Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Decision Based Deep Reinforcement Learning For Energy Harvesting Enabled Mobile Edge Computing

For the next generation of communication systems, low latency is an urging requirement to satisfy the increasing computation requires. In response, mobile edge computing (MEC) with energy harvesting (EH) is a promising technology to achieve sustained improvement of the computation experience. However, the frequently varied harvested energy, coupled with variable computing tasks and changing computation capacity of servers, results in the high dynamics of the computation offloading problem. In order to get satisfactory computation quality for such a high dynamic offloading problem, devices should learn to make multiple continuous and discrete actions when optimizing the system performance, such as latency, energy efficiency, etc. In this paper, we propose a continuous-discrete hybrid decision based deep reinforcement learning algorithm for dynamic computation offloading. Specifically, the actor outputs continuous actions (offloading ratio and local computation capacity) corresponding to every server. On the other hand, the critic outputs the discrete action (server selection) while also evaluates the performance of the actor for neural network updating. Simulation results validate the effectiveness of our proposed algorithm, which demonstrates superior generalization ability and achieves better performance compared with the discrete decision based deep reinforcement learning methods.
Beyond Leaders and Followers: Understanding Participation Dynamics in Event-Based Social Networks

ABSTRACT Understanding user behaviors and social relations in social media has been an important topic in Human-Computer Interaction research. In this paper, we look at an emerging form of social media, Event-based Social Networks (EBSNs), which support a special type of hybrid community where people are connected online to organize themselves for offline gatherings. EBSN users are labeled as either organizers or members on existing platforms, and their behavioral and relationship patterns in offline events have not been described systematically. To understand participation dynamics in EBSNs, we present an interview study with 12 Meetup users and categorize a variety of social roles beyond organizers and members. We identify that different types of organizers, classical leaders and delegated leaders, have different relationship patterns with various types of members, including active contributors, active followers, newcomers and occasional visitors. By comparing these roles with purely offline or online communities, we discuss how participation dynamics in EBSNs reflect the intertwined impacts of hybrid community and implications of our findings for technology designs.
Permanent Supportive Housing With Housing First to Reduce Homelessness and Promote Health Among Homeless Populations With Disability: A Community Guide Systematic Review

Supplemental Digital Content is Available in the Text. Context: Poor physical and mental health and substance use disorder can be causes and consequences of homelessness. Approximately 2.1 million persons per year in the United States experience homelessness. People experiencing homelessness have high rates of emergency department use, hospitalization, substance use treatment, social services use, arrest, and incarceration. Objectives: A standard approach to treating homeless persons with a disability is called Treatment First, requiring clients be “housing ready”—that is, in psychiatric treatment and substance-free—before and while receiving permanent housing. A more recent approach, Housing First, provides permanent housing and health, mental health, and other supportive services without requiring clients to be housing ready. To determine the relative effectiveness of these approaches, this systematic review compared the effects of both approaches on housing stability, health outcomes, and health care utilization among persons with disabilities experiencing homelessness. Design: A systematic search (database inception to February 2018) was conducted using 8 databases with terms such as “housing first,” “treatment first,” and “supportive housing. ” Reference lists of included studies were also searched. Study design and threats to validity were assessed using Community Guide methods. Medians were calculated when appropriate. Eligibility Criteria: Studies were included if they assessed Housing First programs in high-income nations, had concurrent comparison populations, assessed outcomes of interest, and were written in English and published in peer-reviewed journals or government reports. Main Outcome Measures: Housing stability, physical and mental health outcomes, and health care utilization. Results: Twenty-six studies in the United States and Canada met inclusion criteria. Compared with Treatment First, Housing First programs decreased homelessness by 88% and improved housing stability by 41%. For clients living with HIV infection, Housing First programs reduced homelessness by 37%, viral load by 22%, depression by 13%, emergency departments use by 41%, hospitalization by 36%, and mortality by 37%. Conclusions: Housing First programs improved housing stability and reduced homelessness more effectively than Treatment First programs. In addition, Housing First programs showed health benefits and reduced health services use. Health care systems that serve homeless patients may promote their health and well-being by linking them with effective housing services.
Stability Analysis of Impulsive Switched Systems with Time Delays and State-dependent Impulses

This paper studies stability analysis for impulsive switched systems with time delays and state-dependent impulses. To guarantee the uniqueness and existence of the solution, the B-equivalence method is applied to rule out the Zeno and beating phenomena, and impulsive switched time-delay systems with state-dependent impulses are transformed into impulsive switched time-delay systems with time-dependent impulses. The equivalence between the original system and the transformed system is established. With the transformed system and using the Lyapunov-based approach, stability conditions are derived for the original system in terms of linear matrix inequalities. Finally, a numerical example is presented to illustrate the developed results.
Power Allocation and User Clustering in Multicast NOMA based Satellite Communication Systems

This paper investigates the application of multicast non-orthogonal multiple access (MC-NOMA) schemes to the forward link of a satellite communication system. In multicast transmission each frame contains information of multiple users. To benefit from the theory developed in NOMA, the proposed scheme creates two groups of users within each beam. The analysis conducted in this work reveals that the user grouping has an impact on the performance. In the light of this observation, power allocation and user clustering techniques have been derived to either maximize the sum-rate or achieve max-min fairness. The numerical simulation results show that MC-NOMA outperforms multicast orthogonal multiple access (MC-OMA) schemes, where different groups are served in orthogonal resources. Moreover, the gain of MC-NOMA over the MC-OMA becomes more prominent as number of users per group and the transmit power increases. The results show the minimum-rate and the sum-rate of MC-NOMA can be increased by a factor 2 and 1.45 with respect to MC-OMA, respectively.
HotSpot & Cache: an Optimization Method for Small Objects Storage in SWIFT of OpenStack Cloud

In the big-data era, cloud platforms such as OpenStack aims at the optimization of storage performance for large objects. However small files severely hurdle its performance. In this paper a framework, namely HotSpot & Cache, is proposed to optimize the storage performance for small objects in SWIFT of OpenStack cloud. A double-proxy architecture is adopted for HotSpot & Cache architecture, including an external proxy node, an internal proxy node, and a dedicated cache server. Storage performance optimization for small objects is accomplished in the unit of partitions in storage nodes, hot-spot partitions are predicted and cached in the dedicated cache server. A series of facilities are implemented for small objects storage performance optimization, e.g. Sampling & Statistics, Hot-Spot Prediction, and a Request Redirection. The access frequency and the size of a partition is periodically sampled and statistically accumulated to predict the hot-spot of a partition for the next interval. Experimental results demonstrate the effectiveness of the proposed HotSpot & Cache architecture.
Efektifitas Pelatihan Kader Kesehatan dalam Penanganan Tuberkulosis di Wilayah Binaan

Background: Medan is an area with the largest TB patients in the North Sumatra Province. Since 2014, Medan Denai Distric is a target area of Medical Faculty of University Muhammadiyah North Sumatra (FM UMSU). One of the implemented program is formulating a Community Forum on TB Care. The objective of this study is to find out the effectiveness of TB management training by improving the knowledge of health cadres. Method: This was a one-group pre test-and post-test design. Participants of the research were all the TB care Forum members that was selected by inclusive criterias. Data collection was conducted by using quesionnaires of knowledge. Data analysis used Wilcoxon test with significancy level = 0,05. Results: The cadres knowledge significantly rise after the TB management training. Conclusion: TB management training for health cadre was effective in improving knowledge of health cadre.
An experimental study on process modeling for selective laser melting*

This paper addresses process modeling for the selective laser melting (SLM) process. We experimentally investigate the response of the SLM process output (measured by a coaxial near-infrared camera) to changing input laser power. We determined that first and second order models can be used to capture this input-output behavior. Next, we studied the dependency of this transfer function on laser scan speed and other process variables that evolve over a typical part build, such as thermal properties of surrounding medium (bulk powder, build plate, or solidified part) or layer number. The transfer function was found to strongly depend on the material environment (solidified material or bulk powder). Further, transfer function also depended on the layer number, exhibiting transient behavior. We report identified 1st order transfer functions for different scan speeds, locations on the build plate, and different layer numbers. Identified models and quantification of their variability will serve as foundational work for the future implementation of advanced real-time process control algorithms.
Reliability Surveys of Fault Distributions in Rotating Electrical Machines : – Case Study of Fault Detections in IMs –

This paper investigates the condition monitoring field of the rotating electrical machines. Several types of rotating electrical machinery that is present in the industry such as asynchronous machines (induction machines), synchronous machines, DC machines, etc. But, induction motors (IMs) are one of the most important motors used in industrial applications. As it has two rotor types: wound rotor motor and squirrel cage rotor motor. The squirrel cage induction motors (SCIMs) are used more than 90% in industrial and domestic sectors. For this reason, the diagnosis of SCIMs is very important to avoid any damage. Faults like broken rotor bars, magnetic circuit fault, voltage unbalance, stator faults, misalignment, bearing faults, etc. Generally, faults in the SCIMs are categorized as mechanical and electrical types. First, this work focuses on the survey study of fault distributions in SCIM types. We made a detailed survey based on more information derived from several repair workshops. Then a review study of many fault types in induction motor has been presented. This study tries to present a general description of the percentage of many induction motor faults. In addition, an experimental test to verify the increase in amplitude was well presented.
Single Low-Dose CT Image Denoising Using a Generative Adversarial Network With Modified U-Net Generator and Multi-Level Discriminator

Low-dose CT (LDCT) images have been widely applied in the medical imaging field due to the potential risk of exposing patients to X-ray radiations. Given the fact that reducing the radiation dose may result in increased noise and artifacts, methods that can eliminate the noise and artifacts in the LDCT image have drawn increasing attentions and produced impressive results over the past decades. However, recent proposed methods mostly suffer from noise remaining, over-smoothing structures or false lesions derived from noise. In this paper, we propose a generative adversarial network (GAN) with novel architecture and loss function for restoring the LDCT image. Firstly, the inception-residual block and residual mapping are incorporated in the U-Net structure. The modified U-Net is applied as the generator of the GAN network so that the noise feature can be eliminated during the forward propagation. Secondly, a novel multi-level joint discriminator is designed by concatenating multiple convolutional neural networks (CNNs) where the output of each deconvolutional layer in the generator is compared with the corresponding down-sampled ground truth image. The adversarial training can be sensitive to noise and artifacts in different scales with this discriminator. Thirdly, we novely define a loss function consisting of the least square adversarial loss, VGG based perceptual loss, MSE based pixel loss and the noise loss, so that the differences in pixel, visual perception and noise distribution are comprehensively considered to optimize the network. Experimental results on both simulated and official simulated clinical images have demonstrated that the proposed method can provide superior performance to the state-of-the-art methods in noise removal, structure preservation and false lesions elimination.
A System for High Performance Mining on GDELT Data

We design a system for efficient in-memory analysis of data from the GDELT database of news events. The specialization of the system allows us to avoid the inefficiencies of existing alternatives, and make full use of modern parallel high-performance computing hardware. We then present a series of experiments showcasing the system’s ability to analyze correlations in the entire GDELT 2.0 database containing more than a billion news items. The results reveal large scale trends in the world of today’s online news.
Hybrid Deep Learning: An Efficient Reconnaissance and Surveillance Detection Mechanism in SDN

Software defined network (SDN) centralized control intelligence and network abstraction aims to facilitate applications, service deployment, programmability, innovation and ease in configuration management of the underlying networks. However, the centralized control intelligence and programmability is primarily a potential target for the evolving cyber threats and attacks to throw the entire network into chaos. The authors propose a control plane-based orchestration for varied sophisticated threats and attacks. The proposed mechanism comprises of a hybrid Cuda-enabled DL-driven architecture that utilizes the predictive power of Long short-term memory (LSTM) and Convolutional Neural Network (CNN) for an efficient and timely detection of multi-vector threats and attacks. A current state of the art dataset CICIDS2017 and standard performance evaluation metrics have been employed to thoroughly evaluate the proposed mechanism. We rigorously compared our proposed technique with our constructed hybrid DL-architectures and current benchmark algorithms. Our analysis shows that the proposed approach out-performs in terms of detection accuracy with a trivial trade-off speed efficiency. We also performed a 10-fold cross validation to explicitly show unbiased results.
Implementation of IoT-Based Air Quality Monitoring System for Investigating Particulate Matter (PM10) in Subway Tunnels

Air quality monitoring for subway tunnels in South Korea is a topic of great interest because more than 8 million passengers per day use the subway, which has a concentration of particulate matter (PM10) greater than that of above ground. In this paper, an Internet of Things (IoT)-based air quality monitoring system, consisting of an air quality measurement device called Smart-Air, an IoT gateway, and a cloud computing web server, is presented to monitor the concentration of PM10 in subway tunnels. The goal of the system is to efficiently monitor air quality at any time and from anywhere by combining IoT and cloud computing technologies. This system was successfully implemented in Incheon’s subway tunnels to investigate levels of PM10. The concentration of particulate matter was greatest between the morning and afternoon rush hours. In addition, the residence time of PM10 increased as the depth of the monitoring location increased. During the experimentation period, the South Korean government implemented an air quality management system. An analysis was performed to follow up after implementation and assess how the change improved conditions. Based on the experiments, the system was efficient and effective at monitoring particulate matter for improving air quality in subway tunnels.
A Method for Accelerating Convolutional Neural Networks Based on FPGA

Convolutional neural networks (CNNs) have achieved notable success in the field of computer vision, ranging from image classification to object detection. However, characteristics of intensive multiplication computing, large-scale parameters and resource consuming enormously restrict its application in embedded devices. In this paper, we propose a method that accelerates a target CNN module based on FPGA, and the network we used to demonstrate is LeNet-5. Firstly, the LeNet-5 module will be pruned using Dynamic Network Surgery. Secondly, we quantize the parameters to 8-bits in the pruned network by the quantization scheme called Incremental Network Quantization, and recode it to 5-bits for shift operation. Finally, we develop a model-friendly parallel hardware accelerator with 8-bits fixed-point precision using Verilog HDL codes, and integrate it to a SOC system. By converting multiplication operations to shift operations, we design a parallel LUT-based hardware accelerator with no DSP resources utilized. Processed by Xilinx ZC702 FPGA at 100MHz clock, this implementation reaches a peak performance of 33.6 GMACS with only 1.758 W of total on-chip power, and obtains 98.9% accuracy target 10000 images of MNIST test set.
Comparisons between experimental and morphometric water vapor conductance in the eggs of extant birds and crocodiles: implications for predicting nest type in dinosaurs

Estimates of water vapor conductance for the eggs (i.e., diffusive capacity of eggshell; Gh2o, mg H2O·day−1·Torr−1) of extinct archosaur species have been used to infer their nest type (i.e., covered vs. open) because experimental Gh2o values for living archosaur species appear to reflect nest type. The methods used to derive Gh2o for fossil eggs (eggshell morphometrics) differs from that for extant eggs (experimental measurements), and it remains unknown if these two methods are comparable or if morphometric Gh2o values correspond to nest type. Although previous studies assumed that Gh2o values derived from the two methods were comparable, this assumption has not been statistically evaluated in a large sample size that includes both crocodiles and birds. Here, Gh2o values for over 100 species of living archosaurs for both morphometric and experimental methods were compiled and compared using statistical analyses. Results showed that although experimental and morphometric Gh2o values are significantly cor.
Detection of the 23-bp nucleotide sequence mutation in retinoid acid receptor related orphan receptor alpha (RORA) gene and its effect on sheep litter size

Abstract Retinoid acid receptor related orphan receptor alpha (RORA) transcribes steroid-related genes to regulate estrogen synthesis. As an important reproductive trait, litter size relates to estrogen synthesis. Therefore, it is important to investigate the association between RORA gene and sheep litter size. In this study, one 23-bp nucleotide sequence mutation was identified in intron 1 of RORA gene in 532 female Australian White Sheep. Moreover, the polymorphic information content (PIC) values of this locus was 0.219. The litter size of ID genotype was significantly better than II genotype and DD genotype in the second born litter size (p < 0.05). This loci was related to third born litter size and the ID is the dominant genotype (p < 0.05). The association between combined genotypes and average litter size showed that sheep with heterozygote (ID) genotypes had larger lamb than homozygous (DD and II) genotypes. To sum, this study provided theoretical references for the comprehensively research of the function of RORA gene and the breeding of Australian White Sheep. The 23-bp indel variants could be considered as molecular markers for the second and third born litter size of sheep for MAS breeding.
Multi-objective optimisation of risk and business strategy in real-world supply networks in the presence of uncertainty

Abstract Selection of suppliers is very important for a strategic supply network (SN) design. This paper presents a novel multi-objective optimisation model for supplier selection and order allocation. In addition to a standard objective of total SN cost minimisation, two new objectives are considered: minimisation of suppliers’ risk and maximisation of achievement of a manufacturer business strategy. Uncertainty in supply lead times and non-conformance rates of delivered components causes uncertainty in the SN cost objective. These parameters are described using imprecise linguistic terms and modelled using fuzzy numbers. Risk classification of suppliers is carried out using imprecise knowledge which is modelled using fuzzy If-Then rules and embedded in the risk objective. Various experiments are carried out to analyse the trade-off between the considered objectives and the impact of SN network parameters on the suppliers’ selection and order allocation. The size of the problem that the model can handle is analysed also.
Deep Learning based Corn Kernel Classification

This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learning-based approach, the Mask R-CNN architecture, while the classification is performed through a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered. As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have been performed and comparisons with other approaches are provided showing improvements with the proposed pipeline.