Reviewerly/exHarmony huggingface dataset:
Relative deviation learning bounds and generalization with unbounded loss functions

We present an extensive analysis of relative deviation bounds, including detailed proofs of two-sided inequalities and their implications. We also give detailed proofs of two-sided generalization bounds that hold in the general case of unbounded loss functions, under the assumption that a moment of the loss is bounded. We then illustrate how to apply these results in a sample application: the analysis of importance weighting.
Optical neural networks based on optical fiber-communication system

In this work, we propose a novel design for optical neural network system based on reservoir computing. The optical neurons in the system consist of directional couplers, optical fibers, and optical amplifiers. The nonlinear activation function of optical neuron can be obtained by the optical amplifiers. Mach–Zehnder phase modulators and the directional couplers are used to build the readout matrix in the output layer. The performance of the system is studied by a commercial software of optical fiber telecommunication system. The results show that the randomly connected optical neuron networks of the reservoir computing can provide a better performance.
FlowCon

An increasing number of companies are using data analytics to improve their products, services, and business processes. However, learning knowledge effectively from massive data sets always involves nontrivial computational resources. Most businesses thus choose to migrate their hardware needs to a remote cluster computing service (e.g., AWS) or to an in-house cluster facility which is often run at its resource capacity. In such scenarios, where jobs compete for available resources utilizing resources effectively to achieve high-performance data analytics becomes desirable. Although cluster resource management is a fruitful research area having made many advances (e.g., YARN, Kubernetes), few projects have investigated how further optimizations can be made specifically for training multiple machine learning (ML) / deep learning (DL) models. In this work, we introduce FlowCon, a system which is able to monitor loss functions of ML/DL jobs at runtime, and thus to make decisions on resource configuration elastically. We present a detailed design and implementation of FlowCon, and conduct intensive experiments over various DL models. Our experimental results show that FlowCon can strongly improve DL job completion time and resource utilization efficiency, compared to existing approaches. Specifically, FlowCon can reduce the completion time by up to 42.06% for a specific job without sacrificing the overall makespan, in the presence of various DL job workloads.
Self-paced learning with privileged information

Self-paced learning (SPL) is a powerful learning framework, where data from easy ones to more complex ones are gradually involved in the learning process. However, SPL is unable to exploit prior knowledge, so it is prone to overfitting. To alleviate this problem, we propose a framework called self-paced learning with privileged information (SPL+), where privileged information is introduced as prior knowledge to guide the curriculum learned by SPL. Specifically, the learning process using weighted privileged information and the curriculum learning process guided by privileged information are iteratively performed until the final mature curriculum guided by privileged information is learned. As this curriculum learning process can gradually grasp the easy to hard knowledge under the guidance of the robust high level privileged information, a more reliable model can be learned. Moreover, our SPL+ is a generalized framework, which is applicable to various problems. Comprehensive experiments demonstrate that our SPL+ outperforms the conventional SPL based method for three applications including action recognition, scene recognition and handwritten digit recognition.
"Should I Worry?" A Cross-Cultural Examination of Account Security Incident Response

Digital security technology is able to identify and prevent many threats to users accounts.However, some threats remain that, to provide reliable security, require human intervention: e.g., through users paying attention to warning messages or completing secondary authentication procedures.While prior work has broadly explored people's mental models of digital security threats, we know little about users' precise, in-themoment response process to in-the-wild threats.In this work, we conduct a series of qualitative interviews (n=67) with users who had recently experienced suspicious login incidents on their real Facebook accounts in order to explore this process of account security incident response.We find a common process across participants from five countries -with differing online and offline cultures -allowing us to identify areas for future technical development to best support user security.We provide additional insights on the unique nature of incident-response information seeking, known attacker threat models, and lessons learned from a large, cross-cultural qualitative study of digital security.
Tukey-Inspired Video Object Segmentation

We investigate the problem of strictly unsupervised video object segmentation, i.e., the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset. We find foreground objects in low-level vision data using a John Tukey-inspired measure of "outlierness." This Tukey-inspired measure also estimates the reliability of each data source as video characteristics change (e.g., a camera starts moving). The proposed method achieves state-of-the-art results for strictly unsupervised video object segmentation on the challenging DAVIS dataset. Finally, we use a variant of the Tukey-inspired measure to combine the output of multiple segmentation methods, including those using supervision during training, runtime, or both. This collectively more robust method of segmentation improves the Jaccard measure of its constituent methods by as much as 28%.
Generative Adversarial Networks: Recent Developments

In traditional generative modeling, good data representation is very often a base for a good machine learning model. It can be linked to good representations encoding more explanatory factors that are hidden in the original data. With the invention of Generative Adversarial Networks (GANs), a subclass of generative models that are able to learn representations in an unsupervised and semi-supervised fashion, we are now able to adversarially learn good mappings from a simple prior distribution to a target data distribution. This paper presents an overview of recent developments in GANs with a focus on learning latent space representations.
An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal

Copy-move image forgery detection has become a significant research subject in multimedia forensics and security due to its widespread use and its hard detection. In this type of image forging, a region of the image is copied and pasted elsewhere in the same image. Keypoint-based forgery detection approaches use local visual features to identify the duplicated regions. The performance of keypoint-based methods degrades in those cases when the duplicated regions are near to each other and when handling highly textured area. The clustering algorithm that mostly used in keypoint- based methods suffer from high complexity. In this paper, an improved approach for keypoint- based copy-move forgery detection is proposed. The proposed method is based on density-based clustering and Guaranteed Outlier Removal algorithm. Experimental results carried out on various benchmark datasets exhibit that the proposed method surpasses other similar state-of-the-art techniques under different challenging conditions, such as geometric attacks, post-processing attacks, and multiple cloning.
Deep Learning-based Sentiment Analysis: Establishing Customer Dimension as the Lifeblood of Business Management

‘First mover advantage does not go to the first company that launches, it goes to the first company that scales’ (Reid Hoffman, co-founder of LinkedIn, ScaleIt, 2016 ). M. Andreesen defines scale-up as a company that has identified its product/market fit and has reached notable proofs of market traction (e.g., profitability, revenues, active users, registered users, market demand). It has been previously established that five interdependent core dimensions can be used to evaluate scale-up: customer, product, team, business model and financials. Most of the business failures result from falling apart of one or more of these dimensions with others. We in our article discuss the ‘customer dimension’ as the essence of scaling up. For running a profitable business, it is crucial to evaluate customers’ reviews, like their perception and expectation from the product and services in terms of service quality, deliverables, staff and management practices and pricing. This will increase customer satisfaction which will further boost the demand and popularity of the brand and business. The customers today are very vocal in sharing their experiences through social media blogs, channels, review sites and so on. These experiences need to be decrypted by the management to understand the customer’s point of view, their apprehensions and expectations. Sentiment analysis is one of the best ways to tap customer feedback. The usual way of analysis involves the bag of words model using ngrams. A more refined version is an ontology-based analysis. Research has also been done using the radial basis function kernel for support vector machines (SVMs) (for recognizing polarity of sentiments). Naïve Bayes algorithms have also been used in some sentiment studies along with linear kernel SVM classifiers. But we realized during the literature review that it is not enough to just classify the text into positive and negative or a few more categories. It is even more important to know which topics are being discussed by the customers, their intention behind the message and further the need to classify them as complaints, suggestions, appreciation or query and so on. Thus, we in our article discuss the solution to all these types of analyses using ‘deep sentiment analysis’. We discuss the case of a few current social topics and introduce ‘recurrent neural networks’ (RNNs) and ‘convolutional neural networks’ (CNNs) for sentiment and intent analysis. These advanced analytical techniques will help in the ‘pivoting’ of businesses by taking desired actions such as improving product quality, improving quality of services and increasing quality checks in areas with maximum negative reviews. This eventually will help start-up ventures in improving customer experience and gradually help in the scaling of ventures.
Issues in Training a Convolutional Neural Network Model for Image Classification

Convolutional neural networks (CNN) are a boon to image classification algorithms as it can learn highly abstract features and work with less parameter. Overfitting, exploding gradient, and class imbalance are the major challenges while training the model using CNN. These issues can diminish the performance of the model. Proper understanding and use of corrective measures can substantially prevent the model from these issues and can increase the efficiency of the model. In this paper the conceptual understanding of the basic CNN model along with its key layers is provided. The paper summarizes the results of training the deep learning model using CNN on publicly available datasets of cats and dogs. Finally the paper discusses various methods such as data augmentation, regularization, dropout, etc. to prevent the CNN model from overfitting problem. The paper will also help beginners to have a broad comprehension of CNN and motivate them to venture in this field.
Research priorities in immersive learning technology: the perspectives of the iLRN community

This paper presents the perspectives of the immersive learning research network community on the relevance of various challenges to the adoption of immersive learning technology, along three dimensions: access, content production, and deployment. Using a previously validated questionnaire, we surveyed this community of 622 researchers and practitioners during the summer of 2018, attaining 54 responses. By ranking the challenges individually and within each dimension, the results point towards higher relevance being placed on aspects that link immersive environments with learning management systems and pedagogical tasks, alongside aspects that empower non-technical users (educational actors) to produce interactive stories, objects, and characters.
Chocolatine: Outage Detection for Internet Background Radiation

The Internet is a complex ecosystem composed of thousands of Autonomous Systems (ASs) operated by independent organizations; each AS having a very limited view outside its own network. These complexities and limitations impede network operators to finely pinpoint the causes of service degradation or disruption when the problem lies outside of their network. In this paper, we present Chocolatine, a solution to detect remote connectivity loss using Internet Background Radiation (IBR) through a simple and efficient method. IBR is unidirectional unsolicited Internet traffic, which is easily observed by monitoring unused address space. IBR features two remarkable properties: it is originated worldwide, across diverse ASs, and it is incessant. We show that the number of IP addresses observed from an AS or a geographical area follows a periodic pattern. Then, using Seasonal ARIMA to statistically model IBR data, we predict the number of IPs for the next time window. Significant deviations from these predictions indicate an outage. We evaluated Chocolatine using data from the UCSD Network Telescope, operated by CAIDA, with a set of documented outages. Our experiments show that the proposed methodology achieves a good trade-off between true-positive rate (90%) and false-positive rate (2%) and largely outperforms CAIDA's own IBR-based detection method. Furthermore, performing a comparison against other methods, i.e., with BGP monitoring and active probing, we observe that Chocolatine shares a large common set of outages with them in addition to many specific outages that would otherwise go undetected.
Inverse Risk-Sensitive Reinforcement Learning

This work addresses the problem of inverse reinforcement learning in Markov decision processes where the decision-making agent is risk-sensitive. In particular, a risk-sensitive reinforcement learning algorithm with convergence guarantees that makes use of coherent risk metrics and models of human decision-making which have their origins in behavioral psychology and economics is presented. The risk-sensitive reinforcement learning algorithm provides the theoretical underpinning for a gradient-based inverse reinforcement learning algorithm that seeks to minimize a loss function defined on the observed behavior. It is shown that the gradient of the loss function with respect to the model parameters is well defined and computable via a contraction map argument. Evaluation of the proposed technique is performed on a Grid World example, a canonical benchmark problem.
Semantic Segmentation Refinement by Monte Carlo Region Growing of High Confidence Detections

The semantic segmentation produced by most state-of-the-art methods does not show satisfactory adherence to object boundaries. Methods such as fully-connected conditional random fields (CRFs) can significantly refine segmentation predictions. However, they rely on supervised parameter optimization that depends upon specific datasets and predictor modules. We propose an unsupervised method for semantic segmentation refinement that takes as input the confidence scores generated by a segmentation network and re-labels pixels with low confidence levels. More specifically, a region growing mechanism aggregates these pixels to neighboring areas with high confidence scores and similar appearance. To minimize the impact of high-confidence prediction errors, our algorithm performs multiple growing steps by Monte Carlo sampling initial seeds in high-confidence regions. Our method provides both running time and segmentation improvements comparable to state-of-the-art refinement approaches for semantic segmentation, as demonstrated by evaluations on multiple publicly available benchmark datasets.
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis

Cross-domain synthesizing realistic faces to learn deep models has attracted increasing attention for facial expression analysis as it helps to improve the performance of expression recognition accuracy despite having small number of real training images. However, learning from synthetic face images can be problematic due to the distribution discrepancy between low-quality synthetic images and real face images and may not achieve the desired performance when the learned model applies to real world scenarios. To this end, we propose a new attribute guided face image synthesis to perform a translation between multiple image domains using a single model. In addition, we adopt the proposed model to learn from synthetic faces by matching the feature distributions between different domains while preserving each domain's characteristics. We evaluate the effectiveness of the proposed approach on several face datasets on generating realistic face images. We demonstrate that the expression recognition performance can be enhanced by benefiting from our face synthesis model. Moreover, we also conduct experiments on a near-infrared dataset containing facial expression videos of drivers to assess the performance using in-the-wild data for driver emotion recognition.
Key Stakeholders’ Value Propositions for Feature Selection in Software-Intensive Products: An Industrial Case Study

Numerous software companies are adopting value-based decision making. However, what does value mean for key stakeholders making decisions? How do different stakeholder groups understand value? Without an explicit understanding of what value means, decisions are subject to ambiguity and vagueness, which are likely to bias them. This case study provides an in-depth analysis of key stakeholders' value propositions when selecting features for a large telecommunications company's software-intensive product. Stakeholders' value propositions were elicited via interviews, which were analyzed using Grounded Theory coding techniques (open and selective coding). Thirty-six value propositions were identified and classified into six dimensions: customer value, market competitiveness, economic value/profitability, cost efficiency, technology & architecture, and company strategy. Our results show that although propositions in the customer value dimension were those mentioned the most, the concept of value for feature selection encompasses a wide range of value propositions. Moreover, stakeholder groups focused on different and complementary value dimensions, calling to the importance of involving all key stakeholders in the decision making process. Although our results are particularly relevant to companies similar to the one described herein, they aim to generate a learning process on value-based feature selection for practitioners and researchers in general.
Use of blockchains for secure binding of metadata in military applications of IoT

Blockchains offer an interesting solution to some of the security challenges encountered in military applications. They are particularly attractive in the scenarios, where it is difficult or even impossible to establish a common root of trust, e.g., in the context of civil-military collaboration, where military organizations need to build trusted information exchange infrastructure with various types of civilian governmental and non-governmental organizations, local communities, commercial companies and private persons. In our work, we discuss how blockchain can be used to store metadata describing information collected from the IoT devices owned by the federation members as well as crowdsourced from sensors belonging to private users. We present a high level architecture for a metadata binding solution using blockchains compliant with STANAG 4774 and 4778. We also describe a concrete technical solution based on Hyperledger Fabric and some of the open issues.
Multi-label clinical document classification: Impact of label-density

The goal of this work is the classification of Electronic Health Records using Natural Language Techniques. Electronic Health Records (EHRs) convey valuable clinical information, as diagnoses and patient conditions. We explore several Deep Learning classification models for assigning multiple ICD codes to clinical documents. Within the framework of data mining, the aim of multi-label classification is to associate each instance with a set of labels. The multi-label classification is typically carried out based on multiple independent classifiers, in the so-called binary relevance learning approach. Nevertheless, diseases tend to be co-related, independent classifiers are unable to model relationships and do not guarantee the consistency of the predicted label-set. To tackle this, we investigate three Neural Network architectures. We study models that are capable of capturing and modeling label dependencies on the output layer. Moreover, learning from data with low label-density is an inherent challenge in multi-label classification. Thorough experiments were conducted to assess each architecture under different scenarios, varying the language, amount of data and label-density. The results showed that the Bi-GRU model outperform the DNN and both overcome the baseline (BLR). We observed better results with MIMIC than with Osakidetza corpus. Experimental results showed that as the label-density decreases the prediction task becomes harder. It seems that label-density is very much related to the learning ability of the neural networks and another important factor that affects the inference is the amount of training data. The contributions of this work are: (a) a comparison among three classification approaches based on Neural Networks on data sets in English and Spanish to cope with the multi-label classification problem and (b) the study of the impact of label-density in prediction capabilities in the multi-label context.
Facets of Distribution Identities in Probabilistic Team Semantics

We study probabilistic team semantics which is a semantical framework allowing the study of logical and probabilistic dependencies simultaneously. We examine and classify the expressive power of logical formalisms arising by different probabilistic atoms such as conditional independence and different variants of marginal distribution equivalences. We also relate the framework to the first-order theory of the reals and apply our methods to the open question on the complexity of the implication problem of conditional independence.