Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Hop Low Duty Cycle MAC protocol for Wireless Sensor Networks

The proposed model, Multi-Hop Low Duty Cycle Medium Access Control (MLDC-MAC) protocol, assumes that the deployment area is composed of equal-sized grids. The sensor nodes that fall within a grid are considered to be the part of the same cluster. The base station appoints the node with the highest residual energy as the cluster head in each grid. The base station also establishes multi-hop paths for communicating with the cluster heads and broadcasts the schedule for inter-cluster communication in the network. After receiving this information, each cluster head broadcasts a schedule for intra-cluster communication within its cluster. When a cluster head receives data from the source nodes in its cluster, it computes the aggregate and sends it to the base station. The simulation results show that MLDC-MAC increases the network lifetime compared with Low Duty Cycle MAC (LDC-MAC) protocol and Bit-Map-Assisted MAC (BMA-MAC) protocol.
Cross Layer and Management Plane Integration Approach for Detection and Prevention of Malicious Activities in WSN

Wireless Sensor Networks (WSNs) are the next generation envision and have several applications in military, infrastructure monitoring, healthcare, environment monitoring, manufacturing and Internet of Things (IoT) applications. WSN is basically a collection of sensor nodes that are deployed without infrastructure deployed anywhere, anytime, without administrator and every node works as host and router. Sensor nodes are more susceptible to failure because they are deployed in open areas, may be tampered by intruders. Malicious attacks like, Denial of Service (DoS), Sybil attack, Blackhole/Sinkhole attack, Hello Flood attack, Wormhole attacks may modify sensitive information results in performance degradation. The next emerging technology is WSN with IoT, wherein billions of devices are connected with internet and every day millions of new hackers and malicious activities are identified. Hence it has become essential to propose different approaches and different measurements to identify malicious activities and to avoid them is big challenge in WSN. Several researchers have proposed cross layer design to improve QoS by reducing latency and computation overhead. But these proposed ideas have not been standardized in global level networking. So there is a need for designing security models for various devices and conform National Information Security Framework (NISF) policies. One way to detect and identify malicious activities is through analyzing nodes activities pattern in the network. This paper proposes a cross layer and management plane integration approach for detection and prevention of malicious activities in Wireless Sensor Networks
Reducing Risks During Natural Disasters With Optimal Resource Allocation By Multi-Agent Optimization

Natural disasters are notable for the high costs associated with responding to and recovering from them. In this paper we address the issue of critical resources allocation during natural disaster, that incorporates the level of importance of the effected region and cost parameter. Our risk reducing model can be applied to online stochastic environments in the domain of natural disasters. The framework achieves more efficient resource allocation in response to dynamic events and is applicable to problems where disaster evolves alongside the response efforts.
Designing of a Fuzzy Logic Controller Based on Vectored Sum Defuzzification for Inverted Pendulum Model

The inverted pendulum is a system which is very unstable and possesses high nonlinear dynamics. Hence, designing a controller which epitome the current situation remains a challenging task. The process of defuzzification has an important role in the fuzzy logic controller as it generates the final action to be taken in controlling the plant/process. Keeping this in mind, a Fuzzy Logic Controller (FLC) has been designed based on Vectored sum defuzzification method. Comparative analysis was done using the Center of gravity (COG) method to compare the performance of Vectored sum defuzzification in Matlab 7.0 environment.
Method to Obtain Neuromorphic Reservoir Networks from Images of in Vitro Cortical Networks

In the brain, the structure of a network of neurons defines how these neurons implement the computations that underlie the mind and the behavior of animals and humans. Provided that we can describe the network of neurons as a graph. We can employ methods from graph theory to investigate its structure or use cellular automata to mathematically assess its function. Additionally, these graphs can provide biologically plausible designs for networks, which can be integrated as reservoirs to support computing. Although, software for the analysis of graphs and cellular automata are widely available. Graph extraction from the image of networks of brain cells remains difficult. Nervous tissue is heterogeneous, and differences in anatomy may reflect relevant differences in function. Here we introduce a deep learning based toolbox to extracts graphs from images of brain tissue. This toolbox provides an easy- to-use framework allowing system neuroscientists to generate graphs based on images of brain tissue by combining methods from image processing, deep learning, and graph theory. The goals are to simplify the training and usage of deep learning methods for computer vision and facilitate its integration into graph extraction pipelines. In this way, the toolbox provides an alternative to the required laborious manual process of tracing, sorting and classifying. We expect to democratize the machine learning methods to a wider community of users beyond the computer vision experts and improve the time-efficiency of graph extraction from large brain image datasets, which may lead to further understanding of the human mind.
MapReduce : Summarization Design Patterns for Processing Kernel Functions

There is a growing need for pattern analysis algorithms on datasets to extract and analyses information. As datasets grow in size for applications such as topic modeling, recommender systems and internet search queries, there is a need for scalable implementations of pattern analysis algorithms require manual tuning on specialized hardware and methods to parallelize individual learning algorithms on a cluster on a cluster of machines. Practically, MapReduce provides an active device for dripping huge amount of data glitches. But then again, beyond that, MapReduce is vital in how it has changed the way of organization computations at a enormous scale. MapReduce characterizes the first widely accepted step away procedure of von Neumann prototype, which is called bridging model. A Bridging is a conceptual bridge between the physical execution of a hardware and software which has to be executed on that machine. Planning a data through nonlinear function into a appropriate characteristics space allows the use of the similar tools for finding nonlinear patterns. Kernels can make it feasible to use multi-dimensional feature space. The in detailed computation of the feature mapping are avoided. The proposed model of MapReduce Pattern design is done with Hadoop cluster. Algorithm of pattern analysis will take finite sample data from source and pattern may be anything like text, audio, video files. But here only text files are considered. The output of a detected regularity or pattern function will be displayed. The main three features such as efficiency, robustness and stability are expected to expose. The performance of an algorithm to noise in the training examples is inferred by computational efficiency. Summarization kernel function helps to find top view by summing data and grouping data. This helps to generate a performance profile in terms of computations, algorithms and Kernel functions.
Pose Based Trajectory Forecast of Vulnerable Road Users

In this article, we investigate the use of 3D human poses for trajectory forecasting of vulnerable road users (VRUs), such as pedestrians and cyclists, in road traffic. The forecast is based on past movements of the respective VRU and an important aspect in driver assistance systems and autonomous driving, which both could increase VRU safety. The 3D poses represent the entire body posture of the VRUs and can therefore provide important indicators for trajectory forecasting. In particular, we investigate the influence of different joint combinations and input sequence lengths of past movements on the accuracy of trajectory forecasts for pedestrians and cyclists. In addition, we divide VRU movements into the motion types wait, start, move, and stop and evaluate the results separately for each of them. Comparing it to an existing, solely head based trajectory forecast, we show the advantages of using 3D poses. With an input sequence length of 1.0 s, the forecasting error is reduced by 17.9 % for starting, 8.18 % for moving, and 11.0 % for stopping cyclists. For pedestrians, the error is reduced by 6.93 %, 2.73 %, and 5.02 %, respectively. With shorter input sequences, the improvements over the solely head based method remain for cyclists and even increase for pedestrians.
Algorithms and a Tool for Automatic Decryption of Clinical Notes

Natural Language Processing (NLP) presents a set of techniques that are finding applications in modern healthcare, for the extraction and generation text. Clinical notes are classically originated and derived from various sources of narratives such as reports, referral letters, discharge notes and clinical summaries. In this paper, we present algorithms and a tool for enabling each member of a medical team to read and understand each others medical notes, using NLP techniques. We refer to this process as the decryption, or the deciphering, of complex clinical notes into simple readable language. We have presented a tool called the Clinical Note Translator (CNT). Based on the replacement of technical terms in clinical notes, CNT translates these notes to plain text. This solution is expected to assist multidisciplinary medical teams in understanding peer-to-peer expert notes.
People's Adaptive Side-by-Side Model Evolved to Accompany Groups of People by Social Robots

The presented method implements a robot accompaniment in an adaptive side-by-side formation of a single person or a group of people. The method enhances our previous robot adaptive side-by-side behavior allowing the robot to accompany a group of people, not only one person. Adaptive means that the robot is capable to adjust its position and velocity to the behavior of the group being accompanied, without bothering other pedestrians in the environment, as well as facilitating the group navigation to avoid static and dynamic obstacles. Furthermore, the robot can deal with the random factor of human behavior in several situations. Firstly, if other people interfere in the path of the companions, the robot leaves space for the person of the group that has to avoid those other people, by approaching the other companion. Also, without invading any personal space. Secondly, if the people of the group changes their physical position inside the group formation, then the robot adapts to them dynamically by changing from the lateral position to the central position of the formation or otherwise. Thirdly, the robot adapts to changes in the velocity of people in the group and other people that interfere in the path of the group, in magnitude and orientation. Fourthly, the robot can deal with occlusions of one accompanied person by the other. The method has been validated using synthetic experiments and real-life experiments with our robot. Finally, we developed an user study comparing the method with a Wizard of Oz.
Sequential Information Bottleneck Network for RUL Prediction

This work studies the problem of predicting remaining useful life in complex systems. We propose a generic algorithm framework, sequential information bottleneck network(SIBN), which leverages the information criterion to preserve the most relevant information in the learning process. We derive the variational low bound of the information criterion when the data are in the sequential form. By using reparameterization trick, the sequential variational low bound can be optimized using standard stochastic gradient methods, e.g. SGD or Adagrad. The performance of the proposed framework SIBN is investigated on two benchmark datasets: the turbofan dataset and the ball bearing dataset. Experimental results show that our SIBN framework can accelerate network convergence and improve the prediction performance.
Assessing Aesthetics of Generated Abstract Images Using Correlation Structure

Can we generate abstract aesthetic images without bias from natural or human selected image corpi? Are aesthetic images singled out in their correlation functions? In this paper we give answers to these and more questions. We generate images using compositional pattern-producing networks with random weights and varying architecture. We demonstrate that even with the randomly selected weights the correlation functions remain largely determined by the network architecture. In a controlled experiment, human subjects picked aesthetic images out of a large dataset of all generated images. Statistical analysis reveals that the correlation function is indeed different for aesthetic images.
End-to-End Overlapped Speech Detection and Speaker Counting with Raw Waveform

Overlapped speech processing has attracted more and more attention in recent years, and it is a key problem when processing multi-talker mixed speech under the cocktail party scenario. It is commonly observed that the performance of overlapped speech processing can be significantly improved if the number of speakers is given in advance. However, such prior knowledge is often unavailable in real-world conditions, so a robust overlapped speech detection and speaker counting system is demanded. Most existing works focus on combining different handcrafted features to tackle this task, which can be sub-optimal since there are no direct connections between the features and the task. In this work, we try to solve these two problems with an end-to-end manner. First, an end-to-end framework for overlapped speech detection and speaker counting is proposed, which extracts features from the raw waveform directly. Then a curriculum learning strategy is applied to make better use of the training data. The proposed methods are evaluated on multi-talker mixed speech generated from the LibriSpeech corpus. Experimental results show that our proposed methods outperform the model with handcrafted features on both tasks, achieving more than 2% and 4% absolute accuracy improvement on overlapped speech detection and speaker counting respectively.
Hyperspectral and Panchromatic Image Fusion via Adaptive Tensor and Multi-Scale Retinex Algorithm

Fusion of panchromatic image (PANI) and hyperspectral image (HSI) to obtain an output image with high spatial and spectral resolutions has received increasing interests recently. We propose a new image fusion method for HSI and PANI by combining adaptive tensor with a multi-scale Retinex algorithm in this paper. In the proposed method, an adaptive tensor based method is presented to effectively extract the structure information of HSI, and multi-scale Retinex algorithm is introduced to obtain the spatial and structure details of PANI. To integrate spatial structure information, a gradient-based weighted fusion strategy is proposed to combine spatial details of HSI and PANI. The integrated structure details are injected to generate the fused HSI. Experiments using both simulated and real remote sensing data sets demonstrated that the proposed fusion algorithm performs better than the state-of-the-art algorithms in visual inspection and objective assessment.
Evolving Efficient Deep Neural Networks for Real-time Object Recognition

While Deep Neural Networks (DNNs) achieve state-of-the-art performance in many fields, e.g., object recognition, they rely on deep networks with millions or even billions of parameters. Accelerating DNNs by reducing the parameters of DNNs is crucial for real-time object recognition. This paper presents an evolutionary approach to evolve efficient DNNs that can be run on Low-Performance Computing Hardware (LPCH) for real-time object recognition with the fastest possible speed and an accuracy of more than 95%. This approach achieves the goal by means of two design choices. First, NeuroEvolution of Augmenting Topologies (NEAT) is applied to evolve both weights and topology of DNNs starting from simple initial topology, which reduces the number of parameters of DNNs from millions to thousands. Second, we propose the novel fitness functions to further select the evolved DNNs for lower computation time, while maintaining high accuracy. We test the approach to evolve the efficient DNNs on the well-known benchmark MNIST dataset and the self-defined modular robots dataset. Furthermore, com-pared with most current studies, we not only evolve DNNs on the datasets but also implement the best evolved DNN on LPCH to recognize objects real-time in the real world. The experimental results show that the best evolved DNN recognizes the modular robots on a microcomputer, Raspberry Pi 3, with an accuracy of 95.6% and a speed of 5.3 fps. This work can be extended to achieve efficient DNNs for other real-time tasks. We published the source code1 that was used to evolve the efficient DNNs, and the video2 that the best evolved DNN was run on a Raspberry Pi 3 to recognize two modular robots simultaneously in the real world.
Energy-and Performance-Aware Router Design for Chip Multiprocessors

On-chip interconnection networks simplify the challenges of integrating a large number of processing cores. Routers are the backbone of networks. Buffers in router consume significant area and power of the network. Reducing buffers could lead to degradation of network performance. Dual crossbar router architecture combines buffered and bufferless feature to reduce buffer read/write energy. Marching memory buffers achieve highspeed operation with a low power consumption. Switch folding technique is used to reduce wire density and decrease muxes in crossbar. In this paper, we propose Folded Dual Crossbar with Marching memory based buffer (FDCM) router architecture by combining the Dual crossbar, Marching memory, and Folding technique in order to get advantages of these architectures. Simulation results show that there is significant improvement in energy and performance. Energy and performance analysis on a 16-core mesh-based NoC topology running PARSEC and SPLASH-2 benchmark application traffic shows that our proposed router reduces the total energy by 17% and 23% respectively with negligible performance overhead.
An Incremental Learning Ensemble Method for Imbalanced Credit Scoring

Credit scoring is an important task in credit risk forecasting. This kind of task usually handles the imbalanced data, which the number of samples in different classes varies greatly. Moreover, the data doesn’t come into the system at one time but comes continuously as time goes by. In this paper, we propose an incremental ensemble model to deal with the problem of data increment in credit scoring, and design a class balancing strategy to handle the class imbalance problem and the incremental learning task simultaneously. Experimental results show that the proposed model can achieve better performances than some traditional methods in the imbalanced credit-scoring task.
Modeling Analysis and Cost-Performance Ratio Optimization of Virtual Machine Scheduling in Cloud Computing

As an essential feature of cloud computing, dynamic scalability enables the cloud system to dynamically expand or shrink resources according to user needs at runtime. Effectively predicting and optimizing the cost and performance of cloud computing platforms have become one of the key research challenges in the field of cloud computing. In this article, to quantitatively predict the cost and performance of cloud computing platforms, we propose a cloud computing resource analysis model considering both hot/cold startup and hot/cold shutdown of virtual machines (VMs), and use the <inline-formula><tex-math notation="LaTeX">$M/M/N/\infty$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>M</mml:mi><mml:mo>/</mml:mo><mml:mi>M</mml:mi><mml:mo>/</mml:mo><mml:mi>N</mml:mi><mml:mo>/</mml:mo><mml:mi>∞</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq1-2968913.gif"/></alternatives></inline-formula> queuing model to analyze cloud computing platform and acquire accurate performance indicators, such as elasticity indicators, cost indicators, performance indicators, cost-performance ratios, etc. In addition, we establish a multi-objective optimization model to optimize both performance and cost of cloud computing platform. Then the optimal stopping and cost-performance optimization algorithm are applied to obtain the optimal configurations, including the number of hot startup VMs, the system service rate, the hot/cold startup rate of VMs, and the hot/cold shutdown rate. By comparing with existing optimization methods, we demonstrate the superiority of our cost-performance ratio optimization method.
Comparative Analysis of DC-DC Converters for Tracking MPP for Roof-Top PV Power Systems with and without Partial Shading Condition

This paper addresses comparative analysis between three basic converters i.e. Buck, Boost and Buck-Boost converter to interface with a rooftop domestic Photo-Voltaic (PV) system to track maximum power point (MPP) effectively in normal conditions and in partial shading conditions. With partial shading, variable atmospheric conditions and variable load, the working value of impedance at MPP $(\mathbf{Z}_{\mathbf{MPP}})$ may vary widely. The MPPT algorithm should force the PV system work very near to $\mathbf{Z}_{\mathbf{MPP}}$ with the help of power electronic interface for varying load and atmospheric conditions in order to make the system work at MPP. The effectiveness of the MPPT algorithm and the power electronic interface can be inferred from this obligation. Although all the three mentioned DC-DC converters can be used as interface between PV source and desired load, the simulation study verifies that the performance of each converter is different. Here the effect of diverse load having different value than $(\mathbf{Z}_{\mathbf{MPP}})$ on tracking MPP for both with and without partial shading condition is analyzed and it is deduced that Buck-Boost converter successfully tracks MPP for varying loading effect and atmospheric condition most efficiently.
Distributed Average Tracking with Event-Triggered Algorithms of Multi-Agent Systems

Distributed average tracking (DAT) problems are investigated for general linear dynamical systems under undirected connected topology in this paper. A kind of distributed event-triggered DAT algorithms with static gain is designed by using model-based local sampled state information. The control objective of the considered DAT problem is achieved by using the proposed event-triggered DAT algorithms. Meanwhile, the Zeno behavior is excluded. Finally, a simulation example is presented to validate the proposed control laws.