Miracle-dz/UniFAR-FTUs huggingface dataset:
CLEP_A_203023 969..980

Sara Badre-Esfahani 1,2 Mette Bach Larsen 1 Lene Seibæk 2 Lone Kjeld Petersen Jan Blaakær 3 Henrik Støvring 4 Berit Andersen 1 1Department of Clinical Medicine, Aarhus University, Aarhus C DK-8000, Denmark; 2Department of Gynaecology and Obstetrics, Aarhus University Hospital, Aarhus N DK-8200, Denmark; 3Department of Gynaecology and Obstetrics, Odense University Hospital, Odense C DK-5000, Denmark; 4Department of Public Health, Aarhus University, Aarhus C DK-8000, Denmark Background: The combination of organized cervical cancer screening and childhood HPV vaccination programs has the potential to eliminate cervical cancer in the future. However, only women participating in both programs gain the full protection, and combined non-attenders remain at high risk of developing cervical cancer. Our aim was to analyze the association between non-adherence to HPV vaccination and non-participation in cervical cancer screening for the total population and stratified by native background and parental education. Participants: Women born in 1993 eligible for both childhood HPV vaccination and first cervical cancer screening. Analysis: Logistic regression models were used to estimate the odds ratio (OR) of non-participation in cervical cancer screeningwith 95% confidence intervals (CI). Stratified and adjusted logistic regression models were used along with the Wald test in order to test for interaction. Results: 24,828 women were included in the study. Among vaccinated women, 61.4% participated in cervical cancer screening; only 39.0% of unvaccinated women participated in cervical cancer screening. Unvaccinated and unscreened women were often non-native and had the lowest socio-economic status, whereas vaccinated and screened women were often native and had the highest socio-economic status. The adjusted OR for non-participation in cervical cancer screening was 2.07 [95% CI: 1.88–2.28] for unvaccinated compared to vaccinated women. After stratifying by country of origin, unvaccinated natives had the highest adjusted OR of not participating in cervical cancer screening compared to non-native women from both western and non-western countries (adjusted ORs of 2.2 [95% CI: 2.0– 2.4], 1.3 [95% CI: 0.6–2.8], and 1.5 [95% CI: 1.1–2.0], respectively) (Wald test p=0.019). Conclusion: Among natives, non-adherence to HPV vaccination and non-participation in screening seem to be signs of generally poor health-preventive behavior, whereas among non-natives from non-western countries, non-attendance in HPV vaccination and cervical cancer screening seem to be influenced by unrelated factors. Therefore, a differentiated and culturally sensitive approach is needed to enhance overall cervical cancer preventive behavior across different nativities.
Application of Ratio Cut Off of CK-MB/CK in Differential Diagnosis of Myocardial Infarction and Muscle Lesion

To study the differential diagnostic feasibility of ratio cut off of CK-MB/CK for acute myocardial infarction and muscle lesion. Methods:The data of CK and CK-MB were collected from the patients with myocardial infarction (group I) and the patients with muscle lesion (group II) and statistically analyzed respectively,so as to calculate their sensitivity, sepcificity and diagnostic index. Results: About the logarithm mean value of CK, there was no significant differentce between thegroupl [ 617(162-3 495 )u/L] and the group II [ 589(165-5 432)u/L] (t = 1.74, P0.05). The logarithm mean value of CK-MB in the group I [68(9~530)u/L] was significantly higher than that in group II [24(8~ 183)u/L] (t = 5.83, P 0.01), but the general diagnostic index was less than 150 % . The CK-MB/CK ratio in the group I [(11.4 + 4.08)%] was significantly higher than that in group II [(4.47? .07)%] (t = 11.17, P0.01) . When the cut off was determined as 6% , the diagnostic index could reach 176%. The diagnostic efficiency would be higher than that of CK-MB peak value. Four pieces of criterion were suggested for diagnosing myocardial infarction with CK and CK-MB. Conclusion: According to the ratio cut off of CK-MB/CK,it is feasible to differentiate myocardial infarction from muscle lesion.
EEG-Induced Adaptation of Controller Parameter for Closed-Loop Position Control of the End-Effecter in a Robot Arm

Existing brain-computer interfaces for position control of robot arms usually attempt to control the motion of the individual links of the arm in order to control the position of the end-effecter. The above position control schemes suffer from one fundamental limitation in mental pre-selection of the target positions. This paper provides an interesting approach to overcome the above limitation by attempting to directly control the position of the end-effecter in the desired location by mental imagination of its movement rather than attempting to position the individual links of the robot arm. This has been realized by utilizing the benefits of inverse kinematics of the robot arm using neural network based function approximation to serve the computation of inverse kinematics in real time. Apart from the above, the other notable point of interest of the present work lies in controlling the step-size of motion of the end-effecter by brain-inspired position control. An increase in step-size is allowed until the end-effecter crosses any one of the three reference planes of the target position, whereas a decrease in step-size is selected when the end-effecter crosses a reference plane. The decision about crossing the reference plane by the end-effecter is subjective and is also dependent on subject's training. The proposed 3-dimensional position control scheme offers low settling time (0.5 sec), small (<1 %) peak overshoot and also small steady-state error (0.02 %). It can readily be used as an artificial appendage for the people with neuro-motor disability.
A Behavior Sequence Clustering-Based Enterprise Network Anomaly Host Recognition Method

Abnormal host detection is a critical issue in an enterprise intranet data center. The traditional anomaly host detection method mainly focuses on detecting anomaly behavior, and the abnormality determination for a single behavior point often has certain limitations. For example, the entire attack process cannot be completely restored. And it will cause a lot of underreporting. Therefore, in this paper, we propose A Behavior Sequence Clustering-based Enterprise Network Anomaly Host Detection Method to solve the problem of anomaly host detection of an enterprise network. We use the Toeplitz Inverse Covariance-Based Clustering (TICC) algorithm [1] to segment and cluster time series data and mining anomaly host behavior sequences, identify the anomaly host of the enterprise network. The experimental results show that the Behavior Sequence Clustering-based Enterprise Network Anomaly Host Recognition Method can quickly identify the anomaly host and accurately restore the complete attack process.
A Remaining Useful Life Prediction Method with Automatic Feature Extraction for Aircraft Engines

In the aeronautic and astronautic fields, the safety and stability of aircraft engines are indispensable. The remaining useful life prediction can help aircraft engines in assessing health status, scheduling maintenance tasks, and ensuring flight safety. A large number of data-driven methods are applied to the remaining useful life prediction for aircraft engines, but they generally suffer from two defects: (i) feature extraction relies heavily on labor and requires extensive domain knowledge; (ii) model learning rarely takes full account of the chronological order and the temporal correlation of the health monitoring data. To address these challenges, this paper proposes an effective remaining useful life prediction method that combines the Stacked Sparse Autoencoder with the Gated Recurrent Unit. Firstly, the Stacked Sparse Autoencoder is designed to automatically extract representative advanced features, which does not require manual extraction and a lot of domain knowledge. Then, the Gated Recurrent Unit is used as the prediction model to make full use of the temporal correlation of the sequential monitoring data and improve the prediction accuracy. In order to verify the effectiveness of the proposed method, a lot of simulations on the NASA aircraft engine dataset are carried out in this paper. The simulation results show that the proposed method has a promising prospect for industrial applications.
Serverless Parachutes: Preparing Chosen Functionalities for Exceptional Workloads

Function-as-a-Service (FaaS) is an emerging cloud service model that enables composing applications using arbitrary, small, and event-driven code snippets managed by cloud providers and that can be scaled to zero. The scalability properties of FaaS look attractive for handling rare or unexpected high loads that affect only particular functionalities of the application. However, deciding on the component granularity upfront or reengineering the architecture of an entire application for rare workloads is often a very difficult challenge or even infeasible. In this work, we introduce a method that prepares annotated functionalities for handling rare workloads by automatically extracting them from the source code of the application and additionally deploying them as FaaS functions, while keeping the original application's functionalities and architecture unchanged. In this way, the benefits of FaaS can be leveraged without the need to reengineer the application only for rare cases. We validate our method by means of a prototype, evaluate its feasibility in a set of experiments, and discuss limitations and future work.
A Twin-8T SRAM Computation-in-Memory Unit-Macro for Multibit CNN-Based AI Edge Processors

Computation-in-memory (CIM) is a promising candidate to improve the energy efficiency of multiply-and-accumulate (MAC) operations of artificial intelligence (AI) chips. This work presents an static random access memory (SRAM) CIM unit-macro using: 1) compact-rule compatible twin-8T (T8T) cells for weighted CIM MAC operations to reduce area overhead and vulnerability to process variation; 2) an even–odd dual-channel (EODC) input mapping scheme to extend input bandwidth; 3) a two’s complement weight mapping (C2WM) scheme to enable MAC operations using positive and negative weights within a cell array in order to reduce area overhead and computational latency; and 4) a configurable global–local reference voltage generation (CGLRVG) scheme for kernels of various sizes and bit precision. A 64 $\times $ 60 b T8T unit-macro with 1-, 2-, 4-b inputs, 1-, 2-, 5-b weights, and up to 7-b MAC-value (MACV) outputs was fabricated as a test chip using a foundry 55-nm process. The proposed SRAM-CIM unit-macro achieved access times of 5 ns and energy efficiency of 37.5–45.36 TOPS/W under 5-b MACV output.
Self-Service Data Preprocessing and Cohort Analysis for Medical Researchers

Medical researchers are increasingly interested in data-driven approaches to support informed decisions in many medical areas. They collect data about the patients they treat, often creating their own specialized data tables with more characteristics than what is defined in their clinical information system (CIS). Usually, these data tables or sEHR (small electronical health records) are rather small, maybe containing the data of only hundreds of patients. Medical researchers are struggling to find an easy way to first clean and transform these sEHR, and then create cohorts and perform confirmative or exploratory analysis. This paper introduces a methodology and identifies requirements for building systems for self-service data preprocessing and cohort analysis for medical researchers. We also describe a system based on this methodology and the requirements that shows the benefits of our approach. We further highlight these benefits with an example scenario from our projects with clinicians specialized on head&neck cancer treatment.
Two-Step Mesa Structure GaN p-n Diodes With Low ON-Resistance, High Breakdown Voltage, and Excellent Avalanche Capabilities

Vertical structure GaN power devices fabricated on freestanding GaN substrates have high potentials in ideally efficient energy conversion systems. This letter describes successful acquisition of high avalanche capabilities for high breakdown voltage GaN p-n junction diodes. We have already reported the high avalanche capability by applying a punch-through structure in p-GaN layer, however; the structure caused a drawback of increase in on-resistance and turn-on voltage. By applying a newly developed two-step mesa structure consisting of the first inner mesa with partially thinned p-GaN layer and the second outer mesa etched to n-GaN drift layer, the high avalanche capabilities with reversible current-voltage characteristics have been realized at high breakdown voltages of 4.7-4.8 kV without sacrificing the forward I-V characteristics. The two-step mesa structure transferred the position of the peak electric field in the p-n junction from the dry-etch damaged second outer mesa to the first mesa covered by the thinned p-GaN layer, which could lead the mild breakdown. The high avalanche capability was obtained with good reproducibility regardless of the anode electrode diameter. This structure can contribute to the construction of robust power systems.
Prototype Implementation of Edge Encryption in IoT Architecture

In the IoT system, captured plaintext data is sent to the cloud for storing, computing and further processing. But it is a potential concern to secure sensitive information like personal, financial, business, medical, etc. A certain number of researchers established IoT architecture with distinctive layers where data security is solely dependent on cloud service providers (CSPs) which make potential security threats. In this paper, an edge encryption support in the IoT architecture is implemented where the involvement of CSPs for security purposes becomes nullified. As the data become encrypted before it gets sent to the cloud, it becomes more secure and reliable for both the owner and shared users. Additionally, only the IoT node owner and shared users can access those data which provides better security, privacy, and control of data.
Dynamic Adaptation of Application Execution on Heterogeneous Architectures

The era of high-performance computing has rapidly evolved into heterogeneous computing by bringing together architecturally different devices in computer systems; with a promise of opportunities to enhance the application performance using the different computational units. The benefits of heterogeneous computing are yielded through hybrid applications having specific programming models and instructions that help in the execution of tasks on to corresponding computing devices. In a large HPC system, the computational power of the heterogeneous devices is harnessed with judicious mechanisms of tasks execution based on resource availability. In heterogeneous clusters, the jobs in the queue experience increased turnaround time due to demand for accelerators for the execution of code. This paper describes the concept and implementation of an adaptive and dynamic load re-targeting scheme for applications on heterogeneous platforms to reduce the turnaround time of jobs.
Comparative analysis on Prediction Models with various Data Preprocessings in the Prognosis of Cervical Cancer

Cancer is a life-threatening disease, which is considered incurable most of the time. Hence, an early prediction for a possible risk of cancer can be very helpful in order to start treatment as soon as possible. One such cancer is Cervical Cancer. Here, we have used data about the lifestyle and previous medical history of women in order to try and predict if a woman is susceptible to cervical cancer or not. However, the dataset needed a lot of preprocessing for handling missing values. Four different techniques were used in order to fill in the missing values. Soon after, the correlation among the variables was calculated to keep the most effective ones. Then, prediction algorithms namely Decision Tree, Random Forest, Logistic Regression, Naïve Bayes, Support Vector Machine and Neural Network were used to train and test for positive and negative diagnosis. Our findings were that SVM and Logistic Regression had the highest Precision, Recall, F1 Score and Accuracy.
An Analysis of Line of Sight (LoS) in Mobile Ad hoc Networks by varying Impairments

The era of multimedia communication is a major part and partial of each and every user life each task now depends very much on the internet. We believe that line-of-sight is the biggest problem many are facing. In particular, the problem of mobile station tracking at various base stations is considered. The data is transferred more quickly in a flat area as we are all aware. If there were obstacles in between the mobile station and the base station, we discussed some of the issue here. (Or between two host). The overview of Line-of-Sight and various problems might arise are depicted in this paper. The various QoS parameters are considered for the evaluation of LoS with and with obstacles. The overview of the LoS in mobile adhoc networks are dealt.
Topology-Based Spectral Sparsification

Graph sampling is often used to reduce a large graph, with challenges to ensure the sample is representative of the original graph. Spectral sparsification is a related concept that creates a sparsified version of graphs that preserves the spectrum of the original graph.We present TSS, a sampling method combining spectral sparsification with topology-based decomposition of graphs. TSS aims to improve the runtime efficiency of spectral sparsification-based sampling through a Divide-and-Conquer approach using topology-based decomposition and combine it with the superior sampling quality spectral sparsification-based sampling offers over stochastic sampling. We also present DTSS, the distributed version of TSS, aimed for further runtime gains over sequential TSS.Experiments verify that TSS produces samples of the same quality as spectral sparsification-based sampling while attaining significant runtime improvements of up to 60% on real world datasets. DTSS on 5 servers runs up to another 80% faster compared to TSS.
Using GPS for Attitude Determination

The determination of body orientation based on information from Global Positioning System is considered. Two algorithms for solving the problem are considered, depending on the initial information. In the first case, differential range measurements for several points of the object are assumed to be known. In the second case, the coordinates of several points of the object are used. The problem is solved using the least squares method in matrix form. A form of the loss function is proposed, which allows one to obtain simplified expressions for orientation angles for an arbitrary arrangement of satellites and antennas. The influence of the number of measurements on the accuracy of calculations in the presence of interference during measurements is estimated
Sound Source Separation Using Neural Network

We have given an unsupervised separation of the sound for combination of the unknown different sound in the channel based on the DNN i.e. deep neural network. here it can be separate the different voices with the assumption of the dissimilarity measures of the audio signal. It has been shown that the different sound can be classified and separate. Here uses DNN based algorithm to acquire a mapping of mixed signal to the recovered signal. Also, it gives a separation architecture where it constructs an DNN separation module from which the separation of sound is done. After the training and testing of the sound separation it is shows that the performance of the system is better than other sound separation system and its very useful in segregate the multiple mixed signal. sound separation has varied uses in most real-time applications. In this paper, we use the mask for training targets of DNN for speech separation. The experiments are done with adding different noise conditions. The evaluation of this done using STOI evaluation parameter.
Sparse Tensor Decomposition for Multi-task Interaction Selection

Multi-task learning aims to improve the generalization performance of related tasks based on simultaneous learning where prediction models share information. Recently, identifying significant feature interaction attracts more interests because of its practical importance. We propose a second-order interaction method for multi-task learning, which identifies significant linear and interaction terms. We develop a sparse tensor decomposition based on a feature augmentation and a symmetrization trick to express the prediction models of related tasks as the linear combinations of the shared parameters. We show that the proposed method could generate diverse relationships between linear and interaction terms. In minimizing the resulting multiconvex objective function, we select an initial value by deriving unbiased estimators and proposing a tensor decomposition. Experiments on synthetic and benchmark datasets demonstrate the effectiveness of the proposed method.
AQUA+: Query Optimization for Hybrid Database-MapReduce System

MapReduce has been widely recognized as an efficient tool for large-scale data analysis. It achieves high performance by exploiting parallelism among processing nodes while providing a simple interface for upper-layer applications. However, there are many existing applications maintaining their data in a distributed database. It is costly to export those data into the storage system of MapReduce (normally a distributed file system). Moreover, compared to MapReduce, database is equipped with many state-of-the-art techniques, such as index and optimizer. Therefore, a hybrid Database-MapReduce system inheriting the advantages of both systems is preferred. In this paper, we propose AQUA+, a query optimizer tailored for the hybrid system. AQUA+ is an extension work of our previous system AQUA. It generates a plan that adaptively assigns the operators to the database engine and MapReduce engine to optimize the performance. The intuition is to exploit the index, co-partition and other features provided by the database as much as possible and reduce the data volume processed by the MapReduce. Due to the complexity of query optimization, in AQUA+, we introduce a novel tuning technique, learning to optimize. In particular, two neural networks are trained to predict cost and refine query plan, respectively. We train them based on our log of real query processing. Experiments carried out on our in-house cluster confirm the effectiveness of our query optimizer.
A Novel Approach to Recommend Products for Mobile-Commerce site Using Weighted Product Taxonomy

The importance of the World Wide Web has increased enormously in recent years which leads to a large amount of information accessible through the web. The increasing importance of the web provides huge benefits to business, so to improve the business over web recommender systems has been proposed. Recommender systems works based on effective prediction which helps people for convenient access to their products that they might be interested in the real world. In this paper authors proposed a novel approach for product recommendation based on weighted product taxonomy. Product taxonomy is a hierarchical organization of products with different levels of hierarchy. Customer behavior and navigational factors are used for calculation of weight for product taxonomy. Authors also proposed a heuristic algorithm to search product “watch” in weighted product taxonomy. To prove that results of proposed heuristic algorithm are fast, authors apply independent sample ‘t’ test at 5% level of significance.