Reviewerly/exHarmony huggingface dataset:
Dealing with Lack of Training Data for Convolutional Neural Networks: The Case of Digital Pathology

Thanks to their capability to learn generalizable descriptors directly from images, deep Convolutional Neural Networks (CNNs) seem the ideal solution to most pattern recognition problems. On the other hand, to learn the image representation, CNNs need huge sets of annotated samples that are unfeasible in many every-day scenarios. This is the case, for example, of Computer-Aided Diagnosis (CAD) systems for digital pathology, where additional challenges are posed by the high variability of the cancerous tissue characteristics. In our experiments, state-of-the-art CNNs trained from scratch on histological images were less accurate and less robust to variability than a traditional machine learning framework, highlighting all the issues of fully training deep networks with limited data from real patients. To solve this problem, we designed and compared three transfer learning frameworks, leveraging CNNs pre-trained on non-medical images. This approach obtained very high accuracy, requiring much less computational resource for the training. Our findings demonstrate that transfer learning is a solution to the automated classification of histological samples and solves the problem of designing accurate and computationally-efficient CAD systems with limited training data.
Multi-label biomedical question classification for lexical answer type prediction

Question classification is considered one of the most significant phases of a typical Question Answering (QA) system. It assigns certain answer types to each question which leads to narrow down the search space of possible answers for factoid and list type questions. The process of assigning certain answer types to each question is also known as Lexical Answer Type (LAT) Prediction. Although much work has been done to enhance the performance of question classification into coarse and fine classes in diverse domains, it is still considered a challenging task in the biomedical field. The difficulty in biomedical question classification stems from the fact that one question might have more than one label or expected answer types associated with it (also, referred to as a multi-label classification). In the biomedical domain, only preliminary work is done to classify multi-label questions by transforming them into a single label through copy transformation technique. In this paper, we have generated a multi-labeled corpus (MLBioMedLAT) by exploring the process of Open Advancement of Question Answering (OAQA) system for the task of biomedical question classification. We use 780 biomedical questions from BioASQ challenge and assign them appropriate labels. To annotate these labels, we use the answers for each question and assign the question semantic type labels by leveraging an existing corpus and utilizing OAQA system. The paper introduces a data transformation approach namely Label Power Set with logistic regression (LPLR) for the task of multi-label biomedical question classification and compares its performance with Structured SVM (SSVM), Restricted Boltzmann Machine (RBM), and copy transformation based logistic regression (CLR) (previously used for a similar task in the OAQA system). To evaluate the integrity of the introduced data transformation technique, we use three prominent evaluation measures namely MicroF1, Accuracy, and Hamming Loss. Regarding MicroF1, our introduced technique coupled with a new feature set surpasses CLR, SSVM, and RBM with a margin of 7%, 8%, and 22% respectively.
On prominence scale interactions in Hayu: a Harmonic Grammar account

This paper investigates prominence scale interactions in verbal agreement in Hayu (Kiranti). The agreement system is very complex in several ways. First, the person and the number scale interact in interesting ways, i.e. they need to be ranked in order to produce the correct output in cases of conflicting preferences; second, the general ranking seems to be reversed in one particular context. This pattern poses a challenge to existing analysis of scale-driven agreement. I propose a Harmonic Grammar-based analysis where an argument’s prominence is quantified. In this way, all interactions are correctly derived. The apparent exceptions fall out automatically as cumulative effects.
Performance comparison of feedforward neural networks applied to stream flow series forecasting

Feedforward neural networks are those in which the input signal follows only one direction: from the input layer to the output layer, passing through all the hidden layers, in contrast with recurrent architectures. The main examples of this class are the Multilayer Perceptron (MLP) and the Radial Basis Function network (RBF). Recently, other model of this type has received significant attention: Extreme Learning Machines (ELMs). Nonlinear mapping problems, like time series forecasting, can be adequately solved by these methods. In this work, the aforementioned architectures are employed in the prediction of monthly seasonal streamflow series of important Brazilian hydroelectric plants, for different forecasting horizons. The results showed that all the proposals are efficient in solving the task, but, interestingly, the RBF achieved the best performance in most cases. However, the computational cost associated with the training process of the ELM is much smaller than the others.
Emotion-Specific Facial Activation Maps Based on Infrared Thermal Image Sequences

Research on emotion recognition has recently started to gain increased attention.Inner emotions or thought activity can be determined by analyzing facial expression, behavioral responses, audio, and physiological signals.Facial expression is now recognized as an important form of non-verbal interaction.In this paper, emotion-specific activation maps were constructed to establish infrared thermal facial image sequences as an alternative approach to the determination of the correlation between emotional triggers and changes in facial temperature.During the testing process, data stored in the International Affective Picture System were used to create emotional clips that triggered three different types of emotion in the subjects, and their infrared thermal facial image sequences were simultaneously recorded.For processing, an image calibration protocol was first employed to reduce the variance produced by irregular micro-shifts in the faces of the subjects, followed by independent component analysis and statistical analysis protocols to create the facial emotional activation maps.The test results showed that the problem of selecting local regions when analyzing frame temperature had been resolved.The emotion-specific facial activation maps provide visualized results that facilitate the observation and understanding of information.
Exploring Blockchain Value Creation: The Case of the Car Ecosystem

Blockchain is expected to create a variety of new opportunities for businesses.Yet, little is known about how companies can exploit business value from the technology.However, without a clear understanding of how, and corresponding adaption of business practices, the realization of value is doomed to failure.Hence, we contribute to this gap by analyzing and explicating the specificities of value creation from blockchain in the ecosystem of a car.In the course of an exploratory case analysis we conducted interviews and workshops with industry and blockchain experts from five diverse stakeholder groups.In brief, we provide early evidence that (1) blockchain enables value creation through: Distributed Product Innovation, Controlled Customer Intimacy and Shared Operational Efficiency.Further, (2) we derive guidelines and discuss learnings for other businesses aiming to leverage value from blockchain technology.
Warping Deixis

When engaged in communication, people often rely on pointing gestures to refer to out-of-reach content. However, observers frequently misinterpret the target of a pointing gesture. Previous research suggests that to perform a pointing gesture, people place the index finger on or close to a line connecting the eye to the referent, while observers interpret pointing gestures by extrapolating the referent using a vector defined by the arm and index finger. In this paper we present Warping Deixis, a novel approach to improving the perception of pointing gestures and facilitate communication in collaborative Extended Reality environments. By warping the virtual representation of the pointing individual, we are able to match the pointing expression to the observer's perception. We evaluated our approach in a co-located side by side virtual reality scenario. Results suggest that our approach is effective in improving the interpretation of pointing gestures in shared virtual environments.
Instructor Activity Recognition through Deep Spatiotemporal Features and Feedforward Extreme Learning Machines

Human action recognition has the potential to predict the activities of an instructor within the lecture room. Evaluation of lecture delivery can help teachers analyze shortcomings and plan lectures more effectively. However, manual or peer evaluation is time-consuming, tedious and sometimes it is difficult to remember all the details of the lecture. Therefore, automation of lecture delivery evaluation significantly improves teaching style. In this paper, we propose a feedforward learning model for instructor’s activity recognition in the lecture room. The proposed scheme represents a video sequence in the form of a single frame to capture the motion profile of the instructor by observing the spatiotemporal relation within the video frames. First, we segment the instructor silhouettes from input videos using graph-cut segmentation and generate a motion profile. These motion profiles are centered by obtaining the largest connected components and normalized. Then, these motion profiles are represented in the form of feature maps by a deep convolutional neural network. Then, an extreme learning machine (ELM) classifier is trained over the obtained feature representations to recognize eight different activities of the instructor within the classroom. For the evaluation of the proposed method, we created an instructor activity video (IAVID-1) dataset and compared our method against different state-of-the-art activity recognition methods. Furthermore, two standard datasets, MuHAVI and IXMAS, were also considered for the evaluation of the proposed scheme.
δ-Generalized Labeled Multi-Bernoulli Simultaneous Localization and Mapping with an Optimal Kernel-Based Particle Filtering Approach

Under realistic environmental conditions, heuristic-based data association and map management routines often result in divergent map and trajectory estimates in robotic Simultaneous Localization And Mapping (SLAM). To address these issues, SLAM solutions have been proposed based on the Random Finite Set (RFS) framework, which models the map and measurements such that the usual requirements of external data association routines and map management heuristics can be circumvented and realistic sensor detection uncertainty can be taken into account. Rao–Blackwellized particle filter (RBPF)-based RFS SLAM solutions have been demonstrated using the Probability Hypothesis Density (PHD) filter and subsequently the Labeled Multi-Bernoulli (LMB) filter. In multi-target tracking, the LMB filter, which was introduced as an efficient approximation to the computationally expensive δ -Generalized LMB ( δ -GLMB) filter, converts its representation of an LMB distribution to δ -GLMB form during the measurement update step. This not only results in a loss of information yielding inferior results (compared to the δ -GLMB filter) but also fails to take computational advantages in parallelized implementations possible with RBPF-based SLAM algorithms. Similar to state-of-the-art random vector-valued RBPF solutions such as FastSLAM and MH-FastSLAM, the performances of all RBPF-based SLAM algorithms based on the RFS framework also diverge from ground truth over time due to random sampling approaches, which only rely on control noise variance. Further, the methods lose particle diversity and diverge over time as a result of particle degeneracy. To alleviate this problem and further improve the quality of map estimates, a SLAM solution using an optimal kernel-based particle filter combined with an efficient variant of the δ -GLMB filter ( δ -GLMB-SLAM) is presented. The performance of the proposed δ -GLMB-SLAM algorithm, referred to as δ -GLMB-SLAM2.0, was demonstrated using simulated datasets and a section of the publicly available KITTI dataset. The results suggest that even with a limited number of particles, δ -GLMB-SLAM2.0 outperforms state-of-the-art RBPF-based RFS SLAM algorithms.
Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers

Many well-trained Convolutional Neural Network~(CNN) models have now been released online by developers for the sake of effortless reproducing. In this paper, we treat such pre-trained networks as teachers and explore how to learn a target student network for customized tasks, using multiple teachers that handle different tasks. We assume no human-labelled annotations are available, and each teacher model can be either single- or multi-task network, where the former is a degenerated case of the latter. The student model, depending on the customized tasks, learns the related knowledge filtered from the multiple teachers, and eventually masters the complete or a subset of expertise from all teachers. To this end, we adopt a layer-wise training strategy, which entangles the student's network block to be learned with the corresponding teachers. As demonstrated on several benchmarks, the learned student network achieves very promising results, even outperforming the teachers on the customized tasks.
Time Does Not Heal All Wounds: A Longitudinal Analysis of Security-Mechanism Support in Mobile Browsers

Recent market share statistics show that mobile device traffic has overtaken that of traditional desktop computers.Users spend an increasing amount of time on their smartphones and tablets, while the web continues to be the platform of choice for delivering new applications to users.In this environment, it is necessary for web applications to utilize all the tools at their disposal to protect mobile users against popular web application attacks.In this paper, we perform the first study of the support of popular web-application security mechanisms (such as the Content-Security Policy, HTTP Strict Transport Security, and Referrer Policy) across mobile browsers.We design 395 individual tests covering 8 different security mechanisms, and utilize them to evaluate the security-mechanism support in the 20 most popular browser families on Android.Moreover, by collecting and testing browser versions from the last seven years, we evaluate a total of 351 unique browser versions against the aforementioned tests, collecting more than 138K test results.By analyzing these results, we find that, although mobile browsers generally support more security mechanisms over time, not all browsers evolve in the same way.We discover popular browsers, with millions of downloads, which do not support the majority of the tested mechanisms, and identify design choices, followed by the majority of browsers, which leave hundreds of popular websites open to clickjacking attacks.Moreover, we discover the presence of multi-year vulnerability windows between the time when popular websites start utilizing a security mechanism and when mobile browsers enforce it.Our findings highlight the need for continuous security testing of mobile web browsers, as well as server-side frameworks which can adapt to the level of security that each browser can guarantee.
DEVELOPING THE E-COMMERCE MODEL A CONSUMER TO CONSUMER USING BLOCKCHAIN NETWORK TECHNIQUE

E-commerce has increased recently because of the development of the internet and has become a newconcept that is applicable to trade transaction and services providing, using information technology.This is known as e-commerce thatis a means of communicating information،products or services through technical tools.This research proposed model is able to take advantage of Blockchain technology to develop e-commerce especially consumer toconsumer.The proposed model adds some advantages to ecommerce operations, and the possibility of developing them to reach a high percentage of profits by using blockchain technology which led to verify the information of products offered for sale.In addition, to the possibility of distributing feedback to all Blockchain users, through which it develops the mechanism of trust and cooperation between consumers, it is considered a reference point to explore the behaviour of commercial consumers which is stored in the data file of consumers.This model facilitates business processes between consumer andconsumer, eliminates the central role of large business companies in controlling and setting restrictions, and to the development and expansion of this type of trade.
A Novel Effectively Optimized One-Stage Network for Object Detection in Remote Sensing Imagery

With great significance in military and civilian applications, the topic of detecting small and densely arranged objects in wide-scale remote sensing imagery is still challenging nowadays. To solve this problem, we propose a novel effectively optimized one-stage network (NEOON). As a fully convolutional network, NEOON consists of four parts: Feature extraction, feature fusion, feature enhancement, and multi-scale detection. To extract effective features, the first part has implemented bottom-up and top-down coherent processing by taking successive down-sampling and up-sampling operations in conjunction with residual modules. The second part consolidates high-level and low-level features by adopting concatenation operations with subsequent convolutional operations to explicitly yield strong feature representation and semantic information. The third part is implemented by constructing a receptive field enhancement (RFE) module and incorporating it into the fore part of the network where the information of small objects exists. The final part is achieved by four detectors with different sensitivities accessing the fused features, all four parallel, to enable the network to make full use of information of objects in different scales. Besides, the Focal Loss is set to enable the cross entropy for classification to solve the tough problem of class imbalance in one-stage methods. In addition, we introduce the Soft-NMS to preserve accurate bounding boxes in the post-processing stage especially for densely arranged objects. Note that the split and merge strategy and multi-scale training strategy are employed in training. Thorough experiments are performed on ACS datasets constructed by us and NWPU VHR-10 datasets to evaluate the performance of NEOON. Specifically, 4.77% and 5.50% improvements in mAP and recall, respectively, on the ACS dataset as compared to YOLOv3 powerfully prove that NEOON can effectually improve the detection accuracy of small objects in remote sensing imagery. In addition, extensive experiments and comprehensive evaluations on the NWPU VHR-10 dataset with 10 classes have illustrated the superiority of NEOON in the extraction of spatial information of high-resolution remote sensing images.
Semi-supervised encoding for outlier detection in clinical observation data

Electronic Health Record (EHR) data often include observation records that are unlikely to represent the “truth” about a patient at a given clinical encounter. Due to their high throughput, examples of such implausible observations are frequent in records of laboratory test results and vital signs. Outlier detection methods can offer low-cost solutions to flagging implausible EHR observations. This article evaluates the utility of a semi-supervised encoding approach (super-encoding) for constructing non-linear exemplar data distributions from EHR observation data and detecting non-conforming observations as outliers. Two hypotheses are tested using experimental design and non-parametric hypothesis testing procedures: (1) adding demographic features (e.g., age, gender, race/ethnicity) can increase precision in outlier detection, (2) sampling small subsets of the large EHR data can increase outlier detection by reducing noise-to-signal ratio. The experiments involved applying 492 encoder configurations (involving different input features, architectures, sampling ratios, and error margins) to a set of 30 datasets EHR observations including laboratory tests and vital sign records extracted from the Research Patient Data Registry (RPDR) from Partners HealthCare. Results are obtained from (30 × 492) 14,760 encoders. The semi-supervised encoding approach (super-encoding) outperformed conventional autoencoders in outlier detection. Adding age of the patient at the observation (encounter) to the baseline encoder that only included observation value as the input feature slightly improved outlier detection. Top-nine performing encoders are introduced. The best outlier detection performance was from a semi-supervised encoder, with observation value as the single feature and a single hidden layer, built on one percent of the data and one percent reconstruction error. At least one encoder configurations had a Youden's J index higher than 0.9999 for all 30 observation types. Given the multiplicity of distributions for a single observation in EHR data (i.e., same observation represented with different names or units), as well as non-linearity of human observations, encoding offers huge promises for outlier detection in large-scale data repositories. https://github.com/hestiri/superencoder
Agreement in Histological Assessment of Mitotic Activity Between Microscopy and Digital Whole Slide Images Informs Conversion for Clinical Diagnosis

Validating digital pathology as substitute for conventional microscopy in diagnosis remains a priority to assure effectiveness. Intermodality concordance studies typically focus on achieving the same diagnosis by digital display of whole slide images and conventional microscopy. Assessment of discrete histological features in whole slide images, such as mitotic figures, has not been thoroughly evaluated in diagnostic practice. To further gauge the interchangeability of conventional microscopy with digital display for primary diagnosis, 12 pathologists examined 113 canine naturally occurring mucosal melanomas exhibiting a wide range of mitotic activity. Design reflected diverse diagnostic settings and investigated independent location, interpretation, and enumeration of mitotic figures. Intermodality agreement was assessed employing conventional microscopy (CM40×), and whole slide image specimens scanned at 20× (WSI20×) and at 40× (WSI40×) objective magnifications. An aggregate 1647 mitotic figure count observations were available from conventional microscopy and whole slide images for comparison. The intraobserver concordance rate of paired observations was 0.785 to 0.801; interobserver rate was 0.784 to 0.794. Correlation coefficients between the 2 digital modes, and as compared to conventional microscopy, were similar and suggest noninferiority among modalities, including whole slide image acquired at lower 20× resolution. As mitotic figure counts serve for prognostic grading of several tumor types, including melanoma, 6 of 8 pathologists retrospectively predicted survival prognosis using whole slide images, compared to 9 of 10 by conventional microscopy, a first evaluation of whole slide image for mitotic figure prognostic grading. This study demonstrated agreement of replicate reads obtained across conventional microscopy and whole slide images. Hence, quantifying mitotic figures served as surrogate histological feature with which to further credential the interchangeability of whole slide images for primary diagnosis.