Miracle-dz/UniFAR-FTUs huggingface dataset:
ML-based Incast Performance Optimization in Software-Defined Data Centers

Traffic optimization is fundamental to achieve both great application performance and resource efficiency in data centers with heterogeneous workloads, including incast. However, general performance models, providing insights on how various factors affect a certain performance metric used in the network optimization process, are missing. For the special case of incast, the existing models are analytical models, either tightly coupled with a particular protocol version or specific to certain empirical data. This paper proposes an SDN-enabled machine-learning-based optimization framework for incast performance optimization in data center networks that leverages learning-based performance modeling. Evaluations based on intensive NS-3 simulations show that we can achieve accurate performance predictions that enable finding the efficient switch buffer space to achieve optimal incast completion time in different configurations. We expect this framework to be a building block for autonomous data center network management.
Dynamic Average Consensus based on Group Information for Multi-Swarm Systems.

This paper deals with distributed protocols to track the time-varying average of a dynamic Multi-Swarm System (MSS) using group information. To solve the swarm average consensus problem, the agents of each swarm can exchange information at the intra-swarm level. However, only one agent of each swarm, referred to as the swarm communicating agent, can share its group information with the other swarms. We show that the discrete time protocols asymptotically achieve consensus on the dynamic swarm average with a bounded error. The algorithms are developed for both fixed and switching communicating agents configurations with strongly connected and weighted balanced directed graphs. We provide numerical simulations to illustrate the efficiency of our solutions.
Towards a Distributed Testbed for Wireless Embedded Devices for Industrial Applications

Wireless embedded devices are key elements of Internet-of-Things (IoT) and industrial IoT (IIoT) applications. The complexity of these devices as well as the number of connected devices to networks increase steadily. The high intricacy of the overall system makes it error-prone and vulnerable to attacks and leads to the need to test individual parts or even the whole system. Therefore, this paper presents the concept of a flexible and distributed testbed to evaluate correct behavior in various operation or attack scenarios. It is based on the Robot Operating System (ROS) as communication framework to ensure modularity and expandability. The testbed integrates RF-jamming and measurement devices to evaluate remote attack scenarios and interference issues. An energy harvesting emulation cell is used to evaluate different real-world energy harvesting scenarios. A climatic test chamber allows to investigate the influence of temperature and humidity conditions on the system-under-test. As a testbed application scenario, the automated evaluation of an energy harvesting wireless sensor network designed to instrument automotive engine test benches is presented.
Use of ethylene and ethane as primary substrates for aerobic cometabolism of vinyl chloride

A significant problem encountered with anaerobic reductive dechlorination of polychlorinated ethylenes during groundwater remediation is accumulation of vinyl chloride (VC). Even when reduction of VC proceeds to ethylene and/or ethane, low levels of VC may persist. The purpose of this study was to examine use of ethylene and ethane as primary substrates for aerobic cometabolism of VC. Both ethylene- and ethane-grown enrichment cultures (developed with activated sludge inoculum) readily consumed VC. The ethylene culture exhibited an initial preference for VC over ethylene but then switched after several weeks. This culture was unable to use ethane or VC as sole substrates. Although VC inhibited ethylene use, growth on ethylene still occurred in the presence of VC. The ethane-grown culture was able to use both VC and ethylene as sole substrates. When all three compounds were present, ethylene was consumed first, followed by VC and ethane. Thus, the presence of ethylene and/or ethane with VC may eliminate the need to add a primary substrate (for example, methane, toluene, or phenol) to sustain cometabolism of VC below the regulatory limit.
Design and Calibration of Low Cost Sensor Node for Thermal Comfort Estimation

Thermal comfort is a variable that makes it possible to measure a person’s sensation in a given environment. This variable depends on several factors: ambient temperature, relative humidity, air speed, the clothes that the person wears, and the physical activity that they are doing, among others. Given the number of measurements to be carried out, we must already speak of a sensor node for the development of a sensor that allows the measurement of variables to estimate thermal comfort. This work presents the development and calibration of a low-cost sensor node that allows the estimation of thermal comfort through the Fanger method, for which the PMV (Predicted Mean Vote) and PPD (Predicted Percentage Dissatisfied) are estimated. The development of low-cost sensors for estimating thermal comfort will allow the development of platforms to manage energy use in buildings efficiently. Finally, the experimental results of the sensor node prototype are presented and compared with standard instruments in the laboratory stage, showing the certainty of the prototype.
A CoMFA Study of Quinazoline-based Anticancer Agents

Cancer has emerged as one of the leading cause of deaths worldwide. A three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was performed on a series of quinazoline-based anticancer agents. Purpose of the study is to understand the structural basis for their inhibitory activity. Comparative molecular field analysis (CoMFA) technique was employed to develop 3D-QSAR model. Ligand-based alignment scheme was used to generate a reliable CoMFA model. The model produced statistically significant results with a cross-validated correlation coefficient (q) of 0.589 and a non-cross-validated correlation coefficient (r) of 0.928. Model was further validated by bootstrapping and progressive scrambling analysis. This study could assist in the design of novel and more potent anticancer agents.
Improving the energy efficiency of STT-MRAM based approximate cache

Approximate computing applications lead to large energy consumption and performance demand for the memory system. However, traditional SRAM based cache cannot satisfy these demands due to high leakage power and limited density. Spin Transfer Torque Magnetic RAM (STT-MRAM) is a promising candidate of cache due to low leakage power and high density. However, STT-MRAM suffers from high write energy. To leverage the ability of tolerating acceptable quality loss via approximations to data, we propose an STT-MRAM based APProximate cache architecture (APPcache) to write/read approximate data thus largely reducing energy. We find many similar elements (e.g. pixels in images) existing in cache lines while running approximate computing applications. Therefore, APPcache uses several lightweight similarity-based encoding schemes to eliminate the similar elements to reduce the data size thus reducing the write energy of STT-MRAM based cache. Besides, we design a software interface to manually control the output quality. APPcache can significantly eliminate similar elements, thus improving energy efficiency. Experimental results show that our scheme can reduce write energy and improve the image raw data compression ratio by 21.9% and 38.0% compared with the state-of-the-art scheme with 1 % error rate, respectively. As for the output quality, the losses of all benchmarks are within 5% with 1 % error rate.
Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

Medical imaging refers to visualization techniques to provide valuable information about the internal structures of the human body for clinical applications, diagnosis, treatment, and scientific research. Segmentation is one of the primary methods for analyzing and processing medical images, which helps doctors diagnose accurately by providing detailed information on the body’s required part. However, segmenting medical images faces several challenges, such as requiring trained medical experts and being time-consuming and error-prone. Thus, it appears necessary for an automatic medical image segmentation system. Deep learning algorithms have recently shown outstanding performance for segmentation tasks, especially semantic segmentation networks that provide pixel-level image understanding. By introducing the first fully convolutional network (FCN) for semantic image segmentation, several segmentation networks have been proposed on its basis. One of the state-of-the-art convolutional networks in the medical image field is U-Net. This paper presents a novel end-to-end semantic segmentation model, named Ens4B-UNet, for medical images that ensembles four U-Net architectures with pre-trained backbone networks. Ens4B-UNet utilizes U-Net’s success with several significant improvements by adapting powerful and robust convolutional neural networks (CNNs) as backbones for U-Nets encoders and using the nearest-neighbor up-sampling in the decoders. Ens4B-UNet is designed based on the weighted average ensemble of four encoder-decoder segmentation models. The backbone networks of all ensembled models are pre-trained on the ImageNet dataset to exploit the benefit of transfer learning. For improving our models, we apply several techniques for training and predicting, including stochastic weight averaging (SWA), data augmentation, test-time augmentation (TTA), and different types of optimal thresholds. We evaluate and test our models on the 2019 Pneumothorax Challenge dataset, which contains 12,047 training images with 12,954 masks and 3,205 test images. Our proposed segmentation network achieves a 0.8608 mean Dice similarity coefficient (DSC) on the test set, which is among the top one-percent systems in the Kaggle competition.
Modeling of Human Neuraminidase-1 and Its Validation by LERE-Correlation Analysis

Four human neuraminidases (hNEUs1–4) have been identified. Among them, hNEU1 has been studied extensively as a target for sialidosis. It has been desired to understand the biological functions of hNEU1 at the molecular and atomic levels. The three-dimensional structure of hNEU1 is not known at present. In the present work, we constructed a three-dimensional structure of hNEU1 by homology modeling, and then performed correlation analyses between observed and calculated free-energy changes (quantitative structure−activity relationship (QSAR) analyses), coupled with LERE (linear expression by representative energy terms) procedure using the modeled three-dimensional structure in order to confirm the validity of the modeled structure. The atomic coordinates of all atoms in the verified model of hNEU1 are available. The proposed structure of hNEU1 will be useful and helpful for further studies concerning the biological and chemical functions of hNEU1. The present article is one of continuous works derived from the one that won the CBI
Research on the Correlation Between the Timbre Attributes of Musical Sound and Visual Color

This article investigated the timbre of musical sound and visual color; comprehensively used experimental psychology, audio information processing and other experimental methods and technical means; and conducted research on the relationship between timbre attributes and visual color. First, we designed and implemented a subjective perception experiment based on audiovisual cross-modal timbre-color correlation. Through the statistical analysis of the experimental data, we obtained a complete timbre-color cross-modal correlation dataset. Second, through the visual processing and correlation analysis of the timbre-color correlation data, it is proven that there is a certain correlation between the timbre dimension and the color dimension. On this basis, we used three algorithms to construct a timbre-color correlation model, namely, multiple linear regression, BP neural network and SVR, and we verified the accuracy of the three models. The timbre-color correlation dataset constructed in this paper can provide basic data support for audiovisual cross-modal research. The timbre-color correlation model constructed in this paper can provide a theoretical basis for cross-modal audiovisual applications. In addition, the timbre-color cross-modality research method in this paper can provide new research ideas for audio-visual cross-modality research.
Detection of replay attacks in autonomous vehicles using a bank of QPV observers

This paper addresses the problem of replay attack detection in autonomous vehicles. Due to the strong presence of nonlinearities, traditional approaches based on linear approximations of the dynamics would not work effectively. For this reason, the proposed approach is based on a bank of quadratic parameter varying (QPV) observers, designed in such a way that each observer is insensitive to a replay attack that affects one specific sensor channel. This feature allows the development of a decision algorithm, whose effectiveness is validated by means of simulation results.
Prediction of aging of battery for electric vehicles based on a modified version of neural networks

The battery is the key element to store and then supply electricity to the various components of Electric Vehicles (EV). However, the battery cannot be charged and discharged indefinitely. The main objective of this study is to develop a novel methodology for estimation of the ageing rate of a Lithium-Fer-Phosphate (LFP) battery used in electric vehicles in order to predict its Remaining Useful Life (RUL). Indeed, a new method based a modified version of neural networks is proposed. It is used to estimate the LFP battery’s Discharge Capacity (DC) and to conclude about the battery’s end of life.
Surviving Transient Power Failures with SRAM Data Retention

Many computing systems, such as those powered by energy harvesting or deployed in harsh working environment, may experience unpredictable and frequent transient power failures in their life time. The systems may fail to deliver correct computation results or never progress, as computation is frequently interrupted by the power failures. A possible solution could be frequently saving program states to non-volatile memory (NVM), such as using checkpoints, so that the system can incrementally progress. However, this approach is too costly, since frequent NVM writes is time and energy consuming, and may wear out the NVM device. In this work, we propose an approach to enable a system to use volatile SRAM to correctly progress in the presence of transient power failures, since SRAM is capable of retaining its data for seconds or minutes with the charge remained in the battery/capacitor after the CPU core stops at its brown-out voltage. The main problem is to validate whether the data in SRAM are actually retained during power failures. In our approach, we validate only a subset of the program states with Cyclic Redundancy Check for efficiency. The validation technique requires maintaining a backup version of the program states, which additionally provides the system with the ability to progress incrementally. We implement a run-time system with the proposed approach. Experimental results on an MSP430 platform show that the system can correctly progress on SRAM in the presence of transient power failures with low overhead.
Compression behavior of typical silicone rubbers for soft robotics applications at elevated temperatures

In this communication, we analyze the compression behavior of typical silicone rubbers used in soft robotics applications at elevated temperatures. In particular, compression experiments according to ISO 7743 are performed with three mixing ratios (base polymer to curing agent) of Sylgard, Elastosil, and Ecoflex at 24 °C (room temperature), 30 °C, 40 °C, 50 °C, and 60 °C. Furthermore, typical hyperelastic material models are fitted to the measured stress-strain curves for room temperature. It is shown that the (third-order) Yeoh model shows the best overall fit, and the corresponding model parameters are stated.
Stereoselective pharmacokinetics and placental transfer of fluoxetine and norfluoxetine in pregnant sheep under steady-state conditions

Fluoxetine (FX) is one of the most popular SSRIs used for the treatment of depression. Its use in treating depressed pregnant woman has increased substantially in recent years. A considerable amount of attention in the literature regarding the use of FX in pregnancy has been devoted to the assessment of birth outcomes following in utero FX exposures while studies that focus on pharmacokinetics during pregnancy are relatively sparse. Previous FX experiments in pregnant sheep using single dose intravenous (i.v.) bolus administration have focused primarily on the acute exposure and effects of the drug on both the mother and fetus. In the present studies, experiments were conducted to investigate the stereoselective pharmacokinetics of FX and norfluoxetine (NFX) under steady-state conditions in nonpregnant sheep and pregnant sheep at late gestation via continuous 8-day i.v. infusion of FX to the ewe. Drug infusions were conducted at two dose levels (i.e. low dose and high dose) in nonpregnant sheep that allowed the examination of nonlinear pharmacokinetics of FX in sheep. The pregnant sheep infusion experiment was to determine the impacts of pregnancy on the stereoselective pharmacokinetics of FX and NFX, and to assess the exposure of the fetus to FX and NFX enantiomers at steady-state. Moreover, a continuous 4day direct fetal i.v. FX infusion experiment was conducted to study the pharmacokinetics of FX and NFX in fetal lambs in attempt to explore intrinsic drug elimination capability by the
A Reinforcement Learning-based solution for Intra-domain Egress Selection

An ingress router often has multiple potential egress points in an extensive network where it can transmit traffic to external networks. The traditional solution is choosing the closest node (with the shortest path) to the ingress node. This paper claims the drawbacks of this approach in a flexible network system and introduces our proposal called MAB-based Egress Selection. Our approach uses several Reinforcement Learning techniques, which are commonly used to resolve Multi-Armed Bandit (MAB) problem, to allow the ingress router to periodically re-pick egress point, hence optimize the long-term performance of traffic transmission. To formalize the egress selection process as a MAB problem, we use a combined score of delay and loss representing link status as a reward. However, capturing those network metrics encounters some issues due to the distributed control and restricted local view of network nodes. For this purpose, a centralized control architecture, e.g., Software-defined Network (SDN), is a promising candidate. We applied four common algorithms, ϵ-greedy, Softmax, UCB1 and Single Pull UCB2 (SP-UCB2) for egress selection process. The models are evaluated in two simulated network topologies with different scenarios of network traffic condition. The experimental results show that the UCB algorithms produce the best performance, especially in busy network.
Cipher Block Chaining Support Vector Machine for Secured Decentralized Cloud Enabled Intelligent IoT Architecture

The growth of internet era leads to a major transformation in a storage of data and accessing the applications. One such new trend that promises the endurance is the Cloud computing. Computing resources offered by the Cloud includes the servers, networks, storage, and applications, all as services. With the advent of Cloud, a single application is delivered as a metered service to numerous users, via an Application Programming Interface (API) accessible over the network. The services offered via the Cloud are such as the infrastructure, software, platform, database and web services. The main motivation of this application model is to provide computationally secure key generation to protect the data via encryption. This key generation in the cryptography process falls into three categories in this research work. In the first part, SVM based encryption service model is constructed for which the key generation is from the conventional encryption operation mode with some improvements. To make the process more complex, the optimization techniques are taken into account for the key generation in descendant two methods application model that acts computationally more secure specifically for Cloud environment. The results of security analysis confirm the effectiveness of the proposed application model withstands potentially against various attacks such as Chosen Cipher Attack, Chosen Plain text Attack indistinguishable attacks for files. In case of images, it resists well against statistical and differential attacks. Comparative Analysis shows evidence of the efficiency of the developed pioneering application model quality and strength compared with that of the existing services.
Electrodes’ Configuration Influences the Agreement Between Surface EMG and B-Mode Ultrasound Detection of Motor Unit Fasciculation

Muscle fasciculations, resulting from the spontaneous activation of motor neurons, may be associated with neurological disorders, and are often assessed with intramuscular electromyography (EMG). Recently, however, both ultrasound (US) imaging and multichannel surface EMG have been shown to be more sensitive to fasciculations. In this study we combined these two techniques to compare their detection sensitivity to fasciculations occurring in different muscle regions and to investigate the effect of EMG electrodes’ configuration on their agreement. Monopolar surface EMGs were collected from medial gastrocnemius and soleus with an array of 32 electrodes (10 mm Inter-Electrode Distance, IED) simultaneously with b-mode US images detected alongside either proximal, central or distal electrodes groups. Fasciculation potentials (FP) were identified from single differential EMGs with 10 mm (SD1), 20 mm (SD2) and 30 mm (SD3) IEDs, and fasciculation events (FE) from US image sequences. The number, location, and size of FEs and FPs in 10 healthy participants were analyzed. Overall, the two techniques showed similar sensitivities to muscle fasciculations. US was equally sensitive to FE occurring in the proximal and distal calf regions, while the number of FP revealed by EMG increased significantly with the IED and was larger distally, where the depth of FE decreased. The agreement between the two techniques was relatively low, with a percentage of fasciculation classified as common ranging from 22% for the smallest IED to 68% for the largest IED. The relevant number of events uniquely detected by the two techniques is discussed in terms of different spatial sensitivities of EMG and US, which suggest that a combination of US-EMG is likely to maximise the sensitivity to muscle fasciculations.