Miracle-dz/UniFAR-FTUs huggingface dataset:
Reporting Insulin Pump Accuracy: Trumpet Curves According to IEC 60601-2-24 and Beyond

Accuracy of insulin pump basal rate delivery, if tested according to the standard IEC 60601-2-24 for infusion pumps, shall be presented as a trumpet curve. This way of graphical presentation is common; however, it is often misunderstood and misinterpreted by people. It is often assumed that a trumpet curve shows the error rate as a function of time, thus implying an increasing accuracy in the course of time. On the contrary, the horizontal axis of a trumpet curve shows increasingly long observation windows. In addition, trumpet curves display only extreme values, that is, those windows with minimal and maximal deviation, which might not be representative for the total deviation. This commentary provides information regarding the calculations and the interpretation of trumpet curves and proposes alternative approaches.
A Method for Designing Decoupled Filtered Smith Predictor for Square MIMO Systems With Multiple Time Delays

The control of multi-input and multioutput (MIMO) systems with multiple time delays is difficult to be done using classical control techniques, especially if the process is not open-loop stable. Model predictive control (MPC) is the natural choice in industry for this case, but its tuning and implementation are not trivial and, in general, cannot be done in the low level of the process control system. This paper aims to show a method to design MIMO filtered Smith predictors for $n \times n$ processes with multiple time delays based on the decentralized direct decoupling structure. This method allows an easy tuning of the primary controller by simplifying the problem to multiple single loops if the realizability conditions presented in the paper are met. The proposed method can also be used in MPC to remove the interactions between process variables. The effectiveness of the method is illustrated through two case studies.
Cryptanalysis of an Asymmetric Searchable Encryption Scheme

With the rapid growth of information and popularity of outsourced data, most of the IT companies are motivated to outsource their sensitive information on cloud servers. Since these servers are not fully trusted, for preserving privacy and security, these data must be encrypted before being outsourced. For searching on the encrypted data, searchable encryption method plays an important role. Recently, Tajiki et al. have proposed a new asymmetric searchable encryption called Secure Searchable Asymmetric Encryption (SSAE). They claimed their scheme is secure against forgery attack. In this paper, at first, we show that the security of SSAE is not complete by presenting a forgery attack on this scheme. After that we modify the SSAE to withstand this forgery attack.
Malware Detection Using Machine Learning Based on the Combination of Dynamic and Static Features

As millions of new malware samples emerge every day, traditional malware detection techniques are no longer adequate. Static analysis methods, such as file signature, fail to detect unknown programs. Dynamic analysis methods have low efficiency and high false positive rate. We need a detection technique that can adapt to the rapidly changing malware ecosystem. The paper presented a new malware detection method using machine learning based on the combination of dynamic and static features. The characteristic of this experiment involved in many fields of knowledge, including binary program instrumentation, static analysis, assembly instruction analysis, machine learning, etc. Finally, we achieved a good result over a substantial number of malwares.
Massively parallel skyline computation for processing-in-memory architectures

Processing-In-Memory (PIM) is an increasingly popular architecture aimed at addressing the 'memory wall' crisis by prioritizing the integration of processors within DRAM. It promotes low data access latency, high bandwidth, massive parallelism, and low power consumption. The skyline operator is a known primitive used to identify those multi-dimensional points offering optimal trade-offs within a given dataset. For large multidimensional dataset, calculating the skyline is extensively compute and data intensive. Although, PIM systems present opportunities to mitigate this cost, their execution model relies on all processors operating in isolation with minimal data exchange. This prohibits direct application of known skyline optimizations which are inherently sequential, creating dependencies and large intermediate results that limit the maximum parallelism, throughput, and require an expensive merging phase. In this work, we address these challenges by introducing the first skyline algorithm for PIM architectures, called DSky. It is designed to be massively parallel and throughput efficient by leveraging a novel work assignment strategy that emphasizes load balancing. Our experiments demonstrate that it outperforms the state-of-the-art algorithms for CPUs and GPUs, in most cases. DSky achieves 2× to 14× higher throughput compared to the state-of-the-art solutions on competing CPU and GPU architectures. Furthermore, we showcase DSky's good scaling properties which are intertwined with PIM's ability to allocate resources with minimal added cost. In addition, we showcase an order of magnitude better energy consumption compared to CPUs and GPUs.
Analysis of Behavioral Intention to Use Cloud-Based Tools in a MOOC: A Technology Acceptance Model Approach

: MOOC students’ adoption of cloud-based tools has the potential to enrich the learning process and enhance the management of knowledge. The aims of this study are to evaluate the behavioral intention to use cloud-based tools in MOOC context, and to explore the factors that influence this intention, based on extended technology of acceptance model (TAM). This paper reports the findings of a case study conducted on the edX platform. Survey data collected from 133 end-users were analyzed by using structured equation modeling (SEM) to validate the causal relationship among the various constructs of the research model proposed. The findings suggested that the perceived ease of use and the perceived usefulness influence the attitude toward the cloud-based tools used in a MOOC.
Review and comparison of Apriori algorithm implementations on Hadoop-MapReduce and Spark

Abstract Several Apriori algorithm implementations for mining association rules have been proposed in the literature using the Hadoop-MapReduce framework and, more recently, Spark. However, none of the works have made a detailed assessment of its performance, for example, comparing it with other implementations in various characteristics of data sets. In this work, we present a review of the main algorithms proposed for Hadoop-MapReduce and compared their implementations in a single environment under several different situations. Moreover, these algorithms had their implementations adapted to Spark, and also compared under the same circumstances. Based on the results of the experiments, we present a framework for recommending the Apriori implementation most appropriate for solving a given problem, according to the data set characteristics and minimum required support. The results show that Spark implementations overcome Hadoop-MapReduce implementations at runtime in most experiments. However, there is no single implementation that is the best in all the evaluated situations.
Fully Screen Printed Thermocouple and Microheater Applied for Time-of-Flight Sensing in Microchannels

In order to control the flow inside microchannels, measuring the flow velocity of fluids is an important task. A possible way to determine flow velocity is by measuring the thermal time-of-flight. To this end, in this paper, a full screen printed combination of microheater and thermocouple is presented. Screen printing represents a technology that is attractive for fabricating low-cost sensor systems for microfluidic devices which can be directly integrated into the channel. The structure presented here has been manufactured using high-temperature stable screen printing inks. The thermocouple is calibrated and then the sensor setup is used to determine the flow velocity in a microchannel at various flow rates. The measurement is performed using a frequency domain approach by evaluating phase shifts of slow steady-state oscillations, and alternatively in the time-domain by estimating the heat transfer function from a step response measurement. The measurement results are compared to theoretically predicted values and show good agreement for a flow velocity range from 20 <inline-formula> <tex-math notation="LaTeX">$\mu 1$ </tex-math></inline-formula>/min to 70 <inline-formula> <tex-math notation="LaTeX">$\mu 1$ </tex-math></inline-formula>/min.
A Method of Interactively Extracting Region Objects from High-Resolution Remote Sensing Image Based on Full Connection CRF

Aiming at the region objects of high resolution remote sensing images, this paper proposes an interactive region objects extraction method for high-resolution remote sensing images based on fully connected conditional random fields. This method estimates the foreground model by artificial interaction markers. On the basis of using the SLIC algorithm to over segment the input images, combining the color and texture features, the region-based maximum similarity fusion (MSRM) is used to expand the foreground region and establish the global information of the full-connection conditional random field description image. Then, based on the mean-field estimation, the model inference is realized by the high-dimensional Gauss filtering method, and then the contour of the area features is obtained. The experimental results show that the method is effective by extracting the area features such as waters, woodlands, terraces and bare lands on high resolution remote sensing images.
Application of polycaprolactone, chitosan, and collagen composite as a nanofibrous mat loaded with silver sulfadiazine and growth factors for wound dressing

Fabrication of nanofibrous biomaterials composed of natural and synthetic materials that incorporated with antibiotic and growth factors with controlled release manner is an attractive topic in wound healing. The purpose of this study was to prepare optimal composite of materials as biomimetic nanofibrous mats for application in wound healing. The mat was prepared of polycaprolactone (PCL) in the bottom, chitosan/poly ethylene oxide (Cs/PEO) in the middle, and PCL/collagen (PCL/Coll) in the top layer. A panel of standard characterization tests of nanofibrous mat was performed and its compatibilities in strength and integration were confirmed. Middle layer was loaded with epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF), and silver sulfadiazine (SSD) was incorporated in the bottom layer as an anti-infection factor. Then, on the dorsum of rats, a 400-mm2 wound was created and surrounded by a silicone ring to control the usual tissue contractions. Nanofibrous mats with or without growth factors were applied as wound dressings and at day 14, the healing process was evaluated. At day 14, the treated group by designed mat showed faster epithelialization and angiogenesis. Silicone ring in the test group was desirable in wound closure compared to the control group. Reformation of skin tissue was manifested in a shorter time. This composite nanofibrous mat could be introduced as a dynamic and effective candidate for wound dressing.
Consistency-Exclusivity Regularized Deep Metric Learning for General Kinship Verification

While encouraging results have been made so far to advance kinship verification by using facial images, learning a robust genetic similarity measure remains challenging, especially in the setting of general kinship verification, wherein the gender labels of the test samples are unknown in advance. In this paper we present a deep metric learning method with a carefully designed two-stream neural network to jointly learn a pair of deep embeddings for parent-child images. In particular, the deep embeddings are first modeled to explicitly consist of the common and individual components, and then two additional constraints are introduced in deep metric learning: 1) value-aware consistency on the common components, and 2) position-aware exclusivity on the individual components. The proposed hierarchical consistency-exclusivity regularization enables our deep metric learning to harness the sharable and complementary patterns inherent in parent-child images. Empirically, we show improved performance over state of the art metric learning solutions to general kinship verification on two benchmarks.
Automatic Detection of Common Carotid Artery in Transverse Mode Ultrasound Images

Automatic detection of common carotid artery is a precursor to the computer-aided analysis of atherosclerosis. In this paper, we present an approach for common carotid artery detection based on normalized matched filtering technique. The algorithm performance is optimized by introducing vector inner products and norms instead of conventional mean and variance computations. The difficulties of matched filtering are overcome by normalizing the image and template vectors to unit length, yielding a cosine similarity metric. We report common carotid artery detection results on Brno University of Technology, Signal Processing laboratory (SP lab) carotid artery database amounting to a total of 971 transverse mode ultrasound images out of which 538 and 433 images are taken from Ultrasonics and Toshiba ultrasound imaging devices, respectively. The proposed method results in a common carotid artery detection accuracy of 97.63% with a detection time per image of 2.21 seconds. We show that the normalized matched filtering is a reasonable choice for detection of common carotid artery in ultrasound images.
Stochastic output feedback controller design for two tank nonlinear interacting system

In this paper, stochastic output feedback controller is designed for two tank interacting nonlinear multi input multi output (MIMO) system. It is assumed that the control loop is closed over a communication network in which sensor data and the control signal are communicated via the same communication network. Packet delay and dropout are the main disadvantage of a communication network. The goal is to obtain a stochastic output feedback controller that counteracts the effect of packet delay and dropout. A necessary condition is given for the closed loop stability of two tank interacting system in terms of linear matrix inequalities (LMI). The controller gain is then calculated by solving the LMI. The servo and regulatory performance of the designed controller are validated in simulation platform.
Robust Stereo-Match Algorithm for Infrared Markers in Image-Guided Optical Tracking System

The stereo-match to find the corresponding points in the left and right images from an optical tracking system is a crucial step in 3-D point reconstruction. Unfortunately, the presence of ghost markers remains an issue which is caused by indistinct correspondence when several markers are coplanar with two optical centers. Such ghost markers can lead to erroneous tracking of medical instruments and patients. Some methods have been proposed for this issue, but they are rarely applied in tracking systems. To address the issue, we propose a robust stereo-match method for combining the maximum silhouette reprojection consistency with the epipolar constraint into an objective function. For minimization of the objective function, we reconstruct a virtual sphere through optimal triangulation and construct the 3-D distance errors between the inverse projective rays of the marker contour and the reprojection contour on the virtual sphere surface. A contour-detecting method is proposed to obtain the accurate contours of marker blobs in images. Simulative and real experiments are performed to evaluate the performance of the proposed stereo-match method. We compare the proposed method with other methods performed on several real data sets which are acquired from a human phantom. The experimental results demonstrate that the proposed method is more robust and efficient in searching the correct correspondences of the markers.
An Improved Perturb and Observe Maximum Power Point Tracking Algorithm for Photovoltaic Systems

The paper aims to give a comprehensive mathematical analysis of a photovoltaic array. The model representation was implemented using MATLAB/Simulink software. To track the maximum power point, an improved perturb and observe (P&O) algorithm method based was implemented. To demonstrate the performance of the proposed method, a comparative study between the conventional P&O and the proposed algorithms was discussed. The encouraging results indicate the opportunities for the utilization of the strategy covered by this work.
Exploiting Terrestrial Positioning Signals to Enable a Low-Cost Passive Radar

This paper presents results from experiments that utilize Global-Positioning-System-like terrestrial positioning signals in a passive radar system to track aircraft over a metropolitan area. Unlike communication signals, these positioning signals offer unique properties that are advantageous for a passive radar. Exploitation of these properties provides an opportunity to reduce hardware complexity and to enable passive radar systems with a lower cost. This paper provides a detailed description of the signal processing in the context of low-cost hardware.
Joint Placement and Routing of Network Function Chains in Data Centers

In this work, we investigate the problem of joint optimization over placement and routing of network function chains in data centers. In the offline case, we demonstrate that a classical randomization algorithm works well and we derive a new bound on the performance sub-optimality gap. In the online case, we prove a fundamental lower bound in resource violation and propose a new algorithm that combines techniques from multiplicative weight update and primal-dual update paradigms. This online algorithm asymptotically achieves the best possible performance in terms of resource allocation among all online algorithms. We demonstrate the applicability of our solutions to address practical problems by conducting simulation-based evaluations over different data center architectures using data generated from real trace distribution.
Accurate Image Super-Resolution Using Cascaded Multi-Column Convolutional Neural Networks

Image super-resolution (SR) can be seen as finding a nonlinear mapping between low-resolution (LR) image and high-resolution (HR) image. Convolutional neural networks (CNN) based image SR methods have achieved great success based on features extracted from pretrained networks. However, the previous works less consider multiscale features. In this paper, we propose a SR cascaded multi-column convolutional neural network (SRCMCN) to recover HR image by learning multi-scale features from the corresponding LR image. Compared to existing CNN based image SR methods, the proposed method directly takes the original LR image as input to learn the residual between interpolated LR image and corresponding HR image, which will reduce the computational cost of convolution due to the small input size. Besides, in order to achieve high-quality reconstruction, we optimize our networks using mean absolute error (MAE) loss function. Experimental results demonstrate that our proposed method outperforms several state-of-the-art image SR methods in terms of both reconstructed image quality and computational efficiency.
Feasible Trajectory Planning Algorithm for a Skid-Steered Tracked Mobile Robot Subject to Skid and Slip Phenomena

This paper proposes an approach for the motion planning of a constrained skid-steered tracked mobile robot under the hypothesis of non-negligible skid and slip phenomena. Operating environment is firstly discretized with a finite dimensional grid. Then, a weighted graph is defined whose nodes are the above mentioned grid points, and whose arcs denote the trajectory segments. A modified shortest path search algorithm is then proposed to find a trajectory, in terms of succession of arcs, connecting starting and ending nodes. Trajectory feasibility is guaranteed by recurring to set-based arguments. In order to show the effectiveness of the proposed approach, some numerical examples are finally discussed.
The Use of Smart Contracts and Challenges

In December 2017, "CryptoKitties", a game on the Ethereum blockchain became an instant success shortly after its launch. It attracted 180,000 users with over $20 million of spend in Ether, and was at one point taking up 12% of all Ethereum transactions. Technically speaking, "CryptoKitties" is a smart contract - a piece of code with storage capability that resides on a blockchain. Smart contracts are gaining increasing popularity in recent years. We present a comprehensive review of smart contracts with a focus on existing applications and challenges they face. We have covered the smart contract mechanisms, promising use cases, as well as relevant research work and the open issues.
Beam-Time Block Coding With Joint User Grouping and Beamforming for FDD Massive MIMO Systems

A downlink (DL) precoding scheme that needs no DL training nor uplink (UL) feedback for massive MIMO systems operated under frequency-division duplex (FDD) is proposed, whereas most of the schemes in the literature were proposed to compress the overhead of training and feedback. Due to the above advantage, the proposed scheme is suitable for extremely low-latency applications. This scheme exploits angle reciprocity of FDD systems to enable the base station to acquire partial DL channel state information through a previous UL signaling. A joint user grouping and beamforming optimization method without DL training and UL feedback is proposed to enhance spectral efficiency and mitigate interference between multiple UEs during DL data transmission. Simulation results suggest that the proposed scheme possesses a comparable performance to a number of previous grouping-based schemes that require DL training and UL feedback.
Taking common ground into account: Specifying the role of the mentalizing network in communicative language production

Several studies have shown that communicative language production as compared to non-communicative language production recruits parts of the mentalizing or theory of mind network, yet the exact role of this network in communication remains underspecified. In this study, we therefore aimed to test under what conditions the mentalizing network contributes to communicative language production. We were especially interested in distinguishing between situations in which speakers have to consider which information they do or do not share with their addressee (common vs. privileged ground information). We therefore manipulated whether speakers had to distinguish between common and privileged ground in order to communicate efficiently with the listener, in addition to comparing language production in a communicative and a non-communicative context. Participants performed a referential communicative game in the MRI-scanner as well as a similar, non-communicative task. We found that the medial prefrontal cortex, a core region of the mentalizing network, is especially sensitive to communicative contexts in which speakers have to take their addressee’s needs into account in order to communicate efficiently. In addition, we found neural differences between the communicative and the non-communicative settings before speakers started to plan their utterances, suggesting that they continuously update common ground in a communicative context.