Reviewerly/exHarmony huggingface dataset:
Learning Target-Domain-Specific Classifier for Partial Domain Adaptation

Unsupervised domain adaptation~(UDA) aims at reducing the distribution discrepancy when transferring knowledge from a labeled source domain to an unlabeled target domain. Previous UDA methods assume that the source and target domains share an identical label space, which is unrealistic in practice since the label information of the target domain is agnostic. This paper focuses on a more realistic UDA scenario, i.e. partial domain adaptation (PDA), where the target label space is subsumed to the source label space. In the PDA scenario, the source outliers that are absent in the target domain may be wrongly matched to the target domain (technically named negative transfer), leading to performance degradation of UDA methods. This paper proposes a novel Target Domain Specific Classifier Learning-based Domain Adaptation (TSCDA) method. TSCDA presents a soft-weighed maximum mean discrepancy criterion to partially align feature distributions and alleviate negative transfer. Also, it learns a target-specific classifier for the target domain with pseudo-labels and multiple auxiliary classifiers, to further address classifier shift. A module named Peers Assisted Learning is used to minimize the prediction difference between multiple target-specific classifiers, which makes the classifiers more discriminant for the target domain. Extensive experiments conducted on three PDA benchmark datasets show that TSCDA outperforms other state-of-the-art methods with a large margin, e.g. $4\%$ and $5.6\%$ averagely on Office-31 and Office-Home, respectively.
Navo Minority Over-sampling Technique (NMOTe): A Consistent Performance Booster on Imbalanced Datasets

Imbalanced data refers to a problem in machine learning where there exists unequal distribution of instances for each classes. Performing a classification task on such data can often turn bias in favour of the majority class. The bias gets multiplied in cases of high dimensional data. To settle this problem, there exists many real-world data mining techniques like over-sampling and under-sampling, which can reduce the Data Imbalance. Synthetic Minority Oversampling Technique (SMOTe) provided one such state-of-the-art and popular solution to tackle class imbalancing, even on high-dimensional data platform. In this work, a novel and consistent oversampling algorithm has been proposed that can further enhance the performance of classification, especially on binary imbalanced datasets. It has been named as NMOTe (Navo Minority Oversampling Technique), an upgraded and superior alternative to the existing techniques. A critical analysis and comprehensive overview on the literature has been done to get a deeper insight into the problem statements and nurturing the need to obtain the most optimal solution. The performance of NMOTe on some standard datasets has been established in this work to get a statistical understanding on why it has edged the existing state-of-the-art to become the most robust technique for solving the two-class data imbalance problem.
Internet banking in Nigeria: Cyber security breaches, practices and capability

This paper reports the findings from a research project on cyber security in the Nigerian Internet banking industry, by presenting the main cyber security breaches it has experienced, along with its cyber security capability and practices. An online survey was conducted with 100 experienced professional working in both the Nigerian banking and banking security service sectors. Our findings reveal a transformation of the Nigerian cybercrime industry from low-tech cyber-enabled crimes to high-tech sophisticated breaches, with viruses, worms or Trojan infections; electronic spam mails; and hacking being the top three most experienced breaches. In term of cyber security practices, banking professionals have received adequate management in both support and training. The lack of advanced technologies to prevent and address cyber security breaches and the unsatisfactory level of legislative compliance, together, appear to be the primary factors that have reduced cyber security capability in our sample of banks.
Learning Saliency Propagation for Semi-Supervised Instance Segmentation

Instance segmentation is a challenging task for both modeling and annotation. Due to the high annotation cost, modeling becomes more difficult because of the limited amount of supervision. We aim to improve the accuracy of the existing instance segmentation models by utilizing a large amount of detection supervision. We propose ShapeProp, which learns to activate the salient regions within the object detection and propagate the areas to the whole instance through an iterative learnable message passing module. ShapeProp can benefit from more bounding box supervision to locate the instances more accurately and utilize the feature activations from the larger number of instances to achieve more accurate segmentation. We extensively evaluate ShapeProp on three datasets (MS COCO, PASCAL VOC, and BDD100k) with different supervision setups based on both two-stage (Mask R-CNN) and single-stage (RetinaMask) models. The results show our method establishes new states of the art for semi-supervised instance segmentation.
Fast Soft Color Segmentation

We address the problem of soft color segmentation, defined as decomposing a given image into several RGBA layers, each containing only homogeneous color regions. The resulting layers from decomposition pave the way for applications that benefit from layer-based editing, such as recoloring and compositing of images and videos. The current state-of-the-art approach for this problem is hindered by slow processing time due to its iterative nature, and consequently does not scale to certain real-world scenarios. To address this issue, we propose a neural network based method for this task that decomposes a given image into multiple layers in a single forward pass. Furthermore, our method separately decomposes the color layers and the alpha channel layers. By leveraging a novel training objective, our method achieves proper assignment of colors amongst layers. As a consequence, our method achieve promising quality without existing issue of inference speed for iterative approaches. Our thorough experimental analysis shows that our method produces qualitative and quantitative results comparable to previous methods while achieving a 300,000x speed improvement. Finally, we utilize our proposed method on several applications, and demonstrate its speed advantage, especially in video editing.
Visual-Semantic Matching by Exploring High-Order Attention and Distraction

Cross-modality semantic matching is a vital task in computer vision and has attracted increasing attention in recent years. Existing methods mainly explore object-based alignment between image objects and text words. In this work, we address this task from two previously-ignored aspects: high-order semantic information (e.g., object-predicate-subject triplet, object-attribute pair) and visual distraction (i.e., despite the high relevance to textual query, images may also contain many prominent distracting objects or visual relations). Specifically, we build scene graphs for both visual and textual modalities. Our technical contributions are two-folds: firstly, we formulate the visual-semantic matching task as an attention-driven cross-modality scene graph matching problem. Graph convolutional networks (GCNs) are used to extract high-order information from two scene graphs. A novel cross-graph attention mechanism is proposed to contextually reweigh graph elements and calculate the inter-graph similarity; Secondly, some top-ranked samples are indeed false matching due to the co-occurrence of both highly-relevant and distracting information. We devise an information-theoretic measure for estimating semantic distraction and re-ranking the initial retrieval results. Comprehensive experiments and ablation studies on two large public datasets (MS-COCO and Flickr30K) demonstrate the superiority of the proposed method and the effectiveness of both high-order attention and distraction.
Efficiency Implications of Term Weighting for Passage Retrieval

Language model pre-training has spurred a great deal of attention for tasks involving natural language understanding, and has been successfully applied to many downstream tasks with impressive results. Within information retrieval, many of these solutions are too costly to stand on their own, requiring multi-stage ranking architectures. Recent work has begun to consider how to "backport" salient aspects of these computationally expensive models to previous stages of the retrieval pipeline. One such instance is DeepCT, which uses BERT to re-weight term importance in a given context at the passage level. This process, which is computed offline, results in an augmented inverted index with re-weighted term frequency values. In this work, we conduct an investigation of query processing efficiency over DeepCT indexes. Using a number of candidate generation algorithms, we reveal how term re-weighting can impact query processing latency, and explore how DeepCT can be used as a static index pruning technique to accelerate query processing without harming search effectiveness.
Wasserstein Loss based Deep Object Detection

Object detection locates the objects with bounding boxes and identifies their classes, which is valuable in many computer vision applications (e.g. autonomous driving). Most existing deep learning-based methods output a probability vector for instance classification trained with the one-hot label. However, the limitation of these models lies in attribute perception because they do not take the severity of different misclassifications into consideration. In this paper, we propose a novel method based on the Wasserstein distance called Wasserstein Loss based Model for Object Detection (WLOD). Different from the commonly used distance metric such as cross-entropy (CE), the Wasserstein loss assigns different weights for one sample identified to different classes with different values. Our distance metric is designed by combining the CE or binary cross-entropy (BCE) with Wasserstein distance to learn the detector considering both the discrimination and the seriousness of different misclassifications. The misclassified objects are identified to similar classes with a higher probability to reduce intolerable misclassifications. Finally, the model is tested on the BDD100K and KITTI datasets and reaches state-of-the-art performance.
Rethinking Performance Estimation in Neural Architecture Search

Neural architecture search (NAS) remains a challenging problem, which is attributed to the indispensable and time-consuming component of performance estimation (PE). In this paper, we provide a novel yet systematic rethinking of PE in a resource constrained regime, termed budgeted PE (BPE), which precisely and effectively estimates the performance of an architecture sampled from an architecture space. Since searching an optimal BPE is extremely time-consuming as it requires to train a large number of networks for evaluation, we propose a Minimum Importance Pruning (MIP) approach. Given a dataset and a BPE search space, MIP estimates the importance of hyper-parameters using random forest and subsequently prunes the minimum one from the next iteration. In this way, MIP effectively prunes less important hyper-parameters to allocate more computational resource on more important ones, thus achieving an effective exploration. By combining BPE with various search algorithms including reinforcement learning, evolution algorithm, random search, and differentiable architecture search, we achieve 1, 000× of NAS speed up with a negligible performance drop comparing to the SOTA.
Interactive Translation in Echocardiography Training System With Enhanced Cycle-GAN

Interactive translation in echocardiography training system refers to the pixel-wise translation between ultrasound cardiac and theoretical sketch images in the course of hand-on operation.It is capable of efficiently gaining more insights into clinical ultrasound anatomy.However, major studies on the synthesis of ultrasound cardiac image primarily discuss the physical model simulation, while studies on cardiac image segmentation place an emphasis on image processing.Thus, they cannot be easily integrated into one pipeline for interactive translation.This paper presents an enhanced Cycle-GAN for interactive translation.Perceptual loss is introduced to enhance the quality of synthetic ultrasound texture, while Cycle-GAN translates between two modalities.The proposed method is trained on 300 pair images and tested on 68 pair images.As revealed from the experiment results, the proposed method is feasible in interactive translation, and it is superior over Cycle-GAN for ultrasound image synthesis.
Adaptive Posit: Parameter aware numerical format for deep learning inference on the edge

Ultra low-precision (<; 8-bit width) arithmetic is a discernible approach to deploy deep learning networks on to edge devices. Recent findings show that posit with linear quantization has similar dynamic range as the weight and activation values across the deep neural network layers. This characteristic can benefit the data representation of deep neural networks without impacting the overall accuracy. When capturing the full dynamic range of weights and activations, posit with mixed precision or linear quantization leads to a surge in hardware resource requirements. We propose adaptive posit, which has the ability to capture the non-homogeneous dynamic range of weights and activation's across the deep neural network layers. A fine granular control is achieved by embedding the hyper-parameters in the numerical format. To evaluate the overall system efficiency, we design a parameterized ASIC soft core for the adaptive posit encoder and decoder. Benchmarking and evaluation of the adaptive posit is performed on three datasets: Fashion-MNIST, CIFAR-10, and ImageNet. Results assert that on average the performance on inference with <; 8-bit adaptive posits surpasses (2% to 10%) that of posit.
5G Signal Identification Using Deep Learning

Spectrum awareness, including identifying different types of signals, is very important in a cellular system environment. In this paper, a neural network is utilized to identify 5G signals among different cellular communications signals, including Long-Term Evolution (LTE) and Universal Mobile Telecommunication Service (UMTS). We explore the use of deep learning in wireless communications systems. We consider the effects of training dataset size, features extracted, and channel fading in our study. Experiment results demonstrate the effectiveness of deep learning neural networks in identifying cellular system signals, including UMTS, LTE, and 5G.
Robust discriminant feature selection via joint <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2780" altimg="si4.svg"><mml:msub><mml:mrow><mml:mi mathvariant="script">L</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-norm distance minimization and maximization

Discriminative Feature Selection (DFS) is an algorithm, proposed recently for effective feature selection by considering both joint linear discriminant analysis and row sparsity regularization. However, this method is not robust enough to protect the data from outliers, because it utilizes the squared L2-norm distance metric. To overcome this problem, we present in this paper, a novel discriminative feature selection algorithm, which uses the robust L2,1-norm for measuring the distances in DFS. Although the algorithm is apparently simple, it should not be considered trivial because of its non-convexity. Also, we present an analysis of the convergence, both theoretical and empirical. More importantly, we proposed an iterative algorithm to achieve optimal results. Experimental results, using various data sets, demonstrate the effectiveness of the proposed method.
FALCONS: FAst Learner-grader for CONtorted poses in Sports

Isn’t it about time to help judges with the challenging task of evaluating athletes’ performances in sports with extreme poses? To tackle this problem and inspired by human judges’ grading schema, we propose a virtual refereeing network to evaluate the execution of a diving performance. This assessment would be based on visual clues as well as the body joints sequence of the action video. In order to cover the unusual body contortions in such scenarios, we present ExPose: annotated dataset of Extreme Poses. We further introduce a simple yet effective module to assess the difficulty of the performance based on the extracted joints sequence. Finally, the overall score of the performance would be reported as the multiplication of the execution and difficulty scores. The results demonstrate our proposed lightweight network not only achieves state-of-the-art results compared to previous studies in diving but also shows acceptable generalization to other contortive sports.
High-Resolution Dual-Stage Multi-Level Feature Aggregation for Single Image and Video Deblurring

In this paper we address the problem of dynamic scene motion deblurring. We present a model that combines high-resolution processing with a multi-resolution feature aggregation method for single frame and video deblurring. Our proposed model consists of 2 stages. In the first stage, single image deblurring is performed at a very high-resolution. For this purpose, we propose a novel network building block that employs multiple atrous convolutions in parallel. We carefully tune the atrous rate of each of these convolutions to achieve complete coverage of a rectangular area of the input. In this way we obtain a large receptive field at a high spatial resolution. The second stage aggregates information across multiple consecutive frames of a video sequence. Here we maintain a high-resolution, but also use multi-resolution features to mitigate the effects of large movements of objects between images. The presented models rank first and fourth in the NTIRE2020 challenges for single image deblurring and video deblurring, respectively. We apply our framework on current benchmarks and challenges and show that our model provides state-of-the art results.
An open-source, industrial-strength optimizing compiler for quantum programs

Quilc is an open-source, optimizing compiler for gate-based quantum programs written in Quil or QASM, two popular quantum programming languages. The compiler was designed with attention toward NISQ-era quantum computers, specifically recognizing that each quantum gate has a non-negligible and often irrecoverable cost toward a program's successful execution. Quilc's primary goal is to make authoring quantum software a simpler exercise by making architectural details less burdensome to the author. Using Quilc allows one to write programs faster while usually not compromising—and indeed sometimes improving—their execution fidelity on a given hardware architecture. In this paper, we describe many of the principles behind Quilc's design, and demonstrate the compiler with various examples.
Quantum rangefinding

Quantum light generated in non-degenerate squeezers has many applications such as sub-shot-noise transmission measurements to maximise the information extracted by one photon or quantum illumination to increase the probability in target detection. However, any application thus far fails to consider the thermal characteristics of one half of the bipartite down-converted photon state often used in these experiments. We show here that a maximally mixed state, normally viewed as nuisance, can indeed be used to extract information about the position of an object while at the same time providing efficient camouflaging against other thermal or background light.
NonLocal Channel Attention for NonHomogeneous Image Dehazing

The emergence of deep learning methods that complement traditional model-based methods has helped achieve a new state-of-the-art for image dehazing. Many recent methods design deep networks that either estimate the haze-free image (J) directly or estimate physical parameters in the haze model, i.e. ambient light (A) and transmission map (t) followed by using the inverse of the haze model to estimate the dehazed image. However, both kinds of methods fail in dealing with non-homogeneous haze images where some parts of the image are covered with denser haze and the other parts with shallower haze. In this work, we develop a novel neural network architecture that can take benefits of the aforementioned two kinds of dehazed images simultaneously by estimating a new quantity - a spatially varying weight map (w). w can then be used to combine the directly estimated J and the results obtained by the inverse model. In our work, we utilize a shared DenseNet-based encoder, and four distinct DenseNet-based decoders that estimate J, A, t, and w jointly. A channel attention structure is added to facilitate the generation of distinct feature maps of different decoders. Furthermore, we propose a novel dilation inception module in the architecture to utilize the non-local features to make up the missing information during the learning process. Experiments performed on challenging benchmark datasets of NTIRE'20 and NTIRE'18 demonstrate that the proposed method -namely, AtJwD- can outperform many state-of-the-art alternatives in the sense of quality metrics such as SSIM, especially in recovering images under non-homogeneous haze.
DVGAN

Search result diversification aims to retrieve diverse results to cover as many subtopics related to the query as possible. Recent studies showed that supervised diversification models are able to outperform the heuristic approaches, by automatically learning a diversification function other than using manually designed score functions. The main challenge of training a diversification model is the lack of high-quality training samples. Due to the involvement of dependence between documents in the ranker, it is very hard for training algorithms to select effective positive and negative ranking lists to train a reliable ranking model, given a large number of candidate documents within which different documents are relevant to different subtopics. To tackle this problem, we propose a supervised diversification framework based on Generative Adversarial Network (GAN). It consists of a generator and a discriminator interacting with each other in a minimax game. Specifically, the generator generates more confusing negative samples for the discriminator, and the discriminator sends back complementary ranking signals to the generator. Furthermore, we explicitly exploit subtopics in the generator, whereas focusing on modeling document similarity in the discriminator. Through such a minimax game, we are able to obtain better ranking models by combining ranking signals learned by the generator and the discriminator. Experimental results on the TREC Web Track dataset show that the proposed method can significantly outperform existing diversification methods.