Miracle-dz/UniFAR-FTUs huggingface dataset:
of the Operational Research Society Simulation Workshop 2014 ( SW 14 )

This paper deals with the application of modern software development tools on simulation development. Recently, Agile Software Development (ASD) methods enjoy an increasing popularity. eXtreme Programming (XP) techniques, one of the techniques which belong to the ASD group of methods is a software development method which improves software quality and responsiveness of software projects through introducing short development cycles and a Test Driven Development (TDD) philosophy throughout the development. In this paper, we particularly pay attention to the application of the TDD by approaching simulation development from a test-first perspective. This study consists of a feasibility study of applying the TDD technique in simulation development in its various levels, say, acceptance and unit testing. Moreover, a simulation case study of a surgical ward has been considered, designed and implemented using the AnyLogic simulation toolkit. Our study differs from the mainstream in many ways. It addresses the feasibility of Test-Driven Simulation Development in Visual Interactive Modelling and Simulation (VIMS) environments as well as providing an insight into how the test-first concept can further help with the choice of components and acceptance testing.
CCNet: Criss-Cross Attention for Semantic Segmentation

Contextual information is vital in visual understanding problems, such as semantic segmentation and object detection. We propose a criss-cross network (CCNet) for obtaining full-image contextual information in a very effective and efficient way. Concretely, for each pixel, a novel criss-cross attention module harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies. Besides, a category consistent loss is proposed to enforce the criss-cross attention module to produce more discriminative features. Overall, CCNet is with the following merits: 1) GPU memory friendly. Compared with the non-local block, the proposed recurrent criss-cross attention module requires <inline-formula><tex-math notation="LaTeX">$11\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>11</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq1-3007032.gif"/></alternatives></inline-formula> less GPU memory usage. 2) High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 85 percent of the non-local block. 3) The state-of-the-art performance. We conduct extensive experiments on semantic segmentation benchmarks including Cityscapes, ADE20K, human parsing benchmark LIP, instance segmentation benchmark COCO, video segmentation benchmark CamVid. In particular, our CCNet achieves the mIoU scores of 81.9, 45.76 and 55.47 percent on the Cityscapes test set, the ADE20K validation set and the LIP validation set respectively, which are the new state-of-the-art results. The source codes are available at <uri>https://github.com/speedinghzl/CCNethttps://github.com/speedinghzl/CCNet</uri>.
Social learning theory in Second Life

One of the current trends in distance education is the use of multi-user virtual environments (MUVE) as a training platform. MUVEs are being used for both formal and informal online learning. Second Life is a popular MUVEs being used for education today. Teaching and learning in Second Life requires a paradigm shift by educators, researchers, and learners who must adapt to a new environment of teaching and learning. Although things are done differently, many traditional learning theories can apply to Second Life. Examples of Albert Bandura’s social learning theory in Second Life are explored in this paper.
Dynamics of Social Roles in the Context of Group Evolution in the Blogosphere

An important aspect of research carried out on social networks is the analysis of community dynamics. It can have many aspects related to size of community, existing groups and roles performed by users in them. This article focuses on the problem of studying the impact of the types of group reorganization on transitions between user roles. A set of roles has been defined and each role has been assigned to users having similar behavior, and main types of events have been identified that affect groups during their life cycle. A model for the evolution of the community has been developed. Analyzes of transitions between roles of given users as a result of individual events, which have a significant impact on the changing roles in the community by the users, have been carried out. The experiments have been performed on data from the Salon24 - Polish popular blog portal.
Low Restoration-Energy Differential Spin Hall Effect MRAM for High-Speed Nonvolatile SRAM Application

A differential spin Hall effect (DSHE) based nonvolatile static random access memory, named as DSNVM, is proposed in this paper. DSNVM consist of a conventional 6T SRAM cell and a DSHE as nonvolatile element. The data present at storage nodes of SRAM are complementary to each other and are stored in DSHE memory counterpart. The stored data can be retrieved once power supply is resumed. The analysis of DSNVM cell is carried out and is compared with other spintronics based NVSRAMs. DSNVM shows 40% faster data restoration and 16.7% lesser energy consumption as compared to SHE based NVSRAM.
Temperature Effects on the Total Ionizing Dose Response of TaOx-based Memristive Bit Cells

The effects of temperature on the total ionizing dose (TID) response of tantalum oxide (TaOx) memristive bit cells are investigated. The TaOx devices were manufactured by Sandia National Laboratories (SNL). In-situ data were obtained as a function of temperature, accumulated dose, and bias at the Gamma Irradiation Facility (GIF). The data indicate that devices reset into the high resistance off-state exhibit decreases in resistance when the temperature is increased. However, an increased susceptibility to TID at elevated temperatures was not observed.
Predictive Direct Torque Control with Reduced Number of Considered States in Synchronous Reluctance Motor Drive

Model productive control is a well-known and popular control method in motor drive application; but due to its high computational burden, it requires expensive micro-controller. The computational burden in MPC is highly related to the number of equations and states that it has to search in each control cycle. This paper proposes a novel method in predictive direct torque control (MPDTC) of synchronous reluctance motor (SynRM) to reach the optimal state with less numbers of predicted states. Firstly, the space vector sectioning is redefined as a function of torque error. Based on the defined sections and direct torque control (DTC) concept, the reduced optimal switching table is provided. In this method, the number of predicted states for next control cycle reduces by 57%. The results show the effectiveness of the proposed method in torque ripple, flux ripple and total harmonic distortion (THD) of current respect to the conventional MPDTC, which considers all switching states. The reduction in computational burden is not affected the optimal solution but enhanced the MPDTC method capability for implementation.
Adversarial Embedding and Variational Aggregation for Video Face Recognition

Video face recognition is a challenging recognition problem due to low-quality and redundant video data. In order to efficiently address the problem, this paper proposes an adversarial embedding and variational aggregation (AEVA) approach that takes discriminative information from adversarial learning to implicitly constrain the distributions of video features. AEVA contains two parts: adversarial embedding learning and variational aggregation learning. The former contributes to discriminative feature embedding space by a self-adversarial process. It reduces intra-class distance of the same subject in an adversarial way. The latter aims to improve aggregated video representation by forcing latent feature distribution to be as close to real feature distribution as possible. An attentional weighting network and a variational inference structure are used to aggregate the features from one video and generate the latent features. Both parts have neither complex sampling strategies nor the hyperparameter settings from deep metric learning. Our approach achieves state-of-the-art performance for video face recognition on four widely used benchmarks, including YouTubeFace, IJB-A, YouTube Celebrities and Celebrity-1000.
Design and Implementation of a 3 kW Single-Phase BESS Using ARM & FPGA

The purpose of this paper is to design and implementation of a battery energy storage system (BESS) for residential application. BESS Power circuit consists of a two-stage bidirectional DC-DC converter and a full-bridge DC-AC single-phase inverter. In this paper, a proportional-resonant (PR) controller with harmonic cancellation is used as the grid-connected inverter current controller. This method has the ability to control active and reactive injected power to grid with standard total harmonic distortion (THD < 5%). The BESS is simulated using Simscape library of MATLAB and is implemented using ARM and FPGA. Both the simulation and the experimental results prove the satisfactory performance of the BESS.
A Deep Learning Iris Recognition Method Based on Capsule Network Architecture

Iris recognition is one of the most representative identification technologies in biometric recognition, which is widely used in various fields. Recently, many deep learning methods have been used in biometric recognition, owing to their advantages such as automatic learning, high accuracy, and strong generalization ability. The deep convolutional neural network (CNN) is the mainstream method of image processing widely used in many domains, but it has poor anti-noise capacity in image classification and is easily affected by slight disturbances. CNN also needs a large number of samples for training. The recent capsule network not only has high recognition accuracy in classification tasks but can also learn part-whole relationships, increasing the robustness of the model. Furthermore, it can be trained using a small number of samples. In this paper, we propose a deep learning method based on the capsule network architecture in iris recognition. The structure detail of the network is adjusted, and we provide a modified routing algorithm based on the dynamic routing between two capsule layers to make this technique adapt to iris recognition. Migration learning makes the deep learning method available even when the number of samples is limited. Therefore, three state-of-the-art pretrained models, VGG16, InceptionV3, and ResNet50, are introduced. We divide the three networks into a series of subnetwork structures according to the number of their major constituent blocks. They are used as the convolutional part to extract primary features, instead of a single convolutional layer in the capsule network. Our experiments are conducted on three iris datasets, JluIrisV3.1, JluIrisV4, and CASIA-V4 Lamp, to analyze the performance of different network structures. We also test the proposed networks in simulated strong and weak light environments, showing that the networks with capsule architecture are more stable than those without.
International Journal of Advanced Research in Computer Science and Software Engineering

---Cloud computing promises can have several attractive remunerations for business and end users with their future of Collaborative cloud computing. CCC platform interconnects physical resources then enable resource sharing connection between the clouds and provides an essential view of a tremendous amount of resources. If it has two main issues of resource & reputation management might be addressed together at that time they don’t sufficiently efficient or effective. When the previous CCC platform enhances the “Harmony” Method it integrates the resource & reputation management in a partition mode within a single node or multiple nodes. Harmony corporate the key resource innovations, when this multi Qos demands might be time-consuming factors and it can raise some disturbances due to resource sharing time. We propose comprehensive solutions of resource sharing and integrated with log records in every node. By using load prediction algorithm We can explore optimal time period for every node, can store node reputations in virtual machines, calculates load factor and capacity of virtual machines. By using prediction algorithm we provide more security for user’s data and captures future usage results of Virtual Machines.
High Efficiency Electric Vehicle Fast Charging

This paper proposes an electric vehicle fast charging station based on 3-level T-type topology offering much higher efficiency than other three-level inverter topologies. Using two voltage compensation algorithms simultaneously, the proposed charger keeps neutral point voltage constant and prevents grid current distortion during different DC loading scenarios. In addition to balancing the neutral point voltage by established modulation-based control methods, this research overcomes the conventional balancing method limitations by adding an auxiliary circuit to the T-type topology. This additional circuit which comes in the form of a new leg to the inverter and consists of nothing more than an inductor in comparison with the existing legs plays a significant role in DC-link voltage balancing functionality. Furthermore, a novel control method is proposed which manages the simultaneous application of the mentioned voltage balancing methods in an efficient and smart way. Performance of the proposed topology and the new control strategy are validated through simulation and finally their steady-state and dynamic results are presented under different DC loading conditions.
Hand Gesture Recognition and Implementation for Disables using CNN’S

Hand Gestures are playing the major role in today’s industry. The development of the gesture recognition or detection will help people in different ways. Development of many automated technologies like machine learning, deep learning, neural networks and computer vision will help in using futuristic methods like gesture controlling and recognition. These gestures can be used in such a way it can help with people who have difficulty in controlling or operation systems or devices. Here the development of the model is in such a way it can help us to recognition and implementation. For such developments these models will be working with Convolution Neural Networks and Back Propagation methodologies which can help for the easy working of these models.
Secure Transmission Schemes for Two-Way Relay Networks

We investigate the physical-layer security problems of two-way relay networks in this paper. The whole network consists of two legitimate users and one eavesdropper, where the legal users need the help of some intermediate nodes to exchange information. We propose joint jamming and relaying mechanism, where a jamming node is selected from all intermediate nodes, to create interference upon eavesdropper, while other nodes are used to relay data for two users. All intermediate nodes are subjected to per-node power constraints. Based on null-space beamforming, we propose two different transmission schemes when the number of relay nodes is large enough. One scheme is designed to maximize the secrecy sum rate (MSSR) of the network, and the other is designed to maximize the minimum secrecy rate (MMSR) between two users. In the MMSR scheme, a series of semi-definite programming problems with a rank-1 constraint needs to be solved. We prove that this rank-1 constraint does not affect the solutions of original problems. In addition, we propose two modified secure transmission schemes when the number of relay nodes is less abundant. In short, no matter how many intermediate nodes in the communication networks, one can find suitable transmission schemes to ensure communication security.
A Wavelet-based Learning for Face Hallucination with Loop Architecture

Face hallucination is a specific super-resolution problem which aims to generate high-resolution(HR) faces from low-resolution(LR) input. Recently, deep learning methods have been widely applied in single-image super resolution. Considering face images have great similarities in both pixel value and global structure, we propose a wavelet-based deep learning method with loop architecture for face hallucination. In contrast to existing wavelet-based methods that generate wavelet coefficients independently without considering relationships between them, we propose a three-stage method with loop architecture. This alternately updated loop structure explores the statistical relationships among wavelet coefficients and has a maximum use of information flow with a small number of parameters. Because of multi-resolution property of wavelet transform, we adopt a mixed input strategy to train images with different sizes to realize multi-scale face hallucination without retraining and adding extra sub-networks. Experiments demonstrate that our method can get a robust performance with multi-scale face hallucination.
Current Development of siRNA Bioconjugates: From Research to the Clinic

Small interfering RNAs (siRNAs) acting via RNA interference mechanisms are able to recognize a homologous mRNA sequence in the cell and induce its degradation. The main problems in the development of siRNA-based drugs for therapeutic use are the low efficiency of siRNA delivery to target cells and the degradation of siRNAs by nucleases in biological fluids. Various approaches have been proposed to solve the problem of siRNA delivery in vivo (e.g., viruses, cationic lipids, polymers, nanoparticles), but all have limitations for therapeutic use. One of the most promising approaches to solve the problem of siRNA delivery to target cells is bioconjugation; i.e., the covalent connection of siRNAs with biogenic molecules (lipophilic molecules, antibodies, aptamers, ligands, peptides, or polymers). Bioconjugates are “ideal nanoparticles” since they do not need a positive charge to form complexes, are less toxic, and are less effectively recognized by components of the immune system because of their small size. This review is focused on strategies and principles for constructing siRNA bioconjugates for in vivo use.
Rapid Changes in Synaptic Strength After Mild Traumatic Brain Injury

Traumatic brain injury (TBI) affects millions of Americans annually, but effective treatments remain inadequate due to our poor understanding of how injury impacts neural function. Data are particularly limited for mild, closed-skull TBI, which forms the majority of human cases, and for acute injury phases, when trauma effects and compensatory responses appear highly dynamic. Here we use a mouse model of mild TBI to characterize injury-induced synaptic dysfunction, and examine its progression over the hours to days after trauma. Mild injury consistently caused both locomotor deficits and localized neuroinflammation in piriform and entorhinal cortices, along with reduced olfactory discrimination ability. Using whole-cell recordings to characterize synaptic input onto piriform pyramidal neurons, we found moderate effects on excitatory or inhibitory synaptic function at 48 h after TBI and robust increase in excitatory inputs in slices prepared 1 h after injury. Excitatory increases predominated over inhibitory effects, suggesting that loss of excitatory-inhibitory balance is a common feature of both mild and severe TBI. Our data indicate that mild injury drives rapidly evolving alterations in neural function in the hours following injury, highlighting the need to better characterize the interplay between the primary trauma responses and compensatory effects during this early time period.
Scheduling of Data Transcription in Periodically Connected Databases Scheduling of Data Transcription in Periodically Connected Databases

Contemporary data management in applications such as pervasive systems , Web-based supply chain management, data warehouses, and Web crawlers involve the periodic transcription of data onto secondary devices in a networked environment. In this paper, we focus on the scheduling of periodic data transcription in append-only environments, such as e-mail inboxes, newsgroups, technical support bulletin boards, or procurement requests. If the client connects to the server too frequently, the client will nearly always have up-to-date information, but the usage of network resources may be excessive. Conversely, very infrequent connections will conserve network resources, but the client's data may often be signiicantly out of date, which may also be costly (for example, in terms of lost opportunities). Thus, the best transcription policies will make an optimal trade-oo between these costs. Our approach to evaluating this trade-oo is to use modeling techniques from the eld of stochastic processes. The paper presents a general model for data insertions on the server side, using compound nonhomogeneous Poisson processes, and compares several transcription policies in terms of both transcription cost and obsolescence cost. The comparisons use a validation data set from a real data feed, and our models were calibrated using a separate training set from the same feed. We nd that transcription policies based on a nonhomogeneous Poisson arrival model often outperform simpler policies.
A Deep Q-Learning Approach for Continuous Review Policies with Uncertain Lead Time Demand Patterns

Setting safety stock levels and expediting replenishment lead time are tactical policies to hedge against demand fluctuations and supply uncertainties in supply chains. However, the large variable space of planning possibilities makes it difficult to obtain an optimal trade-off policy in a dynamic supply chain environment. In this paper, a deep Q-learning approach for dynamic reorder point decisions of a continuous review system in two-stage supply chains is proposed to explore the proactive decisions of combining uncertain lead time demand patterns and lead time expediting activities. The simulation outcomes show that the deep Q-learning model improves decision performances evidently and identifies the lead time selection tactics depending on expediting cost.
An Anti-Blocking Particle Filter Tracking Algorithm

In order to overcome the problem of target occlusion in target tracking process, this paper proposes an anti-occlusion particle filter tracking algorithm. First, the Camshift algorithm is used to control the clustering and dispersion of particles, so that the particles move toward the maximum posterior probability density of the target state, which better represents the target state and avoids the influence of discrete random isolated particles on the model updating. Then use the particle weights as a basis for subsequent model updating to update the target model, so that the algorithm has strong robustness in the occluded state. The experimental results show that the algorithm has good robustness and can achieve stable tracking of occlusion targets.