Miracle-dz/UniFAR-FTUs huggingface dataset:
Adiponectin levels and arteriosclerotic risk factors in pediatric renal transplant recipients

Abstract: ADPN, a recently discovered adipocytokine, has attracted great attention because of its anti‐atherogenic properties. It was suggested as a protective factor for the cardiovascular system because of its close correlation with several risk factors. Our aim was to investigate serum ADPN levels in pediatric RTR and to document possible relationships between ADPN and arteriosclerotic risk factors. Twenty‐one RTR, aged 16.3 ± 4.0 yr, and 23 healthy age and sex‐matched control subjects were enrolled in this study. Serum lipid/lipoprotein fractions, homocysteine and ADPN levels as well as intima‐media thickness of the cIMT were determined in both groups. Significantly higher serum ADPN (p < 0.001) and homocysteine (p < 0.05) levels as well as higher cIMT (p < 0.001) were found in RTR compared with the control subjects, whereas apolipoprotein B and lipoprotein (a) levels were not significantly different. HDL cholesterol was positively correlated with log ADPN (r = 0.585, p < 0.01). There were inverse correlations between log time post‐transplantation and log ADPN as well as HDL cholesterol (r = −0.438, p < 0.05 and r = −0.578, p < 0.05, respectively). There were no correlation between log ADPN, log homocysteine, log apolipoprotein B, lipoprotein (a), creatinine clearance and cumulative steroid dose. Despite reasonable lipid profiles and remarkably elevated ADPN levels, our pediatric RTR with stable graft function displayed a risk for arteriosclerosis because of increased cIMT and mild hyperhomocysteinemia. Regarding the close positive correlation between ADPN and HDL cholesterol, it could be speculated that ADPN is a novel negative surrogate marker of arteriosclerosis. To our knowledge, this is the only report investigating levels and diverse correlates of ADPN in a pediatric RTR group. Further studies in larger groups of recipients are needed to clarify the interaction between arteriosclerotic risk factors and ADPN.
Identifying Influencers with Ensemble Classification Approach on Twitter

The popular tweets are often originated by ordinary people involving in an event producing interesting or intriguing contents which often be picked up by users so-called influencers who have a mass number of followers. Targeting the users who cost the viral burst in social media among the enormous users producing messages every second is a difficult job. If such influencers can be identified in real time when trends are happening, some incidents can be prevented, protected and even drawn benefits from. Identifying influencers can be done with data classification techniques. Our research proposes a technique for identifying influencers on Twitter social media platform using extracted features from six months of collected tweets from November 2016 to March 2017. The proposed method contains two types of feature which are static and dynamic features. The static features include the weekday, the time period of the first retweet, and its author’s numbers of interaction and relation consist of retweets of the hashtag, followers, followings, friends, favorites, and statuses. The dynamic features are the changes in the author’s numbers of interaction and relation since the first retweet to a target time. The features were extracted according to a period of time according to Bursting State and bursting duration of 30, 60, and 120 minutes. Four ensemble classification algorithms: Bagging, Random Forest, Extra Trees, and Voting were applied. The results demonstrate that Bagging algorithm yields the best results in general for a duration of 120 minutes, and even identifying influencers in the separated topic domain, it also yields better predictive results.
A Wear Leveling Aware Memory Allocator for Both Stack and Heap Management in PCM-based Main Memory Systems

Phase change memory (PCM) has been considered as a replacement of DRAM, due to its potentials in high storage density and low leakage power. However, the limited write endurance presents critical challenges. Various wear leveling techniques have been proposed to mitigate this issue from different perspectives, including both hardware and software levels. This paper proposes a wear leveling aware memory allocator, which (1) always prefers allocating memory blocks with less writes upon memory requests, and (2) leaves blocks allocated more than a threshold value unallocable temporarily. Furthermore, for the first time, this allocator provides a uniform management scheme for both stack and heap areas, thus could better balance writes in stack and heap areas. Experimental evaluations show that, compared to state-of-the-art memory allocators (i.e., glibc malloc, NVMalloc and Walloc), the proposed memory allocator improves the PCM wear leveling, in terms of CoV (a wear leveling indicator) by 41.9%, 30.3%, and 35.8%, respectively.
Extraction of P300 using constrained independent component analysis

A brain computer interface (BCI) uses electrophysiological activities of the brain such as natural rhythms and evoked potentials to communicate with some external devices. P300 is a positive evoked potential (EP), elicited approximately 300ms after an attended external stimulus. A P300-based BCI uses this evoked potential as a means of communication with the external devices. Until now this P300-based BCI has been rather slow, as it is difficult to detect a P300 response without averaging over a number of trials. Previously, independent component analysis (ICA) has been used in the extraction of P300. However, the drawback of ICA is that it extracts not only P300 but also non-P300 related components requiring a proper selection of P300 ICs by the system. In this study we propose an algorithm based on constrained independent component analysis (cICA) for P300 extraction which can extract only the relevant component by incorporating a priori information. A reference signal is generated as this a priori information of P300 and cICA is applied to extract the P300 related component. Then the extracted P300 IC is segmented, averaged, and classified into target and non-target events by means of a linear classifier. The method is fast, reliable, computationally inexpensive as compared to ICA and achieves an accuracy of 98.3% in the detection of P300.
MAVI: Mobility Assistant for Visually Impaired with Optional Use of Local and Cloud Resources

Independent mobility of visually impaired people is key to making an inclusive society for them. Unstructured infrastructure in developing countries pose significant challenges in developing aids to address the mobility problem of visually impaired. Most of the assistive devices available internationally assume a structured and controlled environment severely restricting the applicability of such devices. In this paper, we assess the ability of state-of-the-art assistive devices for addressing the independent outdoor mobility needs of the visually impaired in an unstructured environment. We have created realistic datasets for various scenarios and evaluate deep neural networks for object detection on these datasets. We also present a portable prototype for the task. Further, we have also developed a cloud based solution to address the mobility requirements. We compare the local device based and cloud based solutions in terms of accuracy, latency, and energy. We present and discuss results from these two implementations that can provide insights for an effective solution. The results and insights open up novel research problems for embedded systems.
Spectral Analysis of Familiar Human Voice Based On Hilbert-Huang Transform

Spectral analysis of human voice signals is important to reveal hidden information when is not available in the time-domain. Extracting spectral information from those voice signals will enhance our knowledge in understanding the nature and characteristic of the voice. It concerned with the decomposition method of voice signals into simpler components in frequency and time. The frequency analysis tools are also give beneficial for describing the spectral distribution in a voice signal, very often the methods used by the tools have limitations that restrict us to interpret the data properly. This paper describes a powerful data analysis method called the Hilbert-Huang transform (HHT), which can be used to extract audio frequency components from nonlinear and nonstationary human voice signals. It can describe the audio frequency components locally and adaptively for nearly any oscillating signal. This makes it very extremely versatile to be used for analysing familiar human voices.
A Recovery Model for an EPQ System subject to Supply Disruption with Consideration of Safety Stock

Managers are continuously seeking a balance between an efficient system and cost minimization, and when disruptions occur, the decision making process becomes more complicated, hence requiring the aid of complex problem-solving mechanisms. This paper develops a recovery model of a single stage production-inventory system, where the manufacturer’s original Economic Production Quantity is affected by supply disruption. The proposed model uses safety stock as a mitigation strategy and generates a recovery plan to minimize overall system costs. The developed constrained non-linear optimization model was solved using the Branch and Bound search algorithm. Experimental analysis showed that longer disruption periods resulted in a significant increase in total recovery cost and safety stock levels. Furthermore, a case study at an automotive components manufacturer showed that the optimal recovery plan yield lower total cost as compared to the actual data obtained, which resulted in significant savings in total cost. In conclusion, the proposed model could be a useful tool to assist manufacturers in minimizing the impact of disruption, while improving the efficiency of the supply chain.
TrajSummary: Summarizing Trips of Individuals

City-scale mobility data of people is recorded by multiple location sensing applications. User trajectory summarization is one of the important applications that provides the movement pattern summary of users, which can be used in applications such as city transportation planning, hyper-targeted advertising. We propose TrajSummary: a system for summarizing and quantifying mobility patterns of an individual. We evaluate three novel approaches to cluster user trips – THRESH, L-AWARE and MODE-EST. We show that our techniques provide significantly superior user summary clusters than generic trajectory clustering mechanisms such as SWARM and TRA-CLUS. Our approach is faster; we perform 5x faster than techniques using Dynamic Time Warping based approaches.
RAFS: A RAID-Aware File System to Reduce the Parity Update Overhead for SSD RAID

In a parity-based SSD RAID, small write requests not only accelerate the wear-out of SSDs due to extra writes for updating parities but also deteriorate performance due to associated expensive garbage collection. To mitigate the problem of small writes, a buffer is often added at the RAID controller to absorb overwrites and writes performed to the same stripe. However, this approach achieves only suboptimal efficiency because file layout information is invisible at the block level.This paper proposes RAFS, a RAID-aware file system, which utilizes a RAID-friendly data layout to improve the reliability and performance of SSD-based RAID 5. By leveraging delayed allocation of modern file systems, RAFS employs a stripe-aware buffer policy to coalesce writes to the same file. To reduce parity updates, RAFS compacts buffered updates and flushes back in stripe units to mitigate the parity update overhead. RAFS adopts a stripe-granularity allocation scheme to align writes to stripe boundaries. Experimental results show that RAFS can improve throughput by up to 90%, compared to Ext4.
UW MORSE: The underwater Modular Open Robot Simulation Engine

This paper presents an open-source simulation environment for underwater vehicles and robots. The simulation environment allows the user to simulate underwater robotic vehicles with realistic dynamic behavior in a 3-dimensional virtual environment. The environment is highly configurable, and offers a set of modules for simulating different types of vehicles in a number of underwater scenarios. The simulator can be used for control system development, path planning, risk management and testing in a safe virtual environment. The possibility for virtual testing will lower the cost and reduce time of operations. The simulation environment is an expansion of MORSE - Modular Open Robots Simulation Engine. The modular nature of MORSE allows the user to easily configure the simulations, making new environments and robots, as well as adding sensor and control interfaces. This expansion includes modules for hydrodynamic simulation, thrusters and underwater sensors. These modules enables the user to make a virtual replica of a specific underwater robot system. Such replica may be used as a Software-in-the-loop system for testing and verification of control systems and algorithms. The simulation environment allows interaction with a large set of robotic middlewares.
User Driven Dynamic Frequency Scaling for Power-Aware Mobile Cloud Terminals

The advancement in mobile device capabilities and cloud computing have led to the emergence of Mobile Cloud Computing (MCC). Compute and data-intensive applications are delivered through a network where the mobile terminals act as thin clients. These applications are power hungry however, they tend to have a usage pattern that leaves resources underutilized. The operating parameters of these resources can be tuned to save power without degrading the performance. Dynamic Voltage and Frequency Scaling (DVFS) is a prevalent technique that saves power by scaling the CPU frequency. In this work, we propose a user-driven DVFS scheme (UD-DVFS) that takes user’s feedback into account during decision making. Bayesian classification is used to decide whether to scale the CPU frequency or not. Once the decision is made, the UD-DVFS scheme scales the frequency of the CPU depending upon the application type (CPU-bound or Memory-bound). Experimental evaluations over real applications show that the proposed DVFS scheme is able to increase the battery life 1.08 to 1.23 times the existing DVFS schemes.
Assessing the Demand Response Program in a Network with High Integration of Photovoltaic Plants using Machine Learning

The paper presents the procedure for assessing future demand response program in a network with high integration of photovoltaic plants using machine learning methods. By using scenario-based probabilistic forecasting, the load probability distributions are calculated for the future year. The results derived from probabilistic forecasts are used for assessing the future demand response activation time intervals. Instead of assessing the possible annual peak decrease by computing load duration curves for a past analyzed year, here the median load duration curve was forecasted with corresponding prediction intervals. The main advantage of this procedure is providing a range of load values that could be decreased before implementing the demand response system instead of using a single value, that was estimated from the past data. The proposed procedure in this paper is a follow up to the analyses that were carried out as a part of the demand response project in the scope of the Slovenian-Japanese NEDO project. The procedure can be used by distribution or transmission system operators in order to select an appropriate network for demand response system integration.
Design and Implementation of a Network Robotic Framework Using a Smartphone-Based Platform

With recent advances in robotics along with sensor technologies, robots have richer features and can be used in more diverse applications. However, onboard microcontrollers in robots often take full responsibility for processing data acquired from sensors, controlling actuators, and other tasks. This prohibits us from implementing complex algorithms in microcontrollers. To address this issue, we present a novel design of a network robotic framework using a smartphone-based robotic platform. The design was implemented using a smartphone as a network bridge to distribute computationally burdensome tasks into multiple networked computational resources, as well as, a sensor package that allows the mobile robot to navigate. One of the most practical benefits derived from the outsourced computations is the simplicity in a robot’s design. This provides a sturdy robot that consumes less power. With interaction capabilities to networked resources, more diverse robotic applications can be developed. To demonstrate the feasibility of the proposed network robotic framework, several experiments were conducted for indoor localization and navigation using a novel smartphone-based robotic platform.
Design of Single Poly Flash Memory Cell with Power Reduction Technique at Program Mode in 65nm CMOS Process

This paper focuses on the bit lines in the design of a single-poly flash memory cell during program operation. A conventional flash memory cell uses a floating gate transistor where an extra layer is added between the gate and substrate of the standard MOSFET. This incurs additional process step during fabrication, increasing the cost of the design. The single poly embedded flash memory cell is quite an attractive solution for non-volatile memory applications since it doesn't require any additional process steps. A 5T single-poly flash memory cell was implemented under TSMC 65nm CMOS Process. During program mode of a single-poly flash memory, all the pull-up PMOS of every single cell are turned on even if some of the cells are not to be programmed, causing a short path between the source and ground thus generating static power dissipation. In this paper, power gating technique was being applied to the bit lines. The size of the PMOS were taken for both design and it was found out that in power-gated, the size of the PMOS didn't really affect the power dissipated. The results from comparing each design showed that with power gating method, the static power dissipation was transformed into dynamic power dissipation, reducing power consumption by about 80%. The cell's dimensions are 14.015 µm x 1.930 µm, achieving an area of 27.05 µm2.
Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules

This paper proposes to resolve the limitation of differential evolution (DE) that the difference between the individuals in search behavior has not yet been utilized effectively for guiding the evolution of the population. An adaptive guiding mechanism (AGM) based on the heuristic rules is thus suggested to make possible, individual-dependent guidance. The AGM mainly comprises three stages: <italic>construction</italic>, <italic>separation</italic>, and <italic>guidance</italic>. In the <italic>construction</italic> stage, the elite leadership team (<inline-formula> <tex-math notation="LaTeX">$ELT$ </tex-math></inline-formula>) is established with an adaptive control scheme by using good information of the population. In the <italic>separation</italic> stage, the <inline-formula> <tex-math notation="LaTeX">$ELT$ </tex-math></inline-formula> is divided into distinct elite groups that are allocated to different individuals based on their search behaviors. In the <italic>guidance</italic> stage, the leader that is chosen from the respective elite group, as well as the promising directions extracted from the population, are used together to guide the search of each individual. By incorporating AGM into DE, a novel algorithm framework, named DE with AGM (DE-AGM), is proposed to enhance the performance of DE. As a general framework, DE-AGM can be easily and seamlessly applied to most DE variants. The experimental results on 58 benchmark functions have demonstrated the competitive performance of DE-AGM.
Mathematical Model of Cyber Intrusion in Smart Grid

The impact of cyber attack on the smart grid is a major concern of futuristic grid. Major part of smart grid consists of communication networks interacting with Remote Terminal Units (RTU) through online services. Due to online services using internet or Ethernet the network is prone to cyber attack and cyber war. To detect and mitigate the cyber attack on smart grid, there is a strong urge to understand their different aspects. In this paper, we proposed a mathematical model to understand the cyber attack scenario on smart grid. The proposed model can be easily used to verify the intensity of cyber attack on the given smart grid system.
Architecting Trustworthy Self-Adaptive Systems (Tutorial)

Architecting self-adaptive software systems is challenging. These systems must achieve their goals not only in the environment in which they are deployed initially, but also as this environment changes over time. When self-adaptive systems are used in safety-critical and business-critical applications, this challenge is compounded by the need to also provide guarantees that the system operates correctly at all times. For traditional software systems, such guarantees are provided through assurance cases. These are structured arguments which use comprehensive development-time evidence to explain why a system can be trusted when used for its planned application in a given environment. This tutorial presents the ENTRUST methodology for achieving a similar level of trust in self-adaptive systems [1]. ENTRUST is the first end-to-end methodology for architecting trustworthy self-adaptive systems and dynamic assurance cases guaranteeing the suitability of the software for its intended applications. As advocated by major research initiatives such as the UK-led Assuring Autonomy International Programme (https://www.york.ac.uk/assuring-autonomy/). ENTRUST dynamic assurance cases seamlessly combine evidence obtained during the development of a self-adaptive software system with evidence obtained from its additional verification at runtime. As such, each reconfiguration of an ENTRUST self-adaptive system is accompanied by a new version of the assurance case that confirms the trustworthiness of the reconfigured system architecture.
Latency/Wearout in a Flash-Based Storage System with Replication on Write

We investigate the influence of latency and wearout of writing data on the efficiency of high-performance data storage systems. Using a mathematical model, we propose the strategy of redundant writing with cancelling long write operations. Presented numerical simulation results allow choosing the replication level needed to achieve the trade-off between the latency and wearout that provides desired reliability and efficiency.
Design and Analysis of a Square-Shaped Continuum Robot With Better Grasping Ability

Continuum robots, often taking inspiration from biomimetics, are an exciting novel research field and have a great capability. New continuum robots can be inspired by biologies such as seahorses, pipefish, and pipehorses, which have both strong skeleton and great dexterity. In this paper, the main idea contained is how to effectively employ the square components, which aim to imitate the armor and to connect the square components with the ball pairs and soft surface. For displaying the dexterity features of the square continuum robot (SCR), both the bending and twisting capabilities were studied. Meanwhile, a comparison was also considered in order to validate and analyze the dexterity differences in the numerical model, skeleton prototype, and SCR. The static characters of the SCR were shown both numerically and experimentally so that the mechanical properties are clear. Then, impact experiments were carried out to demonstrate the cushion effect. In addition, the maximum grasping load was also determined to quantize the grasping capability. Finally, a robot system was developed to handle objects with various shapes. Our results reveal that the SCR connected by the soft surface has improved flexibility, cushion stability, and better-grasping capability, with some loss of dexterity compared to SCR’s skeleton.