Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud Processing versus Independent Processing of Independent Data Sets for Distributed Detection

A distributed detection problem where sensors are deployed to observe a common source of interest is studied. For centralized processing, decision is made by utilizing all the data collected at the sensors, which takes more resources of transmission and computation. For independent processing, it takes less resource at the cost of some performance loss. Motivated by the recently proposed cloud radio access network (C-RAN) and cloud radar, this paper proposes the cloud processing, where each sensor directly compresses (quantizes) its data and the central processor makes decision through all the compressed data. To model the quantization effects, the additive quantization noise model (AQNM) is adopted. Then, the performances of the generalized likelihood ratio test (GLRT), independent GLRT (IGLRT) and cloud GLRT (CGLRT) through deflection coefficients are analyzed. We especially focus on the performance comparison of cloud processing and independent processing, which depends on the number of sensors M, the variances of the additive quantization noise $\sigma _{\text{q}}^2$ and the additive noise σ2. Numerical results are conducted to verify the analysis.
Spectral Correspondence Framework for Building a 3D Baby Face Model

Early detection of facial dysmorphology - variations of the normal facial geometry - is essential for the timely detection of genetic conditions, which has a significant impact in the reduction of the mortality and morbidity associated with them. A model encoding the normal variability in the healthy population can serve as a reference to quantify the often subtle facial abnormalities that are present in young patients with such conditions. In this paper, we present the first facial model constructed exclusively from newborn data, the Baby Face Model (BabyFM). Our model is built from 3D scans with an innovative pipeline based on least squared conformal maps (LSCM). LSCM are piece-wise linear mappings that project the training faces to a common 2D space minimising the conformal distortion. This process allows improving the correspondences between 3D faces, which is particularly important for the identification of subtle dysmorphology. We evaluate the ability of our BabyFM to recover the babys facial morphology from a set of 2D images by comparing it to state-of-the-art facial models. We also compare it to models built following an analogous pipeline to the one proposed in this paper but using nonrigid iterative closest point (NICP) to establish dense correspondences between the training faces. The results show that our model reconstructs the facial morphology of babies with significantly smaller errors than the state-of-the-art models (p = 10−4) and the “NICP models” (p < 0.01).
Modeling and Evaluation of Software Defined Networking Based 5G Core Network Architecture

Nowadays, the increase in the number of mobile users and cellular traffic leads to new challenges in the fifth-generation (5G) of cellular networks. The increase in the demand for high data rates brings challenges like scalability and flexibility in the 5G network. Software-defined networking (SDN) is a network paradigm that separates the control plane and data plane in the network and ease the management of the network. In this work, an SDN based 5G core architecture is proposed, in order to introduce flexibility and ease of management in the network. Another benefit of using SDN is to make the network vendor-independent. Furthermore, the explanation of initial attachment and handover procedures in the proposed architecture is provided. A network simulator is built to evaluate the performance of proposed architecture, in terms of end-to-end delay, throughput and resource utilization of controller, under different network factors. A performance comparison, in terms of end-to-end delay, between proposed SDN based 5G architecture and traditional 5G architecture is provided. Results show that the proposed architecture provides 18% to 62% less end-to-end delay, under different factors for different procedures, compared to the traditional 5G architecture. A comparison with previous works is also provided, which indicates similar trends in delay between our work and previous studies.
Ensemble Classification Approach for Opinion Mining in Social Media and Online Newsfeeds for Forex Market Prediction

Opinions hindered in social media, and online news feeds to control the predictability of financial markets such as Forex and cause huge profits and losses for the investors. Due to this reason, financial market prediction through opinion mining has gained significant attention from researchers recently. Current approaches relied more on basic text mining algorithms and overlooked the knowledge generated through the integration of the multiple data sources. To overcome this deficiency, this study harnesses the knowledge required for decisive currency-pair prediction by analyzing forex news and social media data using ensemble classification. Three classification approaches namely Support Vector Machine (SVM), Naïve Bayes and K-Nearest Neighbor (KNN) are used in the ensemble classifier. Based on the classifier, impactful posts/comments are discovered to facilitate forex market prediction.
Simulation of Self-driving Car using Deep Learning

The rapid development of Artificial Intelligence has revolutionized the area of autonomous vehicles by incorporating complex models and algorithms. Self-driving cars are always one of the biggest inventions in computer science and robotic intelligence. Highly robust algorithms that facilitate the functioning of these vehicles will reduce many problems associated with driving such as the drunken driver problem. In this paper our aim is to build a Deep Learning model that can drive the car autonomously which can adapt well to the real-time tracks and does not require any manual feature extraction. This research work proposes a computer vision model that learns from video data. It involves image processing, image augmentation, behavioural cloning and convolutional neural network model. The neural network architecture is used to detect path in a video segment, linings of roads, locations of obstacles, and behavioural cloning is used for the model to learn from human actions in the video.
A Comprehensive Analytic Scheme for Classification of Novel Models

Classification is one of the major tasks in supervised learning. This had a great impact on deep leaning in developing various State-of-the-art architectures every year. These models can capture invariances by extracting the finest features by variant design models developed using Convolution Neural Networks. These deep networks have proven their resilience in the large scale image recognition by overhauling the existing drawbacks in architectures.In this research, a complete analytical scheme is developed to extract features from various models and to depict its performance on Fruits-360 dataset. During the analysis, the data is processed from feature extraction block to classification block. After the classification, the individual models’ performance is evaluated and assessed with standard classification metrics. Out of these, an optimal model is selected in all prospects. It is observed that VGG models tend to outperform the existing models by capturing invariances by providing transferability on variant tasks. The networks VGG-16, VGG-19 obtained the highest precision of 97.2±0.005, 96.6±0.66 respectively. A pair-plot is obtained to analyse the behaviour pattern of predicted labels to that of true labels. This research is a first attempt to analyse the behaviour of pre-trained models on Fruits-360 dataset.
Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures

We give the first outlier-robust efficient algorithm for clustering a mixture of $k$ statistically separated $d$ - dimensional Gaussians ($k$-GMMs). Concretely, our algorithm takes input an $\epsilon$-corrupted sample from a $k$-GMM and outputs an approximate clustering that misclassifies at most $k^{O(k)}(\epsilon+\eta)$ fraction of the points whenever every pair of mixture components are separated by $1-\exp(-poly(k/\eta))$ in total variation distance. This is the statistically weakest possible notion of separation and allows, for e.g., clustering of mixtures with components with the same mean with covariances differing in a single unknown direction or separated in Frobenius distance. The running time of our algorithm is $d^{poly(k/\eta)}$. Such results were not known prior to our work, even for $k=2$. More generally, our algorithms succeed for mixtures of any distribution that satisfies two well-studied analytic assumptions - sum-of-squares certifiable hypercontractivity and anti-concentration. As an immediate corollary, they extend to clustering mixtures of arbitrary affine transforms of the uniform distribution on the $d$-dimensional unit sphere. Even the information theoretic clusterability of separated distributions satisfying our analytic assumptions was not known and is likely to be of independent interest. Our algorithms build on the recent flurry of work relying on certifiable anti-concentration first introduced in [1], [2]. Our techniques expand the sum-of-squares toolkit to show robust certifiability of TV-separated Gaussian clusters in data. This involves giving a low-degree sum-of-squares proof of statements that relate parameter (i.e. mean and covariances) distance to total variation distance by relying only on hypercontractivity and anti-concentration.
An efficient technique for skin cancer classification using deep learning

The development and spreading of abnormal cells in the skin of the human body assumed to be as skin cancer. There are different skin cancer types,. however, melanoma is the most critical one. The prediction of cancer at an early stage could help in better and improved treatment. In this research, a deep convolutional neural network-based technique to extract spatial information is developed as a method for skin cancer classification. For experimentation, the dataset includes real data that is collected from DHQ hospital Faisalabad, Pakistan. The classification results of the our proposed method are compared with state-of-the-art approaches while utilizing a reduced number of factors/feature vectors. The experiments exhibit that the classification accuracy is about 93.29% using our proposed method. The outcome of the experimental investigation shows that our method has higher accuracy in comparison with state-of-the-art techniques in literature.
A Blockchain-based Double-sided Auction Peer-to-peer Electricity Market Framework

The framework presented provides an open-source, blockchain-based, peer-to-peer energy market platform which can be used for testing different setups or for creating a microgrid peer-to-peer trading platform. The framework offers the following possibilities:•variations of the trading horizon, metering intervals;•simulations within a fraction of the real time;•variations of the number of participants;•multiple operated microgrids within one smart contract;•clearing mechanisms with discriminative prices or a market clearing price;•functionality to log data exchanged with the blockchain; production and consumption data of each participant, electricity exchanged within the microgrid and the main grid, token balances of all participants,•variation of the price ranges.
Determinants Analysis of Drone Delivery Service Adoption

As the drone delivery service is still in its infancy, understanding the main determinants for consumers to adopt such a new delivery tool is important for its service providers. Compared with other delivery methods, most empirical research in drone delivery services focuses on its delivery processes and the risks of the machine itself, but rarely considers the preferences of users and their influencing factors. In order to bridge the gap, this paper applied the new unified theory of acceptance and use of technology (UTAUT 2) to investigate the behavioral intention and the actual use of drone delivery service. This integrated model is empirically tested via multiple regression method and intimidation test. The results showed that facilitating factors and effort expectation are strong predictors of behavioral intention to adopt drone delivery; and facilitating factors and habits affect the actual use of drone delivery services through the mediating role of behavior intention.
Feasibility Study of Adding a Third VSC based HVDC Terminal on an Existing Point-Point LCC based HVDC Transmission System

Advantages of Voltage Source Converter (VSC’s) over Line Commutated Converter (LCC’s) urges to have at least one VSC based converter station in a multi terminal hybrid HVDC (MTDC) system. A hybrid HVDC system optimally combines the benefits of LCC (HVDC Classic) and VSC (HVDC Light). The resultant system offers advantages of bulk power transmission, independent control of active and reactive power, capability of interconnection with weak AC network and renewable energy sources, black start capability in absence of grid and no commutation failure issue at VSC inverter. This has made the hybrid HVDC ideally suitable for applications wherein the existing HVDC Classic needs an upgradation to dispatch higher power demands of the utilities. In this scenario, a third VSC terminal may be added to the existing point-to-point LCC HVDC transmission system to handle the large-capacity power transmission over long distances and to connect to the newer load centers or local power generation from distributed energy sources developed in its vicinity. Such a multiterminal arrangement may renew the utilization of existing classic HVDC transmission link with additional operating flexibility, thus saving essential investment cost. In this paper, the study results for a hybrid HVDC system will be presented, where a half-bridge cell based modular multi-level converter (HBMMC) is added as a third station to an existing point to point LCC link with hybrid HVDC breaker (HHB) used as fault clearing or fault ride through technical solution (FRT device). The steady state and dynamic performance of this multiterminal system is analyzed, and the simulation is performed in PSCAD/EMTDC. This paper will illustrate the advantages of the three-terminal hybrid HVDC. It will also address the new challenges due to addition of the third VSC terminal on the point to point LCC HVDC. Solution for mitigating the potential problems is presented. The proposed solutions are also verified by simulating AC and DC faults and the typical simulation results are presented in this paper. The results show that the multi-terminal hybrid HVDC system is able to handle different AC and DC faults with desired performances.
A 9pW/bit 400mV 3T Gain-Cell eDRAM for ULP Applications in 28 nm FD-SOI

The silicon area and power consumption of ultra-low power (ULP) applications is often dominated by embedded memories [1] , [2] . A popular approach for power reduction is scaling the supply voltage ( V DD ) down to the sub-threshold (sub- V T ) region, however, the conventional 6-transistor (6T) SRAM bitcell becomes unreliable at scaled voltages [3] , [4] .
Toward Embedded Sensing Automation and Miniaturization for Portable Smart Cost-Effective Algae Monitor

As an important indicator of water pollution, algae are highly sensitive to changes in their environment and respond to a wide range of pollutants, they provide an early caution signal of worsening ecological condition. In this article, a kind of portable microfluidic lensless depth neural network algae monitor is proposed. The lensless algae image acquisition module, algae segmentation and classification circuit, and touch panel were integrated into the equipment. Therefore, the equipment can collect and analyze algae automatically in wild water body without laboratory. In order to miniaturize the equipment and reduce the cost, lensless microfluidic channel sampling is adopted. In addition, a dual asymmetric quantization algorithm and circuit structure are proposed for the implementation of deep neural network hardware. Finally, the prototype system construction was completed. Compared with the current analysis equipment, the equipment size and hardware cost are greatly reduced, and the accuracy reduction is kept in a small range, which makes a better compromise between the accuracy, hardware cost and circuit power consumption. The performance of the equipment fully meets the needs of the current portable algae monitor equipment, and the cost is greatly reduced compared with the current equipment. The accuracy of the equipment achieves 94.27%, the size achieves 11 * 11 * 17.5cm. These advances in portability and cost are conducive to promoting the transformation of water algae analysis based on artificial intelligence from large servers in the laboratory to portable algae analysis equipment, and promoting the rapid early analysis of water monitoring.
Comparative Analysis of the Properties of Tween‐20, Tween‐60, Tween‐80, Arlacel‐60, and Arlacel‐80

Foamability and foam stability, emulsifying power, surface tension, and interfacial tension were investigated for Tween‐20 (polyoxyethylene sorbitan monolaurate), Tween‐60 (polyoxyethylene sorbitan monostearate), Tween‐80 (polyoxyethylene sorbitan monooleate), Arlacel‐60 (Sorbitan stearate), and Arlacel‐80 (Sorbitan oleate). Among all the surfactants tested for their foaming power and foamabilty, Arlacel‐60 and Arlacel‐80 showed the best results; the foaming power and foamability was found to be 100%. The surfactants having foam stability more than 50% can be considered as metastable and those less than 50% are considered as low‐stability foams. In case of surface tension and interfacial tension property measurements, Arlacel‐80 showed the best results. At 1% surfactant concentration, the surface tension and interfacial tension of Arlacel‐80 was found to be 29.9 dynes/cm and 1.1 dynes/cm at 30°C ambient temperature. Also, Arlacel‐60 was found to exhibit the best emulsifying power among all the surfactants tested. At 30°C, the emulsifying property of Arlacel‐60 was 6 hours.
Multi-label Sentiment Analysis Base on BERT with modified TF-IDF

With the evolution of web technologies, sentiment analysis, especially aspect-based sentiment analysis (ABSA), as a technology to identify and extract user opinions, has attracted widespread attention. According to the characteristics of user comments, we convert ABSA problem into a multi-label classification problem, and propose a classification model based on BERT with modified TF-IDF for ABSA. In the feature extraction phase, a modified TF-IDF method is proposed to better reflect the words’ importance in multi-label classification problem by calculating the different weights in individual classes. And a new feature is generated by combining the BERT embedding with modified TF-IDF for classification task. Then the feature is input into a fully connected layer to fine-tune BERT model for the multi-label classification task. The effectiveness of supervised TF-IDF and proposed model is validated by experiments of multilabel classification on a restaurant reviews dataset.
A Decentralized Approach for Determining Configurator Placement in Dynamic Edge Networks

In today's IoT infrastructures, increasingly newly added computational resources at the edge of a network are added to acquire faster response and increased privacy. Such edge networks bring an opportunity for deploying edge application services in proximity to IoT domains and the end-users. In this paper, we consider the problem of utilizing various computational resources established by multiple heterogeneous edge devices in dynamic edge networks. A new lightweight decentralized mechanism (i.e., configurator) is required to monitor an edge infrastructure to enable deploying, orchestrating, and monitoring edge applications at the edge. In this setting, one critical task is to determine the node where the configurator should be placed (deployed) and run (executed) at the edge. In this paper, we propose an efficient approach that solves the configurator's placement problem on the most suited edge device in a given dynamic edge network. Our approach supports the system coping with the dynamicity and uncertainty of the environment and adapts based on the configurator's service quality. We discuss the architecture, processes of the approach, and the simulations we conducted to validate its feasibility.
Zeroth-Order Diffusion Adaptive Filter Over Networks

Most existing diffusion-based estimation algorithms require the explicit expression of the cost function in order to evaluate the stochastic gradient. In this work, we first discuss the zeroth-order (ZO) gradient for diffusion strategies, and present the ZO-diffusion algorithm that is suitable for applications where the explicit expression of the cost function is unavailable. In addition, to improve the convergence rate of the ZO-diffusion algorithm, we introduce a time-averaging stochastic variance reduced gradient (TA-SVRG) strategy, which is a variant of SVRG algorithm and designed to address online learning problems, and propose a ZO-TA-SVRG diffusion algorithm. Then, we analyze the mean and mean-square stability of the proposed ZO-TA-SVRG diffusion algorithm. Finally, simulation results are provided that demonstrate the performance and effectiveness of the proposed algorithms.
Dynamic Voltage Stability Improvement of Micro-Grid Using Distribution Static Compensator

For effective generation and reliable operation of wind assissted micro-grid for satisfying consumer demands, there arises some dynamic instability issues. Disturbances in voltage at point of common coupling (PCC) makes generated power from micro-grid unstable, these disturbances are triggered in the MG system due to different intermittencies. In past, various invertor based techniques and droop control strategies has been developed, that achieved stable voltages by maintaining reactive power balance. Numerous PID controller digital mapping techniques and inertial control schemes has been developed and improved over time to regulate frequency. This paper presents a new strategy to improve dynamic voltage stability by effective utlization of distribution static compensator (DSTATCOM) a six pulse controller to regulate reactive power within wind farm with intelligent charging of capacitor bank. This new joint strategy for voltage stability enhances the stable real power delivery to end users. A test system is developed and tested in MATLAB to support proposed scheme. The test results shows the efficacy of the proposed scheme in terms of stability improvement of voltage and real power generation.