Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Optimization of Speed Profile and Allocation of Wireless Power Transfer System for Autonomous Driving Electric Vehicles

Optimal allocation of wireless power transfer system (WPTSys) for electric vehicles (EVs) plays an important role in reducing the manufacturing costs of WPTSys. In the conventional approach, the allocation of WPTSys requires information on EV operation such as speed profile and power demand. This paper proposes a new methodology based on mathematical optimization for the simultaneous design of EV speed profile and allocation of WPTSys in a lane segment, with the aim of reducing the cost of WPTSys by minimizing its length. By parameterizing both EV operation and allocation of WPTSys as unknown parameters, an optimization problem with constraints of EV operation and battery sustainability is established for optimizing these unknown parameters to minimize the length of WPTSys. Therefore, the optimal speed profile of EV and optimal allocation of WPTSys can be simultaneously determined. The proposed approach is applied to a scenario of autonomous driving EVs, where it is assumed that the designed speed profile is accurately tracked. A numerical case study is conducted to illustrate the proposed approach. Furthermore, it is shown that our proposed method achieves WPT systems that are shorter in length compared to those obtained using the conventional method under the same operational constraints of EV and battery.
Programming models and systems for Big Data analysis

ABSTRACT Big Data analysis refers to advanced and efficient data mining and machine learning techniques applied to large amount of data. Research work and results in the area of Big Data analysis are continuously rising, and more and more new and efficient architectures, programming models, systems, and data mining algorithms are proposed. Taking into account the most popular programming models for Big Data analysis (MapReduce, Directed Acyclic Graph, Message Passing, Bulk Synchronous Parallel, Workflow and SQL-like), we analysed the features of the main systems implementing them. Such systems are compared using four classification criteria (i.e. level of abstraction, type of parallelism, infrastructure scale and classes of applications) for helping developers and users to identify and select the best solution according to their skills, hardware availability, productivity and application needs. GRAPHICAL ABSTRACT This figure is a word cloud highlighting the most popular words related to Big Data analysis.
Deep Center Supervised Hash for Fast Image Retrieval

With the rapid growth of image data, the current mainstream image retrieval method uses the handcraft visual feature coding, which lack of learning ability, resulting in its image expression is not strong, and the large visual feature dimension which seriously restricting its image retrieval performance. The basic idea of this paper is to learn the hash center of each class, so that the distance of intra-class is closer in Hamming space, while maintaining the Hamming distance between the inter-class. Firstly, the deep implicit relationship of the training image is extracted by using the robust learning ability of the CNN, and enhance the distinguishing and expressive ability of the image hash feature. In particular, we have designed a CNN architecture in which a hash layer is added to encourage the input image to approximate discrete value (e.g. +1/-1). At the same time, we have carefully designed a hash center loss function, so that the distance between the intra-class to disperse, closer within the intra-class distance, and simultaneously imposing regularization on the real-valued outputs to approximate desired discrete values. We have conducted evolution on CIFAR-10 and NUS-WIDE images benchmarks, demonstrating that our approach can provide superior image search accuracy than other state-of-the-art hashing methods.
Satellite network simulator - design and verifcation using AODV and AntHocNet

Satellite Network Simulator Design and Veri cation using AODV and AntHocNet AJ. Merts Department of Electrical and Electronic Engineering, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa. Thesis: MEng (Elec) 2016 As access to space increases, the viability of creating satellite networks and dedicated services for satellite clients also increases. However, satellites are expensive to launch and cannot be easily maintained once in orbit, making it bene cial to simulate network performance before launch. Furthermore there is an extensive wealth of research regarding routing protocols available for terrestrial networks that have not been fully researched for satellite networks thus creating a need for a satellite network simulator. Any network has various ways in which its properties can be interpreted, which means any network metric must be unambiguously de ned. Routing protocols can be classied in various ways, showing their inherent advantages, disadvantages, requirements and limitations. Combining these properties with information on the components of routing protocols, an analysis of routing protocols can be made and candidate routing protocols can be chosen. For this research AODV was chosen to allow functional veri cation and AntHocNet was chosen to show that the simulator is exible enough to simulate various routing protocol functionality. Satellite networks were manually analysed with attention given to the key di erences between terrestrial and satellite networks. Using this information in conjunction with previous research ndings and recommendations, a vast distributed network similar to the Iridium network was chosen as a veri cation scenario for the simulator. A previously proposed network, called the OuterNet, was chosen to further research the transferability of AODV and AntHocNet to other satellite constellations. The expansion of SatSim from a basic point-to-point simulator to a network simulator capable of handling complex routing protocols was documented in detail, with clear information given as to which design choices were made and why. The key design drivers were user-friendliness, modularity and re-usability, and the code was structured to allow users to easily adjust and expand the simulator as needed. Due to the limited replicatable results, further testing for the MAC layer channel access for AODV and AntHocNet implementations was performed. SatSim was successfully expanded to allow for satellite network simulations. Preliminary ndings regarding the suitability of AODV compared to AntHocNet for two di erent satellite constellations are presented. ii Stellenbosch University https://scholar.sun.ac.za
Lean planning and control: Principles and practice

A basic characteristic of lean, agile, and responsive production systems are the use of teams instead of functional departments. Another characteristic is the use of rather simple shop floor control methods to manage the flow of orders. The shop floor control methods provide robust and visual control to cope with the dynamic situations at the shop floor, such as variation in processing times, routing sequences, disturbances, resource allocation, et cetera. Although the main focus of lean is to eliminate unnecessary variation that causes waste, unreasonableness, and unevenness, there will be variation that might be considered to be necessary, valuable, or unavoidable. This type of variation is directly concerned with providing customer value and hence needs to be accommodated in the control system. This paper investigates what fundamental principles are behind lean planning and control methods and how they can be applied in practice. We describe underlying principles that have been developed even before computerized methods such as Material Requirements Planning and Input/Output control became popular. Next we review some recent developments in the use of lean planning and control methods, such as Polca, (M)Conwip, and Cobacabana. We provide several short cases of practical applications to illustrate these concepts.
QUALITATIVE RESEARCH METHODOLOGY IN EDUCATION

Qualitative research is exploratory as its purpose is to discover new ideas and insights or even generate new theories. It focuses on understanding the particular and the distinctive and does not necessarily seek to generalize findings to other contexts. Researcher do not define specific research questions at the outset of the study, as doing so would likely impose their own framework on the research context. Collecting, analyzing and interpreting data are done largely simultaneously, with researcher constantly moving back and forth until new information does not add to their understanding of a topic, a point called data saturation. In fact data analysis steers data collection. Five stages of doing qualitative research; a) Choosing research sites and sample populations; b) Obtaining and encoding quailtative data (taking field notes); c) Preparing for and under taking focused interviews; d) Developing and maintaining field relations; and e) Organizing and analyzing the qualitative data gathered.
Flexible Computing with Virtual Machines

Flexible Computing with Virtual Machines H. Andrés Lagar-Cavilla Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2009 This thesis is predicated upon a vision of the future of computing with a separation of functionality between core and edges, very similar to that governing the Internet itself. In this vision, the core of our computing infrastructure is made up of vast server farms with an abundance of storage and processing cycles. Centralization of computation in these farms, coupled with high-speed wired or wireless connectivity, allows for pervasive access to a highly-available and well-maintained repository for data, configurations, and applications. Computation in the edges is concerned with provisioning application state and user data to rich clients, notably mobile devices equipped with powerful displays and graphics processors. We define flexible computing as systems support for applications that dynamically leverage the resources available in the core infrastructure, or cloud. The work in this thesis focuses on two instances of flexible computing that are crucial to the realization of the aforementioned vision. Location flexibility aims to, transparently and seamlessly, migrate applications between the edges and the core based on user demand. This enables performing the interactive tasks on rich edge clients and the computational tasks on powerful core servers. Scale flexibility is the ability of applications executing in cloud environments, such as parallel jobs or clustered servers, to swiftly grow and shrink their footprint according to execution demands. This thesis shows how we can use system virtualization to implement systems that
Improving the Design and Use of Correlation Filters in Visual Tracking

The volume of video data collected will only increase as the prevalence of automated systems continues to grow and those systems start to rely more on vision sensors to make decisions. Visual tracking is the process of automatically estimating the location of an object through the course of a video. The ability to track objects in video is useful in applications such as autonomous driving, surveillance, and robotics. The ability to track objects allows for more effective decision making processes for tasks such as predictive driving, anomaly detection, and face recognition. With the amount of data to parse and the benefits of doing so accurately, the need for fast and reliable visual tracking is clear. Correlation filters, previously used in detection and recognition tasks within single images, have become a popular approach to visual tracking because of their ability to efficiently match and align two images. Correlation filters have been adapted for visual tracking by developing incremental learning techniques, allowing efficient updating of correlation filters. Tracker elements such as more powerful feature representations and improved scale tolerance have led to state-of-the-art tracking performance. Still, despite the recent improvements in correlation filter trackers, there remain unexplored aspects of the union of correlation filters and visual tracking. This work explores alternative correlation filter designs that have not previously been adapted to visual tracking. We also introduce an occlusion detection system to address situations where the targets are temporarily not visible; one of the most challenging aspects of tracking. We validate our approaches on widely used benchmarks while also introducing a new evaluation metric that reflects the amount of activity that occurs within a given video.
Comparative Study on Perturbation Techniques in Privacy Preserving Data Mining on Two Numeric Datasets

Data Mining is a computational process that able to identify patterns, trends and behaviour from large datasets. With this advantages, data mining has been applied in many fields such as finance, healthcare, retail and so on. However, information disclosure become one of an issue during data mining process. Therefore, privacy protection is needed during data mining process which known as Privacy Preserving Data Mining (PPDM). There are several techniques available in PPDM and each of the techniques has its’ own benefits and drawbacks. In this research, perturbation technique is selected as privacy preserving technique. Perturbation technique is a method that alters the original data value before the application of data mining. In PPDM applications, perturbation technique able to provide a protection of data privacy but the accuracy of data should not be ignored too. In this research, three perturbation techniques are selected which are additive noise, data swapping and resample. For data mining techniques, two methods of classification are selected which are Naive Bayes and Support Vector Machines (SVM). With the selection of these techniques, the experimental results are evaluated based on the hiding failure, accuracy and precision. For overall result, resample is selected as the best perturbation technique in naive bayes and SVM classification for both glass and ionosphere datasets.
Using Multiple Fidelity Models in Motion Planning

Hospitals and warehouses use autonomous delivery robots to increase productivity. Robots must reliably navigate unstructured non-uniform environments which requires efficient long-term operation that robustly accounts for unforeseen circumstances. However, unreliable autonomous robots need continuous operator assistance, which decreases throughput and negates a robot’s benefit. Planning with high fidelity models is more likely to lead to more robust plans, but is not needed in many situations. More specifically, a complex model is an inefficient use of plan-time computation resources when a robot navigates a flat simple environment, but a simple model, that can generate plans quickly, may fail to capture complex environment locales leading to task impediments. This thesis presents a planning framework that reasons about multiple models for efficiently generating a single motion plan without sacrificing execution success. The framework chooses when to switch models and what model is most applicable within a single trajectory. This has the effect of focusing the use of complex models only when necessary. The framework also addresses uncertainty by adding variable uncertainty to models in the form of robot padding. The footprint padding is not fixed but is automatically chosen during plan-time based on the ability to find robust plans. The approach is evaluated by simulating a mobile robot with attached trailer through various hospital environments. Our simulation experiments demonstrate that multi-fidelity model switching increases plan-time efficiency while still maintaining execution success.
Therapeutic drug monitoring in oncology - What’s out there: A bibliometric evaluation on the topic

Pharmacological therapy is the mainstay of treatment for cancer patients. Despite wide interpatient variability in systemic drug concentrations for numerous antineoplastics, dosing based on body size remains the predominant approach. Therapeutic drug monitoring (TDM) is used for few antineoplastics in specific scenarios. We conducted a rapid bibliometric evaluation of TDM in oncology to capture a snapshot of research in this area over time and explore topics that reflect development in the field. Reports with the composite, indexed term ‘therapeutic drug monitoring’ in the title and abstract were extracted from MEDLINE (inception to August 2021). Reports related to applications in cancer were selected for inclusion and were tagged by study design, antineoplastic drugs and concepts related to TDM. We present a timeline from 1980 to the present indicating the year of first report of antineoplastic agents and key terms. The reports in our sample primarily reflected development and validation of analytical methods with few relating to clinical outcomes to support implementation. Our work emphasises evidence gaps that may contribute to poor uptake of TDM in oncology.
An improved real-time object proposals generation method based on local binary pattern

Generating a group of category-independent proposals of objects in an image within a very short time is an effective approach to accelerate traditional sliding window search, which has been widely used in preprocessing step of object recognition. In this article, we propose a novel object proposals generation method to produce an order set of candidate windows covering most of object instances. With combination of gradient and local binary pattern, our approach achieves better performance than BING in finding occluded objects and objects in dim lighting conditions. In experiments on the challenging PASCAL VOC 2007 data set, we show that our approach is significantly more accurate than BING. In particular, using 2000 proposals, we achieve 97.6% object detection rate and 69.3% mean average best overlap. Moreover, our proposed method is very efficient and takes only about 0.006 s per image on a laptop central processing unit. The detection speed and high accuracy of proposed method mean that it can be applied to recognizing specific objects in robot visions.
Efficacy of ozone adjuvant therapy in COVID-19 patients: A meta-analysis study

Introduction Using ozone therapy to manage COVID-19 patients has been accompanied by conflicting results in prior studies. Therefore, we aimed to widely assess the effects of ozone as adjuvant therapy in COVID-19 patients. Methods PubMed, Scopus, Web of Science, Cochrane, ProQuest, Springer, and Sage journals were searched systematically until April 2022. Mortality rate, ICU admission, hospital-length stay, negative PCR, pulmonary, renal, and hepatic functions, as well as inflammatory and blood systems were pooled to compare the efficacy of ozone as adjacent therapy (OZ) and standard treatment (ST). Analyses were run with the random/fixed models, sub-group analysis, funnel plot, and sensitivity analysis using comprehensive meta-analysis (CMA) software version 2.0. Results The results of four randomized clinical trials (RCTs) and four case-control studies with a total of 371 COVID-19 positive patients were analyzed. The OZ group patients had a shorter length of hospital stay (P > 0.05), lower ICU admissions (P > 0.05), and lower mortality rates (P < 0.05) than the ST group cases. After treatment, 41% more COVID-19 patients had negative PCR tests than the ST group (P < 0.05). Serum creatinine and urea levels were not modified in either group (P > 0.05). Moreover, except for albumin serum levels, which decreased significantly in the OZ group, serum bilirubin, ALT, and AST were not modified in either group (P > 0.05). Both arms did not show a decrease in C-reactive protein blood levels (P > 0.05), but the OZ group showed a significant modification in LDH serum levels (P < 0.05). Unlike the d-dimer and WBC serum levels (P > 0.05), platelet levels were increased in the OZ group (P < 0.05). No negative side effects were demonstrated in either group. Conclusion Ozone therapy was effective significantly on PCR test and LDH serum levels, as well as mortality based on overall estimation. Concerning the length of hospital stay and ICU admissions, although the results were insignificant, their effect sizes were notable clinically. More RCT studies are needed to show the efficacy of ozone therapy on other studied variables.
Cognitive-behavioral therapy and relationship therapy in the treatment of children referred for antisocial behavior

The present study evaluated alternative treatments for children (N = 112, ages 7-13) referred for severe antisocial behavior. Children were randomly assigned to one of three treatments: problem-solving skills training (PSST), problem-solving skills training with in vivo practice (PSST-P), which included therapeutically planned activities to extend training to settings outside of treatment, or client-centered relationship therapy (RT). PSST and PSST-P children showed significantly greater reductions in antisocial behavior and overall behavior problems, and greater increases in prosocial behavior than RT children. These effects were evident on measures obtained immediately after treatment and at a 1-year follow-up, and on measures of child performance at home and at school. PSST-P children showed greater changes than PSST children on measures of functioning at school at posttreatment, but these differences were no longer evident at follow-up. Children in both PSST conditions showed significant reductions in deviant behavior and improvements in prosocial behavior from pretreatment to follow-up, whereas RT children tended to remain at their pretreatment level of functioning. Notwithstanding the significant improvements, comparisons with nonclinic (normative) samples revealed that the majority of youth remained outside of the normal range of deviant behavior. Possible directions for improving treatment for antisocial youth are highlighted.
Design of Fuzzy FOPID Controller Optimized by ICA for Control of AVR

In this paper Fuzzy-FOPID controller is used in order to control automatic voltage regulator (AVR) system. This controller has six parameters. In this paper, optimization method is employed to determine the controller parameters. For this purpose, imperialist competitive algorithm (ICA) is used for its high accuracy and good performance. To illustrate performance of proposed controller, some simulations are carried out in MATALB and the results are compared with FOPID controller results. Good performance of proposed controller is validated by results of the simulations.
Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery

Preoperative prediction of recurrence outcome in hepatocellular carcinoma (HCC) facilitates physicians’ clinical decision-making. Preoperative imaging and related clinical baseline data of patients are valuable for evaluating prognosis. With the widespread application of machine learning techniques, the present study proposed the ensemble learning method based on efficient feature representations to predict recurrence outcomes within three years after surgery. Radiomics features during arterial phase (AP) and clinical data were selected for training the ensemble models. In order to improve the efficiency of the process, the lesion area was automatically segmented by 3D U-Net. It was found that the mIoU of the segmentation model was 0.8874, and the Light Gradient Boosting Machine (LightGBM) was the most superior, with an average accuracy of 0.7600, a recall of 0.7673, a F1 score of 0.7553, and an AUC of 0.8338 when inputting radiomics features during AP and clinical baseline indicators. Studies have shown that the proposed strategy can relatively accurately predict the recurrence outcome within three years, which is helpful for physicians to evaluate individual patients before surgery.