Miracle-dz/UniFAR-FTUs huggingface dataset:
HMM-Based Dari Named Entity Recognition for Information Extraction

Named Entity Recognition (NER) is the fundamental subtask of information extraction systems that labels elements into categories such as persons, organizations or locations. The task of NER is to detect and classify words that are parts of sentences. This paper describes a statistical approach to modeling NER in Dari language. Dari and Pashto are low resources languages, spoken as official languages in Afghanistan. Unlike other languages, named entity detection approaches differ in Dari. Since in Dari language there is no capitalization for identifying named entities. We seek to bridge the gap between Dari linguistic structure and supervised learning model that predict the sequences of words paired with a sequence of tags as outputs. Dari corpus was developed from the collection of news, reports and articles based on the original orthographic structure of the Dari language. The experimental result of named entity recognition performance presents 94% accuracy.
An Efficient and Safe Road Condition Monitoring Authentication Scheme Based on Fog Computing

In recent years, with the development of intelligent vehicles and wireless sensor network technology, the research on road safety has attracted much attention in vehicular ad-hoc networks (VANETs). By sensing events on the road, vehicles can broadcast information to inform others of traffic jams or accidents. However, the mobile vehicle network has a large transmission delay, which makes real-time content transmission impossible. In this paper, a new certificateless aggregate signcryption scheme (CLASC) is proposed by using a fog computing framework that supports mobility, low latency, and location awareness. It is combined with online/offline encryption (OOE) technology, which reduces many time-consuming operations and improves the security of vehicle users and the efficiency of message authentication. In addition, the scheme has the characteristics of mutual authentication, anonymity, untraceability, and nondeniability. Based on the difficulty of the discrete logarithm problem (DLP) and the computational Diffie–Hellman (CDH) problem, the scheme is further proved to be unforgeability and confidentiality under the random oracle model. The simulation results show that compared with the existing schemes, this scheme can not only ensure the security requirements of the system but also achieve higher efficiency in computing and communication.
A Review of Audio-Visual Fusion with Machine Learning

For the study of single-modal recognition, for example, the research on speech signals, ECG signals, facial expressions, body postures and other physiological signals have made some progress. However, the diversity of human brain information sources and the uncertainty of single-modal recognition determine that the accuracy of single-modal recognition is not high. Therefore, building a multimodal recognition framework in combination with multiple modalities has become an effective means of improving performance. With the rise of multi-modal machine learning, multi-modal information fusion has become a research hotspot, and audio-visual fusion is the most widely used direction. The audio-visual fusion method has been successfully applied to various problems, such as emotion recognition and multimedia event detection, biometric and speech recognition applications. This paper firstly introduces multimodal machine learning briefly, and then summarizes the development and current situation of audio-visual fusion technology in some major areas, and finally puts forward the prospect for the future.
A Data-Driven Approach to Energy Cost Prediction Of Dwellings Based on Partitioned Parallel Modeling

In today's modern society, residential dwellings make up a large proportion of the world's energy consumption. Therefore, predicting dwellings' energy consumption and needs are highly useful for energy conservation and evaluating future demand. Data-driven energy consumption prediction models are widely researched to forecast energy consumption of buildings. However, in the majority of studies, the training data is collected through smart energy meters and sensors of some target sample buildings. Thus, the source of data intrinsically restricts the usable range of prediction models. In this paper, we build a set of innovative partitioned parallel prediction models include Support Vector Regression (SVR) and Random Forest Regression (RFR) to predict annual heating costs, hot water costs and lighting costs of dwellings. The model training and valuation procedures are basing on the data of the Standard Assessment Procedure and Energy Performance Certificate (SAP-EPC), covering all 13 main regions in the United Kingdom. The feature selection comparative experiments prove that the merge feature selector can select valid feature sets according to different prediction targets The comparative experiment results manifest the fact that the parallel modeling approach can improve the model's accuracy. Meanwhile, the experimental results indicate that the proposed prediction models can accurately forecast the energy consumption of the dwellings.
Multiscale Superpixel-Based Hyperspectral Image Classification Using Recurrent Neural Networks With Stacked Autoencoders

This paper develops a novel hyperspectral image (HSI) classification framework by exploiting the spectral-spatial features of multiscale superpixels via recurrent neural networks with stacked autoencoders. The superpixels can be used to segment an HSI into shape-adaptive regions, and multiscale superpixels can capture the object information more accurately. Therefore, the superpixel-based classification methods have been studied by many researchers. In this paper, we propose a multiscale superpixel-based classification method. In contrast to current research, the proposed method not only captures the features of each scale but also considers the correlation among different scales via recurrent neural networks. In this way, the spectral-spatial information within a superpixel is more efficiently exploited. In this paper, we first segment the HSI from coarse to fine scales using the superpixels. Then, the spatial features within each superpixel and among superpixels are sufficiently exploited by the local and nonlocal similarity measure. Finally, recurrent neural networks with stacked autoencoders are proposed to learn the high-level multiscale spectral-spatial features. Experiments are conducted on real HSI datasets. The results demonstrate the superiority of the proposed method over several well-known methods in both visual appearance and classification accuracy.
Multiple Feature Learning Based on Edge-Preserving Features for Hyperspectral Image Classification

The classification of hyperspectral images is the basis and hotspot in the research of hyperspectral images. In this paper, a classification algorithm of hyperspectral image based on multiple edge-preserving features and multiple feature learning (MFL) is proposed. First, aiming to eliminate the high correlation between adjacent bands and to remove the noise in the image, a new band clustering algorithm is employed to reduce the dimensions, where the dimension-reduced image can be used as spectral information features to extract linearly separable classes. Then, spatial information features are obtained by applying the multiple edge-preserving filter on the reduced-dimensional image. This filter is used to acquire more comprehensive spatial information features of the image for extraction of nonlinearly separable classes. Following that, the locality preserving projections method is applied to retain the representative spatial information from the extracted spatial information for classification accuracy. Finally, the spectral information features and spatial information features are combined for classification using the MFL. The experiments are conducted to verify the validity of the proposed algorithm on three universally adopted hyperspectral datasets.
Hybrid formalism for consensus of a general class of multi-agent systems with biased measurements

The paper presents a hybrid formalism to analyze a decentralized control policy for a network of agents characterized by a class of non-linear heterogeneous dynamics and biased or noisy measurements. Each agent has a continuous-time dynamics and tracks a piecewise constant impulsive reference. The reference jumps depend on the relative distance with respect to some time-varying neighbors. The jumps take place at some updating instants that are decided independently by each agent. Our results provide input-to-state stability guarantees as far as a minimum and maximum dwell-time condition between consecutive updates is satisfied. A numerical example inspired by the formation realization problem for non-holonomic vehicles illustrates the theoretical results.
Online disturbance prediction for enhanced availability in smart grids

A gradual move in the electric power industry towards Smart Grids brings new challenges to the system’s efficiency and dependability. With a growing complexity and massive introduction of renewable generation, particularly at the distribution level, the number of faults and, consequently, disturbances (errors and failures) is expected to increase significantly. This threatens to compromise grid’s availability as traditional, reactive management approaches may soon become insufficient. On the other hand, with grids’ digitalization, real-time status data are becoming available. These data may be used to develop advanced management and control methods for a sustainable, more efficient and more dependable grid. A proactive management approach, based on the use of real-time data for predicting near-future disturbances and acting in their anticipation, has already been identified by the Smart Grid community as one of the main pillars of dependability of the future grid. The work presented in this dissertation focuses on predicting disturbances in Active Distributions Networks (ADNs) that are a part of the Smart Grid that evolves the most. These are distribution networks with high share of (renewable) distributed generation and with systems in place for real-time monitoring and control. Our main goal is to develop a methodology for proactive network management, in a sense of proactive mitigation of disturbances, and to design and implement a method for their prediction. We focus on predicting voltage sags as they are identified as one of the most frequent and severe disturbances in distribution networks. We address Smart Grid dependability in a holistic manner by considering its cyber and physical aspects. As a result, we identify Smart Grid dependability properties and develop a taxonomy of faults that contribute to better understanding of the overall dependability of the future grid. As the process of grid’s digitization is still ongoing there is a general problem of a lack of data on the grid’s status and especially disturbance-related data. These data are necessary to design an accurate disturbance predictor. To overcome this obstacle we introduce a concept of fault injection to simulation of power systems. We develop
Declarative Reflection and its Application as a Pattern Language

The paper presents the reflection facilities of the specification language Slam-sl. Slam-sl is an object oriented specification language where class methods are specified by pre and postconditions. The reflection capabilities allow managing these pre and postconditions in specifications what means that semantic reflection is possible. The range of interesting applications is very wide: formal specification of interfaces and abstract classes, specification of component based software, formalization of design pattern, using Slam-sl as a pattern language, etc. The paper discusses the last two advantages in some detail.
Crowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Smart Cities

Urban planning typically relies on experience-based solutions and traditional methodologies to face urbanization issues and investigate the complex dynamics of cities. Recently, novel data-driven approaches in urban computing have emerged for researchers and companies. They aim to address historical urbanization issues by exploiting sensing data gathered by mobile devices under the so-called mobile crowdsensing (MCS) paradigm. This work shows how to exploit sensing data to improve traditionally experience-based approaches for urban decisions. In particular, we apply widely known Machine Learning (ML) techniques to achieve highly accurate results in predicting categories of local businesses (LBs) (e.g., bars, restaurants), and their attractiveness in terms of classes of temporal demands (e.g., nightlife, business hours). The performance evaluation is conducted in Luxembourg city and the city of Munich with publicly available crowdsensed datasets. The results highlight that our approach does not only achieve high accuracy, but it also unveils important hidden features of the interaction of citizens and LBs.
Wine Critic Scores and Consumer Behavior in a Major USA Metropolitan Market

In this paper, we investigated three questions. First, to what extend do wine critic scores and descriptions influence consumer-buying decisions? Second, to what extent this influence varies with price? Third, how do demographics affect consumer decisions? The experimental design consisted of convenience samples from four different stores in a major United States (US) metropolitan market, with random assignment of consumers to different groups, who completed a total of 240 survey questionnaires. The dependent variable was likelihood to buy wine when presented with varying amount of wine critic information (a control group and three experimental groups with different levels of information). Independent variables included wine price, age, gender, wine interest, store type and location. Major findings include some surprises. For a $20 bottle of wine, the critic information was not a factor on likelihood to purchase, while critic information was a factor for a $50 bottle. These findings varied somewhat based on demographics such as wine club membership, gender, type store, and store location. The findings have implications for wine producer, distributor and retailer marketing methods. Keywords-wine critic; consumer behavior; wine marketing;
Optimization of Flavour Tagging Algorithms for the LHCb Experiment

Studies of CP violation can be used to test the Standard Model and might give insight into New Physics. Therefore, a wide range of CP measurements, including time-dependent decay rate measurements, are performed with the LHCb Experiment. Many of these are subject to mixing of neutral B meson states with their antiparticles. The knowledge of the initial B flavour is essential in these cases which is why several Flavour Tagging algorithms are used to deduce this information from the available event properties. These algorithms must be adjusted to changes in the shape of the event properties, resulting from an upgrade of the LHC centre-of-mass energy to √s = 13 TeV. To simplify this process, the Flavour Tagging software is re-implemented. The tagging power of the muon, electron and kaon tagger is measured based on B+ → J/ψK+ data, which is processed within the new framework. It is found to be (0.782 ± 0.018) %, (0.243 ± 0.011) % and (0.649 ± 0.020) % for Run 1 data, respectively, and (0.67 ± 0.05) %, (0.134 ± 0.019) % and (0.54 ± 0.04) % for Run 2 data. Furthermore a new inclusive strategy for the muon tagger is implemented that improves its tagging power to εeff = (1.09 ± 0.04) % on Run 1 data and εeff = (0.83 ± 0.06) % on Run 2 data.
Do Digital and Communication Technologies Improve Smart Ports? A Fuzzy DEA Approach

The adoption of digital and communication technologies (DCTs) is a critical success factor in port industry. Several pieces of empirical evidence are demonstrating that advanced knowledge infrastructures support port efficiency and competitiveness, for example, through intelligent transport systems, such as sensors, actuators, and platforms. In this perspective, this paper evaluates the impact of investments in DCTs—i.e., interactive websites and social media marketing solutions—on port efficiency. To this end, we perform the nonparametric method of data envelopment analysis, both in its crisp and fuzzy approaches, in order to account for the vague and imprecise nature of some data. Findings pinpoint that port efficiency is generally supported by DCT solutions, and for some ports, the effect is particularly relevant. The outcomes provide managerial suggestions for port authorities, policy makers, and industrial practitioners to identify critical investments for improving port competitiveness.
Electrochemical Performance of Few-Layer Graphene Nano-Flake Supercapacitors Prepared by the Vacuum Kinetic Spray Method

A few-layer graphene nano-flake thin film was prepared by an affordable vacuum kinetic spray method at room temperature and modest low vacuum conditions. In this economical approach, graphite microparticles, a few layers thick, are deposited on a stainless-steel substrate to form few-layer graphene nano-flakes using a nanoparticle deposition system (NPDS). The NPDS allows for a large area deposition at a low cost and can deposit various metal oxides at room temperature and low vacuum conditions. The morphology and structure of the deposited thin films are alterable by changing the scan speed of the deposition. These changes were verified by field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Raman spectroscopy. The electrochemical performances of the supercapacitors, fabricated using the deposited films and H3PO4–PVA gel electrolytes with different concentrations, were measured using a 2-electrode cell. The electrochemical performance was evaluated by cyclic voltammetry, galvanostatic Charge–discharge, and electrochemical impedance spectroscopy. The proposed affordable fabricated supercapacitors show a high areal capacitance and a small equivalent series resistance.
A DDoS Attack Detection Method Based on Machine Learning

Distributed denial-of-service attack, also known as DDoS attack, is one of the most common network attacks at present. With the rapid development of computer and communication technology, the harm of DDoS attack is becoming more and more serious. Therefore, the research on DDoS attack detection becomes more important. Nowadays, some related research work has been done and some progress has been made. However, due to the diversity of DDoS attack modes and the variable size of attack traffic, there has not yet been a detection method with satisfactory detection accuracy at present. In view of this, this paper proposes a DDoS attack detection method based on machine learning, which includes two steps: feature extraction and model detection. In the feature extraction stage, the DDoS attack traffic characteristics with a large proportion are extracted by comparing the data packages classified according to rules. In the model detection stage, the extracted features are used as input features of machine learning, and the random forest algorithm is used to train the attack detection model. The experimental results show that the proposed DDoS attack detection method based on machine learning has a good detection rate for the current popular DDoS attack.
TENT: Technique-Embedded Note Tracking for Real-World Guitar Solo Recordings

The employment of playing techniques such as string bend and vibrato in electric guitar performance makes it difficult to transcribe the note events using general note tracking methods. These methods analyze the contour of fundamental frequency computed from a given audio signal, but they do not consider the variation in the contour caused by the playing techniques. To address this issue, we present a model called technique-embedded note tracking (TENT) that uses the result of playing technique detection to inform note event estimation. We evaluate the proposed model on a dataset of 42 unaccompanied lead guitar phrases. Our experiments showed that TENT can nicely recognize complicated skills in monophonic guitar solos and improve the F-score of note event estimation by 14.7% compared to an existing method. For reproducibility, we share the Python source code of our implementation of TENT at the following GitHub repo: https://github.com/srviest/SoloLa .
Internal Current Return Path for Ground Leakage Current Mitigation in Current Source Inverters

This paper analyzes in detail the effect of a simple solution for ground leakage current mitigation applicable to transformerless three-phase current source inverter (CSI). The circuit modification solution is assessed for both traditional CSI topology and for CSI with an additional seventh switch, in literature named CSI7 (or H7), in particular with the splitting of the dc input inductance. In the present work, the solution is applied to grid-connected converters for string photovoltaic applications: scope of the circuit modification is to provide an internal return path from the wye connected capacitors of the output CL filter. This additional return path is able to significantly reduce the ground leakage current without adversely affecting THD. The performance of the proposed solution is assessed by the numerical simulations in case of a string of photovoltaic (PV) modules and the different behavior of CSI and CSI7 topologies is thoroughly investigated. Furthermore, the definition of $V_{cmZC}$ is assessed by applying it to the common mode equivalent circuits for CSI7 with additional return path and their validation by means of a two-step simulation. The simulation results and experimental validation shows good agreement and confirm that the proposed solution is able to strongly reduce the ground leakage current.
Application of the genetic algorithm to design an optimal PID controller for the AVR system

In this paper, a method for optimal proportional-integral-derivative (PID) controller design is proposed to improve the performance of the turbo generator automatic voltage regulator (AVR). The method utilizes the genetic algorithm (GA) to find the controller gains that minimize a synthesized performance index. The performance index considers the system response to both set point and voltage disturbance. Furthermore, the closed loop robust stability is guaranteed by imposing a finite bound on the maximum sensitivity function. Simulation tests are provided to validate the effectiveness of the proposed scheme.
A K-means based firefly algorithm for localization in sensor networks

Abstract Location information is essential and indispensable for various applications of sensor networks. The multilateration algorithm (MA) is a typical algorithm, which has been widely applied for localization due to its simple model and low requirement on hardware devices. Unfortunately, current methods to find the optimal solution in the MA show poor performance in localization accuracy and convergence. To solve this problem, a K-means based firefly algorithm (KFA) for localization in sensor networks is proposed. A weighted K-means method is designed to optimize the fitness function of the localization model. Then, an inertia weight factor is produced to improve the searching speed of solutions by reasonably adjusting the attractiveness of fireflies. In addition, a new updated solutions strategy is provided to improve the global searching capacity and avoid the local convergence. The simulation experiments demonstrate that the KFA presents better performance in convergence, stability and localization accuracy compared with other tested algorithms. The localization mechanism and applications of the proposed K-means based firefly algorithm in sensor networks.