Reviewerly/exHarmony huggingface dataset:
CutResize: Improved data augmentation method for RGB-D Object Recognition

RGB-D object recognition is an essential but challenging task in computer vision. Most existing methods often exploit popular neural network architectures and various multi-modal fusion manners, which fail to explore the relation between low-level and high-level feature representations. To address this issue, we propose a convolutional recurrent fusion method for RGB-D object recognition. Firstly, a novel regularization strategy, called CutResize, is utilized to generate more reliable multi-scale RGB-D fusion input. Secondly, a convolutional attention gate recurrent unit (ConvAGRU) is proposed to fuse multi-modal features, which can capture the relation between low-level and high-level feature representations. Finally, the network recognizes RGB-D objects in an end-to-end manner. Especially, the CutResize uses the scaled image from another patch to mix with the input image, which avoids the mixed image only contains background and improves the localization ability of the model for small-scale features. The proposed ConvAGRU enhances the context correlation of the network and produces more compact and robust multi-modal fusion features. The proposed method is evaluated on several public datasets (including CIFAR-10, CIFAR-100, JHUIT-50, and OCID datasets). The experiment results show that the proposed method obtains better recognition performance than existing methods and achieves state-of-the-art results.
Collision Detection and Avoidance for Multi-UAV based on Deep Reinforcement Learning

In recent years, the demand for improving the autonomy of UAVs has continued to increase in the civilian and military fields. Collision detection and avoidance is one of the key technologies to this end. In this paper, we propose a fully-distributed collision detection and avoidance method for multi-UAV based on deep reinforcement learning. Different from traditional methods, we implement an end-to-end control, which takes the information of sensor, UAV status and destination as inputs, and directly outputs the control references. Besides, based on the PPO algorithm and the paradigm of centralized training and decentralized execution, we provide the design of the deep reinforcement leaning network and the policy update strategy. In addition, we build a series of training and verification environments, including 2D Stage scenes and 3D Gazebo scenes. The experiment results show that our method can successfully avoid the obstacles and achieve no collision between UAVs.
A Corpus-Based Examination of the Translation of the Suffix –ism into Chinese

The suffix –ism is one of the most productive suffixes in English, which presents complexities and challenges in English-to-Chinese translation. This study examines corpus tools and resources that translators can have access to, aiming at revealing the potential values of corpus resources that translators can draw on to better deal with the suffix in their practice. Our investigation is focused on interrogating a parallel corpus named “Education”, which contains 450,000 pairs of English and Chinese sentences. Compared with traditional tools such as monolingual and English-Chinese (reverse) dictionaries, the parallel corpus exhibits remarkable values. It is easy to access at Sketch Engine, enabling systematic queries on decent coverage of –ism words. The English-Chinese concordance lines present a wealth of resources with which translators can tease out the senses conveyed by the –ism words, retrieve rich translation candidates in meaningful contexts that previous translators have worked with. The technical skills required appeared to be very attainable to translators with average IT competence. The merits of corpus resources are discussed in the context of translator training.
Thinking More about RDMA Memory Semantics

RDMA (Remote Direct Memory Access) provides memory semantics to access the remote memory directly bypassing remote CPUs. It can provide low latency and high throughput that can benefit many data center applications. Though a lot of efforts had been made in the literature, this paper tries to find more opportunities to boost the performance of memory semantic operations in the RDMA network. Similar to the optimizations for local memory operations, we find that the performance can be improved in the RDMA network after considering the vector IO mechanism, the performance asymmetry between sequential and random access, IO consolidation, NUMA effects, as well as the atomic operations (such as compare and swap) provided by the underlying hardware. We have done a comprehensive empirical study on the influences from these factors for the memory semantic operations in RDMA network and provide guidelines to improve applications. Experimental results show that four typical applications, disaggregated hashtable, distributed shuffle, distributed join, and distributed log are improved by 2.7×/5.8×/5.3×/9.1× respectively after considering memory semantics related optimizations.
SAR Target Detection Network Based on Saliency-Combined Single Shot Multi Box Detector

The Single Shot multi-box Detector (SSD) has been successfully applied in synthetic aperture radar (SAR) target detection. Besides, the saliency information in the saliency map has the ability that strengthening the target of interest and suppressing the clutter. It will help to improve the capability of scene understanding. According to the above, a novel SAR target detection network based on saliency-combined SSD is proposed. The proposed method includes two backbone sub-networks, one taking the SAR images as input for extracting the features, and the saliency map obtained from traditional saliency method is used as the input of the other sub-network to obtain the refined saliency information. Through the fusion module is used in multiple scales to integrate the saliency information and the network feature. Finally, we can get the detection results by the convolutional predictors on the multi-scale integrated feature maps. In addition, we apply the dense connection structure in the two sub-networks to utilize context information. The experimental results based on the miniSAR real data show that the proposed method can achieve a good detection performance.
Estimating Usable-Security Through Hesitant Fuzzy Linguistic Term Sets Based Technique

The apparent contradiction between usability and security has been discussed in the literature for several years. This continuous trade-off requires be acknowledging and handling whenever security solutions are introduced. However, some progressive analysts point out that present security solutions are usually very difficult for several users, and they have expressed a willingness to simplify the security product user experience. Usable security is still mostly unexplored territory in computer science. Which we are all aware with security and usability on many levels, usable security has received little operational attention. Companies have recently focused primarily on usable security. As consumers prefer to discard unsatisfactory solutions, attaining well-balanced degrees of both is a prerequisite for adequate security. Usability should be examined from the viewpoint of security to be properly assessed. The aim of this research study is to explore and discuss universally accepted and solution-independent factors influencing the perceived usable-security of institutional websites. An integrated Hesitant Fuzzy Linguistic term sets based technique was chosen as the methodology, and the experts and academicians opinion were collected. The use of predetermined selection criteria resulted in the compilation of statistical findings and were used to determine the usable-security of five different instituational websites.
Learning Steganography with a Strip Transform

This article is devoted to the issues of steganography, i.e., secure data transmission using an image. The introductory part of the article gives a definition of the concept of steganography, and also describes the use of steganography in graphic images. In this paper, the aim of the researchers is to transfer textual data using an image object using a strip transformation method. The main part of the article shows the results obtained during the experimental work and describes the process of obtaining the results.
Efficient High-utility Itemset Mining Based on a Novel Data Structure

High-utility itemset mining (HUIM) is one of the important tasks in data mining. In HUIM, the most profitable products can be found by considering the quantity and profit factors, rather than the frequency factor. The one-phase HUIM algorithms based on utility-list structure have become one of the most efficient algorithms since they can mine high-utility itemsets (HUIs) without generating candidates. However, storing itemset information for utility list is time consuming and memory consuming, especially on the dense datasets with long transactions. To address the problem, this paper proposes an HUIM algorithm based on a novel data structure. The novel data structure is designed by reorganizing the transaction database in order to get all HUIs effectively and reduce memory usage in the depth-first search process. Based on the novel data structure, two upper bounds, which are based on the extensions utility and local transaction weighted utility, are introduced to reduce the search space greatly from width and depth. Experiment are carried out on the dense and sparse benchmark datasets. Compared with some state-of-the-art HUIM algorithms, the proposed HUIM algorithm has obtained better performance for mining HUIs in terms of running time and memory usage.
Quantum circuits generating four-qubit maximally entangled states

Abstract We describe quantum circuits generating four-qubit maximally entangled states, the amount of entanglement being quantified by using the absolute value of the Cayley hyperdeterminant as an entanglement monotone. More precisely we show that this type of four-qubit entangled states can be obtained by the action of a family of $\mathtt{CNOT}$ circuits on some special states of the LU orbit of the state $|0000\rangle$ .
Measuring Text Comprehension for People with Reading Difficulties Using a Mobile Application

Measuring text comprehension is crucial for evaluating the accessibility of texts in Easy Language. However, accurate and objective comprehension tests tend to be expensive, time-consuming and sometimes difficult to implement for target groups of Easy Language. In this paper, we propose using computer-based testing with touchscreen devices as a means to simplify and accelerate data collection using comprehension tests, and to facilitate experiments with less proficient readers. We demonstrate this by designing and implementing a mobile touchscreen application and validating its effectiveness in an experiment with people with intellectual disabilities. The results suggest that there is no difference in terms of task difficulty between measuring comprehension using the mobile application and a traditional paper-and-pencil test. Moreover, reading times appear to be faster in the application than on paper.
REHE: Fast variance components estimation for linear mixed models

Linear mixed models are widely used in ecological and biological applications, especially in genetic studies. Reliable estimation of variance components is crucial for using linear mixed models. However, standard methods, such as the restricted maximum likelihood (REML), are computationally inefficient in large samples and may be unstable with small samples. Other commonly used methods, such as the Haseman-Elston (HE) regression, may yield negative estimates of variances. Utilizing regularized estimation strategies, we propose the restricted Haseman-Elston (REHE) regression and REHE with resampling (reREHE) estimators, along with an inference framework for REHE, as fast and robust alternatives that provide nonnegative estimates with comparable accuracy to REML. The merits of REHE are illustrated using real data and benchmark simulation studies.
Low-dose PET image denoising based on coupled dictionary learning

Reducing the doses of tracers in positron emission tomography (PET) scans can effectively reduce the damage caused to the patient’s health, but at the same time, doing so reduces the quality of the resulting image. The purpose of this study is to reduce the noise in low-dose (LD) PET images through a coupled dictionary learning method and improve the feature similarity between LD images and standard-dose images. The main method in this paper involves training a coupled dictionary with LD and standard-dose PET image samples and then utilizing the updated dictionary to reconstruct a denoised image. This article changes the dictionary training process by not directly updating the joint dictionary, but rather updating the two dictionaries separately in a specific manner. We compare a variety of traditional denoising algorithms that are applied to LD PET images. The proposed method significantly improves the quality of the resulting image, effectively removing the noise from the image and better retaining the image details. In addition, the reconstructed image is closest to the standard-dose PET image.
Synaesthetic Audio-Visual Sound Toys in Virtual Reality

This paper discusses the design of audio-visual sound toys in Cyberdream, a virtual reality music visualization. While an earlier version of this project for Oculus GearVR provided a journey through audio-visual environments related to 1990s rave culture, the most recent iteration for Oculus Quest provides the addition of three audio-visual sound toys, the discussion of which is the main focus of this paper. In the latest version, the user flies through synaesthetic environments, while using the interactive controllers to manipulate the audio-visual sound toys and 'paint with sound'. These toys allow the user to playfully manipulate sound and image in a way that is complementary to, and interfaces with, the audio-visual backdrop provided by the VR music visualization. Through the discussion of novel approaches to design, the project informs new strategies in the field of VR music visualizations.
DataPAL: Data Protection and Authorization Lifecycle framework

This paper introduces a new model for handling data privacy throughout data lifecycle via the introduction of a policy profile using the Abbreviated Language For Authorization (ALFA) policy language. Our approach extends previous models In three complementary ways: (1) By introducing Administration and Delegation Profile (ADP) in ALFA policy where users and companies can restrict the scope of access/usage policies related to data as well as specify a chain of custody for data (moreover such an approach eases up the tasks of handling users' consent); (2) Thanks to our framework Usage Control System Plus (UCS+) users can monitor the usage of data and revoke its usage upon specific conditions or at will; (3) By introducing new states for policy evaluation, i.e. Admissible/NotAdmissible to filter out those applicable policies that were unauthorized in the first place.
Joint Representation of Temporal Image Sequences and Object Motion for Video Object Detection

In this paper, we propose a new video object detection (VoD) method, referred to as temporal feature aggregation and motion-aware VoD (TM-VoD), that produces a joint representation of temporal image sequences and object motion. The TM-VoD generates strong spatiotemporal features for VOD by temporally redundant information in an image sequence and the motion context. These are produced at the feature level in the region proposal stage and at the instance level in the refinement stage. In the region proposal stage, visual features are temporally fused with appropriate weights at the pixel level via gated attention model. Furthermore, pixel level motion features are obtained by capturing the changes between adjacent visual feature maps. In the refinement stage, the visual features are aligned and aggregated at the instance level. We propose a novel feature alignment method, which uses the initial region proposals as anchors to predict the box coordinates for all video frames. Moreover, the instance level motion features are obtained by applying the region of interest (RoI) pooling to the pixel level motion features and by encoding the sequential changes in the box coordinates. Finally, all these instance level features are concatenated to produce a joint representation of the objects. Experiments on the ImageNet VID dataset demonstrate that the proposed method significantly outperforms existing VoDs and achieves performance comparable with that of state-of-the-art VoDs.
He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics

Abstract We investigate how well BERT performs on predicting factuality in several existing English datasets, encompassing various linguistic constructions. Although BERT obtains a strong performance on most datasets, it does so by exploiting common surface patterns that correlate with certain factuality labels, and it fails on instances where pragmatic reasoning is necessary. Contrary to what the high performance suggests, we are still far from having a robust system for factuality prediction.
Short Paper: EMFI for Safety-Critical Testing of Automotive Systems

Electromagnetic Fault Injection (EMFI) is a well known method of introducing faults for security analysis of digital devices. Such faults can be seen as analogous to the faults which are known to naturally occur in digital devices, a known problem with designing safety-critical systems.Numerous standards have been developed for safety-critical systems, including the development of standards for increasing the rate of naturally occurring faults using particle sources. In this work, we demonstrate that desktop EMFI tooling can be used to accomplish similar testing, but with more control, effectively speeding up the evaluation process.We demonstrate using EMFI tooling for safety evaluation to recreate a highly publicized safety issue present in an automotive ECU – one that could not easily be recreated with other techniques.
ChatterHub: Privacy Invasion via Smart Home Hub

Smart-home devices promise to make users’ lives more convenient. However, at the same time, such devices increase the possibility of breaching users’ privacy as they are tightly connected to the users’ daily lives and activities. To address privacy invasion through smart-home devices, we present ChatterHub. This novel approach accurately identifies smart-home devices’ activities with minimal monitoring of encrypted traffic in the home network. ChatterHub targets devices that can only connect to the Internet through a centralized smart-home hub (e.g., Samsung SmartThings) using Zigbee or Z-wave. Specifically, ChatterHub passively eavesdrops on encrypted network traffic from the hub and leverages machine learning techniques to classify events and states of smart-home devices. Using ChatterHub, an adversary can identify smart-home devices’ specific activities without prior knowledge of the target smart home (e.g., list of deployed devices, types of communication protocols). We evaluated the accuracy and efficiency of ChatterHub in three real-world smart-home environments, and the evaluation results show that an attacker can successfully disclose smart-home devices’ behaviors with over 88% F1 score. We further demonstrate that ChatterHub successfully recognizes privacy-sensitive activities, including open and close of a smart door lock and turn on and off of smart LED. Additionally, to mitigate the threats posed by ChatterHub, we introduce two approaches, packet padding and random sequence injection. These mitigation approaches can effectively prevent threats from ChatterHub with only 9.2MB of additional network traffic per day.
Learning Target Class Feature Subspace (LTC-FS) Using Eigenspace Analysis and N-ary Search-Based Autonomous Hyperparameter Tuning for OCSVM

Existing dimensionality reduction (DR) techniques such as principal component analysis (PCA) and its variants are not suitable for target class mining due to the negligence of unique statistical properties of class-of-interest (CoI) samples. Conventionally, these approaches utilize higher or lower eigenvalued principal components (PCs) for data transformation; but the higher eigenvalued PCs may split the target class, whereas lower eigenvalued PCs do not contribute significant information and wrong selection of PCs leads to performance degradation. Considering these facts, the present research offers a novel target class-guided feature extraction method. In this approach, initially, the eigendecomposition is performed on variance–covariance matrix of only the target class samples, where the higher- and lower-valued eigenvectors are rejected via statistical analysis, and the selected eigenvectors are utilized to extract the most promising feature subspace. The extracted feature-subset gives a more tighter description of the CoI with enhanced associativity among target class samples and ensures the strong separation from nontarget class samples. One-class support vector machine (OCSVM) is evaluated to validate the performance of learned features. To obtain optimized values of hyperparameters of OCSVM a novel [Formula: see text]-ary search-based autonomous method is also proposed. Exhaustive experiments with a wide variety of datasets are performed in feature-space (original and reduced) and eigenspace (obtained from original and reduced features) to validate the performance of the proposed approach in terms of accuracy, precision, specificity and sensitivity.
Using Iterative Persona Development to Support Inclusive Research and Assessment

This research paper confirms the value of methodological work and describes our approach to building personas for iterative assessment, beginning from question formation and proceeding through data collection and analysis to follow-up. We discuss how our approach can help teams evaluate both day-to-day work and progress toward long-term goals. We highlight how iteration is an important driver for this method of internal assessment, the overarching framework of “Constructive Distributed Work” (CDW) it seeks to bolster, and our goal of advancing research about ethical collaboration. Readers can adopt our method to their own teams, making adjustments to accommodate their own ethical goals, motivating our use of personas and/or the CDW framework itself.
Multiple two-dimensional active shape model framework for right ventricular segmentation

Segmentation of the right ventricle (RV) in MRI short axis images is very challenging due to its complex shape and various appearance among the different subjects and cross-sections. Active shape models (ASM) have shown potential for segmenting the complex structures, including the RV, through two formulations: two- and three-dimensional modeling with a reported trade-off between accuracy and complexity of each formulation. In this work, we propose a new framework for modeling the RV surface using multiple 2D contours, where information from multiple cross-sectional images are incorporated into the same model. The proposed method was tested using cardiac MRI images from 56 human subjects. Compared to a golden reference of manually delineated RV contours, the proposed method resulted in significantly lower error than (almost one half) that of the conventional 2D ASM especially at the apical slices. The mean absolute distance of the proposed method was 2.9 ± 2 mm while the conventional 2D ASM resulted in an error of 6.6 ± 4.5 mm. In addition, the computation time of the proposed method was 5 s compared to 4 ± 1 min previously reported for the 3D ASM formulation.