Reviewerly/exHarmony huggingface dataset:
Deep Lesion Graph in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalitiesAbnormalities on a large number of radiology imagesRadiology image. Such abnormalitiesAbnormalities, or lesions, have collected over years and stored in hospitals’ picture archiving and communication systemsPicture Archiving and Communication Systems (PACS). However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scaleLarge-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxesBounding box and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates, and sizes. They require little manual annotationManual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clusteringClustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. An algorithm for intra-patient lesion matching is proposed and validated with experiments.
A Learnable Distortion Correction Module for Modulation Recognition

Modulation recognition is a challenging task while performing spectrum sensing in cognitive radio. Recently, deep learning techniques, such as convolutional neural networks (CNNs) have been shown to achieve state-of-the-art accuracy for modulation recognition. However, CNNs are not explicitly designed to undo distortions caused by wireless channels. To improve the accuracy of CNN-based modulation recognition schemes, we propose a signal distortion correction module (CM). The proposed CM is also based on a neural network that can be thought of as an estimator of carrier frequency and phase offset introduced by the channel. The CM output is used to shift the signal frequency and phase before modulation recognition and is differentiable with respect to its weights. This allows the CM to be co-trained end-to-end in tandem with the CNN used for modulation recognition. For supervision, only the modulation scheme label is used and the knowledge of true frequency or phase offset is not required for co-training the combined network (CM+CNN).
Concurrent Think-Aloud Verbalizations and Usability Problems

The concurrent think-aloud protocol—in which participants verbalize their thoughts when performing tasks—is a widely employed approach in usability testing. Despite its value, analyzing think-aloud sessions can be onerous because it often entails assessing all of a user's verbalizations. This has motivated previous research on developing categories to segment verbalizations into manageable units of analysis. However, the way in which a category might relate to usability problems is currently unclear. In this research, we sought to address this gap in our understanding. We also studied how speech features might relate to usability problems. Through two studies, this research demonstrates that certain patterns of verbalizations are more telling of usability problems than others and that these patterns are robust to different types of test products (i.e., physical devices and digital systems), access to different types of information (i.e., video and audio modality), and the presence or absence of a visualization of verbalizations. The implication is that the verbalization and speech patterns can potentially reduce the time and effort required for analysis by enabling evaluators to focus more on the important aspects of a user's verbalizations. The patterns could also potentially be used to inform the design of systems to automatically detect when in the recorded think-aloud sessions users experience problems.
Facing Device Attribution Problem for Stabilized Video Sequences

A problem deeply investigated by multimedia forensics researchers is that of detecting which device has been used to capture a video. This enables us to trace down the owner of a video sequence, which proves extremely helpful to solve copyright infringement cases as well as to fight distribution of illicit material (e.g., child exploitation clips and terroristic threats). Currently, the most promising methods to tackle this task exploit unique noise traces left by camera sensors on acquired images. However, given the recent advancements in motion stabilization of video content, robustness of sensor pattern noise-based techniques is strongly hindered. Indeed, video stabilization introduces geometric transformations to video frames, thus making camera fingerprint estimation problematic with classical approaches. In this paper, we deal with the challenging problem of attributing stabilized videos to their recording device. Specifically, we propose: 1) a strategy to extract the characteristic fingerprint of a device, starting from either a set of images or stabilized video sequences and 2) a strategy to match a stabilized video sequence with a given fingerprint. The proposed methodology is tested on videos coming from a set of different smartphones, taken from the modern publicly available Vision Dataset. The conducted experiments also provide an interesting insight on the effect of modern smartphones video stabilization algorithms on specific video frames.
2-D Stochastic Configuration Networks for Image Data Analytics

Stochastic configuration networks (SCNs) as a class of randomized learner model have been successfully employed in data analytics due to its universal approximation capability and fast modeling property. The technical essence lies in stochastically configuring the hidden nodes (or basis functions) based on a supervisory mechanism rather than data-independent randomization as usually adopted for building randomized neural networks. Given image data modeling tasks, the use of 1-D SCNs potentially demolishes the spatial information of images, and may result in undesirable performance. This paper extends the original SCNs to a 2-D version, called 2DSCNs, for fast building randomized learners with matrix inputs. Some theoretical analysis on the goodness of 2DSCNs against SCNs, including the complexity of the random parameter space and the superiority of generalization, are presented. Empirical results over one regression example, four benchmark handwritten digit classification tasks, two human face recognition datasets, as well as one natural image database, demonstrate that the proposed 2DSCNs perform favorably and show good potential for image data analytics.
PyRanges: efficient comparison of genomic intervals in Python

Abstract Summary Complex genomic analyses often use sequences of simple set operations like intersection, overlap and nearest on genomic intervals. These operations, coupled with some custom programming, allow a wide range of analyses to be performed. To this end, we have written PyRanges, a data structure for representing and manipulating genomic intervals and their associated data in Python. Run single threaded on binary set operations, PyRanges is in median 2.3–9.6 times faster than the popular R GenomicRanges library and is equally memory efficient; run multi-threaded on 8 cores, our library is up to 123 times faster. PyRanges is therefore ideally suited both for individual analyses and as a foundation for future genomic libraries in Python. Availability and implementation PyRanges is available as open source under the MIT license at https://github.com/biocore-NTNU/pyranges and the documentation exists at https://biocore-NTNU.github.io/pyranges/ Supplementary information Supplementary data are available at Bioinformatics online.
Taprint

Smart wristband has become a dominant device in the wearable ecosystem, providing versatile functions such as fitness tracking, mobile payment, and transport ticketing. However, the small form-factor, low-profile hardware interfaces and computational resources limit their capabilities in security checking. Many wristband devices have recently witnessed alarming vulnerabilities, e.g., personal data leakage and payment fraud, due to the lack of authentication and access control. To fill this gap, we propose a secure text pin input system, namely Taprint, which extends a virtual number pad on the back of a user's hand. Taprint builds on the key observation that the hand "landmarks'', especially finger knuckles, bear unique vibration characteristics when being tapped by the user herself. It thus uses the tapping vibrometry as biometrics to authenticate the user, while distinguishing the tapping locations. Taprint reuses the inertial measurement unit in the wristband, "overclocks'' its sampling rate to extrapolate fine-grained features, and further refines the features to enhance the uniqueness and reliability. Extensive experiments on 128 users demonstrate that Taprint achieves a high accuracy (96%) of keystrokes recognition. It can authenticate users, even through a single-tap, at extremely low error rate (2.4%), and under various practical usage disturbances.
Sustaining cultural tourism through higher female participation in Nigeria: The role of corporate social responsibility in oil host communities

Abstract This paper adds to the gender discourse in sustainable African tourism development from the corporate social responsibility (CSR) perspective. Specifically, we examine the impact of CSR on the development of rural women in cultural tourism. A total of 600 rural women were sampled across the Niger Delta. Results from the use of a logit model indicate a significant relationship between CSR and cultural tourism development in oil host communities in Nigeria. This implies that CSR of a multinational oil company is a critical factor for sustaining cultural tourism. The findings suggest increased female participation in General Memorandum of Understanding interventions of multinational oil company and the need to pay close attention to which extent the participation of rural women in the General Memorandum of Understanding projects may be limited by cultural and traditional obstacles.
Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition

Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings. Lexical composition can shift the meanings of the constituent words and introduce implicit information. We tested a broad range of textual representations for their capacity to address these issues. We found that, as expected, contextualized word representations perform better than static word embeddings, more so on detecting meaning shift than in recovering implicit information, in which their performance is still far from that of humans. Our evaluation suite, consisting of six tasks related to lexical composition effects, can serve future research aiming to improve representations.
A novel strong tracking cubature Kalman filter and its application in maneuvering target tracking

The fading factor exerts a significant role in the strong tracking idea. However, traditional fading factor introduction method hinders the accuracy and robustness advantages of current strong-tracking-based nonlinear filtering algorithms such as Cubature Kalman Filter (CKF) since traditional fading factor introduction method only considers the first-order Taylor expansion. To this end, a new fading factor idea is suggested and introduced into the strong tracking CKF method. The new fading factor introduction method expanded the number of fading factors from one to two with reselected introduction positions. The relationship between the two fading factors as well as the general calculation method can be derived based on Taylor expansion. Obvious superiority of the newly suggested fading factor introduction method is demonstrated according to different nonlinearity of the measurement function. Equivalent calculation method can also be established while applied to CKF. Theoretical analysis shows that the strong tracking CKF can extract the third-order term information from the residual and thus realize second-order accuracy. After optimizing the strong tracking algorithm process, a Fast Strong Tracking CKF (FSTCKF) is finally established. Two simulation examples show that the novel FSTCKF improves the robustness of traditional CKF while minimizing the algorithm time complexity under various conditions.
Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation

Automatic 6-month infant brain tissue segmentation of magnetic resonance imaging (MRI) is still less accurate owing to the low intensity contrast among tissues. To tackle the problem, we introduce an accurate segmentation method for volumetric infant brain MRI built upon a densely connected network that achieves state-of-the-art accuracy. Specifically, we carefully design a fully convolutional densely connected network with skip connections such that the information from different levels of dense blocks can be directly combined to achieve highly accurate segmentation results. The proposed network, called 3D-SkipDenseSeg, exploits the advantage of the recently DenseNet for classification task and extends this to segment the 6-month infant brain tissue segmentation of magnetic resonance imaging (MRI). Experimental results demonstrate a competitive performance with regard to both segmentation accuracy and parameter efficiency of the proposed method over the existing methods; namely, the proposed 3D-SkipDenseSeg achieved the best dice similarity coefficient (DSC) of 90.37 ± 1.38% (WM), 92.27 ± 0.81% (GM), and 95.79 ± 0.54% (CSF) among the 21 participating teams in the 6-month infant brain dataset (iSeg-2017) and required only 10–30% of the parameters compared to similar deep learning-based methods.
Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference

Statistical hypothesis testing is among the most misunderstood quantitative analysis methods from data science. Despite its seeming simplicity, it has complex interdependencies between its procedural components. In this paper, we discuss the underlying logic behind statistical hypothesis testing, the formal meaning of its components and their connections. Our presentation is applicable to all statistical hypothesis tests as generic backbone and, hence, useful across all application domains in data science and artificial intelligence.
"I don't know what you're talking about, HALexa"

As Voice User Interfaces (VUI) grow in popularity in both the research and academic world, designers are met with new challenges in delivering on the promises of voice interaction. These promises depict a world where one can just speak to their devices, akin to HAL-9000; yet, existing usability challenges still leave many disappointed. These challenges often make or break the experience users have with VUIs. We argue that what we are missing is a foundation on which to build (and deliver) our promises: it is essential to build a foundation of VUI principles that can guide future designers in the development of voice interaction. We must address the lack of research in developing foundational VUI-specific guidelines that can aid designers in meeting the expectations and promises of true voice interaction.
NTIRE 2019 Challenge on Image Enhancement: Methods and Results

This paper reviews the first NTIRE challenge on perceptual image enhancement with the focus on proposed solutions and results. The participating teams were solving a real-world photo enhancement problem, where the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with Canon 70D DSLR camera. The considered problem embraced a number of computer vision subtasks, such as image denoising, image resolution and sharpness enhancement, image color/contrast/exposure adjustment, etc. The target metric used in this challenge combined PSNR and SSIM scores with solutions' perceptual results measured in the user study. The proposed solutions significantly improved baseline results, defining the state-of-the-art for practical image enhancement.
How does object-oriented code refactoring influence software quality? Research landscape and challenges

Context: Software refactoring aims to improve software quality and developer productivity. Numerous empirical studies investigating the impact of refactoring activities on software quality have been conducted over the last two decades. Objective: This study aims to perform a comprehensive systematic mapping study of existing empirical studies on evaluation of the effect of object-oriented code refactoring activities on software quality attributes. Method: We followed a multi-stage scrutinizing process to select 142 primary studies published till December 2017. The selected primary studies were further classified based on several aspects to answer the research questions defined for this work. In addition, we applied vote-counting approach to combine the empirical results and their analysis reported in primary studies. Results: The findings indicate that studies conducted in academic settings found more positive impact of refactoring on software quality than studies performed in industries. In general, refactoring activities caused all quality attributes to improve or degrade except for cohesion, complexity, inheritance, fault-proneness and power consumption attributes. Furthermore, individual refactoring activities have variable effects on most quality attributes explored in primary studies, indicating that refactoring does not always improve all quality attributes. Conclusions: This study points out several open issues which require further investigation, e.g., lack of industrial validation, lesser coverage of refactoring activities, limited tool support, etc.
Realizing Chosen Ciphertext Security Generically in Attribute-Based Encryption and Predicate Encryption

We provide generic and black box transformations from any chosen plaintext secure Attribute-Based Encryption (ABE) or One-sided Predicate Encryption system into a chosen ciphertext secure system. Our transformation requires only the IND-CPA security of the original ABE scheme coupled with a pseudorandom generator (PRG) with a special security property. In particular, we consider a PRG with an n bit input $$s \in \{0,1\}^n$$ and $$n \cdot \ell $$ bit output $$y_1, \ldots , y_n$$ where each $$y_i$$ is an $$\ell $$ bit string. Then for a randomly chosen s the following two distributions should be computationally indistinguishable. In the first distribution $$r_{s_i, i} = y_i$$ and $$r_{\bar{s}_i, i}$$ is chosen randomly for $$i \in [n]$$ . In the second distribution all $$r_{b, i}$$ are chosen randomly for $$i \in [n], b \in \{0,1\}$$ . We show that such PRGs can be built from either the computational Diffie-Hellman assumption (in non-bilinear groups) or the Learning with Errors (LWE) assumption (and potentially other assumptions). Thus, one can transform any IND-CPA secure system into a chosen ciphertext secure one by adding either assumption. (Or by simply assuming an existing PRG is hinting secure.) In addition, our work provides a new approach and perspective for obtaining chosen ciphertext security in the basic case of public key encryption.
Spatio-Temporal Manifold Learning for Human Motions via Long-Horizon Modeling

Data-driven modeling of human motions is ubiquitous in computer graphics and computer vision applications, such as synthesizing realistic motions or recognizing actions. Recent research has shown that such problems can be approached by learning a natural motion manifold using deep learning on a large amount data, to address the shortcomings of traditional data-driven approaches. However, previous deep learning methods can be sub-optimal for two reasons. First, the skeletal information has not been fully utilized for feature extraction. Unlike images, it is difficult to define spatial proximity in skeletal motions in the way that deep networks can be applied for feature extraction. Second, motion is time-series data with strong multi-modal temporal correlations between frames. On the one hand, a frame could be followed by several candidate frames leading to different motions; on the other hand, long-range dependencies exist where a number of frames in the beginning are correlated with a number of frames later. Ineffective temporal modeling would either under-estimate the multi-modality and variance, resulting in featureless mean motion or over-estimate them resulting in jittery motions, which is a major source of visual artifacts. In this paper, we propose a new deep network to tackle these challenges by creating a natural motion manifold that is versatile for many applications. The network has a new spatial component for feature extraction. It is also equipped with a new batch prediction model that predicts a large number of frames at once, such that long-term temporally-based objective functions can be employed to correctly learn the motion multi-modality and variances. With our system, long-duration motions can be predicted/synthesized using an open-loop setup where the motion retains the dynamics accurately. It can also be used for denoising corrupted motions and synthesizing new motions with given control signals. We demonstrate that our system can create superior results comparing to existing work in multiple applications.
Fusion of Wearable and Contactless Sensors for Intelligent Gesture Recognition

A novel approach of fusing datasets from multiple sensors using a hierarchical support vector machine (HSVM) algorithm is presented. The validation of this method is experimentally carried out using an intelligent learning system that combines two different data sources. The sensors are based on a contactless sensor, which is a radar that detects the movements of the hands and fingers, as well as a wearable sensor, which is a flexible pressure sensor array that measures pressure distribution around the wrist. A HSVM architecture is developed to effectively fuse different data types in terms of sampling rate, data format, and gesture information from the pressure sensors and radar. In this respect, the proposed method is compared with the classification results from each of the two sensors independently. Herein, datasets from 15 different participants are collected and analyzed. The results show that the radar on its own provides a mean classification accuracy of 76.7%, whereas the pressure sensors provide an accuracy of 69.0%. However, enhancing the pressure sensors' output results with radar using the proposed HSVM algorithm improves the classification accuracy to 92.5%.
Image Denoising with Graph-Convolutional Neural Networks

Recovering an image from a noisy observation is a key problem in signal processing. Recently, it has been shown that data-driven approaches employing convolutional neural networks can outperform classical model-based techniques, because they can capture more powerful and discriminative features. However, since these methods are based on convolutional operations, they are only capable of exploiting local similarities without taking into account non-local self-similarities. In this paper we propose a convolutional neural network that employs graph-convolutional layers in order to exploit both local and non-local similarities. The graph-convolutional layers dynamically construct neighborhoods in the feature space to detect latent correlations in the feature maps produced by the hidden layers. The experimental results show that the proposed architecture outperforms classical convolutional neural networks for the denoising task.