Reviewerly/exHarmony huggingface dataset:
“It’s a girl thing”

Cultural taboos and limiting social norms make it challenging to communicate and teach about menstrual health in India. We present findings from an inquiry of current approaches used to educate adolescents about menstruation, examining the perspectives of young adults, parents, teachers, social workers, and health professionals for identifying design opportunities and potential for impact. Our findings from the content analysis of education and training materials in use, an online survey of 391 adults, 52 interviews, and 2 focus groups indicate that although detailed and descriptive information materials are available for use, there exists a disconnect between parents’ and teachers’ expectations regarding who will introduce these topics to adolescents. We also highlight a clear difference in attitudes regarding who must be taught, how, where, and at what stages. Finally, we articulate factors that shape access and receptivity to this knowledge and engage with the lens of feminist HCI to discuss sociotechnical implications for the design of menstrual health education initiatives.
Cross Pixel Optical-Flow Similarity for Self-supervised Learning

We propose a novel method for learning convolutional neural image representations without manual supervision. We use motion cues in the form of optical-flow, to supervise representations of static images. The obvious approach of training a network to predict flow from a single image can be needlessly difficult due to intrinsic ambiguities in this prediction task. We instead propose a much simpler learning goal: embed pixels such that the similarity between their embeddings matches that between their optical-flow vectors. At test time, the learned deep network can be used without access to video or flow information and transferred to tasks such as image classification, detection, and segmentation. Our method, which significantly simplifies previous attempts at using motion for self-supervision, achieves state-of-the-art results in self-supervision using motion cues, and is overall state of the art in self-supervised pre-training for semantic image segmentation, as demonstrated on standard benchmarks.
Video Object Segmentation with Language Referring Expressions

Most state-of-the-art semi-supervised video object segmentation methods rely on a pixel-accurate mask of a target object provided for the first frame of a video. However, obtaining a detailed segmentation mask is expensive and time-consuming. In this work we explore an alternative way of identifying a target object, namely by employing language referring expressions. Besides being a more practical and natural way of pointing out a target object, using language specifications can help to avoid drift as well as make the system more robust to complex dynamics and appearance variations. Leveraging recent advances of language grounding models designed for images, we propose an approach to extend them to video data, ensuring temporally coherent predictions. To evaluate our approach we augment the popular video object segmentation benchmarks, $${\text {DAVIS}}_{{16}}$$ and $${\text {DAVIS}}_{{17}}$$ with language descriptions of target objects. We show that our language-supervised approach performs on par with the methods which have access to a pixel-level mask of the target object on $${\text {DAVIS}}_{{16}}$$ and is competitive to methods using scribbles on the challenging $${\text {DAVIS}}_{{17}}$$ dataset.
Knowledge Management Strategies and Processes in Agile Software Development: A Systematic Literature Review

Knowledge-intensive companies that adopt Agile Software Development (ASD) rely on efficient implementation of Knowledge Management (KM) strategies to promote different Knowledge Processes (KPs) to gain competitive advantage. This study aims to explore how companies that adopt ASD implement KM strategies utilizing practices that promote the KPs in the different organizational layers. Through a systematic literature review, we analyzed 32 primary studies, selected by automated search and snowballing in the extant literature. To analyze the data, we applied narrative synthesis. Most of the identified KM practices implement personalization strategies (81%), supported by codification (19%). Our review shows that the primary studies do not report KM practices in the strategic layer and two of them in the product portfolio layer; on the other hand, in the project layer, the studies report 33 practices that implement personalization strategy, and seven practices that implement codification. KM strategies in ASD promote mainly the knowledge transfer process with practices that stimulates social interaction to share tacit knowledge in the project layer. As a result of using informal communication, a significant amount of knowledge can be lost or not properly transferred to other individuals and, instead of propagating the knowledge, it remains inside a few individuals’ minds.
Latent Filter Scaling for Multimodal Unsupervised Image-To-Image Translation

In multimodal unsupervised image-to-image translation tasks, the goal is to translate an image from the source domain to many images in the target domain. We present a simple method that produces higher quality images than current state-of-the-art while maintaining the same amount of multimodal diversity. Previous methods follow the unconditional approach of trying to map the latent code directly to a full-size image. This leads to complicated network architectures with several introduced hyperparameters to tune. By treating the latent code as a modifier of the convolutional filters, we produce multimodal output while maintaining the traditional Generative Adversarial Network (GAN) loss and without additional hyperparameters. The only tuning required by our method controls the tradeoff between variability and quality of generated images. Furthermore, we achieve disentanglement between source domain content and target domain style for free as a by-product of our formulation. We perform qualitative and quantitative experiments showing the advantages of our method compared with the state-of-the art on multiple benchmark image-to-image translation datasets.
Low-Resolution Face Recognition

Whilst recent face-recognition (FR) techniques have made significant progress on recognising constrained high-resolution web images, the same cannot be said on natively unconstrained low-resolution images at large scales. In this work, we examine systematically this under-studied FR problem, and introduce a novel Complement Super-Resolution and Identity (CSRI) joint deep learning method with a unified end-to-end network architecture. We further construct a new large-scale dataset TinyFace of native unconstrained low-resolution face images from selected public datasets, because none benchmark of this nature exists in the literature. With extensive experiments we show there is a significant gap between the reported FR performances on popular benchmarks and the results on TinyFace, and the advantages of the proposed CSRI over a variety of state-of-the-art FR and super-resolution deep models on solving this largely ignored FR scenario. The TinyFace dataset is released publicly at: https://qmul-tinyface.github.io/ .
The Capacity of Private Information Retrieval With Eavesdroppers

We consider the problem of private information retrieval (PIR) with colluding servers and eavesdroppers (abbreviated as ETPIR). The ETPIR problem is comprised of K messages and N servers where each server stores all K messages, a user who wants to retrieve one of the K messages without revealing the desired message index to any set of T colluding servers, and an eavesdropper who can listen to the queries and answers of any E servers but is prevented from learning any information about the messages. The information theoretic capacity of ETPIR is defined to be the maximum number of desired message symbols retrieved privately per information symbol downloaded. We show that the capacity of ETPIR is C = (1 - (E/N))(1 + (T - E/N - E) + · · · + ((T - E/N - E))K-1)-1 when E <; T, and C = (1 - (E/N)) when E ≥ T. To achieve the capacity, the servers need to share a common random variable (independent of the messages), and its size must be at least (E/N) · (1/C) symbols per message symbol. Otherwise, with less amount of shared common randomness, ETPIR is not feasible and the capacity reduces to zero. An interesting observation is that the ETPIR capacity expression takes different forms in two regimes. When E <; T, the capacity equals the inverse of a sum of a geometric series with K terms and decreases with K; this form is typical for capacity expressions of PIR. When E ≥ T, the capacity does not depend on K, a typical form for capacity expressions of SPIR (symmetric PIR, which further requires data-privacy, i.e., the user learns no information about other undesired messages); the capacity does not depend on T either. In addition, the ETPIR capacity result includes multiple previous PIR and SPIR capacity results as special cases.
A Fuzzy Adaptive Approach to Decoupled Visual Servoing for a Wheeled Mobile Robot

To address the performance bottleneck for image-based visual servoing (IBVS), it is necessary to have appropriate servoing control laws, increased accuracy for image feature detection, and minimal approximation errors. This article proposes a fuzzy adaptive method for decoupled IBVS that allows the efficient control of a wheeled mobile robot (WMR). To address the under-actuated dynamics of the WMR, a decoupled controller is used and translation and rotation are decoupled by using two independent servoing gains, instead of the single servoing gain that is used for traditional IBVS. To reduce the effect of image noise, this article develops an improved bagging method for the decoupled controller that calculates the inverse kinematics and does not use the Moore-Penrose pseudoinverse method. To improve convergence, improved Q-learning is used to adaptively adjust the mixture parameter for the image Jacobian matrix (IQ-IBVS). This allows the mixture parameter can be adjusted while the robot moves under the influence of servo control. A fuzzy method is used to tune the learning rate for the IQ-IBVS method, which ensures effective learning. The results of simulation and experiments show that the proposed method performs better than other methods, in terms of convergence.
Fidelius: Protecting User Secrets from Compromised Browsers

Users regularly enter sensitive data, such as passwords, credit card numbers, or tax information, into the browser window. While modern browsers provide powerful client-side privacy measures to protect this data, none of these defenses prevent a browser compromised by malware from stealing it. In this work, we present Fidelius, a new architecture that uses trusted hardware enclaves integrated into the browser to enable protection of user secrets during web browsing sessions, even if the entire underlying browser and OS are fully controlled by a malicious attacker. Fidelius solves many challenges involved in providing protection for browsers in a fully malicious environment, offering support for integrity and privacy for form data, JavaScript execution, XMLHttpRequests, and protected web storage, while minimizing the TCB. Moreover, interactions between the enclave and the browser, the keyboard, and the display all require new protocols, each with their own security considerations. Finally, Fidelius takes into account UI considerations to ensure a consistent and simple interface for both developers and users. As part of this project, we develop the first open source system that provides a trusted path from input and output peripherals to a hardware enclave with no reliance on additional hypervisor security assumptions. These components may be of independent interest and useful to future projects. We implement and evaluate Fidelius to measure its performance overhead, finding that Fidelius imposes acceptable overhead on page load and user interaction for secured pages and has no impact on pages and page components that do not use its enhanced security features.
Analysis for center deviation of circular target under perspective projection

Purpose Accurate feature localization is a fundamental problem in computer vision and visual measurement. In a perspective projection model of the camera, the projected center of a spatial circle and the center of the projection ellipse are not identical. This paper aims to show how to locate the real projection center precisely in the perspective projection of a space circle target. Design/methodology/approach By analyzing the center deviation caused by projection transformation, a novel method is presented to precisely locate the real projection center of a space circle using projective geometry. Solution distribution of the center deviation is analyzed, and the quadratic equation for determining the deviation is derived by locating vanishing points. Finally, the actual projected center of the circular target is achieved by solving the deviation quadratic equations. Findings The procedures of the author’s method are simple and easy to implement. Experimental data calculated that maximum deviation occurs at approximately between 3π/10 and 2π/5 of the angle between the projection surface and the space target plane. The absolute reduction in error is about 0.03 pixels; hence, it is very significant for a high-accuracy solution of the position of the space circle target by minimizing systematic measurement error of the perspective projection. Originality/value The center deviation caused by the space circle projection transformation is analyzed, and the detailed algorithm steps to locate the real projection center precisely are described.
Visualizing Deep Similarity Networks

For convolutional neural network models that optimize an image embedding, we propose a method to highlight the regions of images that contribute most to pairwise similarity. This work is a corollary to the visualization tools developed for classification networks, but applicable to the problem domains better suited to similarity learning. The visualization shows how similarity networks that are fine-tuned learn to focus on different features. We also generalize our approach to embedding networks that use different pooling strategies and provide a simple mechanism to support image similarity searches on objects or sub-regions in the query image.
DTR-GAN

Video summarization targets the challenge of finding the smallest subset of frames, while still conveying the whole story of a given video. Thus it is of great significance for large-scale video understanding, allowing efficient processing of the large amount of videos that are uploaded every day. In this paper, we introduce a Dilated Temporal Relational Adversarial Network (DTR-GAN) to achieve frame-level video summarization. The dilated temporal relational units in the generator aim to exploit multi-scale temporal context in order to select key frames. To ensure that the model predicts high quality summaries, we present a discriminator that learns to enhance both the information completeness and compactness via a three-player loss. Experiments on the public TVSum dataset demonstrate the effectiveness of the proposed approach.
A Novel Convolutional Neural Network for Image Steganalysis With Shared Normalization

Image steganalysis is to discriminate innocent images (cover images) and those suspected images (stego images) with hidden messages. The task is challenging since modifications to cover images due to message hiding are extremely small. To handle this difficulty, modern approaches proposed using convolutional neural network (CNN) models to detect steganography with paired learning, i.e., cover images and their stegos are both in training set. In this paper, we explore an important technique in CNN models, the batch normalization (BN), for the task of image steganalysis in the paired learning framework. Our theoretical analysis shows that a CNN model with multiple batch normalization layers is difficult to be generalized to new data in the test set when it is well trained with paired learning. To address this problem, we propose a novel normalization technique called shared normalization (SN) in this paper. Unlike the BN layer utilizing the mini-batch mean and standard deviation to normalize each input batch, SN shares consistent statistics for training samples. Based on the proposed SN layer, we further propose a novel neural network model for image steganalysis. Extensive experiments demonstrate that the proposed network with SN layers is stable and can detect the state-of-the-art steganography with better performances than previous methods.
Efficient Straggler Replication in Large-Scale Parallel Computing

In a cloud computing job with many parallel tasks, the tasks on the slowest machines (straggling tasks) become the bottleneck in the job completion. Computing frameworks such as MapReduce and Spark tackle this by replicating the straggling tasks and waiting for any one copy to finish. Despite being adopted in practice, there is little analysis of how replication affects the latency and the cost of additional computing resources. In this article, we provide a framework to analyze this latency-cost tradeoff and find the best replication strategy by answering design questions, such as (1) when to replicate straggling tasks, (2) how many replicas to launch, and (3) whether to kill the original copy or not. Our analysis reveals that for certain execution time distributions, a small amount of task replication can drastically reduce both latency and the cost of computing resources. We also propose an algorithm to estimate the latency and cost based on the empirical distribution of task execution time. Evaluations using samples in the Google Cluster Trace suggest further latency and cost reduction compared to the existing replication strategy used in MapReduce.
Nonlinear industrial soft sensor development based on semi-supervised probabilistic mixture of extreme learning machines

Soft sensors play an important role in process industries for monitoring and control of key quality variables, and calibration of analyzers. Owing to the merits of fast learning speed and good generalization performance, extreme learning machines (ELMs) have been widely accepted to develop soft sensor models for nonlinear industrial processes. However, there still exist some challenges in developing high-accuracy ELM-based soft sensors. Specifically, ELMs with shallow networks seem to have inadequate representation capabilities for complex nonlinearities, while ELMs with deep networks have difficulties in determining the number of hidden layers and hidden nodes for each layer which readily results in overfitting. In addition, in soft sensor applications, labeled samples are usually limited due to technical or economical reasons, which adds obstacles to model training. To deal with these issues, we propose a semi-supervised probabilistic mixture of ELMs (referred to as the ‘S2PMELMs’). In the S2PMELMs, localized ELMs are trained and combined, which are completed in a unified probabilistic way such that process nonlinearities and uncertainties can be accommodated. Moreover, based on the variational Bayes expectation–maximization algorithm, we develop a training algorithm for the S2PMELMs, where unlabeled samples are able to be exploited and the regularization parameter for each ELM can be adaptively determined. The performance of the S2PMELMs is evaluated through two real-world industrial processes, and the results demonstrate the advantages of the proposed method in contrast with several state-of-the-art relevant soft sensing approaches.
Image mosaicing: A deeper insight

Image mosaicing is an effective means of constructing a single seamless image by aligning multiple partially overlapped images. Over the years, the research attention on mosaicing has increased a lot due to the growing applications and subsequently, many algorithms related to mosaicing and its contributing steps have come into existence. Though, the varied approaches for mosaic generation are effective, several difficulties arise in each step of mosaicing which need to be addressed specifically. The aim of this review is to provide an insight into the existing mosaicing algorithms, along with their merits and shortcomings. Additionally, the manuscript provides a classification of these algorithms based on their domain of processing, application, image type, and visual attributes. Furthermore, a comparison among various mosaicing methods is presented to find out which algorithm works best for a particular application and image type. Finally, the paper is concluded with a highlight on future research directions.
Pattern of Local Gravitational Force (<i>PLGF</i>): A Novel Local Image Descriptor

This paper presents a novel local image descriptor called Pattern of Local Gravitational Force (PLGF). It is inspired by Law of Universal Gravitation. PLGF is a hybrid descriptor, which is a combination of two feature components: one is the Pattern of Local Gravitational Force Magnitude (PLGFM), and another is Pattern of Local Gravitational Force Angle (PLGFA). PLGFM encodes the local gravitational force magnitude, and PLGFA encodes the local gravitational force angle that the center pixel exerts on all other pixels within a local neighborhood. We propose a novel noise resistance and the edge-preserving binary pattern called neighbors to center difference binary pattern (NCDBP) for gravitational force magnitude encoding. Finally, the histograms of the two components are concatenated to construct the PLGF descriptor. Experimental results on the existing face recognition databases, texture database, and biomedical image database show that PLGF is an effective image descriptor, and it outperforms other widely used existing descriptors. Even if in complicated variations like noise, and illumination with smaller databases, a combination of PLGF and convolutional neural network (CNN) performs consistently better than other state-of-the-art techniques.
Inference Under Information Constraints I: Lower Bounds From Chi-Square Contraction

Multiple players are each given one independent sample, about which they can only provide limited information to a central referee. Each player is allowed to describe its observed sample to the referee using a channel from a family of channels W, which can be instantiated to capture, among others, both the communication and privacy-constrained settings. The referee uses the players' messages to solve an inference problem on the unknown distribution that generated the samples. We derive lower bounds for the sample complexity of learning and testing discrete distributions in this informationconstrained setting. Underlying our bounds is a characterization of the contraction in chi-square distance between the observed distributions of the samples when information constraints are placed. This contraction is captured in a local neighborhood in terms of chi-square and decoupled chi-square fluctuations of a given channel, two quantities we introduce. The former captures the average distance between distributions of channel output for two product distributions on the input, and the latter for a product distribution and a mixture of product distribution on the input. Our bounds are tight for both publicand privatecoin protocols. Interestingly, the sample complexity of testing is order-wise higher when restricted to private-coin protocols.