state is it i.e. data at rest, data in use, and data in transit. There is a need to pay more attention to it when the third party is included i.e. when the data is stored in the cloud then it requires more security. Since confidential data can reside on a variety of computing devices (physical servers, virtual servers, databases, file servers, PCs, point-of-sale devices, flash drives, and mobile devices) and move through a variety of network access points (wireline, wireless, VPNs, etc.), there is a need of solutions or mechanism that can tackle the problem of data loss, data recovery and data leaks. In this context, the paper presents a holistic view of data protection for sharing and communicating environments for any type of organization. A taxonomy of data leakage protection systems and major challenges faced while protecting confidential data are discussed. Data protection solutions, Data Leakage Protection System's analysis techniques, and, a thorough analysis of existing state-of-the-art contributions empowering machine learning-based approaches are entailed. Finally, the paper explores and concludes various critical emerging challenges and future research directions concerning data protection.
A Large and Diverse Arabic Corpus for Language Modeling

Language models (LMs) have introduced a major paradigm shift in Natural Language Processing (NLP) modeling where large pre-trained LMs became integral to most of the NLP tasks. The LMs are intelligent enough to find useful and relevant representations of the language without any supervision. Perhaps, these models are used to fine-tune typical NLP tasks with significantly high accuracy as compared to the traditional approaches. Conversely, the training of these models requires a massively large corpus that is a good representation of the language. English LMs generally perform better than their other language counterparts, due to the availability of massive English corpora. This work elaborates on the design and development of a large Arabic corpus. It consists of over 500 GB of Arabic cleaned text targeted at improving cross-domain knowledge and downstream generalization capability of large-scale language models. Moreover, the corpus is utilized in the training of a large Arabic LM. In order to evaluate the effectiveness of the LM, a number of typical NLP tasks are fine-tuned. The tasks demonstrate a significant boost from 4.5 to 8.5% when compared to tasks fine-tuned on multi-lingual BERT (mBERT). To the best of my knowledge, this is currently the largest clean and diverse Arabic corpus ever collected.
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning

Subgraph-based graph representation learning (SGRL) has been recently proposed to deal with some fundamental challenges encountered by canonical graph neural networks (GNNs), and has demonstrated advantages in many important data science applications such as link, relation and motif prediction. However, current SGRL approaches suffer from scalability issues since they require extracting subgraphs for each training or test query. Recent solutions that scale up canonical GNNs may not apply to SGRL. Here, we propose a novel framework SUREL for scalable SGRL by co-designing the learning algorithm and its system support. SUREL adopts walk-based decomposition of subgraphs and reuses the walks to form subgraphs, which substantially reduces the redundancy of subgraph extraction and supports parallel computation. Experiments over six homogeneous, heterogeneous and higher-order graphs with millions of nodes and edges demonstrate the effectiveness and scalability of SUREL. In particular, compared to SGRL baselines, SUREL achieves 10$\times$ speed-up with comparable or even better prediction performance; while compared to canonical GNNs, SUREL achieves 50% prediction accuracy improvement.
Algorithmic Improvement and GPU Acceleration of the GenASM Algorithm

We improve on GenASM, a recent algorithm for genomic sequence alignment, by significantly reducing its memory footprint and bandwidth requirement. Our algorithmic improvements reduce the memory footprint by 24$\times$ and the number of memory accesses by 12$\times$. We efficiently parallelize the algorithm for GPUs, achieving a 4.1$\times$ speedup over a CPU implementation of the same algorithm, a 62$\times$ speedup over minimap2's CPU-based KSW2 and a 7.2$\times$ speedup over the CPU-based Edlib for long reads.