Miracle-dz/UniFAR-FTUs huggingface dataset:
Canary: Decentralized Distributed Deep Learning Via Gradient Sketch and Partition in Multi-Interface Networks

The multi-interface networks are efficient infrastructures to deploy distributed Deep Learning (DL) tasks as the model gradients generated by each worker can be exchanged to others via different links in parallel. Although this decentralized parameter synchronization mechanism can reduce the time of gradient exchange, building a high-performance distributed DL architecture still requires the balance of communication efficiency and computational utilization, i.e., addressing the issues of traffic burst, data consistency, and programming convenience. To achieve this goal, we intend to asynchronously exchange gradient pieces without the central control in multi-interface networks. We propose the <italic>Piece-level Gradient Exchange</italic> and <italic>Multi-interface Collective Communication</italic> to handle parameter synchronization and traffic transmission, respectively. Specifically, we design the <italic>gradient sketch</italic> approach based on 8-bit uniform quantization to compress gradient tensors and introduce the <italic>colayer</italic> abstraction to better handle gradient partition, exchange and pipelining. Also, we provide general programming interfaces to capture the synchronization semantics and build the <italic>Gradient Exchange Index</italic> (GEI) data structures to make our approach online applicable. We implement our algorithms into a prototype system called <inline-formula><tex-math notation="LaTeX">${\sf Canary}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Canary</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-3036738.gif"/></alternatives></inline-formula> by using PyTorch-1.4. 0. Experiments conducted in Alibaba Cloud demonstrate that <inline-formula><tex-math notation="LaTeX">${\sf Canary}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Canary</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq2-3036738.gif"/></alternatives></inline-formula> reduces 56.28 percent traffic on average and completes the training by up to 1.61x, 2.28x, and 2.84x faster than BML, Ako on PyTorch, and PS on TensorFlow, respectively.
Johnson $\mathrm{S}_{U}$ distribution in Dispersion Managed Coherent Optical OFDM Transmission Systems

In this paper, signal components after propagation in a dispersion managed (DM) coherent optical fiber link are analyzed, statistically. In this way, the received signal Gaussianity is investigated using two Gaussian tests, the Anderson-Darling and the Jarque-Bera. Deviation from Gaussian distribution is result of increasing the launch power, that is measured by the tests. Next, we use Johnson $\mathbf{S}_{U}$ distribution for distribution fitting and show its good approximation in DM systems. Bit error rate (BER) is employed for performance evaluation that is calculated in three methods: 1) Gaussian based, 2) Johnson $\mathbf{S}_{U}$ based, and, 3) Monte-Carlo (MC) simulations based on the split-step Fourier method. It is demonstrated that in the nonlinear region, using Johnson $\mathbf{S}_{U}$ distribution will have about 2 dB better power prediction than Gaussian in $\mathbf{BER}=10^{-4}$, and the performance fits to the MC method with more accuracy.
A Policy-based Interaction Protocol between Software Defined Security Controller and Virtual Security Functions

Cloud, Software-Defined Networking (SDN), and Network Function Virtualization (NFV) technologies have introduced a new era of cybersecurity threats and challenges. To protect cloud infrastructure, in our earlier work, we proposed Software Defined Security Service (SDS2) to tackle security challenges centered around a new policy-based interaction model. The security architecture consists of three main components: a Security Controller, Virtual Security Functions (VSF), and a Sec-Manage Protocol. However, the security architecture requires an agile and specific protocol to transfer interaction parameters and security messages between its components where OpenFlow considers mainly as network routing protocol. So, The Sec-Manage protocol has been designed specifically for obtaining policy-based interaction parameters among cloud entities between the security controller and its VSFs. This paper focuses on the design and the implementation of the Sec-Manage protocol and demonstrates its use in setting, monitoring, and conveying relevant policy-based interaction security parameters.
A Low-drift Extended-Gate Field Effect Transistor (EGFET) with Differential Amplifier for Cordyceps Sinensis DNA Detection Optimized by gm/ID Theory

An extended-gate field effect transistor (EGFET) integrated with a differential MOSFET amplifier and an open-source Arduino Yun MCU system was realized for detection of Cordyceps Sinensis DNA molecules. A gold microelectrode chip coated with a single-stranded DNA probe, as the extended gate was fabricated by MEMS fabrication processes. The differential MOSFET amplifier and additional coating of alkyl-thiol reduced the drifting by one order of magnitude. Moreover, generalized gm/ID theory was used to study the optimized working regime of the EGFET sensor. The highest electric-electrochemical sensitivity could be achieved in the Moderate Inversion (MI) regime. The sensitivity and limit of detection (LOD) of the EGFET sensor were obtained to be 13.85mV/dec and 10nM, respectively. This low-cost low-drift EGFET sensor system is promising for Internet of Living Things in the near future.
Performance Analysis of Artificial Neural Network Approach on Solar Radio Burst Detection

Solar radio burst is defined as a massive solar radio emission related to the solar flare event occurrences. It is related to space weather events and will triggered an interference in our radio waves signal and affected the electromagnetic spectrum on earth. The solar flare could strike and condemn entire communications line including satellite operation, navigation system, Global Positioning System (GPS), international electrical grid and many more. Solar radio burst is the early warning sign that can helps reducing the effect by taking a precaution action by shutting down system. Because the solar radio is in the low frequency range, the detector system consist of low-frequency receiver is used to detect the burst event. As for Malaysia, solar radio observations are currently carried out using Compact Astronomical Low-cost, Low Frequency Instrument for Spectroscopy and Transportable Observatory (CALLISTO) which been placed at the Malaysia Space Agency (MYSA) Banting, Selangor. The application of Artificial Neural Network (ANN) helps in preparing the proper prediction on solar radio burst using solar radiation readings from the spectrometer. ANN is divided into two main group which are static and dynamic neural network. In static neural network, the data propagates in a single direction from input to the output whereas, in dynamic neural network, the data propagates regardless of its direction. In this paper, both static and dynamic neural network had been applied to the data obtained from CALLISTO to develop a solar radiation prediction model to detect the solar radio burst. Based from the results, it is shown that dynamic neural network given the best results compared to the static neural network.
Artificial Immune System for Economic Dispatch Problems Considering Power Generators having Valve-Point Loadings

Aiming at the power economic dispatch problem (PEDP) of generating units having valve-point loadings, this research investigates a hybrid artificial immune system and multiplier update technique (AIS-MUT). Artificial immune system (AIS) containing a migrating function can actively hunt for extremum values and effectively evaluate answers. Multiplier update technique (MUT) is employed to prevent the Lagrange function which deforms searching difficult. The proposed approach synthesizes AIS and MUT, and its merits are that it can automatically regulate the stochastically assignment penalty to a suitable numeric and need only a minisize population for the PEDP of considering generating plants having valve-point loadings. System simulated results show that this study is very proper than the former studies for considering an actual economic dispatching problem of power systems.
Coordinated Recharging of Heterogeneous Mobile Robot Teams during Continuous Large Scale Missions*

Currently, the coordinated use of autonomous mobile robot teams seems to be one of the most promising and ambitious technologies to provide an efficient solution to a range of economic, military, and environmental problems. Complex large-scale operations in different vast and dynamic environments usually involve long stay activities of vehicles under the limited battery capacity. Since it is not always possible to replace low-battery robots with functionally equivalent, but fully recharged vehicles on schedule, available charging stations on-site are required for long-term mission success. In this context, a reasonable recharging schedule must be generated to maintain continuous and uninterruptible mission implementation. It should be taken into account, that the real-world multi-vehicle systems are partially self-contained and could be subjected to any malfunctions or unforeseen events and thus should be able to adjust the current strategy autonomously if needed. Summarizing the above, two following problems have to be handled at once: to determine the reasonable recharging order for the team over the extended time and to ensure the possibility of regular communication sessions within the group.In this paper, we propose the high-level dynamic mission planner based on the rendezvous point-selection scheme. The idea is to represent a mission as a sequence of time-limited operating periods with final group rendezvous at the end of each period. The planning problem here is to allocate each rendezvous point and assign it to an event of either communication or regrouping (robots leaving the group for recharging or joining the group after recharging) nature to achieve regular recharging in preferably alternate order. While doing this, the group’s parametric and functional heterogeneity should be taken into account to ensure the working squad’s maximal performance capabilities. We propose the hybrid evolutionary approach to implement the suggested planner using specialized heuristics, advanced solution improvement techniques, and original chromosome coding schemes.
Video Semantic Segmentation leveraging Dense Optical Flow

Semantic segmentation is a key step in scene understanding for any autonomous system, as it provides the class of each pixel of an image. The best results on this task are achieved by deep neural networks, however they require in training a significant number of annotated frames which are difficult to create. This work is focused on improving the semantic segmentation network on sparsely labeled video sequences by including optical flow information between consecutive frames, and to provide a meaningful evaluation using densely labeled sequences. The system is composed of a static semantic segmentation, an optical flow and a linking network, which are chosen from existing architectures based on their high accuracy and low computational needs, but were never used together before. After conducting various experiments on the novel Virtual KITTI 2 dataset, we find that the optical flow compensates for the sparsity of annotations in video training sets, and by using just 25% of the labels we achieve the same video semantic segmentation quality as the static network trained on the fully labeled dataset. Since fewer annotations are needed, the cost of ground truth generation is reduced significantly. Moreover, taking advantage of the optical flow ground truth availability, we find the limits of the flow approach despite future advances, when more accurate optical flow networks will be developed. The system’s improved capabilities are also validated on the Cityscapes dataset.
Dual Setting Directional Overcurrent Protection Coordination for Microgrids Considering Single Outage Contingency

Distributed generation units (DGs) are essential components in microgrids (MGs) and modern distribution systems considering their important advantages. But, their presence in microgrids results in complicated protection coordination problems. This paper suggests a protection coordination scheme (PCS) in MGs, which is based on dual setting directional overcurrent relays (DOCRs) considering single outage contingency. In this paper, the conventional and dual setting DOCR-based PCSs are compared in two steps, without and with considering the single outage contingency, respectively. It is shown that in complex and practical cases, the advantages of the dual setting based PCS is more obvious. To reveal the validity of the proposed PCS, the simulation studies are conducted based on the modified version of the IEEE 30-bus test system. Using the short circuit currents obtained by DIgSILENT, the optimization problem is solved in MATLAB environment as a mixed integer non-linear programming problem. The results show the superiority of the Dual setting PCS over the conventional PCS.
Practical Cold boot attack on IoT device - Case study on Raspberry Pi -

Volatile memory like SDRAM, forms an integral part of any computer system. It stores variety of data including sensitive data like passwords and PIN. The data stored in SDRAM is wiped off on power-off. However, by bringing the RAM to freezing cold temperature before power off, the data can persist for several seconds, allowing recovery through cold boot attacks. In this work, we investigate the vulnerability of IoT device such as Raspberry Pi against cold boot attack for the first time. Our study found that even though the boot sequence is different from laptop, personal computer, and smartphone, we demonstrate that it is still possible to steal the RAM data, even when the bootloader is not public. The net cost of the attack was under 10 dollars and 99.99% of the RAM data was successfully recovered.
Hybrid system identification using a mixture of NARX experts with LASSO-based feature selection

The availability of advanced hybrid system identification techniques is fundamental to extract knowledge in form of models from data streams. Starting from the current state of the art, we propose an approach based on a specialized architecture, conceived to address the peculiar integration of nonlinear dynamics and finite state switching behavior of hybrid systems. Following the Mixtures of Experts concept, we combine a set of Neural Network ARX (NNARX) models with a Gated Recurrent Units network with softmax output. The former are exploited to map specific nonlinear dynamical models representing the behavior of the system in each discrete mode of operation. The latter, operating as a neural switching machine, infers the unobserved active mode and learns the state-transition logic, conditioned on input-output data sequences. Besides, we integrate a LASSO based input features and model selection mechanism, aimed to extract the most informative lags over the sequences for each NNARX and calibrate the modes to be employed. The overall system is trained end-to-end. Experiments have been performed on a benchmark hybrid automata with nonlinear dynamics and transitions, showing the capability to achieve improved performances than conventional architectures.
Linear Parameter Varying Robust Control for Wind Turbine Power Regulation

Regulation of speed, torque and output power of a wind energy conversion system (WECS) at nominal values is a crucial safety aspect. It avoids failure of the generator and turbine mechanics in the above-rated wind speed region of wind turbine dynamic system.Linear Parameter Varying (LPV) based H∞ controller is designed to improve the stability, robustness and performance of the wind energy conversion system, in the presence of wind speed variations and output power fluctuations. The control goal is to regulate the generator rotational speed, torque and consequently the output power in the presence of variable wind speed. The regulation the output power is carried out via manipulating the pitch angle. As additional challenge, the incoming wind speed with turbulence intensity more than ± 10% is modeled. To assess the performance and robustness of the LPV controller, simulations are performed with turbulent wind. The time response simulations ensure the robustness of the proposed controller against free-stream velocity variation.
Classification of Chili Plant Growth using Deep Learning

Chili is among top grown crops in Malaysia. Nevertheless, chili plant growth monitoring in Malaysia is still performed manually by human labor, which consumed lots of time, energy and the plant growth can only be monitored at site. Hence, this paper discussed the potential of deep learning algorithm in classifying chili plant growth images from a small dataset. The experiment is performed on 256 chili plant images that under various conditions, where the images were captured using a 12-megapixel and f/1.8 aperture camera. Experimented using ResNet-101 and ResNet-50 of Mask R-CNN models with 75% of the dataset for training and 25% for testing, the results showed that both models were able to detect the correct age of chili plants with accuracy of 96% for Mask R-CNN ResNe-50 which is lower than Mask R-CNN ResNe-101 by 1%.
Ultra-Wideband RCS Reduction Using Polarization Conversion Metasurfaces

A polarization conversion metasurface (PCM) is presented for radar cross-section (RCS) reduction, which is composed of a dielectric substrate and air, sandwiched between two copper sheets. The polarization conversion tiles consist of double-headed arrow unit-cells are arranged with 0°, 90°, 180° and 270° rotation. The proposed structure demonstrates ultra-wideband 5 dB RCS reduction and 10 dB RCS reduction at 6.7-34.2GHz (FWB= 134.5%) and 10.6-31.5GHz (FBW= 99.3%) bands, respectively for normal $x$- and $y$-polarized incident waves.
Improving the Accuracy of Deep Neural Networks Through Developing New Activation Functions

Without activation functions, it would be only possible for the neural network to learn very basic tasks, so the activation function is a key point in the neural network’s architecture. The function allows us to learn more complicated tasks and also it impacts the performance to obtain the outcome. So, activation functions represent the continuous and widespread interest of research to identify the most suitable activation function to a specific task. In this paper, we propose four activation functions that bring improvements for different datasets in the Computer Vision task. These functions are a combination of the popular activation functions such as sigmoid, bipolar sigmoid, Rectified Linear Unit (ReLU), and tangent (tanh). By allowing activation functions to be learnable we obtain models more robust. To validate these functions, we tested using more datasets and more architectures with different depths, showing that their properties are significant and useful. Also, we compared them with other powerful activation functions to see how our proposed activation functions impact accuracy.
Model Adaptation for Dynamic Flotation Process Simulation

Dynamic production processes in mineral beneficiation have complex flowsheets and non-linear, time-varying behavior. They have also limited measurement capabilities. Hence, virtual models are seen as important tools for assisting in design, planning and operation. The development of operational dimension requires not only a suitable software architecture and virtual model, but also continuous validation or adaptation of the virtual model. In this paper, a framework establishing a digital twin for a flotation process is presented. The model adaptation is treated as a trajectory matching problem and realized with a Differential Evolution algorithm. The results are demonstrating the applicability of the presented approach in simulation environment together with a discussion on additional challenges foreseen in implementations to the real processes.
Robust Voice Activity Detection Based on Complementary BLSTM Enhancement Stage

In this paper, we propose a new two-stage deep structure with a joint learning technique to improve Voice Activity Detection (VAD) in different noisy conditions especially in unseen noises. The first stage of our proposed method deals with the enhancement of the noisy signal, which is complementary to the second stage. Bidirectional Long Short-Term Memory (BLSTM) architecture is used in this part so as to take benefit from both previous and upcoming frames. The second stage uses the enhanced frames features to predict the speech presence probability. Based on previous studies, we use Multi-Resolution Cochleagram (MRCG) features to achieve higher robustness. We evaluate our proposed method using the Area Under the Curve (AUC) and precision metrics in TIMIT corpus. Based on our evaluations, the proposed method outperforms other state-of-the-art methods based on deep structures as baseline, both in AUC and precision metrics. The proposed method's AUC improvement versus other methods, in noises not seen in the training step, is significant.
A Graph Theoretic Based Feature Selection Method Using Multi Objective PSO

Feature selection is a dimensionality reduction method known as a main step in data mining and machine learning. The aim of feature selection is to remove redundant and unrelated features. In recent years several feature selection methods based on graph theory and social networking techniques have been proposed . In this study, a feature selection approach based on multi objective PSO algorithm and social network techniques is presented. In the proposed method, Fisher score, node centrality and edge centrality are used to construct the fitness function in order to present a multi objective particle swarm optimization (PSO) approach. The proposed method run over a variety of datasets and the results are compared with the well-known filter-based feature selection methods. The results show that the proposed method is effective and the performance of the proposed method better than other methods in some cases.
Predicting Customer Class using Customer Lifetime Value with Random Forest Algorithm

As there are a lot of booming online retailers in e-commerce industry in the Internet age, the need of maintaining competitive advantages has become to pay attention to customer relationship management (CRM). To build a successful CRM strategy, it is needed to know individual customer class which can be calculated from Customer Lifetime Value (CLV): the monetary value of customers purchased from the business during their lifetime. CLV modelling allows us to identify customer's predicted business value. It provides the retailers for effectively allocating the resource in their business. This predictive model has been taken on the global Super Store Retail dataset with almost ten thousand transactions. Our model will predict the customers' class of the next year based on their CLV that will help the online retailer to decide which customer should be invested to get long term CRM. Random Forest (RF) algorithm is utilized to train our model and Random Search tuning is conducted to get the best predictive accuracy. The experimental analysis is performed to compare with AdaBoost algorithm on the same dataset.