Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical Unclonable Function-based Key Sharing for IoT Security

In many Industry Internet of Things (IIoT) applications, resources like CPU, memory, and battery power are limited and cannot afford the classic cryptographic security solutions. Silicon Physical Unclonable Function (PUF) is a lightweight security primitive that exploits manufacturing variations during the chip fabrication process for key generation and/or device authentication. However, traditional weak PUFs such as Ring Oscillator (RO) PUF generate chip-unique key for each device, which restricts their application in security protocols where the same key is required to be shared in resource-constrained devices. In order to address this issue, we propose a PUF-based key sharing method for the first time. The basic idea is to implement one-to-one input-output mapping with Lookup Table (LUT)-based interstage crossing structures in each level of inverters of RO PUF. Individual customization on configuration bits of interstage crossing structure and different RO selections with challenges bring high flexibility. Therefore, with the flexible configuration of interstage crossing structures and challenges, CRO PUF can generate the same shared key for resource-constrained devices, which enables a new application for lightweight key sharing protocols.
Using Lasso RCCA for cross-language information retrieval

ABSTRACT Restricted canonical correlation analysis and the lasso shrinkage method were paired together for canonical correlation analysis with non-negativity restrictions on datasets, where a sample size is much smaller than the number of variables. The method was implemented in an alternating least-squares algorithm and applied to cross-language information retrieval on a dataset with aligned documents in eight languages. A set of experiments was ran to evaluate the method and compare it to other methods in the field.
Smart Contract-Based Review System for an IoT Data Marketplace

Internet of Things (IoT)-based devices, especially those used for home automation, consist of their own sensors and generate many logs during a process. Enterprises producing IoT devices convert these log data into more useful data through secondary processing; thus, they require data from the device users. Recently, a platform for data sharing has been developed because the demand for IoT data increases. Several IoT data marketplaces are based on peer-to-peer (P2P) networks, and in this type of marketplace, it is difficult for an enterprise to trust a data owner or the data they want to trade. Therefore, in this study, we propose a review system that can confirm the reputation of a data owner or the data traded in the P2P data marketplace. The traditional server-client review systems have many drawbacks, such as security vulnerability or server administrator’s malicious behavior. However, the review system developed in this study is based on Ethereum smart contracts; thus, this system is running on the P2P network and is more flexible for the network problem. Moreover, the integrity and immutability of the registered reviews are assured because of the blockchain public ledger. In addition, a certain amount of gas is essential for all functions to be processed by Ethereum transactions. Accordingly, we tested and analyzed the performance of our proposed model in terms of gas required.
Just Living With Obese Family Members Increases Your Risk of Type 2 Diabetes

IN BRIEF In this new era of accountable care and population health, large provider organizations are looking for new ways to predict diseases in their population, especially for people with diabetes. Although diabetes has been associated with the incidence of obesity, many diabetes patients are not obese. However, we find that just living in a household with one or more obese biologically related family members is a major risk factor for diabetes, even after accounting for all the other traditional risk factors.
Proportional-Resonant Controller And LCL Filter Design For Single-Phase Grid-Connected PV Micro-Inverters

This paper presents a brief behavior description of LCL filters through the analysis of its frequency response and the convenience of its use instead L filters. Moreover, the equations that allow its design and implementation are also described. Regarding the Proportional Resonant (PR) controllers, their behavior, discretization techniques and digital implementation are also studies. The developed systems are evaluated by mathematical model simulation and experimental prototype test. It is concluded that, for inverters operating as a current source, the PR controllers are suitable to follow a sinusoidal reference signal. On the other hand, LCL-type filter largely eliminates the harmonic content of the output current, making it compatible for Grid-Connected PV micro-inverters.
Optimal Control of Payload's Skew Rotation in Crane Systems with State and Control Input Constraints

In this paper, a nonlinear sub-optimal time control is proposed as a vibration controller for the payload's skew rotation process of crane systems. Physical constraints of the actuator including bounded velocity and bounded acceleration are explicitly taken into account in the design process. Clipping-off conjugate gradient method is adopted to solve the bounded optimal control problem, whereas penalty function is employed to relax the state constraint. Both simulation and experimental results are provided to clarify the effectiveness of the optimal control system. The proposed scheme can be directly applied to transfer the payload to desirable skew orientation without any residual oscillation, or it can be utilized as a reference trajectory planner of the skewing control module for a 2-DOFs controller in either overhead or rotary crane systems.
Acetaminophen enhances the reflective learning process

Abstract Acetaminophen has been shown to influence cognitive and affective behavior possibly via alterations in serotonin function. This study builds upon this previous work by examining the relationship between acetaminophen and dual‐learning systems, comprising reflective (rule‐based) and reflexive (information‐integration) processing. In a double‐blind, placebo‐controlled study, a sample of community‐recruited adults (N = 87) were randomly administered acetaminophen (1000 mg) or placebo and then completed reflective‐optimal and reflexive‐optimal category learning tasks. For the reflective‐optimal category learning task, acetaminophen compared to placebo was associated with enhanced accuracy prior to the first rule switch (but not overall accuracy), with needing fewer trials to reach criterion and with a faster learning rate. Acetaminophen modestly attenuated performance on the reflexive‐optimal category learning task compared to placebo. These findings indirectly support two positions that have been proposed elsewhere. First, they are consistent with the view that acetaminophen has an influence on the serotonergic system. Second, the findings are consistent with a proposed link between elevated serotonin function and relative dominance of effortful, rule‐based processing.
Effectiveness of the 2014 European Society of Cardiology guideline on sudden cardiac death in hypertrophic cardiomyopathy: a systematic review and meta-analysis

Objective In 2014, the European Society of Cardiology (ESC) recommended the use of a novel risk prediction model (HCM Risk-SCD) to guide use of implantable cardioverter defibrillators (ICD) for the primary prevention of sudden cardiac death (SCD) in patients with hypertrophic cardiomyopathy (HCM). We sought to determine the performance of HCM Risk-SCD by conducting a systematic review and meta-analysis of articles reporting on the prevalence of SCD within 5 years of evaluation in low, intermediate and high-risk patients as defined by the 2014 guidelines (predicted risk <4%, 4%–<6% and ≥6%, respectively). Methods The protocol was registered with PROSPERO (registration number: CRD42017064203). MEDLINE and manual searches for papers published from October 2014 to December 2017 were performed. Longitudinal, observational cohorts of unselected adult patients, without history of cardiac arrest were considered. The original HCM Risk-SCD development study was included a priori. Data were pooled using a random effects model. Results Six (0.9%) out of 653 independent publications identified by the initial search were included. The calculated 5-year risk of SCD was reported in 7291 individuals (70% low, 15% intermediate; 15% high risk) with 184 (2.5%) SCD endpoints within 5 years of baseline evaluation. Most SCD endpoints (68%) occurred in patients with an estimated 5-year risk of ≥4% who formed 30% of the total study cohort. Using the random effects method, the pooled prevalence of SCD endpoints was 1.01% (95% CI 0.52 to 1.61) in low-risk patients, 2.43% (95% CI 1.23 to 3.92) in intermediate and 8.4% (95% CI 6.68 to 10.25) in high-risk patients. Conclusions This meta-analysis demonstrates that HCM Risk-SCD provides accurate risk estimations that can be used to guide ICD therapy in accordance with the 2014 ESC guidelines. Registration number PROSPERO CRD42017064203;Pre-results.
An Approximate Nearest Neighbour System For Neonatal EEG Recall

Clinical neurophysiologists often find it difficult to recall rare EEG patterns despite the fact that this information could be diagnostic and help with treatment intervention. Traditional search methods may take time to retrieve the archived EEGs that could provide the meaning or cause of the specific pattern which is not acceptable as time can be critical for sick neonates. If neurophysiologists had the ability to quickly recall similar patterns, the prior occurrence of the pattern may help make an earlier diagnosis. This paper presents a system that may be used to assist a clinical neurophysiologist in the recall of neonatal EEG patterns. The proposed system consists of an alignment technique followed by an approximate nearest neighbour search algorithm called locality sensitive hashing. The system was tested on six different neonatal EEG pattern types with 430 events in total and the results are presented in this paper.
Analyzing The Effect Of Antecedents Of Turnover Intention According To Generations

There are many predictors that determine the turnover intention of employees. Among these predictors, mobbing behaviour can be disruptive in the workplace since workplace deviance is a matter which most organizations wrestle today. Organizational commitment is an important variant for employees to achieve organizational success whereas ethical climate is the picture of an organization which demonstrates its’ moral philosophy. This study examines the main effects of ethical climate, mobbing, and organizational commitment in predicting turnover intention among the sample of 166 employees, working in a municipality, particularly in Istanbul province, Turkey. The findings indicate that mobbing affect turnover intention positively, while ethical climate has no effect in estimating turnover intention. Study results also suggest that commitment (continuance and affective) is also found to be negatively related to turnover intention of employees. In addition, for Generation-X members, continuance commitment and for Generation-Y members, affective commitment has a significant effect on turnover intention.
Estimation of the Operating Force From the Human Motion

In this research, we propose a new method to estimate the operating force exerted when moving an object with the hands by observing the human motion only. The required force for moving the object can be estimated without equipping any sensors on the object or on the human body. From the measured joint angles, we calculate the center of mass and the hand’s position using a human model. Then, the operating forces of the hands can be estimated by considering the balance of forces and moments using a dynamic model. In the experiments, we estimated the operating force of the hands for three representative movements of operating a box: pushing, pulling and lifting. By comparing the estimated and measured operating force, we verified the effectiveness of the proposed method with human subjects.
Chronic migraine patients show cognitive impairment in an extended neuropsychological assessment

ABSTRACT The objective of the present study was to assess the presence of cognitive deficits in patients with chronic migraine, and to assess the main factors that trigger cognitive disorders, such as comorbidities or the use of medications. Methods: Chronic migraine and control groups were interviewed in a case-control study. The frequency and intensity of the headache, medication used and associated comorbidities were determined. All patients were submitted to an extended neuropsychological assessment. Results: The chronic migraine group (n = 30) had a worse performance in the Montreal Cognitive Assessment Test (p = 0.00), Verbal Fluency (p = 0.00), Stroop (p = 0.00), Clock Drawing Test (p = 0.00), Digit Span (p = 0.00) and Matrix Reasoning (p = 0.01). After statistical adjustment by linear regression, migraine continued to be the only relevant factor in the poorer performance in the Montreal Cognitive Assessment, Verbal Fluency, Clock Drawing and Stroop tests. Conclusion: Patients with chronic migraine have cognitive deficits in multiple tasks, regardless of the presence of comorbidities or the use of medications.
A Spark-based Analytic Pipeline for Seizure Detection in EEG Big Data Streams

Around 1% of the people in the world suffer from epilepsy, which is the second most neurological disorder in the human after stroke. The spontaneous recurrence of seizures is the main clinical manifestation of the epilepsy. Real time detecting the seizure in the Electroencephalogram (EEG) signal is a clinical way in the diagnosis and treatment of epilepsy. The unpredicted nature of the epileptic seizures, necessitates continuous monitoring and recording of the brain activities using high-throughput neurophysiological data acquisition systems over extended periods of time. The sheer volume and the velocity of the data generated from continuous monitoring the brain activities make real-time seizure detection a big data analytic problem. In this paper, we present a Spark-based machine-learning approach to the seizure detection problem using linear dimensionality reduction and classification. Using this approach, we achieved an average accuracy, sensitivity, specificity across all patients $(\mathrm{N=24)$ of 99.32%, 99.41%, and 95.25%, respectively. Also, the average Iatency of the Spark-based seizure detection framework is about 0.38 ms.
Least Costly Energy Management for Extended Range Electric Vehicles with Start-Up Characterization

In this work, the Energy Management Strategy (EMS) problem is solved considering an Electric Vehicle (EV) equipped with a Range Extender (REX), a device developed to increase the All Electric Range (AER) provided by the battery, which can be switched ON and OFF depending on the need. First, a control-oriented modeling of the powertrain is introduced focusing attention on REX description in terms of power generation, and thermal dynamics. Secondly, the EMS problem is formalized as a mixed-integer convex program. Thus, the optimal energy management policy is obtained by minimizing an objective function taking into account electricity and battery aging costs, REX fuel consumption and start-up costs. The introduction of REX thermal dynamics allows for temperature varying start-up costs and simplified REX aging modeling. To show the effectiveness of the EMS, an electric bus case study is dealt with and a sensitivity analysis is performed over some critical optimization parameters to understand when purchasing a REX is interesting and economically effective.
Intake of fermented and non-fermented dairy products and risk of incident CHD: the Kuopio Ischaemic Heart Disease Risk Factor Study

Abstract Recent dairy product studies have suggested that fermented rather than non-fermented dairy products might provide benefits on cardiovascular health, but the evidence is inconclusive. Therefore, we investigated whether fermented and non-fermented dairy products have distinct associations with the risk of incident CHD in a population with high dairy product intake. The present study included a total of 1981 men, aged 42–60 years, from the Kuopio Ischaemic Heart Disease Risk Factor Study, with no CHD at baseline. Dietary intakes were assessed with instructed 4-d food records. We used Cox’s proportional hazards regression model to estimate the associations with the risk of CHD. Fatal and non-fatal CHD events were ascertained from national registries. During a mean follow-up of 20·1 years, 472 CHD events were recorded. Median intakes were 105 g/d for fermented (87 % low-fat products) and 466 g/d for non-fermented dairy products (60 % low-fat products). After adjusting for potential confounders, those in the highest (v. lowest) intake quartile of fermented dairy products had 27 % (95 % CI 5, 44; P-trend=0·02) lower risk of CHD. In contrast, those in the highest intake quartile of non-fermented dairy products had 52 % (95 % CI 13, 104; P-trend=0·003) higher risk of CHD. When analysed based on fat content, low-fat (<3·5 % fat) fermented dairy product intake was associated with lower risk (hazard ratio in the highest quartile=0·74; 95 % CI 0·57, 0·97; P-trend=0·03), but high-fat fermented dairy and low-fat or high-fat non-fermented dairy products had no association. These results suggest that fermented and non-fermented dairy products can have opposite associations with the risk of CHD.
High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains

PURPOSE Sparsely sampled computed tomography (CT) has been attracting attention as a technique that can reduce the high radiation dose of conventional CT. In general, iterative reconstruction techniques have been applied to sparsely sampled CT to realize high quality images. These methodologies require high computing power due to the modeling of the system and the trajectory of radiation rays. Therefore, the purpose of this study was to obtain high quality three-dimensional (3D) reconstructed images with deep learning under sparse sampling conditions. METHODS We used a deep learning model based on a fully convolutional network and a wavelet transform to predict high quality images. To reduce the spatial resolution loss of predicted images, we replaced the pooling layer with a wavelet transform. Three different domains were evaluated - the sinogram domain, the image domain, and the hybrid domain - to optimize a reconstruction technique based on deep learning. To train and develop a deep learning model, The Cancer Imaging Archive (TCIA) dataset was used. RESULTS Streak artifacts, which generally occur under sparse sampling conditions, were effectively removed from deep learning-based sparsely sampled reconstructed images. However, image characteristics of fine structures varied depending on the application of deep learning technologies. The use of deep learning techniques in the sinogram domain removed streak artifacts well, but some image noise remained. Likewise, when applying deep learning technology to the image domain, a blurring effect occurred. The proposed hybrid domain sparsely sampled reconstruction based on deep learning was able to restore images to a quality similar to fully sampled images. The structural similarity (SSIM) index values of sparsely sampled CT reconstruction based on deep learning technology were 0.85 or higher. Among the three domains studied, the hybrid domain techniques achieved the highest SSIM index values (0.9 or more). CONCLUSION We proposed a method of sparsely sampled CT reconstruction from a new perspective - unlike iterative reconstruction. In addition, we developed an optimal deep learning-based sparse sampling reconstruction technique by evaluating image quality with deep learning technologies.
Robust Formation Control for Multiple Quadrotors With Nonlinearities and Disturbances

In this paper, the robust formation control problem is investigated for a group of quadrotors. Each quadrotor dynamics exhibits the features of underactuation, high nonlinearities and couplings, and disturbances in both the translational and rotational motions. A distributed robust controller is developed, which consists of a position controller to govern the translational motion for the desired formation and an attitude controller to control the rotational motion of each quadrotor. Theoretical analysis and simulation studies of a formation of multiple uncertain quadrotors are presented to validate the effectiveness of the proposed formation control scheme.
Toward Lung Tumor Localization based on Strain Variability of Lung Surface during Video-Assisted Thoracoscopic Surgery

Tumor localization, especially in case of minimally invasive lung tumor resection surgery, is extremely challenging due to the continuous motion of the organ. This motion can be troublesome as it results in spatial discrepancy corresponding to preoperative and intraoperative tumor location. In order to characterize lung tissue stiffness for the purpose of lung tumor localization, in this paper, we present a novel characterization approach based on variability in resistance of the healthy region vs. the tumorous region resulting from lung motion. The proposed approach is numerically validated on a Finite Element (FE) model of the lung with varying surface stiffnesses, where higher stiffness represents tumor and lower stiffness corresponds to healthy lung tissue. The numerical simulation validates the sensitivity of our mechanism for different grades of tumors by demonstrating that the strain on the healthy tissue is 31.8 and 67.1 times higher than that on the tumor surface for a selected relative stiffness variation of 3.6x and 24.4x respectively, at a pressure of 1.6 KPa. Additionally, a framework is developed to validate the proposed approach in a video of a video-assisted thoracoscopic surgery (VATS), where multiple landmarks on the lung surface are tracked. This enables us to quantify the motion of points residing on healthy surface and tumorous surface. The motion data is further analyzed to study the relative surface strain, and it is shown that the proposed approach differentiates a tumor from healthy surface.