Miracle-dz/UniFAR-FTUs huggingface dataset:
A Loop Closure Detection Method Using Binary Feature Descriptors

Aiming at the problem of loop closure detection in simultaneous localization and mapping for mobile robots, we propose a method based on ORB features and BOW model. We extract binary features from the required pictures, then modify a traditional k-means structure according to the characteristics of binary words, obtain the visual dictionary online. The similarity of images can be computed based on the visual words. A Bayesian filter was introduced to select final loop images to guarantee the success rate of the closed-loop detection. Finally, an Epipolar Constraint is performed to remove the wrong images. The proposed approach is detected in several datasets, and the effectiveness of the method is demonstrated by the comparison with several classical algorithms.
Learning Visual Concepts in Images Using Temporal Convolutional Networks

With the advancement of technology and the rapid rise of social media, people now tend to share their experiences and feelings using images. It is useful to effectively mine opinions and identify sentiments from the images available on various platforms for tasks like social media marketing and user profiling. Visual sentiment concepts extracted from tags of online images are known as adjective noun pairs and the sentiment conveyed by them is usually predicted using opinion mining methods. However, the pre-training work is time consuming and requires huge space. Instead of collecting tags for the images, in this paper we propose to automatically predict the corresponding sentiments using deep convolutional networks. Next, to model the sequence of different sentiments in a single image we consider a recurrent neural network. Such a model is able to remember the context of different sentiments in a single image. We applied our method on Flickr dataset and our approach outperformed baselines in the range of 3-20%.
Do Features From Short Durational Segments Classify Epileptic EEG Signals Effectively?

Automated analysis of electroencephalogram (EEG) signal is the current trend in epilepsy diagnosis. Machine learning based automated analysis enables the fast and accurate detection of epileptic diseases. Recent studies have concentrated on developing efficient algorithms for the EEG analysis and diagnosis of epilepsy. A major bottleneck during the algorithm development is that, whether to consider a lengthy signal to analyze or a part of the signal for producing an adequate classification result. In this work, we analyzed the epileptic classification performance of the features from short durational segment of the signal. Two well-known entropy features are extracted from the random segments. The results of a benchmarking epilepsy EEG data set shows that features from short durational segments promisingly classify the epilepsy signals.
Optimization of Dynamic Prices for Electric Vehicle Charging Considering Fairness

For operators of public charging stations, it is economically reasonable to employ a dynamic pricing scheme in order to be able to react to varying operating conditions. However, dynamic prices may lead to the perception of price unfairness with negative consequences for the charging station operator's profit.In this work, the evolutionary optimization of the price offers with respect to the expected profit and the fairness is studied. With the help of numerical experiments, it is shown that a multi-objective optimization is able to increase the perceived fairness without impact on the profit compared to a single-objective optimization of the profit. Furthermore, it is shown that it is possible to achieve promising trade-off solutions by employing appropriate multi-criteria decision making approaches.
Power Network Measurement Placement Using Graph Optimization

In this paper, the minimum cost measurement placement problem guaranteeing power system observability is considered. Contrary to the mainstream studies predominately focused on phasor measurement unit (PMU) placements, this paper considers the placement with traditional units including line power flows and bus power injections. The result in this work serves as a precursor for the placement problem with PMU and traditional measurements in full generality. The placement problem in this paper is formulated as a graph connectivity problem, which is either solved exactly as a mixed integer linear program or approximately via minimum spanning tree and weighted bipartite matching calculations. A numerical case study on IEEE power system benchmarks demonstrates the solution methodologies for the placement problem.
A Single-Channel, 600-MS/s, 12-b, Ringamp-Based Pipelined ADC in 28-nm CMOS

Achieving high linearity and bandwidth with good power efficiency makes the design of ADCs in deep nanoscale CMOS processes very challenging, as the constraints of low-voltage operation and limited intrinsic gain often dictate the use of power-consuming analog circuits and intensive digital calibration. This paper addresses these problems by introducing a pipelined ADC that exploits the low but very constant open-loop gain versus output voltage characteristic of the ring amplifier (ringamp) to achieve both high speed and linearity in low-voltage nanoscale CMOS designs. A tunable ringamp biasing scheme using an anti-parallel arrangement of CMOS transistors and an active ringamp-based common-mode feedback are also introduced. A single-channel prototype ADC is implemented in a standard 28-nm CMOS process, achieving 58.7-dB SNDR and 72.4-dB SFDR at 600 MS/s while consuming 14.5 mW from a single 0.9-V supply, resulting in Walden and Schreier figure-of-merit (FoM) values of 34.4 fJ/conv. -step and 161.9 dB, respectively.
Sign Language Recognition for Speech and Hearing Impaired by Image Processing in MATLAB

The paper presents the model of a sign language interpreter that can verbalize American Sign Language (ASL). This robust model is based on creating a human-computer interface (HCI) using the user's hand gesture only. The combination of Hardware and software interfaces-webcam and MATLAB 2016a-performs the feature extraction process from the image captured from real-time video of hand signs. These features are compared with the features of the database images and after some image processing techniques in MATLAB, the system generates outputs depending on the prediction of highest resemblance. As the model is free from any other apparatus or accessories, it is solely practical and easy to use. This model provided satisfactory accuracy in our tests without any need of any constant or unicolor background. The proposed technique, together with a vast source database, will definitely be highly beneficial for mitigating the communication gap between the people with speaking and hearing abilities and those without them.
Camera Attribution Forensic Analyzer in the Encrypted Domain

Digital media forensics must deal with constantly increasing volumes of data. In order to efficiently scale up, outsourcing computation becomes an appealing solution. However, due to the highly sensitive nature of forensic data, its privacy must be protected when processed in an untrusted environment. This work proposes a new framework to efficiently perform an outsourced PRNU (Photoresponse Non-Uniformity) fingerprint extraction and detection on encrypted images in a fully unattended way. For this purpose, we rely on lattice-based homomorphic cryptosystems paired with advanced optimization strategies. We evaluate our solutions in terms of efficiency, security and performance for real image datasets, showing the feasibility of camera attribution in the encrypted domain.
Novel hybrid intelligent evolutionary algorithm for dynamic optimization problems*

Dynamic optimization is used to make a performance index optimal by controlling operational variables in dynamic model. Intelligent algorithms based on intelligent evolutionary become popular for solving dynamic optimization problems. The general methods based on the gradient method cannot handle the cases that the gradients are not available. For solving this problem, a hybrid genetic algorithm combining the differential evolution and artificial fish swarm algorithm is proposed. The proposed algorithm is successfully tested on several cases and the results show the feasibility and efficiency of the proposed methods.
Self-Organizing Recurrent Interval Type-2 Petri Fuzzy Design for Time-Varying Delay Systems

This paper presents a design of a self-organizing recurrent interval type-2 Petri fuzzy controller (SORIT2PFC) for controlling the time-varying delay systems. The control system comprises of a main controller and a compensation controller; the SORIT2PFC as the main controller is used to imitate an ideal controller, and the simple fuzzy compensator controller is used to eliminate the residual error. By using a self-organizing algorithm, the structure of the proposed network can automatically achieve optimal construction. Recurrent neural network and fuzzy Petri nets are applied to improve system performances and to reduce computational burden. Online tune adaptive laws of the proposed controller are derived by implementing the gradient descent method and the Lyapunov stability theorem. Finally, the control efficacy and effectiveness of the proposed controller are verified by the numerical simulations of the time-varying delay systems.
Effects of Filter on the Classification of Brain MRI Image using Convolutional Neural Network

Brain MRI image produces a clear anatomical view of brain and any small abnormality of brain is perceptible by MRI image. Brain tumor classification, detection, and segmentation are huge concern of researchers. Among various machine-learning algorithms such as -K-Nearest Neighbour, Support Vector Machine, Artificial Neural Network, and Convolutional Neural Network (CNN); CNN acquired better position in image classification. CNN classification accuracy depends on some network parameter as convolutional filters, rectification functions, polling functions, and iteration numbers etc. It is a problem to determine effective values of convolution filter size, number and convolution stride for better accuracy in classification. CNN has capabilities to produce and learn effective features on large datasets. We have provided some directions to determine effective values for CNN filter size and number in Brain MRI image classification. Our research work’s findings are - square filters are better than rectangular filter; accuracy of filter size 3*3 to 10*10 higher than larger filter size; larger number of filters produce more complexities with higher classification accuracy; increasing of convolutional stride results decrease in network accuracy. This work will be helpful and may be a direction for researchers who are interested to work with Convolutional Neural Network in image classification fields.
Flipping the High-School Classroom: Contributions for Learning in a Case Study

This article describes the implementation of a flipped classroom methodology in a Physics course in a technical high school in Argentina (16-year-old students). For the module on fluid mechanics, students watched video lectures before going to the classroom and time at school was used to discuss more examples of the concepts shown in the videos, solve problems and work on experiments. Qualitative and quantitative data were analyzed for this particular case study in order to shed light on the perceptions students have on the influence of this methodology on their learning process. Results showed that students believe that flipping the classroom has a positive impact on their learning experience. Even though each of them worked differently with the video lectures at home (i.e. they performed different cognitive, metacognitive and resource-management tasks), they pointed out several advantages of having the possibility to re-watch the lectures. The instructor highlighted that there was an increased level of engagement in face to face activities. Since the choice of an academic degree could be powerfully influenced by the experiences of high school, how science is taught at this level can significantly affect the decision of a student to choose a scientific degree such as engineering.
Identifying Beneficial Sessions in an e-learning System Using Machine Learning Techniques

The first goal of this project is to investigate the beneficial sessions for each student in an e-learning system. The second goal is to explore the relationships between student session difficulty, workload, engagement and loyalty based on the session outcome. The most difficult problem faced by e-learning instructors is finding which course sessions or what course materials are most beneficial to their students during a course. When instructors do receive insufficient feedback concerning a course session or week, the result can be that students fail the course, drop out, or receive a lower grade on the final exam. In this study, we used machine learning (ML) algorithms and regression analysis to identify beneficial sessions based on students’ workload, engagement, difficulty and loyalty during the course. The results revealed that strong relationships exist between the input student features (engagement, difficulty, workload and loyalty) and the session scores. In addition, the results show that deep learning and random forest models are appropriate ML algorithms for predicting beneficial sessions.
Radiation-Hardened 14T SRAM Bitcell With Speed and Power Optimized for Space Application

In this paper, a novel radiation-hardened 14-transistor SRAM bitcell with speed and power optimized [radiation-hardened with speed and power optimized (RSP)-14T] for space application is proposed. By circuit- and layout-level optimization design in a 65-nm CMOS technology, the 3-D TCAD mixed-mode simulation results show that the novel structure is provided with increased resilience to single-event upset as well as single-event–multiple-node upsets due to the charge sharing among OFF-transistors. Moreover, the HSPICE simulation results show that the write speed and power consumption of the proposed RSP-14T are improved by ~65% and ~50%, respectively, compared with those of the radiation hardened design (RHD)-12T memory cell.
Two Effective Approaches to Data Reduction for Neural Machine Translation: Static and Dynamic Sentence Selection

In this paper, we aim at data reduction for neural machine translation (NMT): selecting a subset from a very large training corpus and training NMT on this subset so as to reduce training time while meantime achieving the same or even higher translation quality. We propose two effective approaches to achieve this goal: a static sentence selection method that selects sentences into a subset before training according to their sentence embeddings, and a dynamic sentence selection method that dynamically selects sentences into epoch during training based on their training cost. We examine the effect of an n-gram based traditional data reduction method originally proposed for statistical machine translation (SMT) on NMT and compare the two proposed approaches against the traditional method. Experiments on the United Nations Parallel Corpus show that the best of the two proposed approaches can reduce training time by half and at the same time achieve improvements in translation quality with up to +0.79 BLEU.
Technology Adoption Models – Adoption of ICT in Educational Institutions in India

Educational technology can be defined as the study and ethical practice of facilitating learning and improving performance by creating, using and managing suitable technological processes and resources. The technology utilizes both educational system and physical hardware. It involves several domains including theoretical learning, computer-based training, web-based learning, etc. Corporates, nations and individuals adopt each evolving technology, only if, it adds value. To recognize the value of such technology, it is essential to be familiar with the technology adoption process and factors affecting it. Well established proven models explain the factors in adoption process. In this paper, the sources, technologies and delivery methods of ICT in Educational Institutions, have been discussed. Six significant models (one model from each decade 1960-1970, 1970-1980, 1980-1990, 1990-2000, 2000-2010 and 2010-2020) have also been discussed for adoption of evolving technologies in Educational Institutions.
Linguistic Divergence of Sinhala and Tamil Languages in Machine Translation

This paper presents a study of the lexical-semantic divergence between Sinhala and Tamil languages. Study of divergence is critical as differences in linguistic and extra-linguistic features in languages play pivotal roles in translation. This research the first study of the divergence between Sinhala and Tamil languages and is based on Dorr's classification. We propose a computer-assisted divergence study procedure using statistical machine translation, which is easy and gives good performance compared to traditional approaches. Accordingly, this research has the twin aims of revisiting classification of divergence types as outlined by Dorr and outlining some of the new divergence patterns specific to Sinhala and Tamil languages. This study proposes a rule-based algorithm to classify a divergence.
Discrete-Time Fractional Order Integral Sliding Mode Control for Piezoelectric Actuators with Improved Performance Based on Fuzzy Tuning

In this paper, the discrete-time fractional order integral sliding mode control design for linear uncertain systems is investigated. First, a new method is proposed to approximate the fractional order integral recursively. Then, the control design based on the proposed approximation is presented. Finally, the fuzzy tuning which adjusts the integral gain and the fractional order of the integral simultaneously is employed to improve the transient response of the control system. The effectiveness of the proposed strategy is verified by both simulations and experiments on a piezo-actuated positioning system.
An Adaptive Mechanism for Recommendation Algorithm Ensemble

Popular recommendation algorithms such as $k$ -nearest neighbors and Slope One are appropriate for different situations. In this paper, we propose an approach, which will be termed as adaptive mechanism for recommendation algorithm ensemble (AMRE). The AMRE consists of three parts, including a set of agents, a reward-function, and a roulette. First, each agent corresponds to a recommendation algorithm. It also contains a reward value to determine whether it should be retained or replaced. Second, a reward-function is designed to update the reward value according to recommendation results. Wrong recommendations bring punishments, while the right ones bring rewards. Finally, the roulette chooses another agent when the reward value is below a given threshold. The experimental results on MovieLens datasets show that the AMRE outperforms 11 well-known algorithms and two classical ensemble methods on Accuracy, Recall, and F1-measure.
Hybrid Deep Learning Model for Stock Price Prediction

In this paper, we propose a novel stock price prediction model based on deep learning. With the success of deep learning algorithms in the field of Artificial Neural Network (ANN), we choose to solve the regression based problems (stock price prediction in our case). Stock price prediction is a challenging problem due to its random movement. This hybrid model is a combination of two well-known networks, Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). We choose the S&P 500 historical time series data and use significant evaluation metrics such as mean squared error, mean absolute percentage error etc., that conventional approaches have used. In experiment section, we have described the effectiveness of each of the component of our model along with its performance gain over the state-of-the-art approach. Our prediction model provides less error by considering this random nature (change) for a large scale of data.
A Novel Dynamic Model-Based Predictive Control of Unmanned Ground Vehicles‚

In this paper, a novel dynamical model for unmanned vehicles is proposed. Based on this model, a linear model predictive controller is then designed in order to implement lateral control of unmanned vehicles. Moreover, an effective longitudinal controller is presented to stabilize the vehicle on the planning speed according to the information of the throttle and brake. It is worth mentioning that the error compensation, which is caused by linearization and discretization of the nonlinear model, is utilized to improve the precision of the novel linear dynamic model. The effectiveness of these two controllers and compensation is verified by simulation results.
Automated cell manipulation through 3D bio-scaffold via dielectrophoresis*

In this paper, we proposed a scaffold which could be used for cell manipulation via dielectrophoresis (DEP). The base of the scaffold was fabricated with a biodegradable polymer, polylactic acid (PLA) by a 3D printer. The fabricated structure was then coated with a thin layer of conductive material, in order to be served as electrodes for DEP manipulation. The designed structure was determined after the electric field simulation by the finite element analysis software COMSOL. MC3T3 cells were seeded onto the PLA and gold coated PLA (G-PLA), in order to investigate the influence of the conductive layer on the attaching and culturing of cells. The fabricated structure was then served as the electrodes for patterning cells via DEP mechanism. Cellular patterns were formed along the side and the corner of the scaffold. The patterned cells were further cultured for a week and could cover the surface of the scaffold. As the experiment results shows, this new scaffold could be used for 3D cell seeding and patterning for the development of bone tissue engineering.
Relevance-Based Automated Essay Scoring via Hierarchical Recurrent Model

In recent years, neural network models have been used in automated essay scoring task and achieved good performance. However, few studies investigated using the prompt information into the neural network. We know that there is a close relevance between the essay content and the topic. Therefore, the relevance between the essay and the topic can aid to represent the relationship between the essay and its score. That is to say, the degree of relevance between the high score essay and the topic will be higher while the low score essay is less similar to the topic. Inspired by this idea, we propose to use the similarity of the essay and the topic as auxiliary information which can be concatenated into the final representation of the essay. We first use a hierarchical recurrent neural network combined with attention mechanism to learn the content representation of the essay and the topic on sentence-level and document-level. Then, we multiply the essay representation and the topic representation to get a similarity representation between them. In the end, we concatenate the similarity representation into the essay's representation to get a final representation of the essay. We tested our model on ASAP dataset and the experimental results show that our model outperformed the existing state-of-the-art models.
A Novel Approach For Data Packing: Using Trapdoor Knapsack

Processing encrypted data is a well-known solution when protecting privacy-sensitive data from untrusted processing units. However, data expansion, as a result of data encryption, makes undesired computational and communicational overheads in the cryptographic applications. Data packing is one of the useful tools to minimize the overheads. In this work, we introduce a novel approach for packing encrypted data based on the subset sum problem. We show that our data packing achieve high performance in reducing the overheads and it is significantly more efficient than existing techniques. Moreover, we show that our approach perfectly matches with secure searching protocols for secure data retrieval.