Miracle-dz/UniFAR-FTUs huggingface dataset:
True-Color and Grayscale Video Person Re-Identification

Person re-identification is an important task in forensics applications. Most existing person re-identification methods focus on matching persons captured by different true-color cameras. In practice, the captured pedestrian videos may be grayscale in some cases due to camera malfunction or special treatment for gray mode. In these cases, the person re-identification between true-color and grayscale pedestrian videos, which we call color to gray video person re-identification (CGVPR), will be needed. Since the color information that is very important to represent a pedestrian is usually intensity information and monochrome in grayscale videos, the CGVPR problem is very challenging. To relieve the difficulties in CGVPR, we propose an asymmetric within-video projection based <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula>emi-coupled <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula>ictionary <inline-formula> <tex-math notation="LaTeX">${P}$ </tex-math></inline-formula>air <inline-formula> <tex-math notation="LaTeX">${L}$ </tex-math></inline-formula>earning (SDPL) approach. SDPL simultaneously learns two within-video projection matrices, a pair of true-color and grayscale dictionaries, as well as a semi-coupled mapping matrix. The learned within-video projection matrices can make each video (true-color or grayscale) more compact. The learned dictionary pair and the mapping matrix can work together to bridge the gap between features of true-color and grayscale videos. To date there exists no true-color and grayscale pedestrian video dataset, so we contribute a new one, called true-<inline-formula> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula>olor and <inline-formula> <tex-math notation="LaTeX">${g}$ </tex-math></inline-formula>rayscale <inline-formula> <tex-math notation="LaTeX">${v}$ </tex-math></inline-formula>ideo person re-<inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>dentification <inline-formula> <tex-math notation="LaTeX">${d}$ </tex-math></inline-formula>ataset (<bold>CGVID</bold>). Our dataset is collected under a real-world scenario and consists of over 50K frames. Extensive evaluations demonstrate that the collected CGVID dataset is very challenging and can be used for further research on person re-identification. The experimental results show that our approach outperforms the compared methods on the CGVPR task.
Heart Rate Estimation from Wrist-Type Photoplethysmographic Signals Corrupted by Intense Motion Artifacts using NLMS Adaptive Filter and Spectral Peak Tracking

Current advances in sensor and microelectronics technology have enable real-time non-invasive Heart Rate (HR) monitoring on wearable devices. Wrist-type Photoplethysmography is one of the most widely used methods to estimate HR in wearable devices. However, Photoplethysmographic (PPG) signals are strongly affected by Motion Artifacts (MA) caused by body movements. This paper presents a method of estimating heart rate from wrist-type photoplethysmographic signals during fast running of increasing speed peaking at 15km/hour. Our proposed method successfully suppresses the motion artifacts by using Normalized Least Mean Square (NLMS) adaptive filter and Spectral Peak Tracking (SPT). The proposed method was tested on datasets recorded from 12 subjects available from IEEE Signal Processing Cup 2015 [7]. Our algorithm produces HR estimations with mean absolute error of 1.57 beat per minute and the standard deviation of 1.11 beat per minute.
Human-in-the-Loop Optimization Control for the Ankle Exoskeleton During Walking Based on Iterative Learning and Particle Swarm Optimization Algorithm

Aiming at enhancing the locomotion capability of human and improving the walking economy, this paper leverages the iterative learning and particle swarm optimization (PSO) algorithm and develops a human-in-the-loop (HIP) optimization control method for the ankle exoskeleton. In the developed optimization control method, PSO is implemented to identify the ankle exoskeleton control parameters, and the iterative learning control algorithm is applied to control the optimized control parameters founded by the PSO optimizer. Since the developed method belongs to the family of customer-individualized control strategy, it may be suited to handle the response variations among different participants and has the potential to improve the walking economy of each participant. In order to validate the efficiency of the developed method, we have conducted an experiment in the case where eight healthy subjects wearing an ankle exoskeleton on their left legs. The experimental results show that the soleus muscle activities of both legs of each subject are reduced, which, to some extent, validates the feasibility of the developed method.
UAV-Based Situational Awareness System Using Deep Learning

Situational awareness by Unmanned Aerial Vehicles (UAVs) is important for many applications such as surveillance, search and rescue, and disaster response. In those applications, detecting and locating people and recognizing their actions in near real-time can play a crucial role for preparing an effective response. However, there are currently three main limitations to perform this task efficiently. First, it is currently often not possible to access the live video feed from a UAV’s camera due to limited bandwidth. Second, even if the video feed is available, monitoring and analyzing video over prolonged time is a tedious task for humans. Third, it is typically not possible to locate random people via their cellphones. Therefore, we developed the Person-Action-Locator (PAL), a novel UAV-based situational awareness system. The PAL system addresses the first issue by analyzing the video feed onboard the UAV, powered by a supercomputer-on-a-module. Specifically, as a support for human operators, the PAL system relies on Deep Learning models to automatically detect people and recognize their actions in near real-time. To address the third issue, we developed a Pixel2GPS converter that estimates the location of people from the video feed. The result – icons representing detected people labeled by their actions – is visualized on the map interface of the PAL system. The Deep Learning models were first tested in the lab and demonstrated promising results. The fully integrated PAL system was successfully tested in the field. We also performed another collection of surveillance data to complement the lab results.
Predicting Oncogenic Missense Mutations

With the rapid progress of cancer genome studies, many missense mutations in populations of somatic cells of different cancer types and at different stages have been identified. However, it is challenging to understand the implications of these cancer-related variants. We have developed a computational method that integrates structural, topographical, and evolutionary information for assessments of biochemical effects and the extent of deleteriousness of the cancer-related variants. We have mapped somatic missense mutations from the Catalogue of Somatic Mutations In Cancer (COSMIC) to 3D structures in the Protein Data Bank (PDB). Our results show that a large portion of these missense mutations is located on protein surface pockets, which often serve as a structural and functional unit of cancer variants. We provide detailed analysis of several examples and assessment on the importance of these variants, including prediction of previously unreported cancer-variants, along with independent evidence from the literature. Furthermore, we show our predictions can inform on the functional roles and the mechanism of predicted cancer variants.
Towards Cluster-wide Deduplication Based on Ceph

In this paper, we design an efficient deduplication algorithm based on the distributed storage architecture of Ceph. The algorithm uses on-line block-level data deduplication technology to complete data slicing, which neither affects the data storage process in Ceph nor alter other interfaces and functions in Ceph. Without relying on any central node, the algorithm maintains the characteristics of Ceph by designing a special hash object to store the data fingerprint, and uses the CRUSH algorithm to judge the data duplication based on calculation, instead of global search. The algorithm replaces the duplicate data with the deduplicated objects, which storage their fingerprints with less storage space. We compare the effects of different block sizes with respect to the performance and deduplication rates through experimental studies, and select the most appropriate block size in our prototype implementation. The experimental results show that the algorithm can not only effectively save the storage space but also improve the bandwidth utilization when reading and writing the duplicate data.
Threat assessment of air target based on dynamic relative entropy ranking method

In the complex and changeable air combat environment, how to accurately assess the threat of incoming targets is the key factor to determine the success or failure of air defense operations. Therefore, a threat assessment method based on dynamic relative entropy ranking method is proposed in this paper. Firstly, the attribute factors of air target are selected, and the target attribute matrix at different times is determined by intuitionistic fuzzy sets. Secondly, the inverse Poisson distribution method is used to determine the weight of time series, and then the time-weighted target attribute matrix is determined, and the target attribute is weighted by intuitionistic fuzzy entropy, so that the attribute-weighted intuitionistic fuzzy decision matrix is obtained. Thirdly, the relative entropy theory is introduced to improve the TOPSIS method, and the relative entropy ranking method is obtained. Then, a threat assessment model based on dynamic relative entropy ranking method is constructed, and the dynamic assessment of incoming targets is completed. Finally, the method is proved to be reasonable and effective by example analysis and comparison.
Schools: a community setting to target for training in cardiopulmonary resuscitation

Knowing cardiopulmonary resuscitation techniques (CPR) means knowing how to save lives. Learning CPR is easy and can be done in only a few hours. Since its foundation, the Spanish Emergency Medicine and Emergency Care Society (SEMES) has been committed to the training of CPR as one of its fundamental pillars. It is known that thousands of persons die each year because of cardiac arrest simply for not having received medical treatment early enough. Eight out of every 10 extrahospital cardiac arrests occur at home, but CPR is only performed by a bystander in 15%. Early recognition of a cardiac emergency by a member of the family, an early call to the Emergency Medical Department (EMD) and early CPR are essential to save lives. It has been demonstrated that with CPR programmes which include the teaching of the use of external automatic defibrillators (EAD) by laypersons implemented in specific public settings, the number of survivors of cardiac arrest due to ventricular fibrillation (VF) outside the hospital doubles in comparison with programmes based on making a rapid call to the EMD and the administration of CPR by specialised personnel 1 . In some studies survival after cardiac arrest may be 3- or 4-fold greater if CPR is carried out by citizens. Moreover, it has been shown that this CPR initiated by bystanders is clearly associated with a better quality of life for the survivors of a cardiac arrest 2
A Systematic Mapping Study of the Empirical MOOC Literature

Massive open online courses (MOOCs) have revolutionized todays education by offering a global accessible form of online learning. Over the years, MOOCs have been an attractive research area and have yielded an ample amount of research publications. However, the existing review studies in MOOCs are characterized by short year coverage or focusing on a specific theme. As such, a systematic mapping methodology was adopted to provide a fine-grain overview of MOOC research domain by identifying the quantity, types of research, available results and publication trends in educational aspects of MOOCs from 2009 to 2018. Key findings show that I) MOOC research have been on the rise since MOOCs became mainstream in 2011. II) MOOC research largely resides in the United States and few European countries. II) Most of MOOC studies focused on addressing learners’ completion/dropout/retention. In addition, we proposed some recommendations for future research on MOOCs.
Discovering Medication Patterns for High-Complexity Drug-Using Diseases Through Electronic Medical Records

An Electronic Medical Record (EMR) is a professional document that contains all data generated during the treatment process. The EMR can utilize various data formats, such as numerical data, text, and images. Mining the information and knowledge hidden in the huge amount of EMR data is an essential requirement for clinical decision support, such as clinical pathway formulation and evidence-based medical research. In this paper, we propose a machine-learning-based framework to mine the hidden medication patterns in EMR text. The framework systematically integrates the Jaccard similarity evaluation, spectral clustering, the modified Latent Dirichlet Allocation and cross-matching among multiple features to find the residuals that describe additional knowledge and clusters hidden in multiple perspectives of highly complex medication patterns. These methods work together, step by step to reveal the underlying medication pattern. We evaluated the method by using real data from EMR text (patients with cirrhotic ascites) from a large hospital in China. The proposed framework outperforms other approaches for medication pattern discovery, especially for this disease with subtle medication treatment variances. The results also revealed little overlap among the discovered patterns; thus, the distinct features of each pattern are well studied through the proposed framework.
Improving Birth Outcomes: Meeting the Challenge in the Developing World

1 Front Matter 2 Executive Summary 3 1. Introduction 4 2. Reducing Maternal Mortality and Morbidity 5 3. Reducing Neonatal Mortality and Morbidity 6 4. Reducing Fetal Mortality 7 5. Improving Birth Outcomes Within Health Care Systems 8 6. The Problem of Low Birth Weight 9 7. Reducing Mortality and Morbidity from Birth Defects 10 8. Preventing Perinatal Transmission of HIV 11 9. Summing Up: The Way Forward 12 Appendix A: Workshop Agenda 13 Appendix B: Defining Developing Countries 14 Appendix C: The Essential Competencies of a Skilled Birth Attendant 15 Appendix D: Committee Biographies 16 Appendix E: Dissenting Note 17 Glossary 18 Acronyms 19 Index
Interference in Automotive Radar Systems: Characteristics, mitigation techniques, and current and future research

This article examines the problem of interference in automotive radar. Different types of automotive radar as well as mechanisms and characteristics of interference and the effects of interference on radar system performance are described. The interference-to-noise ratio (INR) at the output of a detector is a measure of the susceptibility of a radar to interference. The INR is derived from different types of interfering and victim radars and depends on the location of both as well as parameters such as transmit power, antenna gain, and bandwidth. In addition, for victim radar with beamscanning, INR depends on the location of the target the victim radar is attempting to detect. Analysis is presented to show the effects of various interference scenarios on the INR. A review of the current state of the art in interference mitigation techniques previously deployed as well as areas of research currently being addressed is then provided. Finally, important future research directions are suggested.
Investigation of Chaotic and Spiking Dynamics in Mid-Infrared Quantum Cascade Lasers Operating Continuous-Waves and Under Current Modulation

This study investigates chaotic and spiking dynamics of mid-infrared quantum cascade lasers operating under external optical feedback and emitting at 5.5 <inline-formula><tex-math notation="LaTeX">$\mu {\text{m}}$</tex-math></inline-formula> and 9 <inline-formula><tex-math notation="LaTeX">$\mu {\text{m}}$</tex-math></inline-formula>. In order to deepen the understanding, the route to chaos is experimentally studied in the case of continuous-wave and current modulation operation. The non-linear dynamics are analyzed with bifurcation diagrams. While for quasi-continuous wave operation, chaos is found to be more complex, pure continuous wave pumping always leads to the generation of a regular spiking induced low-frequency fluctuations dynamics. In the latter, results show that by combining external optical feedback with periodic forcing and further induced current modulation allows a better control of the chaotic dropouts. This work provides a novel insight into the development of future secure free-space communications based on quantum cascade lasers or unpredictable optical countermeasure systems operating within the two transparency atmospheric windows hence between 3 <inline-formula><tex-math notation="LaTeX">$\mu {\text{m}}$</tex-math></inline-formula>–5 <inline-formula><tex-math notation="LaTeX">$\mu {\text{m}}$</tex-math></inline-formula> and 8.5 <inline-formula><tex-math notation="LaTeX">$\mu {\text{m}}$</tex-math></inline-formula>–11 <inline-formula><tex-math notation="LaTeX">$\mu {\text{m}}$</tex-math></inline-formula>.
An Improved Sub-Networks Partitioning Method for Urban Traffic Networks

Coordinated control of traffic sub-networks is an effective method to alleviate traffic congestion and improve the efficiency of traffic network operations. The essentials in achieving coordinated control is about sub-networks partitioning. In this paper, an improved Newman fast clustering algorithm was proposed based on a weighted traffic network. The algorithm adjudges each partition in terms of the contribution degrees of each sub-network to the value of the modularity of the entire network, so as to cope with the shortcomings of local search in conventional Newman fast algorithm and improve the modularization to some extent. Furthermore, the designed algorithm are implemented in the MATLAB environment. With respect to the experimental traffic network, the effectiveness of the designed partitioning method is compared with conventional Newman fast algorithm, in terms of the values of the partitioning modularity. The simulated experiments show that the improved Newman fast algorithm can obtain better results of sub-network partitioning.
Flexible Fractional Supercapacitor Model Analyzed in Time Domain

Supercapacitors have been rapidly adopted to replace batteries in many instances from power tools to automotive and aviation. Designing systems incorporating supercapacitors necessitate an accurate model of supercapacitor to maintain system efficiency. However, due to varying impedance property of supercapacitor, modeling becomes critical with likelihood of introducing errors. This work proposes an enhanced model to predict the system dynamics under various initial state of charge in supercapacitors. The proposed flexible model is suitable to represent actual supercapacitor impedance without prior assumption on the model structure. The use of one model to cover the other various impedance structures is simple and possible to estimate accurate supercapacitor’s impedance. Selection of impedance structure is self-regulated to improve the accuracy with minimum variables. To illustrate the variable structure fractional model, experimental results are shown with three different values of supercapacitors from three different manufacturers.
Intra-Task Priority Assignment in Real-Time Scheduling of DAG Tasks on Multi-Cores

Real-time scheduling and analysis of parallel tasks modeled as directed acyclic graphs (DAG) have been intensively studied in recent years. However, no existing work has explored the execution order of eligible vertices within a DAG task. In this paper, we show that this intra-task vertex execution order has a large impact on system schedulability and propose to control the execution order by vertex-level priority assignment. We develop analysis techniques to bound the worst-case response time for the proposed scheduling strategy and design heuristics for proper priority assignment to improve system schedulability as much as possible. We further extend the proposed approach to the general setting of multiple recurrent DAG tasks. Experiments with both realistic parallel benchmark applications and randomly generated workload show that our method consistently outperforms state-of-the-art methods with different task graph structures and parameter configurations.
Security Assessment Of Internet Of Things In Healthcare Environment

Internet of things is the new generation of the networks that allows interconnected devices detecting, communicating and understand each other. These devices include: gadgets, sensors, etc. Internet of Things Network requires Security Assessment. This paper deals with security assessment, detection and remediation of attacks on IoT networks through the presence and absence of AODV delay defense and AODV black hole defense. The improving of the data reception rate was 7.5% and 12.67% respectively, while improving data destruction rate reduction 7.3% and 12.7% respectively.
Predicting Bone Modeling Parameters in Response to Mechanical Loading

In vivo studies in mechanobiology and mechanotransduction explained the importance of mechanical loading in promoting osteogenesis (new bone formation) and thus, in preventing the bone loss. The literature suggests that the cyclic loading parameters viz. loading cycles, strain and frequency regulate the extent of new bone formation. Nevertheless, the amount of regulation has not been defined. As a result, researchers have been trying a data driven approach to estimate the new bone formation by proposing different empirical models. The models proposed so far have mainly focused on some specific bone modelling parameters such as mineral apposition rate (MAR). The literature, however, suggests that there are equally important bone modelling parameters which are also influenced by the change in cyclic loading parameters. Therefore, the results obtained from earlier computer modelling studies remain incomplete. This paper presents an improved empirical model which attempts to establish a relation between bone modelling parameters mineral apposition rate (MAR) and mineralising surface (MS/BS), and cyclic loading parameters. The results indicate that the proposed model has better accuracy in terms of prediction as compared to the state-of-the-art models involving only one bone modelling parameter i.e., MAR. The model may be useful in designing the optimal loading regimen to induce a desired new bone response. Based on these outcomes, a better bio-mechanical intervention may be developed in future to check bone loss.