Miracle-dz/UniFAR-FTUs huggingface dataset:
Heading Control of Unmanned Marine Vehicles Based on an Improved Robust Adaptive Fuzzy Neural Network Control Algorithm

A robust adaptive fuzzy neural network control (RAFNNC) algorithm is proposed based on a generalized dynamic fuzzy neural network (GDFNN), proportion–integral–differential (PID), and improved bacterial foraging optimization (BFO) algorithm, for heading the control of the unmanned marine vehicle (UMV) in the presence of a complex environment disturbance. First, the inverse dynamic model of the motion control of UMV is established based on the GDFNN for the uncertain disturbance caused by the complex environment disturbance. Then, the adaptive rate of the fuzzy neural network is designed based on the error between the real UMV heading angle and designed reference heading angle, so as to further adjust the weight parameter of the GDFNN, and then, the output control value of the neural network is obtained. In order to further reduce the computation amount and computation time of the RAFNNC, the parameters of the PID control algorithm were optimized in advance by using the improved BFO algorithm. The fractal dimension step size and the intelligent probe operation are integrated into the BFO algorithm, in order to optimize the operation time and accuracy of the algorithm. Stability of the designed RAFNNC algorithm for the heading control of the UMV in the presence of complex marine environment disturbance is proved by the Lyapunov stability theory, and the effectiveness and accuracy of the control algorithm proposed are verified by semi-physical simulation experiment carried out in our laboratory.
iRide: A Privacy-Preserving Architecture for Self-Driving Cabs Service

Despite the popularity Ride Hailing Services (RHSs) have gained recently, they pose significant privacy risks. In particular, a user wishing to benefit from a RHS is required to disclose her precise spatio-temporal data to the RHS provider. The provider is, thus, able to infer and harvest further sensitive information about the user, including, e.g., her social behavior. Previous work on protecting privacy in such a context assumes service provider to not collude with drivers. This assumption does not hold in the scenario of self-driving cabs, as driverless vehicles replace drivers and, thus, the service provider has to control and collude with her fleet. In this paper, we tackle the open issue of service provider colluding with her fleet by analyzing the scenario of self-driving cab services. We present iRide, a privacy-preserving architecture for self-driving cab service that relies on Intel SGX to provide strong privacy guarantees. iRide maintains the convenience of the functionality while offering strong privacy guarantees, that is, we do not introduce or rely on trade-offs between functionality and privacy. The introduced overhead in iRide design is relatively small and rather acceptable under practical aspects. To our best knowledge, this is the first work that tackles privacy protection in self-driving cab services.
Hand Swifter:A Real-Time Computer Controlling System Using Hand Gestures

In the last decade, the trends in Human-Computer Interaction have changed dramatically with the invention of new and emerging technologies and devices. Controlling computer without the traditional peripherals is one of that trends. Our system is a new step to reduce the use of conventional mouse and keyboard. The proposed approach is a real-time hand tracking process based on webcam to navigate a computer. With the help of Kalman filter mouse pointer flickering was reduced and gained a better accuracy and smoothness for mouse pointer movement. The system focuses on navigating computers, reading documents and controlling multimedia like videos, pictures etc. So, we designed our system for three different modes as, Navigation, Reading, and Multimedia mode. We implemented Leap Motion device for switching to different modes and developed different types of hand gestures as swiping and tilting hand. This approach can ensure user comforts and prevent ergonomic injuries. The developed system Hand Swifter - not only improves better computer using experience but also helps to prevent repetitive strain injuries caused by traditional input devices such as mouse and keyboard.
Facial Emotion Recognition Using Computer Vision

This paper examines how human emotion, which is often expressed by face expression, could be recognized using computer vision. The study is performed by analyzing journals and researches related to this topic, ranging from psychological to technological journals. A number of algorithms and techniques have been reviewed, and at the end of this paper, a summary of recommendation for performing facial emotion recognition based on the reviews of the techniques/methods is given.
Design of a Tunable Anti-Aliasing Filter for Multistandard RF Subsampling GNSS Receivers

A novel reconfigurable dual-bandpass filter with independently controlled center frequencies and fixed bandwidths is presented. It was designed to be integrated as a radio frequency anti-aliasing filter in a reconfigurable multi-bandpass sampling global navigation satellite system (GNSS) receiver. The center frequency tuning ranges are 1190–1210 MHz and 1560–1590 MHz in the lower and the upper L-bands, respectively, with a fixed bandwidths of 35 MHz. Varactor tuning technology associated with open loop stub loaded resonator microstrip topology were investigated to achieve these goals. The proposed dual-bandpass filter design showed the agility to independently control the center frequencies in the two L-bands and to deactivate one of the passbands. Furthermore, it shows enough flexibility to adapt to the deployment of new GNSS standards specifications. It exhibits on all the tuning ranges an insertion and a return losses of less than 4.5 dB and more than 10 dB, respectively.
Active Learning for Neural Machine Translation

Neural machine translation (NMT) normally requires a large bilingual corpus to train a high-translation-quality model. However, building such parallel corpora for many low-resource language pairs is rather expensive. In this paper, we propose to select informative source sentences to build a parallel corpus under the active learning framework so as to reduce the cost of manual translation as much as possible. Particularly, we propose two novel and effective sentence selection methods for active learning: selection based on semantic similarity and decoder probability. Experiments on Indonesian-English and Chinese-English show that our selection approaches are superior to random selection and two conventional selection methods.
$4\times25$  Gbps Polarization Diversity Silicon Photonics Receiver With Transfer Printed III-V Photodiodes

We demonstrate the transfer-printing-based integration of III-V substrate-illuminated p-i-n photodetectors onto a 4-wavelength-channel silicon photonics polarization diversity receiver circuit. <inline-formula> <tex-math notation="LaTeX">$4\times 25$ </tex-math></inline-formula> Gbps operation is demonstrated for 2.5-nm wavelength channel spacing in the <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula>-band. Transfer printing is an enabling technology for massively parallel heterogeneous III-V-on-Si integration.
Reverse logistics models for the collection of Waste Electrical and Electronic Equipment: the Brazilian case

In 2010, the legislation regarding the Brazilian Policy of Solid Waste (BPSW) was introduced in Brazil. It is the legal framework for solid waste management that differentiates what is recyclable and from what is not. The BPSW features innovations such as Reverse Logistics (RL) which determines that manufacturers, importers, distributors and retailers must perform the collection of used packaging and products like batteries, light bulbs, electronics, etc. The aim of this paper is to propose a framework that helps indicating which would be, among the National Collective and Clearing House models used in European countries, the most suitable scheme for reverse logistics of Waste Electrical and Electronic Equipment (WEEE) according to the Brazilian reality. For this purpose, the paper analyses the existing literature and various relevant practices used in Europe. Moreover, data regarding the Brazilian environmental legislation and the characteristics of each state were used to evaluate the possible use of the European reverse logistics models. The framework helps to define the Brazilian case in three stages and suggests that the most suitable model for Brazil is the National Collective. This decision is urgent since it has been proposed that many European countries should also implement this model instead of the currently employed. Finally, a structured framework is proposed, facilitating the decision-making process to be carried out by various stakeholders such as the Brazilian Federal Government, municipal authorities, industries, recyclers and service providers. In this way, the WEEE management and logistics organization would be easier in the Brazilian territory.
A Parametric Study of Crossover Operators in Multi-objective Evolutionary Algorithm

The performance of Multi-Objective Evolutionary Algorithms (MOEAs) depends on the various parameter settings such as population size, generation size, crossover, mutation and so on. It is often difficult to know the appropriate parameter setting for a real-world optimization problem in advance. Besides, the optimal parameter values might depend on each optimization problem and MOEA itself. However, there are few studies for investigating the effect of parameters even in benchmark problems. Therefore, in this study, the effects on performance due to the crossover operators and MOEAs are widely investigated by using eight benchmark problems, including DTLZ and WFG benchmark problems. The number of objectives is set to three and six. We consider five major crossover operators: Simulated Binary crossover (SBX), Simplex crossover (SPX), Differential Evolution operator (DE), Parent Centric crossover (PCX), and Unimodal Normal Distribution crossover (UNDX). As MOEAs, we adopt Non-dominated sorting genetic algorithm-II (NSGAII), Non-dominated sorting genetic algorithm-III (NSGA-III),-Dominance-based Evolutionary Algorithm (-MOEA), Indicator-Based Evolutionary Algorithm (IBEA) and Multi-Objective Evolutionary Algorithm with decomposition (MOEA/D) in this study. The experimental results on benchmark problems show that the effect of the crossover operator on each MOEA is almost the same in both three and six objectives. This indicates that the knowledge has been obtained so far could adapt to the other MOEAs and more than three objectives. In addition, parameters of some crossover operators such as SBX have little impact on the performance. This indicates that these crossover operators can be set to a value used so far without the need of tuning.
Discriminant analysis and feature selection for emergency department readmission prediction

Readmission to the emergency department is an important indicator of the quality of care. By estimating the risk of readmission, measures may be taken and revisits avoided. In this study we use an optimization-based discriminant analysis model (DAMIP), in combination with different heuristics for feature selection, to predict emergency department readmission within 72 hours. For feature selection random search, particle swarm optimization, and tabu search are considered. The model offers the possibility to not classify some patients in order to ensure acceptable prediction accuracy. A balancing technique is used to generate balanced training sets to improve the prediction of the small readmission group.The proposed method is tested on French healthcare data including diagnosis-related group (DRG) and patient characteristics of over 12 million emergency stays, for which approximately 5% of the patients return within 72 hours to the emergency department. We compare the results to different classification algorithms, including linear discriminant analysis, naive Bayesian, support vector machine, logistic regression, classification tree, random forest, nearest shrunken centroid, and neural network. The proposed method is shown to achieve similar or higher prediction accuracies than classical classification algorithms.
Analysing Vibrotactually Stimulated EEG Signals to Comprehend Object Shapes

Tactile feedback has the capability of reducing the workload on the visual channel, during visual feedback in brain-computer interfaces (BCIs). It is requisite to analyse the brain signals corresponding to the tactile stimulations. This work is aimed at analysing the brain signals while the users are vibrotactually stimulated. The brain signals are acquired non-invasively by electroencephalography (EEG), while brushless coin-type vibration motors are actuated in particular patterns to convey the object shape information on subjects’ skin surface in form of vibrations. The acquired EEG signals are pre-processed to eliminate the effect of various types of noises and to extract the EEG signals corresponding to relevant frequency bands. Adaptive autoregressive (AAR) parameters are extracted from the pre-processed EEG signals and are finally classified by Naive Bayesian $(NB)$ approach, in order to recognize the vibratotactually stimulated object shapes from brain signals. In addition to the classifier output, subjects’ verbal responses about the object shape they perceived are also noted for validation. Three successive sessions of shape recognition from vibrotactile pattern show an improvement in EEG classification accuracy from 63.75% to 74.37%, and also depicted learning of the stimulus from subjects’ psychological response which is observed to increase from 75% to 95%. This observation substantiates the learning of vibrotactile stimulation in user over the sessions which in turn increases the system efficacy.
Emotion Classification on Indonesian Twitter Dataset

The rapid growth of Twitter usage attracts many researchers to utilize Twitter data for several purposes, including emotion analysis. However, there is a resource limitation in standard dataset for emotion analysis task for under-resourced language, especially Indonesian. In this study, we build an Indonesian twitter dataset for emotion classification task which is publicly available. In addition, we conduct feature engineering to decide the best feature in emotion classification. The features used in this research are lexicon-based, Bag-of-Words, word embeddings, orthography and Part-Of-Speech (POS)tag features. We test those features in two datasets with different characteristics. F1-score is employed as an evaluation metric. The results of our experiments show that implementing the combination of all proposed features in our built dataset can achieve 69.73% of F1-Score, which outperforms the baseline model by 26.64%.
Multilevel Programming-Based Coordinated Cyber Physical Attacks and Countermeasures in Smart Grid

Since the Ukraine blackout in 2015, coordinated cyber-physical attacks (CCPAs) have been emerging and are used to mask line outages in the smart grid. In this paper, we investigate the features of CCPAs and constitute the mathematic formulation with respect to topologies and electric parameters of a power grid before and after attacks. With the objective of maximizing the number of overloaded lines, a bilevel programming model is developed to describe the interaction between the adversary and the control center. The most damaging CCPA can be determined by transforming the developed bilevel model to a single mixed-integer linear programming problem using the Karush–Kuhn–Tucker conditions. Based on the features of the bilevel model, the countermeasure is expressed as a trilevel model with one leader and multiple followers. The implicit enumeration-based searching strategy is proposed to solve the trilevel model to identify the protected meters. Both the implementation of CCPAs and the effectiveness of the developed countermeasure are verified on the modified IEEE 14-bus system.
Pareto Dominance-based MOEA with Multiple Ranking methods for Many-objective Optimization

Pareto Dominance-based Multi-objective Evolutionary algorithms (PDMOEAs) have issues while handling many-objective optimization problems (MaOPs) due to the lack of selection pressure provided by the Pareto dominance to guide the search process towards the convergence. Hence, most of the PDMOEAs proposed rely on additional selection criterion to establish preferences between the solutions. In this paper, we propose a PDMOEA with multiple ranking methods (PDMOEAMR), an extension to the proposed PDMOEA with ranking methods for MaOPs which assigns priority rank based upon Ranking methods and niche radius. In PDMOEA with ranking methods for MaOPs, ranking methods such as Average rank (AR) in PDMOEA-AR, and weighted sum of objectives (WS) in PDMOEA-WS, are used. Instead of using two ranking methods separately, in the proposed PDMOEA_MR, both the ranking methods AR and WS are incorporated into a common framework and a different strategy is adopted to assign priority rank. The performance of proposed method is analyzed on 16 test problems.
Performance of Coordinated Scheduling in Downlink LTE-A under User Mobility

Resource scheduling, in LTE, plays a pivotal role to determine the user equipment (UE) throughput. The performance of a scheduler is primarily affected by the associated latency and UE mobility. These can significantly deteriorate the overall system efficiency. Coordinated multipoint (CoMP) is considered as one of the primary downlink transmission technique to improve data transmission efficiency. A key feature of CoMP is coordinated scheduling (CS). This coordination can be centralized or distributed. These improve interference management but at the cost of an increased latency and backhaul traffic, which potentially reduces the overall achievable throughput of the system. The effect becomes significantly important when UE mobility is considered which further degrades the received UE signal-to-interference-plus-noise-ratio (SINR). This degrades the performance of a scheduler. In this paper, we have simulated, analyzed and compared several LTE downlink parameters between centralized coordinated scheduling (C-CS) and distributed coordinated scheduling (D-CS) for various channel bandwidths and UE densities under multiple UE velocity to determine which CS scheme is comparatively more efficient for high velocity UEs and under large UE densitites.
Facial Expression recognition via neurons partially activated discriminated ELM

Facial expression recognition is a very meaningful and challenging research topic and it has been widely used in many problems in the artificial intelligence era. Such as human computer interaction, intelligent family robot, patient intelligence diagnosis, and driver safety. In this paper, we proposed a new approach for facial recognition based on ELM(Extreme Learning Machine). Traditional ELM is famous for it simple structure and high accuracy both in classification and regression problems. However, the number of neurons is hard to determine in a given problem. The core idea is force the l2,1-norm on the weight matrix of hidden layer to select the activated neurons, thus to obtain a simple network structure. Then, we add the migration factor into loss term to enlarge the distance of different types of facial expressions. Compared to state-of-the-art method at present, our method is competitive in accuracy and robustness. The experiments of facial expression recognition are carried out on JAFFE facial expression database and getting excellent performance.
Comparison and Analysis of Feature Method and Direct Method in Visual SLAM Technology for Social Robots*

Simultaneous localization and mapping (SLAM) technology is adopted for localization and environmental map reconstruction in unknown environments. Visual SLAM provides rich three-dimensional map information that helps social robots complete complex tasks. Visual odometry (VO) is an important component of Visual SLAM. Here, we first review the principles of the feature and direct methods of VO. Thereafter, we introduce a social robot platform for testing the two VO methods. We detail the implementation and setup of the feature and direct methods of VO. Finally, we compare and analyze the two methods and provide suggestions for their applications.
Hypernym-Hyponym Relation Extraction from Indonesian Wikipedia Text

This study aims to automatically acquire a collection of hypernym-hyponym word pairs in Bahasa Indonesia using pattern analysis approach. The Wikipedia text is utilized as the data source to obtain the hypernym-hyponym relations. The presented method to acquire relations is defined in systematic procedures, which are seed building, text pre-processing, sentence extraction, pattern extraction, and pattern matching. The number of extracted relations can be gradually increased using bootstrapping algorithm.
Adaptive Speech Recognition System Using Data From Keyboard

Automatic Speech Recognition (ASR) system is one of the important components of developing a true intelligence in personal assistants as the personal assistants depend on its accuracy for responses. However, speech recognition is a highly challenging task due to the limitations of training data coverage i.e., continuous emergence of Out of Vocabulary (OOV) words, as these words are mostly nouns and are not generic to the language and change from user to user and from location to location. The words that we type through keyboard during chats, web searches, etc. has a rich collection of OOV words and there is a high likelihood that these words are used in a query or a conversation with personal assistants. In this paper, we propose a system to use the data from the keyboard to improve n-gram model based Automatic Speech Recognition (ASR) systems accuracy and also, learn and adapt to specific user’s dictionary and location dictionary by generating two different OOV Language Models(LM), (1) Specific to user-User Language Model(ULM), (2) Specific to a location-Location Language Model (LLM). We then show how our initial evaluation of this system has achieved better speech recognition when compared to traditional speech recognition.
A New Cellular-Based Redundant TSV Structure for Clustered Faults

Due to the winding level of the thinned wafers and the surface roughness of silicon dies, the quality of through-silicon vias (TSVs) varies during the fabrication and bonding process, which greatly reduces the yield of 3-D-ICs. The basic method to repair faulty TSVs (FTSVs) is to transfer the signals on FTSVs through regular TSVs. Many redundant TSV (RTSV) structures have been proposed to repair uniformly distributed FTSVs. For clustering FTSVs, a router-based RTSV structure appears to be a good scheme. But it is not an economical method, since the structure consumes many more hardware resources than normal structures. In this paper, we propose a cellular-based RTSV structure to utilize hardware resources more efficiently for a higher yield. We propose a corresponding algorithm for recovery-route searching. Simulation results show that for 1E6 TSVs and a TSV failure rate of 0.01%, our design consumes only 4.5% more area of all STSVs to achieve a yield above 99.9%. We compare our structure with several other designs and demonstrate the cost-effectiveness of the proposed technique.