Reviewerly/exHarmony huggingface dataset:
Towards a Low-cost WiFi based Real-time Human Activity Recognition System

Current implementations of human monitoring systems based on video, audio, and wearables offer better data but at the cost of privacy and convenience. While research has focused on systems using off-the-shelf WiFi hardware as an alternative to existing systems, most of them have been implemented using the Intel WL5300 WiFi network adapter, which requires a dedicated computer to function. Our research focuses on using low-cost Raspberry Pi 3B+ devices as an alternative for human activity recognition using WiFi CSI data through classification. In this paper, we propose a real-time implementation of a deep learning based human activity recognition system through classification using Raspberry Pi. We have created a public dataset of human activity data for six activities. A Convolutional LSTM model is used for the classification of activity data. A prototype system has also been developed for the real-time recognition of human activity data. We have achieved an accuracy of 95% for the model for the experiments performed in two test environments across six activities, including one for no movement. We have also evaluated the performance of our real-time human activity recognition system with acceptable performance in a static environment.
SATAn: Air-Gap Exfiltration Attack via Radio Signals From SATA Cables

This paper introduces a new type of attack on isolated, air-gapped workstations. Although air-gap computers have no wireless connectivity, we show that attackers can use the SATA cable as a wireless antenna to transfer radio signals at the 6 GHz frequency band. The Serial ATA (SATA) is a bus interface widely used in modern computers and connects the host bus to mass storage devices such as hard disk drives, optical drives, and solid-state drives. The prevalence of the SATA interface makes this attack highly available to attackers in a wide range of computer systems and IT environments. We discuss related work on this topic and provide technical background. We show the design of the transmitter and receiver and present the implementation of these components. We also demonstrate the attack on different computers and provide the evaluation. The results show that attackers can use the SATA cable to transfer a brief amount of sensitive information from highly secured, air-gap computers wirelessly to a nearby receiver. Furthermore, we show that the attack can operate from user mode, is effective even from inside a Virtual Machine (VM), and can successfully work with other running workloads in the background. Finally, we discuss defense and mitigation techniques for this new air-gap attack.
You only search once

Benefiting from the search efficiency, differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural networks (DNNs). We note that DNNs must be executed under strictly hard performance constraints in real-world scenarios, for example, the runtime latency on autonomous vehicles. However, to obtain the architecture that meets the given performance constraint, previous hardware-aware differentiable NAS methods have to repeat a plethora of search runs to manually tune the hyper-parameters by trial and error, and thus the total design cost increases proportionally. To resolve this, we introduce a lightweight hardware-aware differentiable NAS framework dubbed LightNAS, striving to find the required architecture that satisfies various performance constraints through a one-time search (i.e., you only search once). Extensive experiments are conducted to show the superiority of LightNAS over previous state-of-the-art methods. Related codes will be released at https://github.com/stepbuystep/LightNAS.
Digital Tools Adopted by Public Health Agencies to Support COVID-19 Case Investigation and Contact Tracing, United States, 2020-2021

During the COVID-19 pandemic, public health agencies implemented an array of technologies and digital tools to support case investigation and contact tracing. Beginning in May 2020, the Association of State and Territorial Health Officials compiled information on digital tools used by its membership, which comprises 59 chief health officials from each of the 50 states, 5 US territories, 3 freely associated states, and the District of Columbia. This information was presented online through a publicly available technology and digital tools inventory. We describe the national landscape of digital tools implemented by public health agencies to support functions of the COVID-19 response from May 2020 through May 2021. We also discuss how public health officials and their informatics leadership referenced the information about the digital tools implemented by their peers to guide and refine their own implementation plans. We used a consensus-based approach through monthly discussions with partners to group digital tools into 5 categories: surveillance systems, case investigation, proximity technology/exposure notification, contact tracing, and symptom tracking/monitoring. The most commonly used tools included the National Electronic Disease Surveillance System Base System (NBS), Sara Alert, REDCap, and Maven. Some tools such as NBS, Sara Alert, REDCap, Salesforce, and Microsoft Dynamics were repurposed or adapted for >1 category. Having access to the publicly available technology and digital tools inventory provided public health officials and their informatics leadership with information on what tools other public health agencies were using and aided in decision making as they considered repurposing existing tools or adopting new ones.
Specific emitter identification based on the multi‐discrepancy deep adaptation network

Specific emitter identification task often encounters the problem of channel inconsistency between the collected signal and the signal to be recognised, that is, different channel environments would cause inconsistency in the sample distribution, which would result in a significant decrease in recognition accuracy; the same problem is encountered in transfer learning. The authors focus on an important category of methods in transfer learning called domain adaptation to solve the above problems. Motivated by the idea of the Fisher discriminant and metric learning, a novel domain adaptation method called the multi-discrepancy deep adaptation network is proposed. In contrast to the current approach of reducing the discrepancies between the source and target domain distributions without considering category information, the basic idea of the proposed method is to increase interclass discrepancies between the source and target domains while decreasing interclass discrepancies. To adjust the weights of interclass and interclass discrepancies, a dynamic balancing factor that adaptively adjusts the weights between the interclass variance and interclass variance according to the latest trends of both variances is introduced, which is effective in improving performance. Dynamic balancing and learning weight factors, which are used to adjust the training weights of interclass and interclass discrepancies during the training process are introduced. The authors applied the network to a specific emitter identification problem under different channels on the ORACLE radio frequency Fingerprinting Data set and demonstrated a maximum domain adaptability increase of 15.4%, which outperformed existing domain adaptation methods.
JPEG Status and Progress Report 2022

This is an updated report of the work of the JPEG Committee formed by the International Telecommunication Union (ITU), the International Organization for Standardization (ISO), and the International Electrotechnical Commission (IEC), known as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Joint Photographic Experts Group (JPEG)</i> . A detailed introduction to JPEG’s work was already presented in Ref. 1. In this report, the focus is more on updated work since 2020 and on new work items and explorations.
Fuzzy Comprehensive Evaluation of APTS Dispatcher Working Level Based on Cloud Model

Many cities in China have built an advanced public transportation system (APTS) in recent years. These systems cannot carry out automated dispatches but rely on the dispatcher to ensure normal operations. Based on the theory of fuzzy comprehensive evaluation, this study created a hierarchical evaluation model to evaluate the work level of the dispatcher. Firstly, a hierarchical evaluation system was established based on the data of line complexity and dispatch workload provided by the APTS. Secondly, we assigned the weight of the evaluating indicator by combing an expert scoring method and the entropy weight method. We then transformed the quantitative data into qualitative evaluation data with a cloud model. Through a comparison with the standard cloud, the work level of an APTS dispatcher can be defined. The evaluation results for 12 cities in China showed that there was no obvious connection between the working level and bus company size, but the work level was always higher in large cities. We finally obtained the standard range of the evaluation indicator through cluster analysis. This method can be used as a reference to evaluate the work level of dispatchers in other cities, and can reflect the correlation between the work level and dispatching system construction, population and city size.
Learning the Distribution-Based Temporal Knowledge With Low Rank Response Reasoning for UAV Visual Tracking

In recent years, the constraint based correlation filter has shown good performance in unmanned aerial vehicle (UAV) tracking, which gains a lot popularity in many intelligence transportation applications. In this work, a distribution-based temporal knowledge driven method is proposed to leverage the temporal translation property in UAV tracking. Instead of focusing on the traditional issues in the correlation filter, we provide a new method of learning parametric distribution on temporal knowledge by Wasserstein distance which is successfully embedded to solve the problem of temporal degeneration in learning process of tracking. Furthermore, we approximate optimal response reasoning with low-rank constraint over response consistency. Furthermore, the proposed method is solved by a simple iterative scheme with alternating direction multiplication ADMM algorithm. We demonstrate the superior tracking performance in several public standard UAV tracking benchmarks compared with state-of-the-art algorithms.
Learning Pre- and Post-contrast Representation for Breast Cancer Segmentation in DCE-MRI

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a considerable role in high-risk breast cancer diagnosis and image-based prognostic prediction. The accurate and robust segmentation of cancerous regions is with clinical demands. However, automatic segmentation remains challenging, due to the large variations of cancers in shape and size, and the class-imbalance issue. To tackle these problems, we offer a two-stage framework, which leverages both pre- and post-contrast images for the segmentation of breast cancer. Specifically, we first employ a breast segmentation network, which generates the breast region of interest (ROI) thus removing confounding information from thorax region in DCE-MRI. Furthermore, based on the generated breast ROI, we offer an attention network to learn both pre- and post-contrast representations for distinguishing cancerous regions from the normal breast tissue. The efficacy of our framework is evaluated on a collected dataset of 261 patients with biopsy-proven breast cancers. Experimental results demonstrate our method attains a Dice coefficient of 91.11% for breast cancer segmentation. The proposed framework provides an effective cancer segmentation solution for breast examination using DCE-MRI. The code is publicly available at https://github.com/2313595986/BreastCancerMRI.
Propositional equality for gradual dependently typed programming

Gradual dependent types can help with the incremental adoption of dependently typed code by providing a principled semantics for imprecise types and proofs, where some parts have been omitted. Current theories of gradual dependent types, though, lack a central feature of type theory: propositional equality. Lennon-Bertrand et al. show that, when the reflexive proof refl is the only closed value of an equality type, a gradual extension of the Calculus of Inductive Constructions (CIC) with propositional equality violates static observational equivalences. Extensionally-equal functions should be indistinguishable at run time, but they can be distinguished using a combination of equality and type imprecision. This work presents a gradual dependently typed language that supports propositional equality. We avoid the above issues by devising an equality type of which refl is not the only closed inhabitant. Instead, each equality proof is accompanied by a term that is at least as precise as the equated terms, acting as a witness of their plausible equality. These witnesses track partial type information as a program runs, raising errors when that information shows that two equated terms are undeniably inconsistent. Composition of type information is internalized as a construct of the language, and is deferred for function bodies whose evaluation is blocked by variables. We thus ensure that extensionally-equal functions compose without error, thereby preventing contexts from distinguishing them. We describe the challenges of designing consistency and precision relations for this system, along with solutions to these challenges. Finally, we prove important metatheory: type safety, conservative embedding of CIC, weak canonicity, and the gradual guarantees of Siek et al., which ensure that reducing a program’s precision introduces no new static or dynamic errors.
Weakly perceived object detection based on an improved CenterNet

Nowadays, object detection methods based on deep neural networks have been widely applied in autonomous driving and intelligent robot systems. However, weakly perceived objects with a small size in the complex scenes own too few features to be detected, resulting in the decrease of the detection accuracy. To improve the performance of the detection model in complex scenes, the detector of an improved CenterNet was developed via this work to enhance the feature representation of weakly perceived objects. Specifically, we replace the ResNet50 with ResNext50 as the backbone network to enhance the ability of feature extraction of the model. Then, we append the lateral connection structure and the dilated convolution to improve the feature enhancement layer of the CenterNet, leading to enriched features and enlarged receptive fields for the weakly sensed objects. Finally, we apply the attention mechanism in the detection head of the network to enhance the key information of the weakly perceived objects. To demonstrate the effectiveness, we evaluate the proposed model on the KITTI dataset and COCO dataset. Compared with the original model, the average precision of multiple categories of the improved CenterNet for the vehicles and pedestrians in the KITTI dataset increased by 5.37%, whereas the average precision of weakly perceived pedestrians increased by 9.30%. Moreover, the average precision of small objects (AP_S) of the weakly perceived small objects in the COCO dataset increase 7.4%. Experiments show that the improved CenterNet can significantly improve the average detection precision for weakly perceived objects.
A Backstepping Approach to Nonlinear Model Predictive Horizon for Optimal Trajectory Planning

This paper presents a novel trajectory planning approach for nonlinear dynamical systems; a multi-rotor drone, built on an optimization-based framework proposed by the authors named the Nonlinear Model Predictive Horizon. In the present work, this method is integrated with a Backstepping Control technique. The goal is to remove the non-convexity of the optimization problem in order to provide real-time computation of reference trajectories for the vehicle which respects its dynamics while avoiding sensed static and dynamic obstacles in the environment. Our method is applied to two models of multi-rotor drones to demonstrate its flexibility. Several simulation and hardware flight experiments are presented to validate the proposed design and demonstrate its performance improvement over earlier work.
Remote Sensing Image Instance Segmentation Based on Attention Balanced Feature Pyramid

In recent years, with the development of remote sensing technology and the enhancement of the value of remote sensing images in military and civil fields, remote sensing image object segmentation has also received more and more attention. This paper mainly studies the application of instance segmentation based on deep convolutional neural network in the remote sensing image. This paper proposes an attention balanced feature pyramid module, which strengthens multi-level features and uses the attention module to suppress the interference features of noise in the complex background. In addiction, Soft-NMS is introduced to improve the performance of the network, and GIoU loss is introduced to improve the effect of object detection. The proposed network improves the average detection and segmentation accuracy (mAP) values from [Formula: see text] and [Formula: see text] to [Formula: see text] and [Formula: see text], respectively.
Learning and visualizing chronic latent representations using electronic health records

Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches.We propose the use of the Denoising Autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with different chronic conditions. Furthermore, this representation can be also used to characterize the patient's health status evolution, which is of paramount importance in the clinical setting.To obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hypertension, diabetes and multimorbidity. The procedure allowed us to find patients with the same main chronic disease but different clinical characteristics. Thus, we identified two kinds of diabetic patients with differences in their drug therapy (insulin and non-insulin dependant), and also a group of women affected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most significant diagnoses and drugs associated with chronic patients.Our results highlighted the value of ML techniques to extract clinical knowledge, supporting the identification of patients with certain chronic conditions. Furthermore, the patient's health status progression on the two-dimensional space might be used as a tool for clinicians aiming to characterize health conditions and identify their more relevant clinical codes.
A Node-Merging based Approach for Generating iStar Models from User Stories (ADPBD)

User story is a widely adopted requirement notation in agile development.Generally, user stories are written by customers or users in natural language with limited form to describe user's needs for the software system from their perspectives.However, since user stories are generally presented in a flat list, the relations derived from the user stories are difficult to capture.It reduces the understanding of the system as a whole.One solution to this problem is to build goal-oriented models that provide explicit relations among user stories.But extracting concepts and relationships from a large number of discrete user stories often take a lot of time for the agile development team.This paper proposes an iStar model generating approach based on node-merging from user stories.The method first extracts the iStar nodes from the semi-structured user stories, then uses a BERT (Bidirectional Encoder Representations from Transformers) model to measure the similarity between the nodes, and then nodes to be merged are identified and the edges between the iStar nodes are connected.Experiments are designed to illustrate the effectiveness of the proposed approach.
Trace4PF: A tool for Automated Decomposition of Problem Diagrams with Traceability

This paper provides a support tool for Jackson's Problem Frames approach -named Trace4PF for decomposing a global problem diagram into sub-problem diagrams.The tool provides a web browser interface with features such as drawing, editing and performing syntactical checking, and highlighting the trace of causal chain.A video demonstration of the tool is available at https : //youtu.be/XSUV GGqEKkw.
Improving Internet of Things Platform with Anomaly Detection for Environmental Sensor Data

Internet of things has an essential role in various application domains.The number of Internet of Things applications makes researchers try to formulate how to design the architecture of the Internet of Things platform so that it can be used generically in various domains.Commonly used architectural designs consist of data collecting, data preprocessing, data analysis, and data visualization.However, sensor data that enters the platform often experiences anomalies such as constant values or being stuck-at zero, which are processed manually at the data preprocessing stage.In this research, we try to design an anomaly detection system on the Internet of Things platform that can automatically improve the platform's performance in detecting anomalies.In this study, we compared the False Positive Rate of several anomaly detection algorithms tested to real datasets in the environmental sensor data application domain.The results showed that the anomaly detector system on the Internet of Things platform had an optimal False Positive Rate of 0.9%.
Graph Segmentation-Based Pseudo-Labeling for Semi-Supervised Pathology Image Classification

Pathology image classification is an important step in cancer diagnosis and precision treatment.Training a pathology image classification model in a fully supervised manner requires exhaustive pixel-level manual annotations from pathologists, which may not be practical in real applications.Semisupervised learning (SSL) has been widely used to exploit large amounts of unlabeled data to facilitate model training with a small set of labeled data.However, due to the limited annotations, it still suffers from the issue of inaccurate pseudo-labels of unlabeled data.In this paper, we propose a novel framework for semi-supervised pathology image classification, which incorporates graph-based segmentation to refine initial pseudo-labels of tissue regions by considering local and global contextual relationships of patches in whole-slide images (WSIs).Moreover, we define a new energy function for graph construction that allows the graph to take into account the uncertainty of network predictions on unlabeled data.Extensive experiments on two different pathology image datasets demonstrate the effectiveness of our method compared with state-of-the-art SSL baselines.In particular, when using 5% labeled data, our approach outperforms a strong baseline by 2.81% AUC.