Miracle-dz/UniFAR-FTUs huggingface dataset:
Solar Cell Using Hourglass-Shaped Silicon Nanowires for Increased Light-Trapping Path

Silicon nanowires (SiNWs) are promising structures with excellent photovoltaic properties for low-cost, high-efficiency solar cells and have the potential to overcome the limits of bulk silicon solar cells. There is still a need to enhance light absorption in SiNWs without increasing the surface area. Here, we introduce a novel design, hourglass-shaped silicon nanowire (HG-SiNW) to significantly increase the light-trapping path. Electrical and optical measurements show the superiority of HG-SiNW as an antireflective layer. In addition, whispering gallery mode identified by optical simulations accounts for the greatly improved absorption at the broadband solar spectrum. The HG-SiNW solar cells show an increase in short-circuit current (JSC) and 1.85 times higher efficiency with only simple diameter variations.
Detection and Classification of Occluded Traffic Sign Boards

Traffic sign board detection and recognizing is essential in vehicular driving assistance. It's a one of the recent trends where many of the automotive industries are carrying out their research. It becomes very much important to recognize traffic signs when they are occluded. Due to the occlusion of traffic signs it may even mislead the vehicular driver and also may cause severe problems while driving on a road. There are some existing automatics traffic sign detection methods that have been developed in previous studies, yet the methods designed for generalized traffic sign. This paper explains how occluded sign boards are recognized and occluded sign boards are classified depending upon their structures. It also helps method helps in identifying the partial or completely occluded sign boards and achieve betteraccuracy.
Feature selection via kernel sparse representation

To solve the "dimension disaster" problem caused by high-dimensional data, this paper proposes a new feature selection algorithm that combines kernel functions with sparse learning. Firstly, each dimension feature of the data set is mapped to a high-dimensional kernel space by using a kernel function, and each dimension of the data set performs linear feature selection in the high-dimensional kernel space to achieve nonlinear feature selection in low-dimensional space. Secondly, we sparsely reconstruct the features mapped to the kernel space to get a sparse representation of the original data set. Meanwhile, we use l1-norm to construct feature scoring selection mechanism and select the optimal feature subset. Finally, this data subset is used for classification experiments. Experimental results on public datasets showed that the proposed algorithm can conduct feature selection better, and the accuracy of classification can be increased by about 4%.
Yorùbá Gender Recognition from Speech Using Neural Networks

The impressive improvement in performance obtained using neural networks for automatic speech recognition (ASR) have motivated the application of neural networks to other speech technologies such as speaker, emotion, language, and gender recognition. Prior work has shown significant improvement in gender recognition from images and videos. This paper uses speech to build a gender recognition system based on neural networks. Three types of neural networks are investigated to find the best model for gender recognition system using Yorùbá, namely, feed-forward artificial neural networks (Multilayer Perceptrons), Recurrent neural networks (long short-term memory), and Convolutional neural networks. All the classifier models obtained the state-of-the-art performance in speech-based gender recognition with 99% in accuracy and F1score.
Orthogonal Time Frequency Space Modulation: Pilot Power Allocation and Nonlinear Power Amplifiers

This paper deals with a pilot power allocation in the orthogonal time-frequency space (OTFS) modulation system. In our simulations, the pilot is used for a channel estimation. Subsequently, the channel effects are equalized via a deconvolution. It is shown that proper solution of the pilot power allocation problem has significant impact on the bit-error rates. We also show that the increase of the pilot power causes notable increase of the peak-to-average (PAPR) power ratio. In consequence, high PAPR is one of the limiting factors of the pilot power increase. Considering a non-linear power amplifier, high PAPR is demonstrated to limit the OTFS bit-error rate performance.
Twitter Analytics for Discovering Socially Important Locations for Business Improvement

This work aims to investigate the role of social media for profiling businesses at specific locations within the Kingdom of Saudi Arabia. The applied methodology focuses exclusively on the location information linked to the customer's feedback about an organization or business. In this regard, nearly 10,000 tweets that were posted within the Saudi Arabia region were collected from Twitter, which is a very popular Micro-blogging forum. Data exploratory methods and clustering model were employed to analyze the spatial patterns across the geo-located customer tweets. The data exploratory method was used to determine how information about a particular organization is spread around the world in the Saudi Arabia Twitter community. The clustering technique is applied to discover socially important locations for profiling new businesses or expanding the existing ones. The experimental results are encouraging and it is greatly expected that it will open a new way to identify the most important locations within the Kingdom of Saudi Arabia for profiling businesses leading up to customer satisfaction.
Heart Disease Predictions Using Numerous Classification Techniques and Dynamic LSTM Model

Now-a-days, a lot of clinical data are used for utilizing the intellectual advancements of clinical basis leadership. In the area of health sector, the need for improving the nature of patient's lives and decreasing the expenses as well as measures the work engaged with their everyday medicinal services condition is very important. An Electronic health record (EHRs) is a data record set which resides patient diagnostic lab records with different parameters, physician/consultants' histories, also in the accounts of different sections of the hospital. Earlier, in the case of heart disease prediction, we have attained a bulk unorganized set of data since the time series of the EHR system. By dividing and taking out these time-sensitive EHRs information, we can distinguish/perceive the aggregate factors among all the obtained set of clinical data for our research purpose. Despite the fact that it is troublesome undertaking to utilize the current EHR information legitimately, on the grounds that they might be repetitive, not in a standardized structure, and some of the data missed/blanked. Consequently, this paper has been proposed a viable and solid design of the Dynamic LSTM model for coronary illness forecast. The main aim of doing this paper has been used to predict heart sickness utilizing an alternate information mining arrangement system. In this paper, we have designed a Dynamic LSTM using classification techniques and generated a training and test model which produce a more accurate result as compare to previously LSTM risk algorithm. One of the advantages of the proposed algorithm is it can work on dynamic dataset records and capable to produce a more efficient classified result for heart disease prediction.
Peak Detection, Feature Extraction and Clustering of Peptides Fragments Ions

This work presents a peak detection technique used to detect Proteomics fragments peaks and investigates if shape- based features and clustering can group the peaks such that the clusters are homogeneous, i.e. contain peaks from a single class. We used Continuous Wavelet Transformation (CWT) and two Gaussian Mixture Model (GMM); K=2 and K=15; for peak detection and clustering, respectively. GMM(K=15) performed better than GMM(K=2) with an f1-score of 0.81 and 0.57 for the good class and the bad class, respectively. Additional features and other clustering techniques need to be investigated to improve the homogeneity of the clusters.
Performance Evaluation of Network Layer Routing Protocols on Wireless Sensor Networks

The wireless sensor network is increasingly gaining attention due to the ubiquitous Internet of Thing (IoT). The wireless sensor network (WSN) basically consists of several nodes which are having the capability to communicate with each other over the wireless media. Further, in terms of network architecture, the most part of the WSN is infrastructure-less, however, the core of such network may comprise of the infrastructure including an access point (AP). The implementation of a particular routing protocol from the bunch of available, is of a great dilemma. Thus, this paper evaluates the effectiveness of different routing protocols for wireless sensor network applications. The effectiveness of the routing protocols is evaluated on the basis of different network performance matrices such as average End-to-End Delay (ETD) and Packet Delivery Ratio (PDR). To evaluate, the performance of WSN, QualNet 5.2, a network simulator is used which is accepted as one of the best network simulators to correctly represent the real-time scenario.
A Reliable Deployment Strategy for Public Electric Vehicle Charging Stations: A Discrete Event Simulation Model for Power Grid and Traffic Networks

Today, with the help of low-cost and clean electricity, electric vehicles (EVs) show great potential for future urban transportation with little greenhouse gas emissions and air pollution. However, the use of EVs is still in its infancy with limited numbers of users worldwide. Public charging infrastructures can be one of the drivers to enhance the prevalence of EVs. In this paper, we aim to develop a framework to establish a reliable urban public charging infrastructure. We first determine the optimal locations and size of those stations by a robust optimization model incorporating uncertain traffic flows and existing power grid networks; and then we use a discrete event simulation approach to model more realistic charging demands. We further improve these optimal solutions and verify the EV charging infrastructure considering both power and transportation networks. We validate our models with real traffic and power grid data from a Southeast Chinese municipality.
Mental Workload Classification Using Short Duration EEG Data: an Ensemble Approach Based on Individual Channels

Real-time mental workload assessment using EEG signals is an active research area that can potentially revolutionize the workplace for human operators. To make mental workload estimation practical in online every-day scenarios, it is vital to design classification models that can attain high accuracy when using short data fragments. In this study a subject-specific mental workload classifier has been designed that can classify two levels of mental workload with high accuracy when using data instances as short as 1.1s. The proposed model is an ensemble leaner that automatically extracts features of EEG channel data. Each base model of the ensemble classifier was specialized to learn spectral-temporal features of a single particular channel. Spatial information was added to the decision of the model by combining outputs of the channel-specific base classifiers through a majority voting method. The ensemble classifier was tested using different durations of data instances. Performance of the proposed ensemble classifier was better than a single classifier that used all channel data as input, for all instance durations tested. The proposed ensemble structure in this study can open new avenues to design ensemble learners based on different brain regions in EEG classification tasks.
Symmetric group testing with noise

Group testing problem is to find all unknown defective elements (samples) of a search space, using subsets of the search space as tests (queries). We consider symmetric group testing (SGT), one of known group testing models. In SGT the response on a test ℱ equals 0 iff no defective elements belong to ℱ, equals 1 iff all elements of ℱ are defective, and equals {0, 1} otherwise. We derive a new upper bound on the number of tests needed to recover τ or less defective elements in presence of noise. Also, we recall the connection of SGT with cover-free codes, multiple access channel type A and fingerprinting codes for multimedia.
Attentively Conditioned Generative Adversarial Network for Semantic Segmentation

Generative Adversarial Network has proven to produce state-of-the-art results by framing a generative modeling task into a supervised learning problem. In this paper, we propose Attentively Conditioned Generative Adversarial Network (ACGAN) for semantic segmentation by designing a segmentor model that generates probability maps from images and a discriminator model which discriminates the segmentor’s output from the ground truth labels. Additionally, we conditioned the discriminator’s dual inputs with extra information as a conditional adversarial model such that, an attention obtained probability distribution of the segmentor’s feature maps is incorporated, and the ground truth is also accompanied by a vector of the class label. We demonstrate that our proposed model can provide better semantic segmentation results while stabilizing the discriminator to model long-range dependencies as a result of the supplementary inputs to the network. The attention network particularly provides more insights by extracting cues from the feature locations, and alongside the class label vector, gives the model an advantage to inform better spectral sensitivity. Experiments on the PASCAL VOC 2012 and the CamVid datasets show that our adversarial training technique yields improved accuracy.
Kannada Named Entity Recognition and Classification using Bidirectional Long Short-Term Memory Networks

This paper focuses on carrying out the Named Entity Recognition and Classification (NERC) task on Kannada, a major Dravidian language spoken in India. Low resource conditions such as absence of external linguistic resources and gazetteers in Kannada and other Dravidian languages pose obstacles to the NERC task. LSTM networks, with their capability of learning long-term dependencies, present an effective solution to this task without the need of a deeper understanding of the semantics of the language. This paper describes a novel supervised machine learning model for Kannada NERC using Bidirectional LSTM networks. The network model is trained and validated on a manually annotated corpus, and gives encouraging results in terms of various evaluation metrics.
Real Time Twitter Based Disaster Response System for Indian Scenarios

Twitter is a popular social media platform with more than 1 million daily active users. Mostly, all breaking news is posted earlier in twitter than any mainstream media. Hence, this microblogging social network experiences a deluge of information flow during natural disasters. Situation based mining of information from the twitter data, can play a significant role in disaster response and recovery. The large volume and velocity of data flow on twitter during disaster makes it tedious for the disaster rescue volunteers to manually analyze and retrieve information from them. An automated system that could retrieve relevant information from this enormous twitter data during a disaster, could be useful for the disaster relief volunteers to accomplish their duty efficiently amidst the chaos. During disasters, the volunteer's team may service more efficiently, if they had a classification based on the victims who request for donation and those who request rescue. In this paper, we propose an artificial intelligence based real time disaster response system-Disastro, which assists the volunteers by identifying the relevant tweets from the real time twitter data and classifying them under the domains "rescue" and "donation". Disastro is empirically validated across various machine learning algorithms for classification using the tweets posted during Chennai rains and Kerala floods. The versatility of Disastro across different disasters and its improved classification accuracy makes it flexible and robust to handle any location-based emergencies.
Traffic Analysis for Accidents Reduction in VANET’s

Road accident and traffic congestion is a current application in vehicular Ad-hoc networks, VANET system is used to enhance road safety and reduce the road accident chance between moving vehicle. Connectivity and traffic congestion analysis is an important part of VANET System. We have created a different four-lane path on SUMO (Simulation Of urban mobility) and analysis the result with the help of nets. In this paper, we have Simulated the results of vehicle connectivity in VANET system based on physical parameters such as throughput, and time
On-Line Permissive Supervisory Control of Discrete Event Systems for scLTL Specifications

We propose an on-line supervisory control scheme for discrete event systems (DESs), where a control specification is described by a fragment of linear temporal logic. On the product automaton of the DES and an acceptor for the specification, we define a ranking function that returns the minimum number of steps required to reach an accepting state from each state. In addition, we introduce a permissiveness function that indicates a time-varying permissive level. At each step during the on-line control scheme, the supervisor refers to the permissiveness function as well as the ranking function in order to guarantee the control specification while handling the tradeoff between its permissiveness and acceptance of the specification. The proposed scheme is demonstrated in a surveillance problem for a mobile robot.
Anomaly Detection Techniques using Deep Learning in IoT: A Survey

IoT technologies is improving life quality by enhancing several real-life smart applications. IoT includes large number of devices generating huge amount of data which needs large computation. Anomaly detection and security is the major concern in the IoT domain. This survey paper provides an overview of anomaly detection using machine learning and deep learning methods in IoT applications. Machine learning and deep learning are powerful tools for analyzing normal and abnormal behavior of IoT components and devices. In this paper we outline key issues in research and challenges using deep anomaly detection techniques for resource constrained devices in real-world problems of IoT. Fog computing move the computation at the device or the edge to solve some of the issues of network security and delay in computation.
EEG Absence Seizure Detection with Autocorrelation Function and Recurrent Neural Network

Epilepsy patients experience challenges in daily life, and epilepsy seizures might cause injuries or endanger the life of the patients or others. The electroencephalogram (EEG) signals, recorded by a machine or device, are often used to analyze the brain electrical activity, which is noninvasive. Locating the seizure period in EEG recordings is usually difficult and time consuming for doctors. Therefore, automatic detection of seizures is necessary. In this paper, we use the autocorrelation function to extract the EEG features, and propose a method based on Recurrent Neural Network to detect the seizure period of the EEG signal, which combines the gated recurrent unit and a 1-D convolutional embedding head. We use the clinical EEG recording of 15 patients to simulate the results of our proposed method. The experimental results demonstrate that our method achieves an excellent performance with 99.6% detection accuracy for Absence Seizure, which can greatly reduce the workload of doctors in clinical diagnosis.
Multi-modal Multi-objective Optimization: Problem Analysis and Case Studies

In many real-world applications, multi-objective optimization problems may have more than one Pareto sets. The goal of multi-modal multi-objective optimization is to find all Pareto sets in the decision space. As a relatively new research area, difficulties in solving multi-modal multi-objective optimization problems have not been carefully analyzed in the literature. In this paper, first we point out that standard evolutionary multi-objective algorithms show difficulties when solving multimodal multi-objective optimization problems. Next, using the concept of genetic drift, we clearly explain the decrease of diversity in the decision space during the evolutionary process. Then, we report performance evaluation results of state-of-the- art evolutionary multi-modal multi-objective algorithms using scalable test problems.
A Programmably Compliant Origami Mechanism for Dynamically Dexterous Robots

We present an approach to overcoming challenges in dynamical dexterity for robots through programmably compliant origami mechanisms. Our work leverages a one-parameter family of flat sheet crease patterns that folds into origami bellows, whose axial compliance can be tuned to select desired stiffness. Concentrically arranged cylinder pairs reliably manifest additive stiffness, extending the programmable range by nearly an order of magnitude and achieving bulk axial stiffness spanning 200–1500 Nm<inline-formula><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula> using 8 mil thick polyester-coated paper. Accordingly, we design origami energy-storing springs with a stiffness of 1035 N m<inline-formula><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula> each and incorporate them into a three degree-of-freedom (DOF) tendon-driven spatial pointing mechanism that exhibits trajectory tracking accuracy less than 15% rms error within a (<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>2 cm)<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> volume. The origami springs can sustain high power throughput, enabling the robot to achieve asymptotically stable juggling for both highly elastic (1 kg resilient shotput ball) and highly damped (“medicine ball”) collisions in the vertical direction with apex heights approaching 10 cm. The results demonstrate that “soft” robotic mechanisms are able to perform a controlled, dynamically actuated task.
Alternative Loss Functions in AlphaZero-like Self-play

Recently, AlphaZero has achieved outstanding performance in playing Go, Chess, and Shogi. Players in AlphaZero consist of a combination of Monte Carlo Tree Search and a deep neural network, that is trained using self-play. The unified deep neural network has a policy-head and a value-head, and during training, the optimizer minimizes the sum of policy loss and value loss. However, it is not clear if and under which circumstances other formulations of the loss function are better. Therefore, we perform experiments with different combinations of these two minimization targets. In contrast to many recent papers who adopt single run experiments and use the whole history Elo ratings from self-play, we propose to use repeated runs. The results show that this method can describe the training performance quite well within each training run, but there is a high self-play bias, such that it is incomparable among different training runs. Therefore, inspired by the AlphaGo series papers, a self-play bias avoiding performance assessment, final best player Elo rating, is adopted to evaluate the playing strength in a direct competition between the evolved players. For relatively small games, based on this new evaluation method, surprisingly, minimizing only value loss achieves the strongest playing strength in the final best players’ round-robin tournament. These results indicate that more research is needed into the relative importance of value function and policy function in small games.