Reviewerly/exHarmony huggingface dataset:
Breaking Shortcuts by Masking for Robust Visual Reasoning

Visual reasoning is a challenging but important task that is gaining momentum. Examples include reasoning about what will happen next in film, or interpreting what actions an image advertisement prompts. Both tasks are "puzzles" which invite the viewer to combine knowledge from prior experience, to find the answer. Intuitively, providing external knowledge to a model should be helpful, but it does not necessarily result in improved reasoning ability. An algorithm can learn to find answers to the prediction task yet not perform generalizable reasoning. In other words, models can leverage "shortcuts" between inputs and desired outputs, to bypass the need for reasoning. We develop a technique to effectively incorporate external knowledge, in a way that is both interpretable, and boosts the contribution of external knowledge for multiple complementary metrics. In particular, we mask evidence in the image and in retrieved external knowledge. We show this masking successfully focuses the method's attention on patterns that generalize. To properly understand how our method utilizes external knowledge, we propose a novel side evaluation task. We find that with our masking technique, the model can learn to select useful knowledge pieces to rely on.
Using human-centred design to develop an innovative female condom

Objective The female condom is a non-hormonal barrier method that can protect from unintended pregnancy and sexually transmitted infections such as HIV. Female condoms are an important contribution to women’s reproductive health globally as they are the only woman-initiated method currently available that can provide dual protection. This article describes how human-centred design (HCD) was applied to the development of the Woman’s Condom—a second-generation female condom. Methods A multidisciplinary team pioneered the application of HCD principles to develop a novel reproductive health product. The Woman’s Condom design incorporated feedback from both female and male users from multiple sites in the USA and Cuernavaca, Mexico; Durban, South Africa and Khon Kaen, Thailand to inform product development. Results We developed and tested more than 50 design iterations reflecting various solutions to user-related concerns. The final locked design confirmed that the Woman’s Condom was easy to use, stable, comfortable and provided satisfactory sensation during sex for both partners. The ‘dissolving capsule’ to facilitate insertion and ‘soft cling’ design are key innovative features of the Woman’s Condom. Conclusion The Woman’s Condom is a second-generation female (or internal) condom product that has been shown to be highly acceptable to users throughout the world. The Woman’s Condom’s special design features enable easy insertion, secure fit during use, good sensation and easy removal. Engaging users as codesigners through an HCD approach resulted in a female condom that meets the needs of women and men from diverse regions.
Gated Convolutional Neural Networks for Text Classification

The popular approach for several natural language processing tasks involves deep neural networks, and in particular, recurrent neural networks (RNNs) and convolutional neural networks (CNNs). While RNNs can capture the dependency in a sequence of arbitrary length, CNNs are suitable for extracting position-invariant features. In this study, a state-of-the-art CNN model incorporating a gate mechanism that is typically used in RNNs, is adapted to text classification tasks. The incorporated gate mechanism allows the CNNs to better select which features or words are relevant for predicting the corresponding class. Through experiments on various large datasets, it was found that the introduction of a gate mechanism into CNNs can improve the accuracy of text classification tasks such as sentiment classification, topic classification, and news categorization.
Accessible authentication: dyslexia and password strategies

Purpose The purpose of this paper is to reveal the lived experiences of dyslexics in engaging with all kinds of alphanumeric authentication mechanisms. Design/methodology/approach A significant proportion of the world’s population experiences some degree of dyslexia, which can lead to spelling, processing, sequencing and retention difficulties. Passwords, being essentially sequences of alphanumeric characters, make it likely that dyslexics will struggle with these, even more so than the rest of the population. Here, this study explores the difficulties people with dyslexia face, their general experiences with passwords, the coping strategies they use and the advice they can provide to developers and others who struggle with passwords. This paper collects empirical data through semi-structured interviews with 13 participants. Thematic analysis was used to provide an in-depth view of each participant’s experience. Findings The main contribution of this paper is to provide evidence related to the inaccessibility dimensions of passwords as an authentication mechanism, especially for dyslexics and to recommend a solution direction. Research limitations/implications There is a possible volunteer bias, as this study is dealing with self-reported data including historical and reflective elements and this paper is seeking information only from those with self-declared or diagnosed dyslexia. Furthermore, many expressed interest or curiosity in the relationship between dyslexia and password difficulties, for some a motivation for their participation. Finally, given that the participants told us that dyslexics might hide, it is possible that the experiences of those who do hide are different from those who chose to speak to us and thus were not hiding. Originality/value A few authors have written about the difficulties dyslexics face when it comes to passwords, but no one has asked dyslexics to tell them about their experiences. This paper fills that gap.
Cooperative Field Prediction and Smoothing via Covariance Intersection

This work studies the field prediction and smoothing problems, where the spatio-temporal field in 2-D is described by a stochastic dynamical system and observed by a number of spatially deployed sensors. We adopt a finite-element technique to approximate the field dynamics with piece-wise Gaussian functions, leading to a high-dimensional linear stochastic system. By exploiting its sparsity, a local covariance intersection-based filter and smoother are developed in each sensor only for a moderate number of state variables via communications with nearby sensors. Such a cooperative scheme is both communication and computation efficient. We prove the uniform stability of the local filter and smoother under mild conditions, and validate their effectiveness on two application examples: the temperature prediction of a metal rod and the source localization of a PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> field with a real dataset in a city of China.
An Efficient Bit Allocation Scheme for Weighted Random Graph Signal Sampling and Quantization

We study the quantization effect and bit allocation problem for the reconstruction of bandlimited graph signals from a subset of nodes, selected by a weighted random sampling strategy. Under a limited total rate condition, we design an optimal rate allocation scheme for the sampling set which minimizes the reconstruction error due to quantization. However, this scheme requires the estimation of the eigenvectors of a large graph Laplacian matrix. In order to reduce the computational complexity associated with such matrix calculation, a computational efficient method was then proposed. By considering various graph examples and by means of computer simulations, it is shown that the proposed scheme achieves better performance than the uniform rate assignment especially under a 1-bit restrict condition.
Compact One-Stage Object Detection Network

The targets in aerial images captured by drones are difficult to detect due to their small size, those neural networks with better detecting accuracy are too complicated to run real-time job on drone-mounted computer. This paper proposes a network combined residual network and YOLOv3-Tiny, residual network is used to merge different level features for improving YOLOv3-Tiny's small object detecting performance. During the experiment, the proposed network gets 2.9 higher mAP than YOLOv3-Tiny.
Reactive Concept Drift Detection Using Coresets Over Sliding Windows

The change of underlying data is one of the biggest challenges in non-stationary environments. While several algorithms have been proposed to detect these changes, substantial problems remain in the case of higher dimensional data. Thus, we propose a novel Concept Drift detector based on Minimum Enclosing Balls, with the capability to quickly process higher dimensional data. Additionally a kernelized version of this detector is derived, to process non-linear streaming data. We also propose a method to measure the performance of drift detectors with a binary classification evaluation technique, the confusion matrix, which enables calculating statistics like the F1 score. Our experiments show, that this novel technique is superior to existing state-of-the-art Concept Drift detectors regarding its true positive detection rate.
Domain Generalization Using Shape Representation

CNN-based representations have greatly advanced the state of the art in visual recognition, but the community has primarily focused on the setting where training and test set belong to the same dataset/distribution
Real Time Pedestrian Detection Using Robust Enhanced YOLOv3+

Autonomous pedestrian detection plays a vital role in Computer Vision tasks such as smart video surveillance, smart traffic monitoring system and smart obstacle detections for building smart cities. Real-time performance is much required in self-driving cars particularly while detecting smaller pedestrians without losing any detection accuracy. The proposed paper introduces an anti-residual module in the robust Enhanced YOLOv3+ network to improve feature extraction. The proposed network is optimized by reducing bounding box loss error. This network is trained on Pascal VOC-2007+ 12 dataset, only on the extracted pedestrian images. Experimental results show this network achieves 79.86% detection accuracy while detecting smaller pedestrians and still meets the real-time requirements.
Forensic Insights From Smartphones Through Electromagnetic Side-Channel Analysis

The increasing use of smartphones has increased their presence in legal and corporate investigations.Unlike desktop and laptop computers, forensic analysis of smartphones is a challenging task due to their limited interfaces to retrieve information of forensic value.Electromagnetic side-channel analysis (EM-SCA) has been recently proposed as an alternative window to acquire forensic insights from computers, in particularly from Internet of Things devices.Along this line, this work experimentally evaluates the potential of extracting information of forensic value from smartphones through their EM radiation.Initially, a group of smartphones representing a diverse set of system-on-chip (SoC) processors were used to acquire EM radiation traces.Later, deep learning models were trained to detect various internal software behaviours running on the SoCs.The results of this work indicates that a wide variety of insights can be extracted from smartphones through EM side-channel, increasing the potential opportunities for digital forensic investigators.
A secure distributed machine learning protocol against static semi-honest adversaries

Machine learning has been successfully applied to various fields over the last few years. However, it still faces two critical challenges. On one hand, the concern for the security issue in machine learning is increased. On the other hand, data exists in the form of isolated islands across different organizations. In this work, we focus on the privacy-preserving issue on a non-parametric machine learning algorithmize, i.e., the k Nearest Neighbor Classification (k NNC) in which, training data are split vertically among multiple servers. We propose a novel protocol that is secure against static semi-honest adversaries. In specific, the clients can obtain the label of his/her query without disclosing the servers’ data, the client’s query, and the client’s output to others. We use the state-of-the-art lattice-based fully homomorphic encryption to realize the privacy-preserving distance computation. In order to protect data access patterns, permutation technique and Oblivious Transfer are used in the top-k selection phase. We proved the security via the simulation paradigm. Meanwhile, we implemented our protocol and performed extensive experiments. Results show that our protocol performs well, especially in a large-width environment. Compared to the existing solution, our protocol leaks no information about the participants’ private input and output in both centralized and distributed architectures. Meanwhile, our protocol runs faster than existing solutions.
Object Recognition with Continual Open Set Domain Adaptation for Home Robot

Object recognition ability is indispensable for robots to act like humans in a home environment. For example, when considering an object searching task, humans can recognize a naturally arranged object previously held in their hands while ignoring never observed objects. Even in such a simple task, we need to deal with three complex problems: domain adaptation, open-set recognition, and continual learning. However, most existing datasets are simplified to focus on one problem and do not measure the object recognition ability for home robots when multiple problems are simultaneously present. In this paper, we propose the COSDA-HR (Continual Open Set Domain Adaptation for Home Robot) dataset that requires dealing with the above three problems simultaneously. The COSDA-HR dataset focuses particularly on the scenario in which naturally arranged objects in a room are recognized by training with handheld objects towards the goal of creating a user-friendly teaching system for home robots. We provide various baselines to address the problems in the COSDA-HR dataset by combining state-of-the-art methods from each research area and analyze the limitations of such simple combinations. We consider that it is necessary to study the methods of handling multiple problems simultaneously instead of solving each problem to realize practical object recognition systems for home robots.
RETRACTED: Material database management system based on heterogeneous multi-processor and computer embedded system

The development of different Data Base Management System (DBMSs) and computer networks and the proliferation of communication led to an increase in the distribution of DBMS network displays. Major diversity issues and challenges include Different types of information, different types of rules and semantics, different DBMSs (recycling, etc.). Address here is a long-term goal of a diversified Database Management System (DBMS), no node. The network, where the user can give an integrated and customized view or mode, cannot support the data. It resides in a single database or database, managed separately by the same DBMS type (according to a single user claims) or by different DDBMSs. The main way to go about data sharing and access: downloading / loading and downloading to the computer from existing top business files and databases created as a result and long-distance desire in the existing DBMS, common interface. Covers are also an item of commercial availability that can become a problem with motivational installation, opportunity cost, and the need to share data with a diverse world. These platforms are partly part of the long-term goals that major research and development projects seek to develop. The calculations were first done on desktop-based systems, and therefore data-based. However, embedded time-dependent systems are required to measure the output events or output events of a given period, run at regular intervals, and more widely. Embedded course Advanced course basket is an effective teaching time-related program. Programming early learning habits are difficult to break in the current situation. Introducing time-related programs for beginners or sophomores, involving a balance between abstraction and resource awareness can provide a good foundation. Combined with a clean microcontroller and a timer, the synchronous state machine calculation model can provide such a balance.
A Bottom-up Paradigm for Traffic Scene Graph Representation

With increasing hardware computing power and model capacity, visual tasks for scene cognitive understanding have attracted more attention, such as visual relationships inference. The scene graph representation formed by a coupling of objects, attributes and relationships nodes displayed by different modalities of information, including original image, foreground things, background stuff and scene attributes, strongly promotes the progress of research area. In this paper, we address the scene graph representation of traffic scenarios for autonomous driving. It should be noted that the universal representation are the specific needs of cognitive understanding of traffic scenes: on the one hand, there is a lack of fine-grained description of key objects and attributes; on the other hand, there are redundant descriptions of objects and relationships. To tackle these problems, we take advantage of the fine-grained instance-level annotation of the traffic scene, proposing a bottom-up representation paradigm. It makes full use of the hierarchical structure of the traffic scene and the sparsity of element classes. In addition, on the basis of the existing methods, we optimize the relationship list of traffic scene graph representation. Moreover, we improve the scene graph annotation methods, proposing a "ground-vision joint location method" to better describe the spatially-distributed visual knowledge. The case analysis showed that compared with existing methods, our paradigm for scene graph can represent more abundant traffic scene information.
A Variational Information Bottleneck Based Method to Compress Sequential Networks for Human Action Recognition

In the last few years, deep neural networks' compression has become an important strand of machine learning and computer vision research. Deep models require sizeable computational complexity and storage when used, for instance, for Human Action Recognition (HAR) from videos, making them unsuitable to be deployed on edge devices. In this paper, we address this issue and propose a method to effectively compress Recurrent Neural Networks (RNNs) such as Gated Recurrent Units (GRUs) and Long-Short-Term-Memory Units (LSTMs) that are used for HAR. We use a Variational Information Bottleneck (VIB) theory-based pruning approach to limit the information flow through the sequential cells of RNNs to a small subset. Further, we combine our pruning method with a specific group-lasso regularization technique that significantly improves compression. The proposed techniques reduce model parameters and memory footprint from latent representations, with little or no reduction in the validation accuracy while increasing the inference speed several-fold. We perform experiments on the three widely used Action Recognition datasets, viz. UCF11, HMDB51, and UCF101, to validate our approach. We show that our method achieves over 70 times greater compression than the nearest competitor with comparable accuracy for action recognition on UCF11.
Dual-Arm Needle Manipulation with the da Vinci<sup>®</sup>Surgical Robot

This paper presents a method of planning and performing dual-arm needle grasp and regrasp manipulations using the da Vinci <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Robotic Surgical System. This method implements a hybrid-object-based state-space Rapidly-Exploring Random Tree (RRT) algorithm to manage needle regrasps. Previous methods have attempted to pick up a loose surgical needle in the environment and then use the same grasp to begin suturing; however, the initial needle grasp is not always ideal in terms of the needle pose with respect to the gripper or the workspace of the arm. Thus, it is advantageous to regrasp the needle in a more optimal manner before suturing proper begins. To solve the problem, a hybrid-object state space (HySS) planner is implemented under the RRT - Connect motion planning framework. This planner is able to find a collision-free handoff manipulation path connected by a series of intermediate hybrid states. We show that this algorithm produces trajectories which can be successfully executed by a simulated da Vinci <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> robot and result in grasps that are optimal as defined by [1].
A Unified Framework for Compressive Video Recovery from Coded Exposure Techniques

Several coded exposure techniques have been proposed for acquiring high frame rate videos at low bandwidth. Most recently, a Coded-2-Bucket camera has been proposed that can acquire two compressed measurements in a single exposure, unlike previously proposed coded exposure techniques, which can acquire only a single measurement. Although two measurements are better than one for an effective video recovery, we are yet unaware of the clear advantage of two measurements, either quantitatively or qualitatively. Here, we propose a unified learning-based framework to make such a qualitative and quantitative comparison between those which capture only a single coded image (Flutter Shutter, Pixel-wise coded exposure) and those that capture two measurements per exposure (C2B). Our learning-based framework consists of a shift-variant convolutional layer followed by a fully convolutional deep neural network. Our proposed unified framework achieves the state of the art reconstructions in all three sensing techniques. Further analysis shows that when most scene points are static, the C2B sensor has a significant advantage over acquiring a single pixel-wise coded measurement. However, when most scene points undergo motion, the C2B sensor has only a marginal benefit over the single pixel-wise coded exposure measurement.
Fuzzy Based Decision-Making Approach for Estimating Usable-Security of Healthcare Web Applications

Usability and security are often considered contradictory in nature.One has a negative impact on the other.In order to satisfy the needs of users with the security perspective, the relationship and trade-offs among security and usability must be distinguished.Security practitioners are working on developing new approaches that would help to secure healthcare web applications as well increase usability of the web applications.In the same league, the present research endeavour is premised on the usable-security of healthcare web applications.For a compatible blend of usability and security that would fulfill the users' requirments, this research proposes an integration of the Fuzzy AHP-TOPSIS method for assessing usable-security of healthcare web applications.Since the estimation of security-usability accrately is also a decision making problem, the study employs Multiple Criteria Decision Analysis (MCDA) for selecting the most decisive attributes of usability as well as security.Furthermore, this study also pinpoints the highest priority attributes that can strengthen the usable-security of the healthcare web applications.The effectiveness of the suggested method has been tested on the healthcare web applications of local hospitals in Mecca, Saudi Arabia.The results corroborate that Fuzzy AHP-TOPSIS is indeed a reliable technique that will help the developers to design a healthcare web applications that delivers optimum usable-security.