Miracle-dz/UniFAR-FTUs huggingface dataset:
MRD-Nets: Multi-Scale Residual Networks With Dilated Convolutions for Classification and Clustering Analysis of Spacecraft Electrical Signal

The fault detection of spacecraft electronic load systems is a crucial part of the spacecraft prognostics and health management system. To detect the abnormal state of spacecraft electronic load systems, complex electrical signals should be processed rapidly and accurately. For the fault detection of spacecraft electronic load systems, a robust unsupervised clustering analysis method and an accurate supervised classification method are of great importance. However, the traditional machine learning methods have poor performance when processing high-dimensional signal data because of the lack of ability to extract complex features from the signals. Therefore, neural-network-based deep learning (DL) models which can extract features from signals automatically are more suitable in this situation. In this paper, a novel convolutional neural network (CNN) module, the multi-branch residual module with dilated convolutions (MRD module), is proposed to extract multi-scale features from the electrical signal. Then, a well-designed CNN model named MRD-CNN is presented for the supervised classification task of signal. Furthermore, for the unsupervised clustering task, the clustering variational autoencoder with MRD modules (MRD-CluVAE) is proposed. The MRD-CluVAE can extract high-quality features from signal data and output the clustering results directly. To evaluate the performance of the proposed models, comparisons among the proposed models and other baseline algorithms are carried out. The experimental results show that the MRD-CNN model achieves higher classification performance and stability, while the MRD-CluVAE has a higher clustering accuracy than other algorithms. These methods can be utilized to resolve the classification and recognition problems of spacecraft electronic load signals.
Un modèle sémantique basé sur les ontologies pour le partage et la réutilisabilité des itinéraires cliniques à travers le contexte (ShaRE-CP)

Clinical pathways (CP) enable a standardized and an efficient management of patients with common pathologies. As operational tools, they take into account knowledge from guidelines and from the context (e.g. availability of resources) in which the different interventions are to be carried out. Mastering the coherence of interactions between all these knowledge domains is a major challenge for the implementation of CP. This thesis led to the development of an ontology called Shareable and Reusable Clinical Pathway Ontology (ShaRE-CP) which integrates four knowledge domains (CP, guidelines, resources and context) and to the establishment of existing semantic links between them. The consistency of this semantic model has been validated by representing some corpora related to these knowledge domains and by solving some use cases using reasoners, SWRL rules or SPARQL queries. ShaRE-CP can serve as a basis for the development of a decision support system for planning and managing patient care.
The Effects of Product Feature Complexity, Market Activity, and Update Scheduling on Mobile App Life Cycles

Rapid advancements in telecommunication devices and the emergence of the mobile app ecosystem have immensely impacted our lives. Innovative apps have helped improve market efficiency in agriculture, contributed to environmental sustainability through peer-to-peer sharing services, and stimulated financial inclusion in developing economies. However, mobile app developers have to deal with challenges that can hinder the app to reach its full potential. In order to achieve commercial success in the hyper-competitive business landscape where freemium business models are dominating, developers need deep understanding on how non-price operational levers such as product design, delivery, and continued service lead to user adoption. From the two essays that comprise this dissertation, the first essay aims to explain user downloads of free mobile apps during the introduction stage in the lifecycle based on app feature designs and launch timings. The second essay estimates the effect of app enhancement updates on app downloads and explores contextual factors such as update regularity, lifecycle stage, and market activity levels that may further influence the effectiveness of the enhancements. Research questions proposed in the essays are answered by statistical analysis of heteroskedasticity-based instrumental variables regression and difference-in-differences analysis on free iOS mobile game app data acquired from app market Application Programming Interface (API) that contains daily performance observations over a 3.5-year time horizon. Data extraction and sample construction relied on naive Bayes tf-idf document classification algorithms and Bass diffusion model predictions which are performed via multi-thread processing on a high-performance cluster computing (HPC) server.
Verification of a Smart Power Control System using Hybrid Actors

This paper proposes an approach to modelling and analysis of time-critical cyber-physical systems (CPSs). The approach combines discrete-time actors with continuous modes which reproduce the dynamical laws by which some external variables change their value. In particular, the article introduces Hybrid Theatre which is an original actor formalism and an implementation framework in Java, for the development of CPSs. A Hybrid Theatre model is mapped onto Uppaal hybrid timed automata for property assessment by statistical model checking. A smart power control system in a micro grid is presented as a case study to demonstrate the usefulness of the proposed approach.
Eye-open monitor using two-dimensional counter value profile

A simple eye-opening monitor (EOM) system based on two dimensional (2-D) counter-value pro ﬁ le is designed. The proposed EOM can be applied to adaptive equalizer coe ﬃ cient control for better bit error rate (BER) performance in the high-speed serial interface. Input data is sampled 2048 times with 32 di ﬀ erent clock phases on 32 di ﬀ erent decision threshold amplitudes to the clocked comparator and the sampled outputs of ‘ 1 ’ or ‘ 0 ’ are recorded in the designated counter. The counter values at each phase and decision threshold amplitude are compared with a refer- ence of 1024 for eye-opening monitor. The estimated eye-diagram is displayed on the monitor. Through the estimated eye-diagram, the optimal sampling timing can also be determined. The chip for sampling data and gathering the counter value has been designed through 180-nm CMOS process and 86mW including I / O block is consumed on 2Gb / s data rate.
An Overview of Service Placement Problem in Fog and Edge Computing

To support the large and various applications generated by the Internet of Things (IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution, and heterogeneity of edge computational nodes make service placement in such infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complicate the deployment problem. This article presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new classification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.
Latency and Reliability-Aware Workload Assignment in IoT Networks With Mobile Edge Clouds

Along with the dramatic increase in the number of IoT devices, different IoT services with heterogeneous QoS requirements are evolving with the aim of making the current society smarter and more connected. In order to deliver such services to the end users, the network infrastructure has to accommodate the tremendous workload generated by the smart devices and their heterogeneous and stringent latency and reliability requirements. This would only be possible with the emergence of ultra reliable low latency communications (uRLLC) promised by 5G. Mobile Edge Computing (MEC) has emerged as an enabling technology to help with the realization of such services by bringing the remote computing and storage capabilities of the cloud closer to the users. However, integrating uRLLC with MEC would require the network operator to efficiently map the generated workloads to MEC nodes along with resolving the trade-off between the latency and reliability requirements. Thus, we study in this paper the problem of Workload Assignment (WA) and formulate it as a Mixed Integer Program (MIP) to decide on the assignment of the workloads to the available MEC nodes. Due to the complexity of the WA problem, we decompose the problem into two subproblems; Reliability Aware Candidate Selection (RACS) and Latency Aware Workload Assignment (LAWA-MIP). We evaluate the performance of the decomposition approach and propose a more scalable approach; Tabu meta-heuristic (WA-Tabu). Through extensive numerical evaluation, we analyze the performance and show the efficiency of our proposed approach under different system parameters.
Targeting MYCN in Pediatric Solid Tumors

Author(s): Meyerowitz, Justin Gabriel | Advisor(s): Weiss, William A; Shokat, Kevan M | Abstract: MYC proteins are critical drivers of a wide range of cancers, and as transcription factors are generally considered undruggable. MYCN is the neuroblastoma-derived isoform of MYC and is a driving oncogene in neuroblastoma and medulloblastoma, two deadly solid tumors of childhood. Here we describe, in three chapters, (1) MYC proteins and their role in neuroblastoma and medulloblastoma, and (2, 3) two strategies for MYC-directed therapeutics using ATP-competitive small-molecule inhibitors of kinases. This first strategy entails targeting MYCN for proteolysis through inhibition of Aurora Kinase A (Aurora A). MYCN is ubiquitinated and degraded by the proteasome, and Aurora A physically interacts with ubiquitinated MYCN to prevent its degradation. However, this MYCN-stabilizing function is completely independent of its kinase activity, as conventional Aurora A inhibitors have no effect on MYCN degradation. In chapter 2 we describe a new class of conformation-disrupting (CD) inhibitors of Aurora A that bind the ATP binding pocket and sufficiently alter the conformation of Aurora A as to prevent this physical interaction with ubiquitinated MYCN. This new class of compounds effects potent and rapid loss of MYCN protein in MYCN-expressing neuroblastoma and medulloblastoma, both in cell lines as well as in vivo, and reduces tumor burden and extends survival in an animal model. Furthermore, cell line profiling demonstrates that this new strategy for targeting MYCN is both a MYC- and MYCN-directed therapy, as MYC-like expression signature and MYC family overexpression predicts sensitivity to Aurora A conformation-disrupting inhibitors. These CD compounds represent a new strategy for targeting MYCN by way of inducing its degradation. The second strategy for targeting MYCN, described in Chapter 3, is through the reliance of MYCN on mTOR signaling through 4EBP, the latter a main regulator of cap-dependent translation. 4EBP and c-MYC have been shown to cooperate in oncogenesis, through c-MYC inhibition of the senescence induced by 4EBP and 4EBP inhibition of the apoptosis promoted by c-MYC. Here we show that rapamycin, an allosteric inhibitor of mTOR, blocks signaling through rpS6 but not 4EBP, whereas MLN0128, an ATP-competitive inhibitor of mTOR, blocks signaling through both rpS6 and 4EBP. Accordingly, in MYCN-driven medulloblastoma, MLN0128 but not rapamycin promotes apoptosis, and this apoptosis is consistent with loss of 4EBP phosphorylation but not loss of phosphorylation of other mTOR targets. We also show that the efficacy of mTOR kinase inhibitors is unrelated to any change in MYCN protein levels, and that MLN0128 can reduce mTOR signaling in a mouse model of MYCN-driven medulloblastoma.
A Recommendation System for Predicting Privacy Leaks in Mobile Traffic

Author(s): Asgari Mehrabadi, Milad | Advisor(s): Markopoulou, Athina | Abstract: Today’s smart phones have access to personal stored data, including personally identifiable information (PII) that can be used to uniquely identify users. It is well-known that a wide range of mobile applications transmit this data to remote servers, including their own servers, third-party advertisers, and trackers, which clearly poses a threat to user privacy. The present study’s goal is to detect PII in packets transmitted out of a mobile device, referred to as “privacy leaks”. This study build on prior work that developed systems for intercepting each network packet and inspecting it to detect PII, typically using deep-packet inspection (DPI) and/or machine learning techniques. This thesis, develop a lightweight mechanism that can predict if an outgoing packet contains any PII, based on minimal information, namely (i) the application name (package name) that generated the packet and (ii) the second-level destination domain. The problem is formulated as a recommendation system combining baseline and neighborhood predictors that exploit the similarity of mobile app behavior and PII leak types. Two different datasets of popular apps are used to get insights into privacy leak patterns. It is shown that the present framework can successfully detect 89% and 84% of PII in network packets on average while achieving F1 score as high as 0.97 and 0.91 in both datasets.
Segmentation of Salient Objects in Digital Image using Hybridization Gradient Based Technique

-Segmentation of salient objects is essential application for classification of image, recognition of object and CBIR tasks. In this paper, authors implement a new approach for segmentation of salient region from a digital image using hybridization gradient techniques. Here authors project, smoothing a low intensity values using L0 gradient minimization technique. After smoothing an image, applying gradient based segmentation. In experimental results, we use Flowers-17 database to check the effectiveness of the proposed approach. Authors show that proposed approach outperforms with gradient methods using recall, precision and F1-score.
Taking Standards for Better Health Forward in East Kent Coastal Teaching Primary Care Trust:A staff development project exploring pathways towards implementation of standards of care - Final Report

The publication of Standards for Better Health in February 2004 has re-emphasised the importance of the delivery of high quality care. As Primary Care Trusts (PCTs) are now making a major contribution towards the achievement of these standards in the primary care arena, there is an urgent need to assess the local managerial and clinical infrastructure necessary for their implementation. Given that PCT structural and organisational changes are imminent across the UK (DoH 2005), it will be important to capture current information on how standards are understood and applied. This is particularly so in relation to revealing good models of practice, in order to promote knowledge transfer and uniformity of approach within the new structures. This report describes an eight month project funded by East Kent Coastal Teaching PCT that sought the views of primary care workers in relation to standards of care and their implementation. Specifically, the project: ? describes current knowledge, understanding, attitudes and infrastructures surrounding clinical standards ? ascertains what is needed to work towards implementing best practice within the seven standard domains of Standards for Better Health ? makes recommendations for policy and practice
Space GNSS Receiver Performance Results With Precise Real-Time On-board Orbit Determination (P2OD) in LEO Missions

The paper presents a low-cost Space GNSS receiver for Cubesats promoted by European Space Agency, with the aim of providing real-time precise on-board orbit determination (P2OD). The activity is oriented to a Low-Earth Orbit in-orbit demonstration of the concept in 2020-2021. The GNSS Receiver is based on Deimos’ FPGA-based dual frequency GPS / Galileo receiver (L1/E1 and L5/E5a), integrated in a space qualified Software Defined Radio (SDR) hardware platform produced by GomSpace, and a Precise Point Positioning (PPP) software library provided by Fugro. The University of Nottingham will contribute to the validation campaign. The paper describes preliminary performance results obtained during system design and the on-going qualification campaign.
Estimation of Mean Surface Current and Current Variability in the East Sea using Surface Drifter Data from 1991 to 2017

동해의 평균 표층 순환과 표층 해류의 변동성을 이해하기 위하여 1991년부터 2017년까지 동해를 지나간 표층 뜰개들의 궤적을 분석 하였다. 표층 뜰개 자료를 분석하여 동해 표층 해류들을 그 주경로 별로 분류하고, 이들 해류의 변동을 조사하였다. 동한난류는 한국 동해안을 따라 북쪽으로 흐르며 36∼38°N에서 이안한 후 동해 중앙(131∼137°E)에서 동쪽으로 흐른다. 이때 해류 경로의 평균 위도 는 36∼40°N의 범위를 가지며, 남북으로 큰 진폭을 갖고 사행한다. 표층 뜰개 경로의 평균 위도가 37.5°N 이남(이북)일 때 사행진폭 이 상대적으로 크며(작으며) 진폭은 약 100 (50) km이다. 동해 중앙에서 표층 뜰개들은 37.5∼38.5°N를 따라 동쪽으로 흐르는 경로 를 가장 빈번하게 지나간다. 동해 북부 블라디보스토크 연안에 투하된 표층 뜰개들은 연안을 따라 남서쪽으로 이동하다가 일본분지 서쪽에서 반시계방향 순환을 따라 남동쪽으로 이동한 후 39~40°N에서 동쪽으로 사행하여 이동한다. 다음으로 동해를 0.25° 간격으 로 격자를 나누어 각 격자를 통과하는 표층 뜰개들의 이동 속도 벡터 자료로 동해 평균 표층 해류 벡터장과 속력장을 구하였다. 그리 고 0.5° 격자 간격으로 해류장의 분산타원을 계산하였다. 울릉분지 서쪽에서는 동한난류의 경로가 매년 변화하고, 야마토분지에서 는 해류의 사행과 소용돌이가 많아 해류의 변동성(분산)이 크다. 표층 뜰개의 주 이동 경로, 평균 해류 벡터장, 분산을 모두 반영하여 표층 뜰개 자료에 근거한 동해 표층 해류 모식도를 제시하였다. 이 연구는 그동안 인공위성 고도계 자료를 이용하여 구한 표층 지형 류와 해양수치모델로 모의한 해류를 중심으로 연구해 왔던 동해 표층 순환을 라그랑지 관측 자료를 통해 정리했다는 데 의의가 있다. To understand the mean surface circulation and surface currents in the East Sea, trajectories of surface drifters passed through the East Sea from 1991 to 2017 were analyzed. By analyzing the surface drifter trajectory data, the main paths of surface ocean currents were grouped and the variation in each main current path was investigated. The East Korea Warm Current (EKWC) heading northward separates from the coast at 36∼38°N and flows to the northeast until 131°E. In the middle (from 131°E to 137°E) of the East Sea, the average latitude of the currents flowing eastward ranges from 36 to 40°N and the currents meander with large amplitude. When the average latitude of the surface drifter paths was in the north (south) of 37.5°N, the meandering amplitude was about 50 (100) km. The most frequent route of surface drifters in the middle of the East Sea was the path along 37.5‒38.5°N. The surface drifters, which were deployed off the coast of Vladivostok in the north of the East Sea, moved to the southwest along the coast and were separated from the coast to flow southeastward along the cyclonic circulation around the Japan Basin. And, then, the drifters moved to the east along 39-40°N. The mean surface current vector and mean speed were calculated in each lattice with 0.25° grid spacing using the velocity data of surface drifters which passed through each lattice. The current variance ellipses were calculated with 0.5° grid spacing. Because the path of the EKWC changes every year in the western part of the Ulleung Basin and the current paths in the Yamato Basin keep changing with many eddies, the current variance ellipses are relatively large in these region. We present a schematic map of the East Sea surface current based on the surface drifter data. The significance of this study is that the surface ocean circulation of the East Sea, which has been mainly studied by numerical model simulations and the sea surface height data obtained from satellite altimeters, was analyzed based on in-situ Lagrangian observational current data.
BoSMoS: A Blockchain-Based Status Monitoring System for Defending Against Unauthorized Software Updating in Industrial Internet of Things

The role of the Industrial Internet of Things (IIoT) in critical infrastructure sectors, such as power, chemistry, and manufacturing, will be increasingly important as we move toward Industry 5.0. For example, IIoT devices are deployed in factories to help the manufacturing companies (e.g., automotive) gain in-depth insight into the various states of production, and thus improving production efficiency and achieving cost reductions. However, malicious code may compromise IIoT devices if either the devices are exposed to outside or unexposed inner devices are updated unauthentically. Due to their limited resources and features, it is challenging to implement strong security solutions for such embedded devices. In this article, we propose a blockchain-based software status monitoring system, called BoSMoS. The system is designed to monitor the software status of IIoT devices to detect and respond to identified malicious behaviors (e.g., intrusions). BoSMoS takes a snapshot of the statue of monitored software and monitors its file system calls. In order to ensure the software integrity information, we use blockchain as the distributed ledger to store a snapshot of software status. The blockchain network of BoSMoS can employ different consensus algorithms. We also evaluate the performance of BoSMoS, in terms of exception response delay, resistance performance to various intrusions, and scalability. The experimental results justify that BoSMoS is practical and sound. In addition, the evaluation of scalability and security demonstrates that the system can carry deployment of large-scale IIoT devices and can guarantee authenticated software updating, as well as detect unauthorized software status.
Simultaneous localization and mapping in underwater robots

Water covers more than 70% of the surface of our planet, and there are still areas that remain largely unexplored. Underwater engineering research offers scientist a variety of technologies including robots and specialized instrumentation to explore this environment. Marine robot development faces different challenges from its construction to its control and navigation due to the highly dynamic and harsh conditions of this scenario, limitations in communication, instrumentation, and energy. In this dissertation, we aim to extend the development of underwater robot technologies by investigating and implementing robotics vehicles and, applying and evaluating localization and mapping approaches towards autonomous navigation. This thesis is organized as a collection of research manuscripts based on articles already published or submitted to internationally refereed conferences and journals. In this dissertation, we research two main challenges in underwater robots. First, we focus on the implementation of underwater robots for scientific studies. We present the implementation of a novel Remotely Operated Vehicle (ROV)-based acquisition system based on current underwater sensors for scientific studies. The design and preliminary tests of the data acquisition are presented. Then we propose a robot framework based on a novel low-level expansion board which applies to underwater robots. We upgrade two underwater robots based on the framework including a simulation environment and Robot Operating System (ROS) integration. Second, we focus on Simultaneous Localization and Mapping (SLAM) algorithms and their application to underwater scenarios. We review three main SLAM approaches and use them over collected data from a simulation for comparison. Then, we center in visual SLAM, for which, we gathered and made public available a collection of datasets from different underwater locations in various illumination conditions. We evaluate the performance of feature detectors and descriptors in matching features over consecutive frames of the datasets. Finally, we apply a visual SLAM method based on Oriented FAST and Rotated BRIEF (ORB) features and graph optimization. We present the resulting maps and trajectories generated and evaluate the algorithm over the datasets. We also offer the proper conditions and the challenges for its application.