Reviewerly/exHarmony huggingface dataset:
Diagnostic Assessment of Deep Learning Algorithms for Detection and Segmentation of Lesion in Mammographic Images

Computer-aided detection or diagnosing support methods aims to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. This system relates to the use of deep learning for automated detection and segmentation of soft tissue lesions at the early stage. This paper presents a novel deep learning approach, based on a two stage object detector combining an enhanced Faster R-CNN with the Libra R-CNN structure for the Object Detection segment. A segmentation network is placed on top of previous structure in order to provide accurate extraction and localization of masses various features, i.e: margin, shape. The segmentation head is based on a Recurrent Residual Convolutional Neural Network and can lead to an additional feature classification for specific instance properties. A database of digital mammograms was collected from one vendor, Hologic, of which 1,200 images contained masses. The performance for our automated detection system was assessed with the sensitivity of the model which reached a micro average recall: 0.892, micro average precision: 0.734, micro average F1 score: 0.805. Macro average recall: 0.896, macro average precision: 0.819, macro average F1 score: 0.843. The segmentation performance for the same test set was evaluated to a mean IOU of 0.859.
Cross-Domain Recommendation with Multiple Sources

Data sparsity remains a challenging and common problem in real-world recommender systems, which impairs the accuracy of recommendation thus damages user experience. Cross-domain recommender systems are developed to deal with data sparsity problem through transferring knowledge from a source domain with relatively abundant data to the target domain with insufficient data. However, two challenging issues exist in cross-domain recommender systems: 1) domain shift which makes the knowledge from source domain inconsistent with that in the target domain; 2) knowledge extracted from only one source domain is insufficient, while knowledge is potentially available in many other source domains. To handle the above issues, we develop a cross-domain recommendation method in this paper to extract group-level knowledge from multiple source domains to improve recommendation in a sparse target domain. Domain adaptation techniques are applied to eliminate the domain shift and align user and item groups to maintain knowledge consistency during the transfer learning process. Knowledge is extracted not from one but multiple source domains through an intermediate subspace and adapted through flexible constraints of matrix factorization in the target domain. Experiments conducted on five datasets in three categories show that the proposed method outperforms six benchmarks and increases the accuracy of recommendations in the target domain.
Regularized ELM bagging model for Tropical Cyclone Tracks prediction in South China Sea

This paper aims to improve the prediction accuracy of Tropical Cyclone Tracks (TCTs) over the South China Sea (SCS) with 24 h lead time. The model proposed in this paper is a regularized extreme learning machine (ELM) ensemble using bagging. The method which turns the original problem into quadratic programming (QP) problem is proposed in this paper to solve lasso and elastic net problem in ELM. The forecast error of TCTs data set is the distance between real position and forecast position. Compared with the stepwise regression method widely used in TCTs, 8.26 km accuracy improvement is obtained by our model based on the dataset with 70/1680 testing/training records. By contrast, the improvement using this model is 16.49 km based on a smaller dataset with 30/720 testing/training records. Results show that the regularized ELM bagging has a general better generalization capacity on TCTs data set.
Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments

In this work we focus on improving the efficiency and generalisation of learned navigation strategies when transferred from its training environment to previously unseen ones. We present an extension of the residual reinforcement learning framework from the robotic manipulation literature and adapt it to the vast and unstructured environments that mobile robots can operate in. The concept is based on learning a residual control effect to add to a typical sub-optimal classical controller in order to close the performance gap, whilst guiding the exploration process during training for improved data efficiency. We exploit this tight coupling and propose a novel deployment strategy, switching Residual Reactive Navigation (sRRN), which yields efficient trajectories whilst probabilistically switching to a classical controller in cases of high policy uncertainty. Our approach achieves improved performance over end-to-end alternatives and can be incorporated as part of a complete navigation stack for cluttered indoor navigation tasks in the real world. The code and training environment for this project is made publicly available at https://sites.google.com/view/srrn/home.
A Buffer-Based Rate Adaptation Approach for Video Streaming Over HTTP

The demand for high quality multimedia streaming services has increased rapidly in the past decade. Sustaining this growth while maintaining the quality desired by the end users poses several challenges to the service providers. Perhaps the most important challenge is the fact that the underlying networks for these streaming services are fundamentally best-effort, and that all the attempts to offer an acceptable quality of experience (QoE) to the users were necessarily an afterthought. Nevertheless, some service providers have managed to offer very competitive streaming QoE thanks to the well-refined dynamic adaptive streaming over HTTP (DASH) technology. In this work, we propose a buffer-based streaming algorithm for dynamic adaptive video streaming over HTTP. The proposed algorithm aims at jointly reducing quality variations as well as playback interruptions by improving bandwidth utilization. This is done by maintaining the occupancy of the client playback buffer around a level that enables the streaming approach to absorb any possible undesirable network dynamics. Specifically, the client decides the segment bit rate by considering the variations in the video quality, buffered playback time in addition to bandwidth utilization. The simulation results show that the proposed system can maintain the playback process while gradually switching between the available video qualities while enhancing the bandwidth utilization.
BR-GAN: Bilateral Residual Generating Adversarial Network for Mammogram Classification

Mammogram malignancy classification with only image-level annotations is challenging due to a lack of lesion annotations. If we can generate the healthy version of the diseased data, we can easily explore the lesion features. An intuitive idea of such generation is to use existing Cycle-GAN based methods. They achieve the healthy generation regarding healthy images as reference domain, while maintaining the original content by cycle consistency mechanism. However, healthy mammogram patterns are diverse which may lead to uncertain generations. Moreover, the back translation from healthy to the original remains an ill-posed problem due to lack of lesion information. To address these problems, we propose a novel model called bilateral residual generating adversarial network(BR-GAN). We use the Cycle-GAN as a basic framework while regarding the contralateral as generation reference based on the bilateral symmetry prior. To address the ill-posed back translation problem, we propose a residual-preserved mechanism to try to preserve the lesion features from the original features. The generated features and the original features are aggregated for further classification. BR-GAN outperforms current state-of-the-art methods on INBreast and in-house datasets.
S<sup>6</sup>: Semi-Supervised Self-Supervised Semantic Segmentation

Semi-supervised learning provides a means to leverage unlabeled data when labels are expensive to obtain. In this work, we propose a constrained framework that better learns from unlabeled data. The proposed algorithm adds a self-supervised task, image reconstruction, to the target segmentation task. The extra reconstruction task improves the model's geometric reasoning about different textures in an image. It happens that this improvement is transferable from reconstruction to segmentation since they both share some common parts of the architecture. Such extra task allows the trained model to have richer representations and better geometric understanding. Our results show that the proposed constrained framework achieves an improvement in mean Intersection over Union by 18% over unconstrained one, using only 2% of labeled examples. The performance gain and reduction in amounts of labeled data is crucial for applications in which obtaining labels is expensive and labor intensive such as Biomedical Imaging and Seismic Interpretation.
A Robust Copy Move Forgery Classification Using End to End Convolution Neural Network

In this digital world it is not surprising to do manipulation with digital images. With advantage of such technologies it has become very easy to misguide the observer about the reality appearing in the images. The objective behind such manipulation for fun and entertainment is acceptable but when such things are applied on sensitive information such as evidences used in judiciary system, to prove certain claim, using such manipulated images on social media becomes dangerous. Although many types of forgeries that could be performed with images such as copying certain part of image then pasting it in same image somewhere else in document with such precision that it appears normal to observer called copy move forgery. Other forgeries include splicing of images, image morphing, retouching etc. Two different categories of approaches are being used till recently for this problem of copy-move forgery detection. These are block based and Keypoint based approach wherein block based is computationally intensive and suffers from many disadvantages and other is based interest points or high textured areas whose features vectors are formed for comparison to find the duplicated regions. In this paper, a deep neural network based approach has been proposed with promising results that can classify images based whether any copy move forgery has been there in the images. The proposed work aims to classify all the images having copy move forgery with presence of scaling, rotation, different compression level. A new CNN model has been researched for this problem to obtain the accuracy of around 93-95 percent for different datasets alone as well on the combination of two or more datasets.
Building a formal model for hate detection in French corpora

This paper investigates the development of a formal model in order to analyse online hate in French corpora. Relevant concepts are identified by exploiting several sources: the cognitive foundations of the appraisal theory, according to which people's emotional response are based on their own evaluative judgments or appraisals of situations, events or objects; a linguistic model of how different kinds of modalities applied to predicative contents are expressed in textual data; several definitions of a hate speech. Based on those inputs, a formal model was developed to describe online hate speech. The model highlights different categories of hate targets and actions, and emphasizes the importance of context for online hate detection.
Ten-Year Compilation of #SaveKPK Twitter Dataset

Politic is one of the most favorite topics to discuss in social media for people in Indonesia. It was proven when a people movement to support the Commission of Corruption Eradication (KPK) named #SaveKPK has been enlived for ten years and become a trending topic on Twitter for several times. In this research, all tweets contain `#SaveKPK' are crawled and compiled using an alternative algorithm to get twitter historical data instead of using Twitter API. The result described the characteristic of the dataset statistically, from the most frequently used words to the most active users. A clustering algorithm named Latent Dirichlet Allocation (LDA) was run over the gathered text dataset to discover most relevant keywords using unsupervised learning approach.
Deeply scale aggregation network for object counting

Object counting is a fundamental and essential task to build the high-level cognition for the given scene in the computer vision field. For the common scale variant issue in the object counting, this paper designs a deeply scale aggregation network for object counting task. Specially, we design a dense multi-scale (DMS) block to extract the initial scale-aware feature, and thus stack multiple DMS blocks in the specific structure to implement the adaptive network depth, so as to learn the more mighty scale-aware feature. Extensive experimental results are reported in this paper, using up to six public object counting benchmarks, which demonstrate that the proposed method has an effective performance for object counting task. And the ablation studies validate the structural rationality of the proposed method.
Tackling unsupervised multi-source domain adaptation with optimism and consistency

It has been known for a while that the problem of multi-source domain adaptation can be regarded as a single source domain adaptation task where the source domain corresponds to a mixture of the original source domains. Nonetheless, how to adjust the mixture distribution weights remains an open question. Moreover, most existing work on this topic focuses only on minimizing the error on the source domains and achieving domain-invariant representations, which is insufficient to ensure low error on the target domain. In this work, we present a novel framework that addresses both problems and beats the current state of the art by using a mildly optimistic objective function and consistency regularization on the target samples.
Implicit Saliency In Deep Neural Networks

In this paper, we show that existing recognition and localization deep architectures, that have not been exposed to eye tracking data or any saliency datasets, are capable of predicting the human visual saliency. We term this as implicit saliency in deep neural networks. We calculate this implicit saliency using expectancy-mismatch hypothesis in an unsupervised fashion. Our experiments show that extracting saliency in this fashion provides comparable performance when measured against the state-of-art supervised algorithms. Additionally, the robustness outperforms those algorithms when we add large noise to the input images. Also, we show that semantic features contribute more than low-level features for human visual saliency detection. Based on these properties and performances, our proposed method greatly lowers the threshold for saliency detection in terms of required data and bridges the gap between human visual saliency and model saliency.
Evaluating Robustness of AI Models against Adversarial Attacks

Recently developed adversarial attacks on neural networks have become more aggressive and dangerous, because of which Artificial Intelligence (AI) models are no longer sufficiently robust against them. It is important to have a set of effective and reliable methods to detect malicious attacks to ensure the security of AI models. Such standardized methods can also serve as a reference for researchers to develop robust models and new kinds of attacks. This study proposes a method to assess the robustness of AI models. Six commonly used image classification CNN models were evaluated when subjected to 13 types of adversarial attacks. The robustness of the models is calculated unbiased and can be used as a reference for further improvement. It is distinguished from prior related works that our algorithm is attack-agnostic and is applicable to neural network model.
GPNPU: Enabling Efficient Hardware-Based Direct Convolution with Multi-Precision Support in GPU Tensor Cores

To tailor for DNN (Deep Neural Network) acceleration, GPU has migrated to new architectures such as NVIDIA Volta and Turing that incorporate dedicated Tensor Cores. Although good at GEMM (generic matrix-matrix multiplication), Tensor Cores still have inefficiency facing convolutions with certain layer structures. This paper proposes a GPNPU (General-Purpose Neural-network Processing Unit) architecture, which offers another option of direct convolution in GPU. It stitches the direct convolution dataflow into the Tensor Cores with little hardware support, and resorts to regulated data layout with stripe-mined convolution execution to achieve higher performance and power efficiency, while retaining the general programability as GPU. We further apply a unified core design to support varied operand types and precision for higher computing throughput. The evaluation shows that GPNPU can outperform Tensor Cores on typical DNNs by 1.4X for inference (FP16) and 1.2X for training with much reduced power. The INT8 performance even increases to 2.4X. Our study demonstrates that it is possible and appealing to refine the Tensor Cores for greater DNN acceleration, while conforming to GPU architecture for the programmability necessary in future DNN evolution.
Collision Avoidance of Fixed-Wing UAVs in Dynamic Environments Based on Spline-RRT and Velocity Obstacle

It is crucial to online plan a smooth, continuous and collision-free path to navigate a fixed-wing unmanned aircraft through complex environments. In a static environment, given priori knowledge of the map, the sampling-based RRT (rapidly exploring random tree) method and its variants are efficient to provide global path planning to navigate the fixed-wing aircraft through static obstacles. However, in complex and dynamic situations, the fixed-wing aircraft may encounter dynamic obstacles when following the planned global path. In the presence of dynamic obstacles within the sensing range of the fixed-wing aircraft, it is challenging to on-line generate a new collision-free and smooth path. In this paper, a real-time collision-free path planning strategy is proposed for fixed-wing aircraft in dynamic environments. Specifically, the proposed collision-free path planner named as Spline-RRT-VO is presented incorporating the spline-RRT algorithm and the velocity obstacle (VO) method to avoid a high-speed dynamic obstacle. In the proposed spline-RRT-VO approach, a random tree grows in the local area, meanwhile, the VO method is used to extend tree edges and reject unavailable nodes. It improves the tree growing in a more efficient and smooth manner. Simulation results verify the effectiveness of the spline-RRT-VO method to navigate the fixed-wing UAVs through dynamic environments.
A Lightweight Waterproof Casing for an Aquatic UAV using Rapid Prototyping

Aquatic unmanned aerial vehicles (UAVs) are a unique type of UAVs which have the ability to operate in both air and water. However, the vastly different medium properties of air and water pose significant challenges to the design of such vehicles. One of these is the contradicting requirement of minimising weight in air while ensuring that the vehicle is sufficiently dense to obtain near neutral buoyancy in water. In addition, being submerged requires the vehicle to be adequately waterproof, which will inevitably contribute to a significant portion of the vehicle's weight and volume. Here, we look into the design and manufacturing of such a waterproof casing for a proposed aquatic UAV design. Rapid prototyping using low cost desktop 3D printers was used to develop and produce a customised hull casing with complex geometry for the vehicle. Through the use of different materials, printing methods, and hardware design to seal the access openings, a small lightweight casing can create a waterproof hull while achieving the desired vehicle dynamics in both mediums.
Comprehensive Study on Sentiment Analysis: Types, Approaches, Recent Applications, Tools and APIs

Sentiment analysis can be considered a major application of machine learning, more particularly natural language processing (NLP). As there are varieties of applications, Sentiment analysis has gained a lot of attention and is one among the fastest growing research area in computer science. It is a type of data analysis which is observed from news reports, user reviews, feedbacks, social media updates etc. Responses are collected and analyzed by researchers. All sentiments can be classified into three categories-Positive, Negative and Neutral. The paper gives a detailed study of sentiment analysis. It explains the basics of sentiment analysis, its types, and different approaches of sentiment analysis. The recent tools and APIs along with various real world applications of sentiment analysis in various areas are also described briefly.
Range Estimation and Visual Servoing of a Dynamic Target using a Monocular Camera

This paper delves into the problem of tracking an unknown maneuvering target using only monocular visual feedback. It is usually difficult to perform target tracking using only monocular vision due to the absence of depth information. This restricts the use of commonly used Image and Position-based Visual Servoing methods. Hence, range estimation from monocular images has paramount importance if a conventional position based path planning algorithm is to be utilized. In this paper, we circumvent this problem using the angle subtended by the target on the follower in both 3D and image coordinate systems. Also, an Extended Kalman Filter is used to filter and get accurate range estimates of the moving target. The proposed method is extensively validated on a simulated setup created in Gazebo. Also, a Proportional Derivative (PD) controller is used to maintain a fixed standoff distance from the moving target while following it.
Grab’Em: A Novel Graph-Based Method for Combining Feature Subset Selectors

Feature selection is one of the most frequent tasks in data mining applications. Its ability to remove useless and redundant features improves the classification performance and gains knowledge about a given problem makes feature selection a common first step in data mining. In many feature selection applications, we need to combine the results of different feature selection processes. The two most common scenarios are the ensembles of feature selectors and the scaling up of feature selection methods using a data division approach. The standard procedure is to store the number of times every feature has been selected as a vote for the feature and then evaluate different selection thresholds with a certain criterion to obtain the final subset of selected features. However, this method is suboptimal as the relationships of the features are not considered in the voting process. Two redundant features may be selected a similar number of times due to the different sets of instances used each time. Thus, a voting scheme would tend to select both of them. In this article, we present a new approach: instead of using only the number of times a feature has been selected, the approach considers how many times the features have been selected together by a feature selection algorithm. The proposal is based on constructing an undirected graph where the vertices are the features, and the edges count the number of times every pair of instances has been selected together. This graph is used to select the best subset of features, avoiding the redundancy introduced by the voting scheme. The proposal improves the results of the standard voting scheme in both ensembles of feature selectors and data division methods for scaling up feature selection.