Reviewerly/exHarmony huggingface dataset:
Reliable and Safe Use of Machine Translation in Medical Settings

Language barriers between patients and clinicians contribute to disparities in quality of care. Machine Translation (MT) tools are widely used in healthcare settings, but even small mistranslations can have life-threatening consequences. We study how MT is currently used in medical settings through a qualitative interview study with 20 clinicians–physicians, surgeons, nurses, and midwives. We find that clinicians face challenges stemming from lack of time and resources, cultural barriers, and medical literacy rates, as well as accountability in cases of miscommunication. Clinicians have devised strategies to aid communication in the face of language barriers including back translation, non-verbal communication, and testing patient understanding. We propose design implications for machine translation systems including combining neural MT with pre-translated medical phrases, integrating translation support with multimodal communication, and providing interactive support for testing mutual understanding.
Fidelity Restorable Universal Linear Optics

Universal multiport interferometers that can be programmed to perform any unitary or linear transformation turn into an important building block for both classical and quantum photonics. These interferometers typically utilize the mathematical framework of U(2) unitary matrix decomposition techniques and comprise a mesh of 2 × 2 beam splitters and phase shifters. All of them are, however, inherently fidelity limited as their U(2)‐based deployment approach leads to imbalanced path losses without supporting any fidelity restoration mechanism. Herein, a novel Universal Generalized Mach–Zehnder Interferometer (UGMZI) architecture is presented that migrates from U(2) decomposition techniques and adopts a recursive U(N) decomposition, exploiting cascaded size‐augmenting N N beam splitters and phase shifters. The design can natively support fidelity restoration and safeguard absolute fidelity, while outperforming the state‐of‐the‐art designs also with respect to phase‐error‐induced fidelity performance properties. Finally, it is demonstrated that its fidelity restoration properties turn the design into the optimal architecture for constructing any real/complex‐valued matrix via the singular value decomposition (SVD) scheme.
Extending CloudSim to simulate sensor networks

With the enormous growth of sensing devices tending to the use of Internet of everything, data aggregated by these devices are the biggest data streams generated in the history of IT. Thus, aggregating such data in the cloud for leveraging powerful cloud computing processing and storage is essential, and it eventually led to the emergence of Sensor-Cloud concept. This has allowed aggregation of the sensors’ data to the cloud for further processing, storage, and visualization. Furthermore, virtualization makes the sensors accessible to other end-user applications that require such data. All of these features are expected to be provided by the Sensor-Cloud invisibly, without the end-user application developer being aware of the sensor location or hardware specifications. For these reasons, a simulation platform where Sensor-Cloud infrastructure agents and components may be modeled, scheduling policies defined, and execution time assessed is essential to assure performance and quality of service. The aim of this study is to develop such a platform by enhancing CloudSim, the most well-known and powerful simulation tool for cloud computing. A user-friendly Java Script Swing-based graphical user interface (GUI) has been carefully designed and implemented for this purpose. The user can then utilize the specific interface to define the Cloudlet type as well as the scheduling on a single virtual machine. Finally, a simulation study is carried out on the platform to demonstrate its efficiency and accuracy. We were able to fully model the needed scenarios and acquire real-time results, displaying good accuracy in terms of application response time with a mean absolute percentage error (MAPE) of 3.37%, demonstrating the increased proposed platform’s proper operation.
Blockchain Technology in the Insurance Industry

Application of blockchain technology is feasible in the insurance sector, thus authorising data transmission, preventing fraud, enabling automation, and safeguarding audit trails. The present study aims at understanding the use cases of blockchain in the insurance industry and examining the challenges and opportunities of blockchain technology in the insurance industry. This study is approached from a theoretical perspective, and literature search strategy was performed for all working and published research articles and book chapters indexed under different databanks (e.g., CEI, Google Scholar, IEEE, Research Gate, Web of Science, etc.). Only articles accessible in English and published between 2010 to 2021 are included in the study. Blockchain is gaining ever-increasing attention from various industries and research domains. A quantum leap in skills and technical know-how influences all segments of industries, and this revolution is absolutely necessary in the field of insurance. Thus, the implementation of blockchain in the insurance sector has turned out to be a default platform.
An Algorithmic Framework for Bias Bounties

We propose and analyze an algorithmic framework for "bias bounties" — events in which external participants are invited to propose improvements to a trained model, akin to bug bounty events in software and security. Our framework allows participants to submit arbitrary subgroup improvements, which are then algorithmically incorporated into an updated model. Our algorithm has the property that there is no tension between overall and subgroup accuracies, nor between different subgroup accuracies, and it enjoys provable convergence to either the Bayes optimal model or a state in which no further improvements can be found by the participants. We provide formal analyses of our framework, experimental evaluation, and findings from a preliminary bias bounty event.1
An optimized hexagonal quadtree encoding and operation scheme for icosahedral hexagonal discrete global grid systems

Although research on the discrete global grid systems (DGGSs) has become an essential issue in the era of big earth data, there is still a gap between the efficiency of current encoding and operation schemes for hexagonal DGGSs and the needs of practical applications. This paper proposes a novel and efficient encoding and operation scheme of an optimized hexagonal quadtree structure (OHQS) based on aperture 4 hexagonal discrete global grid systems by translation transformation. A vector model is established to describe and calculate the aperture 4 hexagonal grid system. This paper also provides two different grid code addition algorithms based on induction and ijk coordinate transformation. We implement the transformation between OHQS codes and geographic coordinates through the ij, ijk and IJK coordinate systems. Compared with existing schemes, the scheme in this paper greatly improves the efficiency of the addition operation, neighborhood retrieval and coordinate transformation, and the coding is more concise than other aperture 4 hexagonal DGGSs. The encoding operation based on the ijk coordinate system is faster than the encoding operation based on the induction and addition table. Spatial modeling based OHQS DGGSs are also provided. A case study with rainstorms demonstrated the availability of this scheme.
Using Deep Neural Networks for Human Fall Detection Based on Pose Estimation

Requests for caring for and monitoring the health and safety of older adults are increasing nowadays and form a topic of great social interest. One of the issues that lead to serious concerns is human falls, especially among aged people. Computer vision techniques can be used to identify fall events, and Deep Learning methods can detect them with optimum accuracy. Such imaging-based solutions are a good alternative to body-worn solutions. This article proposes a novel human fall detection solution based on the Fast Pose Estimation method. The solution uses Time-Distributed Convolutional Long Short-Term Memory (TD-CNN-LSTM) and 1Dimentional Convolutional Neural Network (1D-CNN) models, to classify the data extracted from image frames, and achieved high accuracies: 98 and 97% for the 1D-CNN and TD-CNN-LSTM models, respectively. Therefore, by applying the Fast Pose Estimation method, which has not been used before for this purpose, the proposed solution is an effective contribution to accurate human fall detection, which can be deployed in edge devices due to its low computational and memory demands.
6Forest: An Ensemble Learning-based Approach to Target Generation for Internet-wide IPv6 Scanning

IPv6 target generation is the critical step for fast IPv6 scanning for Internet-wide surveys. Existing techniques, however, commonly suffer from low hit rates due to inappropriate space partition caused by the outlier addresses and short-sighted splitting indicators. To address the problem, we propose 6Forest, an ensemble learning-based approach for IPv6 target generation that is from a global perspective and resilient to outlier addresses. Given a set of known addresses, 6Forest first considers it as an initial address region and then iteratively divides the IPv6 address space into smaller regions using a maximum-covering splitting indicator. Before a round of space partition, it builds a forest structure for each region and exploits an enhanced isolation forest algorithm to remove the outlier addresses. Finally, it pre-scans samples from the divided address regions and based on the results generates IPv6 addresses. Experiments on eight large-scale candidate datasets indicate that, compared with the state-of-the-art methods in IPv6 worldwide scanning, 6Forest can achieve up to 116.5% improvement for low-budget scanning and 15× improvement for high-budget scanning.
Time domain graph-based anomaly detection approach applied to a real industrial problem

Detecting anomalies in industrial processes is a critical task. Prior fault detection can reduce company costs, and most importantly, may prevent accidents and environmental damage. Anomaly detection can be treated as drift detection, as both aims at identifying changes in data that happen unexpectedly over time. In this paper, a graph-based approach to anomaly detection is presented. Based on graph theory and set coverage, the method brings results similar to deep autoencoders, without the need to extensively set parameters.
TrojanFlow: A Neural Backdoor Attack to Deep Learning-based Network Traffic Classifiers

While deep learning (DL)-based network traffic classification has demonstrated its success in a range of practical applications, such as network management and security control to just name a few, it is vulnerable to adversarial attacks. This paper reports TrojanFlow, a new and practical neural backdoor attack to DL-based network traffic classifiers. In contrast to traditional neural backdoor attacks where a designated and sample-agnostic trigger is used to plant backdoor, TrojanFlow poisons a model using dynamic and sample-specific triggers that are optimized to efficiently hijack the model. It features a unique design to jointly optimize the trigger generator with the target classifier during training. The trigger generator can thus craft optimized triggers based on the input sample to efficiently manipulate the model’s prediction. A well-engineered prototype is developed using Pytorch to demonstrate TrojanFlow attacking multiple practical DL-based network traffic classifiers. Thorough analysis is conducted to gain insights into the effectiveness of TrojanFlow, revealing the fundamentals of why it is effective and what it does to efficiently hijack the model. Extensive experiments are carried out on the well-known ISCXVPN2016 dataset with three widely adopted DL network traffic classifier architectures. TrojanFlow is compared with two other backdoor attacks under five state-of-the-art backdoor defenses. The results show that the TrojanFlow attack is stealthy, efficient, and highly robust against existing neural backdoor mitigation schemes.
Learning Fair Representation via Distributional Contrastive Disentanglement

Learning fair representation is crucial for achieving fairness or debiasing sensitive information. Most existing works rely on adversarial representation learning to inject some invariance into representation. However, adversarial learning methods are known to suffer from relatively unstable training, and this might harm the balance between fairness and predictiveness of representation. We propose a new approach, learningFAir Representation via distributional CONtrastive Variational AutoEncoder (FarconVAE), which induces the latent space to be disentangled into sensitive and non-sensitive parts. We first construct the pair of observations with different sensitive attributes but with the same labels. Then, FarconVAE enforces each non-sensitive latent to be closer, while sensitive latents to be far from each other and also far from the non-sensitive latent by contrasting their distributions. We provide a new type of contrastive loss motivated by Gaussian and Student-t kernels for distributional contrastive learning with theoretical analysis. Besides, we adopt a new swap-reconstruction loss to boost the disentanglement further. FarconVAE shows superior performance on fairness, pretrained model debiasing, and domain generalization tasks from various modalities, including tabular, image, and text.
Hybrid Touch/Tangible Spatial Selection in Augmented Reality

Abstract We study tangible touch tablets combined with Augmented Reality Head‐Mounted Displays (AR‐HMDs) to perform spatial 3D selections. We are primarily interested in the exploration of 3D unstructured datasets such as cloud points or volumetric datasets. AR‐HMDs immerse users by showing datasets stereoscopically, and tablets provide a set of 2D exploration tools. Because AR‐HMDs merge the visualization, interaction, and the users' physical spaces, users can also use the tablets as tangible objects in their 3D space. Nonetheless, the tablets' touch displays provide their own visualization and interaction spaces, separated from those of the AR‐HMD. This raises several research questions compared to traditional setups. In this paper, we theorize, discuss, and study different available mappings for manual spatial selections using a tangible tablet within an AR‐HMD space. We then study the use of this tablet within a 3D AR environment, compared to its use with a 2D external screen.
Mudra: A Multi-Modal Smartwatch Interactive System with Hand Gesture Recognition and User Identification

The great popularity of smartwatches leads to a growing demand for smarter interactive systems. Hand gesture is suitable for interaction due to its unique features. However, the existing single-modal gesture interactive systems have different biases in diverse scenarios, which makes it intractable to be applied in real life. In this paper, we propose a multi-modal smartwatch interactive system named Mudra, which fuses vision and Inertial Measurement Unit (IMU) signals to recognize and identify hand gestures for convenient and robust interaction. We carefully design a parallel attention multi-task model for different modals, and fuse classification results at the decision level with an adaptive weight adjustment algorithm. We implement a prototype of Mudra and collect data from 25 volunteers to evaluate its effectiveness. Extensive experiments demonstrate that Mudra can achieve 95.4% and 92.3% F1-scores on recognition and identification tasks, respectively. Meanwhile, Mudra can maintain stability and robustness under different experimental settings.
When Wireless Federated Learning Meets Physical Layer Security: The Fundamental Limits

It has been shown that when federated learning (FL) is implemented in wireless communication systems, the noise of the channel directly serves as a privacy-inducing mechanism, which indicates that un-coded transmission can achieve privacy “for free” However, due to the broadcast nature of wireless communication, the wireless FL is susceptible to eavesdropping, and different from the privacy requirement of the FL (to preserve the accuracy of data analysis, information leakage is allowed in privacy mechanisms), the physical layer security (PLS) requirement is that the information leakage caused by the eavesdropper should vanish, which indicates that the previous un-coded transmission strategy is not available for the wireless FL in the presence of PLS. In this paper, we propose a novel architecture satisfying both the privacy requirement of the FL and the PLS requirement of the wireless channels, and characterize the corresponding capacity-equivocation region under privacy-utility constraint. The study of this paper is further explained via numerical examples and simulation results.
An Efficient Algorithm for Object Detection in Thermal Images using Convolutional Neural Networks and Thermal Signature of the Objects

In recent years, thermal imagery techniques have gained even more relevance because of its application and uses in various domains starting from agriculture to electrical engineering and going all the way to military and surveillance to name a few. In this paper, the use of thermal imagery and deep learning techniques for object detection has been explored. The objects chosen are commonly available entities such as: cat, dog, man and a car. The dataset consists of thermal images and objects are detected on the basis of different heat signatures and further these images are used to train a Convolutional Neural Network (CNN) based model. Also, model training is done using idea of transfer learning and pre-trained models to evaluate and compare performance metrics against the exiting Keras based transfer learning algorithms which are utilized here. The best model achieved an average accuracy of 91.94%. The results were also verified against a test dataset.
Smartphone-Aided Human Activity Recognition Method using Residual Multi-Layer Perceptron

Human activity recognition (HAR) has received intensely attention in many applications, such as healthcare, human-computer interaction, and smart home. Existing HAR methods based on deep learning (DL) have been proposed in the last several years. However, these DL-based HAR methods are hard to balance between performance and cost, which truly limited the applications in practical scenarios. To solve this problem, this paper proposes a smartphone-aided HAR method using the residual multi-layer perceptron (Res-MLP). It composes of two linear layers and Gaussian error linear unit (GELU) activation function, and obtains Res-MLP network through residual. Experimental results show that the proposed HAR method can achieve a high classification accuracy of 96.72% based on the public UCI HAR dataset.
Multi-label text classification via hierarchical Transformer-CNN

Traditional multi-label text classification methods, especially deep learning, have achieved remarkable results, but most of these methods use the word2vec technique to represent continuous text information, which fails to fully capture the semantic information of the text. To solve this problem, we built a hierarchical Transformer-CNN model and applied it in multi-label classification. Taking into account the characteristics of natural language, a hierarchical Transformer-CNN model is constructed to capture the semantic information of different levels of the text at the word and sentence levels using multi-headed self-attention mechanism, and a sentence convolutional neural network was used to extract key semantic features. For the hierarchical Transformer-CNN model we proposed, sufficient experiments have been conducted on the RCV1 and AAPD data sets to verify the model's effectiveness.
Mercury: A Simple Transport Layer Scheduler to Accelerate Distributed DNN Training

Communication scheduling is crucial to improve the efficiency of training large deep learning models with data parallelism, in which the transmission order of layer-wise deep neural network (DNN) tensors is determined for a better computation-communication overlap. Prior approaches adopt tensor partitioning to enhance the priority scheduling with finer granularity. However, a startup time slot inserted before each tensor partition will neutralize this scheduling gain. Tuning the optimal partition size is difficult and the application-layer solutions cannot eliminate the partitioning overhead. In this paper, we propose Mercury, a simple transport layer scheduler that does not partition the tensors, but moves the priority scheduling to the transport layer at the packet granularity. The packets with the highest priority in the Mercury buffer will be transmitted first. Mercury achieves the near-optimal overlapping between communication and computation. It leverages immediate aggregation at the transport layer to enable the coincident gradient push and parameter pull. We implement Mercury in MXNet and conduct comprehensive experiments on five DNN models in an 8-node cluster with 10Gbps Ethernet. Experimental results show that Mercury can achieve about 1.18 ~ 2.18 × speedup over vanilla MXNet, and 1.08 ~ 2.04× speedup over the state-of-the-art tensor partitioning solution.
A Profit-Maximizing Model Marketplace with Differentially Private Federated Learning

Existing machine learning (ML) model marketplaces generally require data owners to share their raw data, leading to serious privacy concerns. Federated learning (FL) can partially alleviate this issue by enabling model training without raw data exchange. However, data owners are still susceptible to privacy leakage from gradient exposure in FL, which discourages their participation. In this work, we advocate a novel differentially private FL (DPFL)-based ML model marketplace. We focus on the broker-centric design. Specifically, the broker first incentivizes data owners to participate in model training via DPFL by offering privacy protection as per their privacy budgets and explicitly accounting for their privacy costs. Then, it conducts optimal model versioning and pricing to sell the obtained model versions to model buyers. In particular, we focus on the broker’s profit maximization, which is challenging due to the significant difficulties in the revenue characterization of model trading and the cost estimation of DPFL model training. We propose a two-layer optimization framework to address it, i.e., revenue maximization and cost minimization under model quality constraints. The latter is still challenging due to its non-convexity and integer constraints. We hence propose efficient algorithms, and their performances are both theoretically guaranteed and empirically validated.