Reviewerly/exHarmony huggingface dataset:
AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Sequential fine-tuning and multi-task learning are methods aiming to incorporate knowledge from multiple tasks; however, they suffer from catastrophic forgetting and difficulties in dataset balancing.To address these shortcomings, we propose AdapterFusion, a new two stage learning algorithm that leverages knowledge from multiple tasks.First, in the knowledge extraction stage we learn task specific parameters called adapters, that encapsulate the task-specific information.We then combine the adapters in a separate knowledge composition step.We show that by separating the two stages, i.e., knowledge extraction and knowledge composition, the classifier can effectively exploit the representations learned from multiple tasks in a non-destructive manner.We empirically evaluate AdapterFusion on 16 diverse NLU tasks, and find that it effectively combines various types of knowledge at different layers of the model.We show that our approach outperforms traditional strategies such as full fine-tuning as well as multi-task learning.Our code and adapters are available at AdapterHub.ml.
Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs

Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples.In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-shot link prediction in KGs, namely predicting new triples about a relation by only observing a few associative triples.We solve few-shot link prediction by focusing on transferring relation-specific meta information to make model learn the most important knowledge and learn faster, corresponding to relation meta and gradient meta respectively in MetaR.Empirically, our model achieves stateof-the-art results on few-shot link prediction KG benchmarks.
FastBERT: a Self-distilling BERT with Adaptive Inference Time

Pre-trained language models like BERT have proven to be highly performant.However, they are often computationally expensive in many practical scenarios, for such heavy models can hardly be readily implemented with limited resources.To improve their efficiency with an assured model performance, we propose a novel speed-tunable FastBERT with adaptive inference time.The speed at inference can be flexibly adjusted under varying demands, while redundant calculation of samples is avoided.Moreover, this model adopts a unique selfdistillation mechanism at fine-tuning, further enabling a greater computational efficacy with minimal loss in performance.Our model achieves promising results in twelve English and Chinese datasets.It is able to speed up by a wide range from 1 to 12 times than BERT if given different speedup thresholds to make a speed-performance tradeoff.
ARBERT &amp; MARBERT: Deep Bidirectional Transformers for Arabic

Pre-trained language models (LMs) are currently integral to many natural language processing systems.Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training.We remedy these issues for a collection of diverse Arabic varieties by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT.To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation.ARLUE is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions.When fine-tuned on ARLUE, our models collectively achieve new state-of-theart results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets).Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large (∼ 3.4× larger size).Our models are publicly available at https://github.com/UBC-NLP/marbertand ARLUE will be released through the same repository.
Network Intrusion Detection: Based on Deep Hierarchical Network and Original Flow Data

Network intrusion detection plays a very important role in protecting computer network security. The abnormal traffic detection and analysis by extracting the statistical features of flow is the main analysis method in the field of network intrusion detection. However, these features need to be designed and extracted manually, which often loses the original information of the flow and leads to poor detection efficiency. In this paper, we do not manually design the features of the flow but directly extract the raw data information of the flow for analysis. In addition, we first proposed a new network intrusion detection model named the deep hierarchical network, which integrates the improved LeNet-5 and LSTM neural network structures, while learning the spatial and temporal features of flow. By designing a reasonable network cascading method, we can train our proposed hierarchical network at the same time instead of training two networks separately. In this paper, we use the CICIDS2017 dataset and the CTU dataset. The number and types of flow in these two datasets are large, and the attack types are relatively new. The experimental results show that the performance of the proposed hierarchical network model is significantly better than other network intrusion detection models, which can achieve the best detection accuracy. Finally, we also present an analysis method for traffic features which has an important contribution to abnormal traffic detection and gives the actual meanings of these important features.
Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data

Considerable effort has been made to address the data sparsity problem in neural grammatical error correction.In this work, we propose a simple and surprisingly effective unsupervised synthetic error generation method based on confusion sets extracted from a spellchecker to increase the amount of training data.Synthetic data is used to pre-train a Transformer sequence-to-sequence model, which not only improves over a strong baseline trained on authentic error-annotated data, but also enables the development of a practical GEC system in a scenario where little genuine error-annotated data is available.The developed systems placed first in the BEA19 shared task, achieving 69.47 and 64.24 F 0.5 in the restricted and low-resource tracks respectively, both on the W&I+LOCNESS test set.On the popular CoNLL 2014 test set, we report state-of-theart results of 64.16 M 2 for the submitted system, and 61.30M 2 for the constrained system trained on the NUCLE and Lang-8 data.
End-to-End Learnt Image Compression via Non-Local Attention Optimization and Improved Context Modeling

This article proposes an end-to-end learnt lossy image compression approach, which is built on top of the deep nerual network (DNN)-based variational auto-encoder (VAE) structure with Non-Local Attention optimization and Improved Context modeling (NLAIC). Our NLAIC 1) embeds non-local network operations as non-linear transforms in both main and hyper coders for deriving respective latent features and hyperpriors by exploiting both local and global correlations, 2) applies attention mechanism to generate implicit masks that are used to weigh the features for adaptive bit allocation, and 3) implements the improved conditional entropy modeling of latent features using joint 3D convolutional neural network (CNN)-based autoregressive contexts and hyperpriors. Towards the practical application, additional enhancements are also introduced to speed up the computational processing (e.g., parallel 3D CNN-based context prediction), decrease the memory consumption (e.g., sparse non-local processing) and reduce the implementation complexity (e.g., a unified model for variable rates without re-training). The proposed model outperforms existing learnt and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, on both Kodak and Tecnick datasets with the state-of-the-art compression efficiency, for both PSNR and MS-SSIM quality measurements. We have made all materials publicly accessible at https://njuvision.github.io/NIC for reproducible research.
Dual-level Collaborative Transformer for Image Captioning

Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning. However, they are still criticized for the lack of contextual information and fine-grained details, which in contrast are the merits of traditional grid features. In this paper, we introduce a novel Dual-Level Collaborative Transformer (DLCT) network to realize the complementary advantages of the two features. Concretely, in DLCT, these two features are first processed by a novel Dual-way Self Attenion (DWSA) to mine their intrinsic properties, where a Comprehensive Relation Attention component is also introduced to embed the geometric information. In addition, we propose a Locality-Constrained Cross Attention module to address the semantic noises caused by the direct fusion of these two features, where a geometric alignment graph is constructed to accurately align and reinforce region and grid features. To validate our model, we conduct extensive experiments on the highly competitive MS-COCO dataset, and achieve new state-of-the-art performance on both local and online test sets, i.e., 133.8% CIDEr on Karpathy split and 135.4% CIDEr on the official split.
DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems

Deep learning (DL) has achieved remarkable progress over the past decade and has been widely applied to many industry domains. However, the robustness of DL systems recently becomes great concerns, where minor perturbation on the input might cause the DL malfunction. These robustness issues could potentially result in severe consequences when a DL system is deployed to safety-critical applications and hinder the real-world deployment of DL systems. Testing techniques enable the robustness evaluation and vulnerable issue detection of a DL system at an early stage. The main challenge of testing a DL system attributes to the high dimensionality of its inputs and large internal latent feature space, which makes testing each state almost impossible. For traditional software, combinatorial testing (CT) is an effective testing technique to balance the testing exploration effort and defect detection capabilities. In this paper, we perform an exploratory study of CT on DL systems. We propose a set of combinatorial testing criteria specialized for DL systems, as well as a CT coverage guided test generation technique. Our evaluation demonstrates that CT provides a promising avenue for testing DL systems.
Fully Quantized Network for Object Detection

Efficient neural network inference is important in a number of practical domains, such as deployment in mobile settings. An effective method for increasing inference efficiency is to use low bitwidth arithmetic, which can subsequently be accelerated using dedicated hardware. However, designing effective quantization schemes while maintaining network accuracy is challenging. In particular, current techniques face difficulty in performing fully end-to-end quantization, making use of aggressively low bitwidth regimes such as 4-bit, and applying quantized networks to complex tasks such as object detection. In this paper, we demonstrate that many of these difficulties arise because of instability during the fine-tuning stage of the quantization process, and propose several novel techniques to overcome these instabilities. We apply our techniques to produce fully quantized 4-bit detectors based on RetinaNet and Faster R-CNN, and show that these achieve state-of-the-art performance for quantized detectors. The mAP loss due to quantization using our methods is more than 3.8x less than the loss from existing methods.
Massively Multilingual Transfer for NER

In cross-lingual transfer, NLP models over one or more source languages are applied to a lowresource target language.While most prior work has used a single source model or a few carefully selected models, here we consider a "massive" setting with many such models.This setting raises the problem of poor transfer, particularly from distant languages.We propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively.Evaluating on named entity recognition, we show that our techniques are much more effective than strong baselines, including standard ensembling, and our unsupervised method rivals oracle selection of the single best individual model.
Novel frontier of photonics for data processing—Photonic accelerator

In the emerging Internet of things cyber-physical system-embedded society, big data analytics needs huge computing capability with better energy efficiency. Coming to the end of Moore’s law of the electronic integrated circuit and facing the throughput limitation in parallel processing governed by Amdahl’s law, there is a strong motivation behind exploring a novel frontier of data processing in post-Moore era. Optical fiber transmissions have been making a remarkable advance over the last three decades. A record aggregated transmission capacity of the wavelength division multiplexing system per a single-mode fiber has reached 115 Tbit/s over 240 km. It is time to turn our attention to data processing by photons from the data transport by photons. A photonic accelerator (PAXEL) is a special class of processor placed at the front end of a digital computer, which is optimized to perform a specific function but does so faster with less power consumption than an electronic general-purpose processor. It can process images or time-serial data either in an analog or digital fashion on a real-time basis. Having had maturing manufacturing technology of optoelectronic devices and a diverse array of computing architectures at hand, prototyping PAXEL becomes feasible by leveraging on, e.g., cutting-edge miniature and power-efficient nanostructured silicon photonic devices. In this article, first the bottleneck and the paradigm shift of digital computing are reviewed. Next, we review an array of PAXEL architectures and applications, including artificial neural networks, reservoir computing, pass-gate logic, decision making, and compressed sensing. We assess the potential advantages and challenges for each of these PAXEL approaches to highlight the scope for future work toward practical implementation.
CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion

Depth Completion deals with the problem of converting a sparse depth map to a dense one, given the corresponding color image. Convolutional spatial propagation network (CSPN) is one of the state-of-the-art (SoTA) methods of depth completion, which recovers structural details of the scene. In this paper, we propose CSPN++, which further improves its effectiveness and efficiency by learning adaptive convolutional kernel sizes and the number of iterations for the propagation, thus the context and computational resource needed at each pixel could be dynamically assigned upon requests. Specifically, we formulate the learning of the two hyper-parameters as an architecture selection problem where various configurations of kernel sizes and numbers of iterations are first defined, and then a set of soft weighting parameters are trained to either properly assemble or select from the pre-defined configurations at each pixel. In our experiments, we find weighted assembling can lead to significant accuracy improvements, which we referred to as "context-aware CSPN", while weighted selection, "resource-aware CSPN" can reduce the computational resource significantly with similar or better accuracy. Besides, the resource needed for CSPN++ can be adjusted w.r.t. the computational budget automatically. Finally, to avoid the side effects of noise or inaccurate sparse depths, we embed a gated network inside CSPN++, which further improves the performance. We demonstrate the effectiveness of CSPN++ on the KITTI depth completion benchmark, where it significantly improves over CSPN and other SoTA methods 1.
Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and low-light conditions, respectively, with annotated objects/faces. We launched the UG <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.
Model-based Reinforcement Learning: A Survey

Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This monograph surveys an integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps: dynamics model learning and planning-learning integration. In this comprehensive survey of the topic, the authors first cover dynamics model learning, including challenges such as dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. They then present a systematic categorization of planning-learning integration, including aspects such as: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop. In conclusion the authors discuss implicit model-based RL as an end-to-end alternative for model learning and planning, and cover the potential benefits of model-based RL. Along the way, the authors draw connections to several related RL fields, including hierarchical RL and transfer learning. This monograph contains a broad conceptual overview of the combination of planning and learning for Markov Decision Process optimization. It provides a clear and complete introduction to the topic for students and researchers alike.
The prospects of quantum computing in computational molecular biology

Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to "hype", and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics.
Collective Reconstructive Embeddings for Cross-Modal Hashing

In this paper, we study the problem of cross-modal retrieval by hashing-based approximate nearest neighbor (ANN) search techniques. Most existing cross-modal hashing work mainly addresses the issue of multi-modal integration complexity using the same mapping and similarity calculation for data from different media types. Nonetheless, this may cause information loss during the mapping process due to overlooking the specifics of each individual modality. In this work, we propose a simple yet effective cross-modal hashing approach, termed Collective Reconstructive Embeddings (CRE), which can simultaneously solve the heterogeneity and integration complexity of multi-modal data. To address the heterogeneity challenge, we propose to process heterogeneous types of data using different modalityspecific models. Specifically, we model textual data with cosine similarity based reconstructive embedding to alleviate the data sparsity to the greatest extent, while for image data we utilize the Euclidean distance to characterize the relationships of the projected hash codes. Meanwhile, we unify the projections of text and image to the Hamming space into a common reconstructive embedding through rigid mathematical reformulation, which not only reduces the optimization complexity significantly but also facilitates the inter-modal similarity preservation among different modalities. We further incorporate the code balance and uncorrelation criteria into the problem, and devise an efficient iterative algorithm for optimization. Comprehensive experiments on four widely-used multimodal benchmarks show that the proposed CRE can achieve superior performance compared to the state-of-the-arts on several challenging cross-modal tasks.
Obstacle avoidance of mobile robots using modified artificial potential field algorithm

In recent years, topics related to robotics have become one of the researching fields. In the meantime, intelligent mobile robots have great acceptance, but the control and navigation of these devices are very difficult, and the lack of dealing with fixed obstacles and avoiding them, due to safe and secure routing, is the basic requirement of these systems. In this paper, the modified artificial potential field (APF) method is proposed for that robot avoids collision with fixed obstacles and reaches the target in an optimal path; using this algorithm, the robot can run to the target in optimal environments without any problems by avoiding obstacles, and also using this algorithm, unlike the APF algorithm, the robot does not get stuck in the local minimum. We are looking for an appropriate cost function, with restrictions that we have, and the goal is to avoid obstacles, achieve the target, and do not stop the robot in local minimum. The previous method, APF algorithm, has advantages, such as the use of a simple math model, which is easy to understand and implement. However, this algorithm has many drawbacks; the major drawback of this problem is at the local minimum and the inaccessibility of the target when the obstacles are in the vicinity of the target. Therefore, in order to obtain a better result and to improve the shortcomings of the APF algorithm, this algorithm needs to be improved. Here, the obstacle avoidance planning algorithm is proposed based on the improvement of the artificial potential field algorithm to solve this local minimum problem. In the end, simulation results are evaluated using MATLAB software. The simulation results show that the proposed method is superior to the existing solution.