Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer Learning for Humanoid Robot Appearance-Based Localization in a Visual Map

Autonomous robot visual navigation is a fundamental locomotion task based on extracting relevant features from images taken from the surrounded environment to control an independent displacement. In the navigation, the use of a known visual map helps obtain an accurate localization, but in the absence of this map, a guided or free exploration pathway must be executed to obtain the images sequence representing the visual map. This paper presents an appearance-based localization method based on a visual map and an end-to-end Convolutional Neural Network (CNN). The CNN is initialized via transfer learning (trained using the ImageNet dataset), evaluating four state-of-the-art CNN architectures: VGG16, ResNet50, InceptionV3, and Xception. A typical pipeline for transfer learning includes changing the last layer to adapt the number of neurons according to the number of custom classes. In this work, the dense layers after the convolutional and pooling layers were substituted by a Global Average Pooling (GAP) layer, which is parameter-free. Additionally, an $L_{2}$ -norm constraint was added to the GAP layer feature descriptors, restricting the features from lying on a fixed radius hypersphere. These different pre-trained configurations were analyzed and compared using two visual maps found in the CIMAT-NAO datasets consisting of 187 and 94 images, respectively. For evaluating the localization tasks, a set of 278 and 94 images were available for each visual map, respectively. The numerical results proved that by integrating the $L_{2}$ -norm constraint in the training pipeline, the appearance-based localization performance is boosted. Specifically, the pre-trained VGG16 and Xception networks achieved the best localization results, reaching a top-3 accuracy of 90.70% and 93.62% for each dataset, respectively, overcoming the referenced approaches based on hand-crafted feature extractors.
DeepARC: An Attention-based Hybrid Model for Predicting Transcription Factor Binding Sites from Positional Embedded DNA Sequence

The binding of transcription factors (TFs) to transcription factor binding sites (TFBS) plays a pivotal role in regulating gene expression and evolution. Accurately modeling the specificity of DNA and searching for TFBS helps understand the genome’s function and evolution. In recent years, computational identification of TFBS has become an active field of research. Here, we propose DeepARC, an attention-based hybrid approach combining convolutional neural network (CNN) and recurrent neural network (RNN) for predicting TFBS. We employ a position-based embedding strategy to embed a DNA sequence into a matrix with distributed representation contenting the position information and then feed the distributed representations of the sequence into a CNN-BiLSTM-Attention-based framework to classify whether there is a TFBS in a sequence. Take the advantage of the attention mechanism, DeepARC can obtain more valuable information about TFBS and add interpretability to the TFBS search process. Moreover, sufficient experiments prove that DeepARC has better performance than existing predictors. The DeepARC web server is available at http://deeparc.denglab. org.
A multi-view approach for predicting microbedisease associations by fusing the linear and nonlinear features

Microbes play a crucial role in human health and disease. Understanding the relationship between microbes and diseases is conducive to the treatment and diagnosis of diseases. Recently, many computational methods have been proposed to predict disease-microbe associations. However, most of the existing methods only consider a single model and explore the disease-microbe associations from a single view. To improve the prediction accuracy, we propose a novel multi-view approach that fuses the linear and nonlinear features to predict new potential associations between diseases and microbes. We first design a non-negative matrix tri-factorization method to extract the linear features of diseases and microbes. We input the linear features from the non-negative matrix tri-factorization model and bi-random walk model into a capsule neural network to obtain the diseases and microbes’ nonlinear features. Finally, we leverage a logistic regression model to combine the non-negative matrix tri-factorization model predictions, bi-random walk model predictions and the capsule neural network predictions to obtain the final association scores between microbes and diseases. We apply our method to predict human microbedisease associations. Experimental results show that our fusion model outperforms the non-negative matrix tri-factorization model, bi-random walk model and other existing models.
Multi-Head Self-Attention Transformation Networks for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to analyze the sentiment polarity of an input sentence in a certain aspect. Many existing methods of ABSA employ long short-term memory (LSTM) networks and attention mechanism. However, the attention mechanism only models the local certain dependencies of the input information, which fails to capture the global dependence of the inputs. Simply improving the attention mechanism fails to solve the issue of target-sensitive sentiment expression, which has been proven to degrade the prediction effectiveness. In this work, we propose the multi-head self-attention transformation (MSAT) networks for ABSA tasks, which conducts more effective sentiment analysis with target specific self-attention and dynamic target representation. Given a set of review sentences, MSAT applies multi-head target specific self-attention to better capture the global dependence and introduces target-sensitive transformation to effectively tackle the problem of target-sensitive sentiment at first. Second, the part-of-speech (POS) features are integrated into MSAT to capture the grammatical features of sentences. A series of experiments carried on the SemEval 2014 and Twitter datasets show that the proposed model achieves better effectiveness compared with several state-of-the-art methods.
Consensus in Networks of Nonlinear Integrators with Applications to Coordinated Path Following Control of Fixed-Wing UAVs

This paper investigates the problems of consensus in networks of nonlinear integrators, as well as the coordinated path following control of multiple fixed-wing unmanned aerial vehicles (UAVs). Firstly, by employing the notions related to reduction theorem, it is shown that the classic consensus protocol is still effective for networks of nonlinear integrators, which are the nonlinear generalization of integrators with bounded inputs. The requirement to achieve consensus is still that the underlying digraphs contain directed spanning trees. Then the results are employed to design a novel coordinated path following control law for heterogeneous fixed-wing UAVs with control input constraints. Asymptotic stability of the closed-loop system is theoretically guaranteed, as long as the requirement for consensus is satisfied.
A Binary Linear Programming-Based K-Means Algorithm For Clustering with Must-Link and Cannot-Link Constraints

Clustering is probably the most extensively studied problem in unsupervised learning. Traditional clustering algorithms assign objects to clusters exclusively based on features of the objects. Constrained clustering is a generalization of traditional clustering where additional information about a dataset is given in the form of constraints. It has been shown that the clustering accuracy can be improved substantially by accounting for these constraints. We consider the constrained clustering problem where additional information is given in the form of must-link and cannot-link constraints for some pairs of objects. Various algorithms have been developed for this specific clustering problem. We propose a binary linear programming-based k-means approach that can consider must-link and cannot-link constraints. In a computational experiment, we compare the proposed algorithm to the DILSCC algorithm, which represents the state-of-the-art. Our results on 75 problem instances indicate that the proposed algorithm delivers better clusterings than the DILSCC algorithm in much shorter running time.
Structured Information Extraction of Pathology Reports with Attention-based Graph Convolutional Network

Electronic medical data contains biochemical, imaging, pathological information during diagnosis and treatment. The pathology report is a kind of highly liberalized unstructured textual data, which is the basis and gold standard of cancer diagnosis and is very important for the prognosis and treatment of patients. The application of information extraction technology to pathological reports can obtain structured data that can be understood and analyzed by computers, helping pathologists make appropriate decisions. In this work, we proposed an attention-based graph convolutional network (GCN) for converting unstructured pathological reports into a structured form suitable for computer analysis to improve the current pathologist’s workflow, collected medical data from different platforms, and provided more accurate assistance for diagnosis and treatment. We used pathology reports data from TCGA (The Cancer Genome Atlas) database with fine-grained annotations on 3632 pathology reports including four types of cancers. Our method performs better in our pathology report dataset with higher F1 score than traditional methods and deep learning methods. The results indicate that our method is robust, thus may work with other types of cancer pathology report.
An Enhanced EPP-MPPT Algorithm With Modified Control Technique in Solar-Based Inverter Applications: Analysis and Experimentation

In this paper, an optimized adaptive perturb-perturb (PP) based algorithm is presented. The modified algorithm has a predictive variable step size calculated through the Newton-Raphson procedure, making its programming effort simple. This combination merits fewer calculations, faster response time and can simply be applied effectively in both bright and shady conditions. The algorithm is developed as a C language code linked to the PSIM simulation representing a typical photovoltaic module system. The proposed algorithm’s simulation results proved faster tracking time response with a reduced error than the standard system. The tracking time is ten times faster than the MPPT method and reduced by 10 seconds in a 100 kHz converter. The measured error is less than 0.03% at steady state. A modified control modulation scheme is blended with the algorithm as well. Experimental results are provided using a 10 W prototype for telecom applications and another 300W practical micro inverter as a proof of concept, and in agreement with both modelling and simulation results. In addition, the results validate the viability of the proposed algorithm in the cases of linear (resistor) and non-linear (brushless motor) loads. The PSIM and experimental setups are provided to prove the concept of the proposed methodology, which is critical for universal solar-inverter applications.
A New Class of Differential Beamformers

Differential microphone arrays (DMAs) have been used in a wide range of applications for high-fidelity acoustic signal acquisition and enhancement. In the design of differential beamformers, three of the widely used measures are the directivity factor (DF), the front-to-back ratio (FBR), and the white noise gain (WNG). The former two have been used to obtain optimal differential beamformers, e.g., the hypercardioid and supercardioid, and the third one is generally used to analyze and control the robustness of the beamformer with respect to array imperfections due to sensors’ self noise, mismatch among sensors, and sensors’ placement errors. In this paper, we present a new measure called directivity factor and front-to-back ratio (DFBR), which is a generalization of DF and FBR. With this new measure, three different kinds of beamformers are derived. The first one is the maximum DFBR beamformer, which is deduced by maximizing DFBR with a joint diagonalization method. The second one is the $\psi$-cardioid beamformer, which is the maximum DFBR beamformer corresponding to a distortionless constraint. The last one is the reduced-rank differential beamformer, which is obtained by properly choosing the dimension of the signal subspace and maximizing WNG subject to the distortionless constraint. The developed beamformers have many interesting properties, which are justified by both simulations and experiments.
SecSky: A Secure Dynamic Skyline Query Scheme With Data Privacy

Skyline computation is a typical multi-objective optimization problem and has been a hot spot for current research. Most of them have concentrated on improving the efficiency of skyline computing, while the security issues associated with executing skyline computation in the cloud server are rarely taken into account. The cloud data stored in plaintext form is attacked by hackers or malicious administrators, resulting in potential leakage of users’ private information. One of the most effective approaches to solve this problem is to encrypt the private data before storing it in the cloud server. However, how to compute the skyline on the encrypted data stored on a cloud server efficiently is still challenging. In this paper, we present a novel framework called SecSky. Firstly, the improved B+ tree structure and symmetric encryption were combined to construct a secure storage structure. Then, the pruning idea was applied to reduce unnecessary calculation and to achieve efficient skyline query and dynamic update. The whole process was directly completed on the ciphertext data. Finally, both the security and the efficiency of SecSky were analyzed through theoretical proof and simulation experiments. The results showed that our scheme is of sound efficiency on the premise of ensuring private data security.
Design of a New Five Level MLM based Multilevel Inverter

In this paper, a novel cascaded multi-level inverter(MLI) with a multi-level module (MLM) is introduced. The inverter consists of sub multi-level units and a full bridge circuit. The added advantage of the novel topology is the reduction in components count as the level of output voltage raise. By increasing the number of output voltage levels the power quality can be improved. The harmonic content in the output voltage of the MLI can be minimized by applying a PWM optimization strategy. Here a multi carrier phase disposition (PD) PWM with constant frequency method is used to trigger the gate pulses. The novel topology has better performance and low THD when operating at desired voltage and power. The operation and performance of the multi-level module based 5 level MLI has been simulated in PSIM.
An Unsupervised Ensemble Clustering Approach for the Analysis of Student Behavioral Patterns

Specialized services and management must understand students’ behavioral patterns in a timely and accurate manner. Based on these patterns, we can make targeted rules, especially for unexpected patterns. To perform this type of work, a questionnaire method is usually used to collect data and analyze students’ behavioral states. However, the effectiveness of this method is greatly influenced by the timeliness and validity of the feedback data. To address this problem, we propose an unsupervised ensemble clustering framework to use student behavioral data to discover behavioral patterns. Because the behavioral data produced by students on campus are available in real time without intentional bias, clustering analysis can be relatively efficient and reliable. The proposed framework extracts behavior features from the two perspectives of statistics and entropy and then combines density-based spatial clustering of applications with noise (DBSCAN) and ${k}$ -means algorithms to discover behavioral patterns. To evaluate the performance of the proposed framework, we carry out experiments on six types of behavioral data produced by undergraduates in a university in Beijing and analyze the relationships between different behavioral patterns and students’ grade point averages (GPAs). The results show that the framework can not only detect anomalous behavioral patterns but also find mainstream patterns. The findings from this research can assist student-related departments in providing better services and management, such as psychological consulting and academic guidance.
Design and Development of Bit Error Measurement using FPGA for Visible Light Communication

Bit error rate (BER) is a fundamental performance measurement of data transmission and communication link. BER measures. Incorporating with digital baseband processing in transceiver, BER measurement can be integrated in a field programmable gate array (FPGA). A Cyclone IV E EP4CE115 is implemented as BER tester to measure performance of visible light communication (VLC) link. The flexibility of FPGA allows the proposed BER measurement system design to add features such as pseudo random bit sequence (PRBS) generator, burst error generator and delay control for synchronization. It shows that design capable to measure VLC link up to 2 Mbps due to limitation of VLC analog front end (AFE).
Efficient Secure Building Blocks With Application to Privacy Preserving Machine Learning Algorithms

Nowadays different entities (such as hospitals, cyber security companies, banks, etc.) collect data of the same nature but often with different statistical properties. It has been shown that if these entities combine their privately collected datasets to train a machine learning model, they would end up with a trained model that often outperforms the human experts of the corresponding field(s) in terms of classification accuracy. However, due to judicial, privacy and cost reasons, no entity is willing to share their data with others. We have the same problem during the classification (inference) stage. Namely, the user doesn’t want to reveal any information about his query or its’ final classification, while the owner of the trained model wants to keep this model private. In this article we overcome these drawbacks by firstly introducing novel efficient secure building blocks for general purpose, which can also be used to build privacy preserving machine learning algorithms for both training and classification (inference) purposes under strict privacy and security requirements. Our theoretical analysis and experimentation results show that our building blocks (hence also our privacy preserving algorithms which are built on top of them) are more efficient than most (if not all) of the state-of-the-art schemes in terms of computation and communication cost, as well as security characteristics in the semi-honest model. Furthermore, and to the best of our knowledge, for the Naïve Bayes model we extend this efficiency for the first time to also deal with active malicious users, which arbitrarily deviate from the protocol.
Probabilistic Sizing of Battery Energy Storage System for Solar Photovoltaic Output Smoothing

Malaysia is strategically located in the tropical region which is abundant in solar resources. Nevertheless, countries with tropical climatic condition is known for the high passing cloud events. This phenomenon has introduced technical challenges to the integration of solar photovoltaic (PV) system to electricity networks. The high intermittency of solar irradiance will lead to rapid solar output power fluctuations, which is harmful to the operation of the electricity grid. Hence, this paper aims to utilize a battery energy storage system to smoothen solar output fluctuations under real weather conditions in Malaysia. More specifically, two smoothing methods were considered to determine the required battery capacity probabilistically for a grid-connected solar PV system installed at the Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka.
Modeling, simulation and prediction of protein structures for the design of oncolytic viruses

One of the main problems in the fight against cancer is represented by the immunosuppression it causes. Several research lines have been undertaken to address this issue; one of the most promising is represented by oncolytic viruses. Oncolytic viruses are designed from live attenuated recombinant viruses and are designed to preferentially infect cancer cells, within which they replicate causing their lysis and enhance, through various mechanisms, innate and adaptive immunity. In collaboration with the Etna Biotech company, which was involved in designing five protein constructs composed of five different variants of a monoclonal antibody linked to a viral surface protein, we worked on the modeling and evaluation of these proteins associated with the Measles Virus. The software we used was Swiss-model (https://swissmodel.expasy.org/), a bioinformatics webserver dedicated to homology modeling of 3D protein structures, and Pro-Q (https://proq.bioinfo.se/ProQ/ProQ.html) to perform quality control. We shared our results with the company, which completed laboratory tests for two of the five constructs, confirming our predictions. Transfection experiments are currently underway with two other constructs, while the last one has not yet been subjected to such investigations.
A Simple Thermoelectric Effect Setup for Determining the Conductivity Type of Thin Film Materials

In this article, we present a simple and low-cost experimental setup for thermoelectric effect measurements of thin film materials near room temperature, which can be used to determine their conductivity types. Bi<sub>2</sub>Te<sub>3</sub> and Sb<sub>2</sub>Te<sub>3</sub> thin films grown by the molecular beam epitaxy technique were used as the tested samples. Their Seebeck coefficients were determined to be <inline-formula> <tex-math notation="LaTeX">$(-141\pm 1)\,{\mathrm{\mu V/K}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$(39\pm 2)\,{\mathrm{\mu V/K}}$ </tex-math></inline-formula>, respectively, confirming that the former is an n-type material and the latter is a p-type material. A heterostructure composed of Sb<sub>2</sub>Te<sub>3</sub> and Bi<sub>2</sub>Te<sub>3</sub> was characterized by electrical transport measurements. Data fitting was carried out for its current–voltage characteristics with the Shockley diode model and a real diode model proposed by Cataldo <italic>et al.</italic>, and some physical parameters of the heterostructure were extracted, including its ideality factor and saturation current. Based on its rectifying current–voltage behavior, we confirm that the aforementioned heterostructure is a p-n junction, which echoes the contrast in the conductivity types of Sb<sub>2</sub>Te<sub>3</sub> and Bi<sub>2</sub>Te<sub>3</sub> as determined by the thermoelectric effect measurements.
Analysis of Transformers by Acoustic Emission

The article deals with an on-line noncontact measuring method for the analysis of transformer operation by measuring acoustic, noise and electromagnetic emissions. The electric non-rotating machine generates electromagnetic and acoustic waves, by means of which it is possible to analyze not only operating states but also its possible fault states. We performed measurements of acoustic emission on the traction transformers 25/6 kV and 110/3 kV that were in real operation. We studied the spatial distribution of the acoustical field as well as its spectra. Preliminary results have shown that acoustic analysis may be a useful alternative option for high-power transformer's health condition.