Miracle-dz/UniFAR-FTUs huggingface dataset:
The Fuzzy Scheduling Algorithm for the Parallel Key Searching Problem on Cloud Environment

Rainbow table is the well-known cryptanalytic method for searching the encryption key or the plaintext. The user can precompute several lookup tables in order to increase the success probability. These tables can be distributed to multiprocessor or parallel machines for decreasing the makespan. Searching problem using the Rainbow table differs from the others traditional problem. The problem can only be solved on some node. However, if the problem can't be solved on current node, it must be re-executed on the other node. Thus, using FCFS algorithm is not the proper solution. We proposed the fuzzy scheduling for the parallel key searching problem, using Rainbow table. The algorithm is implemented and evaluated by Cloudsim. The result shows the proposed method reduces the makespan from the traditional FCFS.
Fast and Simple Model - For Free Hanging, Pre-impregnated Carbon Fibre Material

Automated one-of-a-kind grasping and draping of pre-impregnated (prepreg) fiber plies onto complex molds is a hitherto unsolved problem. In the project FlexDraper, we have gathered an international consortium with industrial keyplayers for addressing this gap. Our approach is based on modeland simulation-based control of the shaping of the plies during the draping procedure, which requires fast computable models in the draping control loop. In this paper, we present such a fast computable mathematical model for the shape of free hanging prepreg fiber plies with perpendicular fiber directions, where the ply is held (clamped) at a set of locations that are not too far apart. Rather than using expensive physically based models, e.g. FEM simulations, our model is only based on interpolations between the held locations satisfying curve length constraints in fiber directions. The paper also contains experimental tests for the accuracy of the model.
Automatic Properties Classification Approach for Guiding the Verification of Complex Reconfigurable Systems

This paper deals with reconfigurable discrete event/control systems (RDECSs) that dynamically change their structures due to external changes in environment or user requirements. Reconfigurable Timed Net Condition/Event Systems (R-TNCESs) are proposed as an extension of the Petri nets formalism for the optimal functional and temporal specification of RDECSs. The correct design of these systems continues to challenge experts in both academia and industry, since bugs not covered early can be extremely expensive at the final deployment. The classic model-checking using computation tree logic (CTL) and its extensions (extended CTL, Timed CTL, etc) produces a large number of properties, possibly redundant, to be verified in a complex R-TNCES. To control the complexity and to reduce the verification time, a reduction technique of properties is proposed. The novelty consists in the classification of CTL properties according to their semantic relationships for guiding an efficient verification. An algorithm is proposed for the automatic classification of CTL properties before starting model-checking process. A case study is exploited to illustrate the impact of using this technique. The current results show the benefits of the paper’s contribution.
A Soft Tissue Scalpel Cutting Robotic System with Sucker Fixation†

Soft tissue cutting is a fundamental operation in clinical treatments. Autonomous soft tissue cutting using scalpel, one of soft tissue cutting methods, is an emerging field. However, soft tissue displacement during cutting by robotic system is a bottleneck. In this paper, a robotic system is proposed with sucker that can fix soft tissue before and during cutting. The system consists of scalpel fixation and three degrees of freedom (DoF) motion structure and surrounding suckers at the bottom. Cutting curve is abstracted from the cut soft tissue imaged by X-ray. Then the curve is fitted using 10-order B-spline function. The experimental results provide a pilot study towards high precision soft tissue cutting robot.
Design and Analysis of an Electro-Optic Type Pressure Transmitter Using Bellows as Primary Sensor

In process industries, an optical transmitting system has a great impact especially for long distance transmission through inflammable and hazardous areas. A combination of bellows and Mach-Zehnder Interferometer (MZI)-based pressure transmitting system has been proposed in this paper. This novel approach is capable to transmit pressure reading to a remote location using an electro-optic modulation in the MZI. The proposed system is the hybrid type and consists of two sections. First part is the electrical type which is responsible for transmission of measured information in an electrical domain (primary) and a second section is designed for transmission of measured information in an optical domain (secondary). Primary part is the combination of bellows, a Hall probe sensor, and a signal conditioning circuit, and the second part consists of the MZI with three electrodes. The pressure is directly applied to the bellows. The magnet is placed at the top of the Bellows and the variation of the magnet positions is sensed by the Hall probe sensor. The response of the Hall probe sensor, in terms of millivolt range, is not capable to perform the electro-optic modulation in the MZI, so it is further amplified by an instrumentation amplifier and a calibrating circuit. The output from the signal conditioning circuit is fed to the MZI so that the intensity of light which is propagating in the MZI is modulated due to the electro-optic effect. Transmission of the optical signal is very much beneficial for the inflammable industry to prevent the sparking like situation. The operation of the system has been explained with the help of derived theoretical equations. The experiment has been done and the performance of the system along with the experimental results is reported in this paper. The proposed system is linear and provides good repeatability with the applied pressure.
Flexible Motion Planning for Object Manipulation in Cluttered Scenes

The work implements a new real-time flexible motion planning method used for reactive object manipulation in pick and place tasks typical of in-store logistics scenarios such as shelf replenishment of retail stores. This method uses a new hybrid pipeline to recognize and localize an object observed through a depth camera, by integrating and optimizing state of the art techniques. The proposed algorithm guarantees recognition robustness and localization accuracy. The desired object is then manipulated. The motion planner, based on the obstacles detected in the scene, plans a collision-free path towards the target pose. The planned trajectory optimizes a cost function that reflects the best solution among those available and produces natural and smooth path through a smart IK constrained solution which avoids robot unnecessary reconfigurations. A reactive control based on distributed proximity sensors is finally adopted to locally modify the planned trajectory in real time to avoid collisions with uncertain or dynamic obstacles. Experimental results in a supermarket scenario populated with cluttered obstacles demonstrate smoothness of the robot motions and reactive capabilities in a typical fetch and carry task.
A Comparative Study on the Performance of MOPSO and MOCS as Auto-tuning Methods of PID Controllers for Robot Manipulators

An auto-tuning method of PID controllers for robot manipulators using multi-objective optimization technique is proposed. Two approaches are introduced based on the multi-objective particle swarm optimization (MOPSO) and multi-objective cuckoo search (MOCS), respectively. The main goal of this work is to introduce a comparative study on the performance of both algorithms with respects to their applicability to the auto-tuning process. For this sake, necessary metrics are considered such as the hyperarea difference and the overall Pareto spread, among others. In order to generate a sufficient amount of statistical data, a simulation of the robot Puma 560 is implemented. Using a relatively accurate model of the robot dynamics, a PID controller is applied and an optimization problem is configured. Two objective functions are defined, namely the integral of absolute error and the variance of control action. In addition, two constraints are considered regarding the maximal position error and maximal motor torque. After defining the optimization problem, the two algorithms are implemented as auto-tuning methods of the controller gains. Execution of the tuning process is repeated 30 times to test the statistical power of the obtained results. After that, an experiment on a real robot is performed to gain an overview on the practical application of the proposed method. Finally, the performance of both algorithms are compared and conclusions about the efficiency of each one are made.
Tactile Sensing Glove-Based System for Objects Classification Using Support Vector Machine

Peripheral neuropathies affect around 2% to 8% of the adult population and are usually caused by diseases such as diabetes, hanseniasis, and alcoholism. The sensitivity loss resulting from this type of injury makes it much more difficult to the patients cope with daily activities such as writing, driving, and eating. Electronic gloves are capable of reproducing the human sensorial capacities regarding the classification and the differentiation of objects. In this paper, we present an intelligent low-cost glove using pressure sensors to classify objects by using a Support Vector Machine (SVM) algorithm. Two capacitive pressure sensors were manufactured from thin sheets of copper and a layer of Ethyl Vinyl Acetate (EVA). These sensors were attached to the thumb and the index fingers of a glove of polyamide-spandex. The obtained capacitance values, represented in the form of digital levels, were analyzed by a software developed in Matlab, which is responsible for both the serial communication and the interface using a Graphical User Interface (GUI). It was possible to observe that the measurements conducted for different objects gather in clusters, which may be distinguished with an error smaller than 2% for an 11-object-training session using an average Gaussian SVM classifier. The results allowed developing an electronic system for objects classification which can help patients with neuropathies and the emerging assistive technologies.
FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies

Detecting high-order epistasis in genome-wide association studies (GWASs) is of importance when characterizing complex human diseases. However, the enormous numbers of possible single-nucleotide polymorphism (SNP) combinations and the diversity among diseases presents a significant computational challenge. Herein, a fast method for detecting high-order epistasis based on an interaction weight (FDHE-IW) method is evaluated in the detection of SNP combinations associated with disease. First, the symmetrical uncertainty (SU) value for each SNP is calculated. Then, the top-k SNPs are isolated as guiders to identify 2-way SNP combinations with significant interaction weight values. Next, a forward search is employed to detect high-order SNP combinations with significant interaction weight values as candidates. Finally, the findings were statistically evaluated using a G-test to isolate true positives. The developed algorithm was used to evaluate 12 simulated datasets and an age-related macular degeneration (AMD) dataset and was shown to perform robustly in the detection of some high-order disease-causing models.
Assessing the accuracy of commercially available gas sensors for the measurement of ambient ozone and nitrogen dioxide

Abstract The objective of the National Institute for Occupational Safety and Health (NIOSH) accuracy criterion is to ensure that measurements from monitoring devices are within ±25% of the true concentration of the analyte with 95% certainty. To determine whether NO2 and O3 sensors meet this criterion, three commercially available units (Cairclip O3/NO2, Aeroqual NO2, and Aeroqual O3 sensors) were co-located three times with validated instruments (NOx chemiluminescence [NO2mon] and photometric O3 analyzers [O3mon]) at an outdoor monitoring station. As cofactors of sensor performance such as temperature (T) and relative humidity (RH) potentially influence the response of NO2 and O3 sensors, corrections for cofactors were made by using T, RH, and the sensor measurements to predict measurements made by NO2mon and O3mon during the first co-location period (training dataset). The developed models were tested in the merged data obtained from the second and third co-location periods (testing dataset). In the training and testing datasets, the mean NO2 as measured by NO2mon was 4.6 ppb (range = 0.4–35 ppb) and 9.4 ppb (range = 1–37 ppb), respectively. The mean O3 in the training and testing datasets as measured by O3mon was 38.8 ppb (range = 1–65 ppb) and 35.7 ppb (range = 1–61 ppb), respectively. None of the sensor measurements in the training dataset were within the NIOSH accuracy criterion (mean error ≥25%). After correcting for cofactors of sensor performance, the accuracy of the Cairclip O3/NO2 and the Aeroqual O3 sensors considerably improved when tested with the testing dataset (mean error = -1% and 14%, respectively). However, the Aeroqual NO2 sensor had an error that was not within ±25%. Raw measurements from the tested sensors may be unsuitable for assessing workers’ exposure to NO2 and O3. Corrections for cofactors of Cairclip O3/NO2 and Aeroqual O3 sensor performance are required for more accurate occupational exposure assessment.
Dynamic Event Detection Using a Distributed Feature Selection Based Machine Learning Approach in a Self-Healing Microgrid

A self-healing function is an attractive feature in any modern microgrid. Once a fault occurs, it is imperative for a grid to monitor its status, take action based on the level of severity, and after the contingency has been cleared, restore the system. With an increasing number of microgrids and distributed generation stations, deploying a centralized control is no longer a cost-effective option, and therefore distributed control is a likely solution. In an interconnected network, it is important to detect the underlying events taking place in each of the distributed stations, otherwise operational decisions become noncoherent. This paper proposes a novel, feature selection-based distributed machine learning approach to detect the dynamic signatures of different power system events. The purpose is to facilitate a postfault decision-making process in order to restore a stand-alone microgrid without the intervention of a central station. The proposed method detects meaningful features from the generator data and then applies a multiclass classification algorithm to the feature data. Each class represents one dynamic event taking place. The methodology is demonstrated in an interconnected two-area-based microgrid with multiple types of energy generation schemes.
Theory for 1D full waveform inversion of surface GPR data

In one dimension, full waveform inversion is shown to be a linear problem under several conditions. I show that if the magnetic permeability can be assumed constant and electric conductivity to be zero, measuring the magnetic field at the surface or in the air suffices as input data. I present the theory using integral equations that describe the electric field inside the medium in terms of contrast sources. The electric field inside the medium can be computed from the measured magnetic field by solving a Marchenko equation. Once this field is known only the contrast function is unknown and can be found by matrix inversion. If the electric field is also measured the inverse problem can be solved recursively. In one dimension depth is intrinsically unknown and I use recording time as a replacing coordinate. After the electric permittivity is known as a function of one-way travel time from surface to a depth level inside the medium, the depth level can be found by an integral. This produces electric permittivity as a function of depth and full waveform inversion is complete. A simple numerical example demonstrates the method.
Evolutionary history and spatio-temporal dynamics of dengue virus serotypes in an endemic region of Colombia

Dengue is a prevalent disease in Colombia and all dengue virus serotypes (DENV-1 to -4) co-circulate in the country since 2001. However, the relative impact of gene flow and local diversification on epidemic dynamics is unknown due to heterogeneous sampling and lack of sufficient genetic data. The region of Santander is one of the areas with the highest incidence of dengue in Colombia. To provide a better understanding of the epidemiology of dengue, we inferred DENV population dynamics using samples collected between 1998 and 2015. We used Bayesian phylogenetic analysis and included 143 new envelope gene sequences from Colombia, mainly from the region of Santander, and 235 published sequences from representative countries in the Americas. We documented one single genotype for each serotype but multiple introductions. Whereas the majority of DENV-1, DENV-2, and DENV-4 strains fell into one single lineage, DENV-3 strains fell into two distinct lineages that co-circulated. The inferred times to the most recent common ancestors for the most recent clades of DENV-1, DENV-2, and DENV-4 fell between 1977 and 1987, and for DENV-3 was around 1995. Demographic reconstructions suggested a gradual increase in viral diversity over time. A phylogeographical analysis underscored that Colombia mainly receives viral lineages and a significant diffusion route between Colombia and Venezuela. Our findings contribute to a better understanding of the viral diversity and dengue epidemiology in Colombia.
Systematic Development of a Wireless Sensor Network for Piezo-Based Sensing

A low-power wireless sensor/actuator network was specially developed and optimized for piezoceramic transducer-based active sensing applications. Wireless sensor network promises increased system flexibility, lower system cost, and increased robustness through decentralization. Piezoceramic signal conditioning circuit, actuating circuit, power management, and wireless microcontroller were integrated in the hardware design. IEEE 802.15. 4 wireless stack protocol was implemented on the hardware, and user input/output management together with a shell provided easier debugging and configuring interface. The designed system provides a low-power wireless solution towards many applications such as wireless structural health monitoring and wireless structural vibration control.
Reduced Volume ‘Daily Max’ Training Compared to Higher Volume Periodized Training in Powerlifters Preparing for Competition—A Pilot Study

The present study looked to examine reduced volume ‘daily max’ (near max loads) training compared to higher volume periodized training in powerlifters preparing for competition. Ten competitive powerlifters were split into 2 groups (MAX group and PER group) and participated in a 10-week training intervention either following a “daily max” training protocol or a traditional periodized training protocol while preparing for competition. All participants underwent 1RM testing for squat (SQ), bench press (BP) and deadlift (DL) prior to the 10-week intervention. The MAX group performed single sets of single repetitions using a load equating to an RPE rating of 9–9.5 while the PER group performed higher volume periodized training with loads ranging from 70%1RM up to 93%1RM as well as a taper at the final weeks of the training intervention. Both groups were tested after the 10-week training intervention at the Greek IPF-affiliate National Championships. In the PER group, powerlifting (PL) total increased for P1 and P3 by 2% and 6.5% respectively while P2 experienced no change. In the MAX group PL total increased for P1 and P2 by 4.8% and 4.2% respectively while it decreased by 0.5%, 3.4% and 5% for P3, P4 and P5 respectively. In the MAX group peri PL total increased for P1–4 by 3.6%, 4.2%, 4.5% and 1.8% respectively while it decreased by 1.2% for P5. The results of this pilot study show that single-set, single-rep, RPE based ‘daily max’ training may be a favorable strategy for some beginner-intermediate powerlifters preparing for competition while it may lead to performance decreases for others. Further, it suggests that performance may be comparable to traditional periodized training during shorter training cycles, though future work with larger samples is needed to further test this. Practically ‘daily max’ training may be useful for PL athletes looking to maintain strength during periods with limited training time available.
A Lightweight Incognito Key Exchange Mechanism for LTE-A Assisted D2D Communication

Security, privacy, and availability are the crucial factors in Device-to-Device (D2D) communication. 5G is enabled with IPv6 to provide IP to the growing D2D communication and they are expected to operate over the LTE-Advanced (LTE-A) networks. With the increase in the 5G devices, there is a major security threat as any high-level device like smartphones has the ability to hack the network. The higher capability device can spoof IP of lower capability devices like household equipment to gain access into a trusted environment. Henceforth, there is a need for promising security architecture to protect a wide range of 5G devices and data. With the increase in the 5G devices, the device security and mobility management task at the core network will increase tremendously and impose huge core network overhead. Meanwhile 5G, multi-tier architecture has a stringent latency requirement which brings new security challenges due to frequent handovers and a wide range of authentication mechanisms in 5G small cells. Therefore we propose a Lightweight Incognito Key Exchange (LIKE) protocol for LTE-A supported D2D authentication and secret key sharing. Our objective is to provide a secure and user privacy ensured D2D communication that can promise a secure, low-cost and highly available D2D service for the upcoming delay and energy constrained 5G devices.
Bi-Objective-Based Cost Allocation for Cooperative Demand-Side Resource Aggregators

This paper presents a cooperative game theoretic approach to tackle the cost allocation problem for a virtual power plant which consists of multiple demand-side resource aggregators (DRAs) participating in the short-term two settlement electricity market. Given the considered game is balanced, we propose to employ the cooperative game theory’s core cost allocation concept to efficiently allocate the bidding cost to the DRAs. Since the nonempty core contains many potential solutions, we develop a bi-objective optimization framework to determine the core cost allocation solution that can achieve efficient tradeoff between stability and fairness. To solve this problem, we jointly employ the ${\epsilon }$ -constraint and row constraint generation methods to construct the Pareto front, based on which we can specify a desired operation point with reasonable computation effort. Numerical studies show that our proposed design can efficiently exploit the nonempty core to find a cost allocation for the participants, achieve the desirable tradeoff between stability and fairness, and can address the practical DRAs’ large-scale cooperation design.