Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Perceived Synthesized Speech Quality with Wav2Vec2 Features on Small Dataset

The perceived quality of the synthesized audio is one of many factors that may determine the success of a speech synthesizer system in the market. Assessing the perceived speech audio quality is usually done subjectively and objectively. Since the subjective approach is costly and time-consuming, a faster and more efficient objective approach is developed. In an ob-jective approach, only the audio signal is used; The assessment is done either by comparing the difference between the original and synthesized speech or by creating a model to predict the quality. In a text-to-speech system, there is no audio input in the implementation phase. Therefore, evaluating its quality should be done by model prediction. However, human-labeled data is scarce, which leads to generalization issues with machine learning methods. In this work, we propose a transfer-learning method for predicting perceived synthesized speech quality, where the features are extracted from self-supervised pretrained Wav2Vec2. We experimented and evaluated the difference in the prediction performance of the basic Mel spectrogram features with the Wav2Vec2's feature-encoder, which learns the features from the input signal and contextual-encoder features, which learns the encoded features passed from the feature-encoder and using its embedding as the label for the self-supervised training purpose. Our result shows that the use of feature-encoder features yields better performance compared to the one using contextual-encoder features.
Dual Resolution Detection and Identification of Circulating Cancer Cells from Immunofluorescence Microscopy

Accurate detection of tumor-derived cells in cancer patients' peripheral blood under fluorescence microscope has been a challenging task because of their rarity and diversity. We present a deep learning framework for automatically searching, detecting, and identifying the circulating tumor cells (CTCs) and circulating stromal cells (CSCs) from the immunofluorescence microscopy images of patient blood smears. We devise a novel highly sensitive compact network called C-YOLO that analyzes the scanned low-resolution images on-the-fly while the imager sweeps across the slide with a 4X objective, to search and locate all candidates in a background densely populated with leukocytes, debris, and artifacts. After scanning, each detected blob is re-imaged with a 40X objective and analyzed by a convolutional neural network for cell identification, leading to an increased specificity. The method demonstrates promising sensitivity, specificity and accuracy of CTC/CSC detections as illustrated with a small training dataset. The proposed learning framework can be integrated with liquid biopsies as a companion tool for metastatic cancer diagnosis, monitoring, treatment, and prognosis.
Reducing Losses and Energy Storage Requirements of Modular Multilevel Converters With Optimal Harmonic Injection

Due to the single phase characteristic of the individual arms of the Modular Multilevel Converter (MMC) topology, the difference between the instantaneous AC and DC side power must be buffered in the module capacitors. This results in large module capacitors compromising the power density and cost of the MMC. In this paper, a multi-objective optimization scheme is formulated that aims at reducing the required module capacitance and the semiconductor losses at the same time. Further attention is paid to the maximum AC voltage amplitude or the maximum current. The optimization scheme is based on the injection of circulating current and AC common mode voltage harmonics. Unlike most existing optimization schemes it considers the actual trajectories of capacitor voltage and arm output voltage to maximize the savings in module capacitance and semiconductor losses. For an exemplary medium voltage MMC parameter set, capacitance value reductions of more than 50% are achieved while the semiconductor losses decrease by 8- 18%. Based on a volume estimation for MMC modules, this results a volume reduction of up to 45%.
Enhanced Two-stage Ship Detection Algorithm in Remote Sensing Image

Remote sensing images have the characteristics of extreme high resolution, small object and sparse distribution. , which bring huge difficulties for ship detection in the sea. Traditional object detection models based on deep learning can not be directly applied to remote sensing images. This paper proposes an efficient ship detection framework based on Cascade R-CNN network. We use the YOLOv5 model as the first stage detection and simplify the Cascade R-CNN detector head. This new model is particularly suitable for detecting sparse objects in high resolution image.
An integrated Machine Learning Model for Manufacturing industry

The purpose of this article is to examine the process of developing services based on machine learning technologies in a manufacturing environment context. Over the past decade, there has been a resurgence of interest in technology, with prominent technology business organisations and several start-ups incorporating it into their operations. The technology's widespread applications capacity for obtaining a competitive advantage, combined with the prospect of task automation for increased operational efficiency, renders it a significant resource for large corporations. Yet, the rate at which most of manufacturing firms are implementing Machine Learning services into their operations is very insignificant. Technical expertise has dominated study in this field, with minimal participation from other areas. Thus, the goal of this article is to identify the development benefits of Machine Learning services and to provide a process model for their deployment, based on service development and value theory.
An RNS-Based NDN Forwarding Plane: Design and Evaluation

Due to the variable length and uniqueness of names in NDN, traditional IP address-based routing and forwarding architecture is no longer suitable for NDN networks. It is still a challenge to design a high-performance forwarding engine to solve the problem of variable name length and uniqueness in NDN. In this article, Residue Number System (RNS) is applied to the routing and forwarding of NDN for the first time, and a new forwarding engine framework based on the RNS is designed. The simulation results show that this method has a significant performance increase in memory space and system latency. At the core-level node, the RNS-based process requires only about 5 percent of Bloom Filter (BF) in terms of memory space and has a 9.8 percent decrease in system latency compared to applying BF (P=1E-08). This method not only achieves less memory usage and reduces the system latency but also fundamentally solves the false positives and has good scalability due to the superiority of the algorithm itself. The proposed work is admirably suited for realizing large-scale NDNs by hardware implementation of NDN routers.
MDFA: A Malware Detection Framework Based on Attention Mechanism with Bi-LSTM

As the existing malware intelligent detection methods have shortcomings and low accuracy of manual feature extraction and feature processing, a Malware Detection Framework with Attention mechanism based on Bi-directional Long Short-Term Memory (MDFA) was proposed. To training this method, We have collected 15, 893 malware and 10, 959 benign program samples and constructed a dataset. Then, the sample data was labeled and sanitized by determining the results based on VirusTotal analysis. Finally, the Bi-directional Long Short-Term Memory model injected into the attention mechanism was built and trained to complete the malware detection. Experimental results show that the accuracy of MDFA was improved by at least 2. 3%, and the recall rate was improved significantly compared with the existing methods, verifying the effectiveness of MDFA.
Risk-Aware Particle Filtering for State Estimation in Recirculating Aquaculture Systems

We design a sequential non-linear risk-aware estimator based on particle filtering to compute estimates and approximate the system state posterior distribution. For this purpose, we consider the risk given by the expected variance of the squared error between the system state and the estimate conditioned on the observations. We compare the proposed estimator with existing risk-neutral estimators in terms of error variance, mean squared error, and execution time per iteration. The comparison is carried out using a simulation of a Recirculating Aquaculture System, a case study for non-linear critical systems, where the performance in estimation for low probability events becomes an important aspect. Our simulation results demonstrate a competitive estimation performance while ensuring a lower risk. 11The reader can find the code for the proposed estimators in github.com/uia-wisenet/Risk_Aware_MMSE_PF.
Modeling the Relationship between Perisaccadic Neural Responses and Location Information

Eye movements are essential for the brain to collect visual information from the environment. Visual images projected on the retina change abruptly during rapid ballistic eye movements (saccades), but our perception of the visual world is continuous. To generate a stable visual perception, the spatiotemporal sensitivity of visual neurons needs to change quickly prior to and during saccades. This study uses a modeling framework to characterize the fast dynamics of neuronal responses across saccades, thereby quantifying the contribution of perisaccadic response dynamics to the readout of location information during saccades. We apply this approach to neuronal responses recorded from the visual cortex of nonhuman primates during a visually-guided saccade task with visual stimulations. Using the model-predicted responses and a classification method, we measure the spatial discriminability performance of neurons at pre-saccadic and post-saccadic receptive field locations. Characterizing the readout of perisaccadic spatial information and its precise time course can provide insights into how neurons integrate spatial information across saccades to generate a continuous visual experience.
Classification of Fundus Images Using Modified Stacking Ensemble

Glaucoma and diabetic retinopathy are serious eye diseases that can impair vision if untreated. According to a WHO study, the number of diabetics who have retinopathy is expected to rise by more than 50% by 2030. The most frequent cause of permanent blindness is glaucoma, affecting at least 12 million people worldwide and leaving nearly 1.2 million people in India permanently blind. For early detection of glaucoma, fundus imaging datasets are collected from various sources and provided in open source for further research. The dataset is preprocessed through morphological closing and CLAHE, and data augmentation is considered to increase the size of the dataset. Fundus images are classified into three classes: glaucoma, diabetic retinopathy (DR), and normal through transfer learning algorithm of 5 CNN models: VGG19, ResNet50, MobileNetV3Large, EfficientNetB0, and VGG16. The accuracy is further improved through stacking ensemble procedure providing an accuracy of 96.7%. A novel technique of stacking ensemble is considered for the best 3 transfer learning models of ResNet50, VGG19, and MobileNetV3Large, with which the achieved accuracy is 98.3%.
A Scrutinized Outliers Rate for One Class Classification of Green Landscape

The Green area is unarguably the most crucial part of the Sustainable Development Goals (SDG). The importance of the green regions includes preserving biodiversity and stabilizing climates. However, sophisticated mapping tool such as LIDAR is considerably expensive and also not accessible by people in common. This study offers the one-class classification of green regions using authentic drone images. The research manages to scrutinize the outliers rate based on literature. The methodology is cheaper and very applicable-the classification results in 95 % accuracy and 93 % weighted-average F1 score. The technology behind the method includes a lightweight neural-network architecture, a weighted Huber loss and a final Softmax function. The results show that this study is promising for future use.
VocabGCN-BERT: A Hybrid Model to Classify Disaster Related Tweets

When it comes to classifying tweets about disasters, Deep Neural Network-based models have shown great potential over conventional machine learning models. In particular, Bidirectional Encoder Representations from Transformers(BERT) are effectively used to capture the contextual information present in a tweet. But it is not effectively capturing the global structural information of tweets. A Graph Convolutional Network's(GCN) power lies in its ability to capture global information. In this study, we present a novel hybrid model called VocabGCN-BERT by combining the GCN made from a vocabulary graph of tweets and the pre-trained BERT model. A powerful representation for classifying tweets is created by combining local contextual information acquired from BERT with global structural information acquired from VocabGCN. The results of the experiments demonstrate that the proposed VocabGCN-BERT performs better than the currently available state-of-art models based on GCN on seven publicly available datasets by a margin of +1.66% to +4.79% in weighted average F1 score and +1.45% to +4.34% in accuracy.
A Mobility-Aware Successive Feedback Scheme for 5G and Beyond Multi-User MIMO

To support gains in frequency division duplex multi-user multiple-input multiple-output scenarios, Channel State Information (CSI) is required at the Base Stations (BSs). For this purpose, Mobile Stations (MSs) report quantized CSI based on Grassmannian codebooks, that are common to MSs and BSs. Depending on the speed of the MSs, the channel changes over time and CSI must be performed frequently to maintain good performance. To reduce the amount of overhead bits in the limited feedback channel, we developed a differential algorithm to track channel changes efficiently. Compared to independent CSI, the MS reports residuals of past feedback via sparse coding. The results show gains in the sum throughput compared to 5G New Radio Type II feedback solution while requiring less overhead at the same time.
Extended Folded Cube with Cross Connection: A New Fault Tolerant Interconnection Network for Parallel Computing

A framework has been provided for designing interconnection network topology based on extended folded cube named as Extended Folded Cube with Cross Connection represented as EFCC(k, i). The proposed network topology is hierarchical, recursive and structurally expansive. The network diameter, average distance and constant degree of nodes of the proposed topology has reduced. The predecessors of the proposed network are less reliable and degree of fault tolerance is less. In presence of some faulty nodes and communication links this topology provides better fault tolerance. The different topological properties like degree connectivity, diameter, local and global average distance of the proposed network topology EFCC(k,i) has been discussed. These metrices proves the proposed topology is a better choice for parallel processing environment as compared to its predecessor. This model has the advantage of increasing higher levels of VLSI process. The study proves that Extended Folded Cube with Cross Connection EFCC(k, i) is suitable for VLSI implementation.
Classification of Pretrial vs. Encoding stage for Working Memory Task among Subjects with pFC Lesions and Healthy Controls using Directed Information

This paper analyzes the scalp electroencephalogram (EEG) recorded from 14 human subjects with pFC lesions and 20 healthy controls while performing lateral visuospatial working memory tasks to identify the directional brain networks responsible for memory encoding. First, we show that effective connectivity features using directed information (DI) are more accurate and robust than the functional connectivity measure of correlation coefficients in classifying the memory encoding stage from the pretrial phase, with a mean accuracy of 99.36%. Second, we identify the functional segregation of memory encoding to a much smaller sub-network by showing that the top 2.5% of the observed DI features can distinguish memory encoding from the pretrial phase with a mean accuracy of 93.1%. Finally, using graph features, we reveal the increased significance of frontocentral, centroparietal, and temporal regions in memory encoding for subjects with pFC lesions and reduced information flow in the prefrontal, frontal and parietooccipital areas when compared to healthy control.
A Tenable Approach to Detect Brain Haemorrhage Using Deep Learning

One of the most important stages for detection of brain haemorrhage in patients is the early diagnosis which requires radiologist's assessment of CT scans. In order to automate the procedure for early diagnosis, CNN models are built which classifies a CT scan brain image to normal images (without haemorrhage) and abnormal images (with haemorrhage). The dataset used are 752,803 DCM files provided by the “Radiological Society of North America (RSNA)”. Out of total number of files 215k is considered as total number of DCM files for training and testing for the model, in which 100k files are for training, 65k for testing and 45k for validation. The DenseNet121 model has 90% accuracy on each class and 89.6% accuracy on each image. The ResNet50 model has 88% accuracy on each class and 87.9% accuracy on each image. From both models DenseNet121 model performance better in predicting the classes.
Meta-Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Terrains

Driving energy consumption plays a major role in the navigation of autonomous mobile robots in off-road scenarios. However, real-time constraints often limit the accuracy of the energy estimations, especially in scenarios where accurate wheel-terrain interactions are complex to model. In this paper, an adaptive deep meta-learning energy-aware path planner is proposed that can provide energy estimates of a mobile robot traversing complex uneven terrains with varying and unknown terrain properties. A novel feature of the method is the integration into the meta-learning framework of a 1D convolutional neural network to analyze the terrain sequentially, in the same temporal order as it would be experienced by the robot when moving, and efficiently adapt its energy estimates to the local terrain conditions based on a small number of local measurements. The performance of the method is assessed in a realistic 3D-body dynamic simulator over several typologies of deformable terrains and unstructured geometries. We provide evidence of the benefit of the proposed approach to retain 83 % r2 score of the original simulator at 0.55% of the computing time. Finally, the method is compared with alternative state-of-the-art deep learning solutions. In this way, we show indications of its improved robustness to provide more informed driving energy estimations and energy-efficient paths when navigating over challenging uneven terrains.
Semi Supervised Approach for Relation Extraction in Agriculture Documents

In this work, we propose a semi-supervised boot-strapping approach for relation extraction in domain specific texts, specifically focusing on agricultural domain. Our approach utilizes the BERT model with dependency parsing for relation extraction. The proposed model, focuses on identifying five inter subdomain relations viz., Soil_Location, Soil_Crop, Disease_Pathogen, Pathogen_Crop, and Chemical_Crop. We created a corpus of 30,000 sentences extracted from recognised agriculture sites to evaluate the model. The labeled relations were then manually checked to evaluate the prediction accuracy. We used a test corpus with 700 sentences that included 3500 triplets for the evaluation. The proposed approach presents an average macro F -Score of 86.4 %, which is quite promising for semi-supervised domain specific relation extraction systems. Experimental results show the efficacy of the proposed approach in classifying relational phrases in a semi-supervised set-up for the agricultural domain.
Fast Distributed Beamforming without Receiver Feedback

Distributed multi-agent beampattern matching inherently enables covert communication by constructively combining the transmitted signals at target receivers and forming nulls towards the adversaries. Most existing beamforming methodologies achieve this by relying on partial or full-state real-time feedback from the receivers. This paper proposes a novel distributed beamforming technique that does not assume any feedback from the receivers or channel parameters, such as multipath fading. The proposed algorithm works in a server-agent architecture of the beamforming agents, eliminating the need for receivers' feedback. Our algorithm is built on the classical gradient-descent (GD) method. However, when the problem is ill-conditioned, GD requires many iterations to converge and is unstable against system noise. We propose an iterative pre-conditioning technique to mitigate the deleterious effects of the data points' conditioning on the convergence rate, facilitating a rapid establishment of communication links with far-field targets. The empirical results demonstrate the proposed beamforming algorithm's favorable convergence rate and robustness against unknown multipath fading in realistic environments.
Convexified Coupled Active Contour Segmentation of Clue Cells for Assessing Bacterial Vaginosis with Immunofluorescence Microscopy

Clue cells are vaginal epithelial cells heavily covered with bacteria. Presence of 20% or more clue cells is a critical criterion in the diagnosis of bacterial vaginosis (BV), which is associated with an increased risk of HIV infection. Clue cell segmentation is a prerequisite task towards the enumeration of clue cells. We adopt a two-band immunofluorescence microscope to capture the high-resolution images of clue cell candidates for segmentation. We present a convexified coupled active contour (CCAC) model that takes the advantage of the cell structure and background relationship across the bands, enabling jointly segmenting the boundaries of the epithelial cell, its nucleus, and distinct bacteria. Convexification is formulated on top of the dual band, region-based level set segmentation framework using characteristic functions. This approach provides a global optimal solution and does not require good initializations. Efficacy of the method is demonstrated on clinical data, showing robust segmentation results in a wide range of scales. Our method achieves a high sensitivity of 98.6%.
A stochastic competitive learning algorithm

We introduce a new stochastic competitive learning algorithm (SCoLA). Here the criterion for selecting the winning neuron consists of a deterministic component and a stochastic component. The deterministic component is inversely proportional to the distance between the input vector and the weight vector, whereas the stochastic component is a zero-mean normal random variable whose variance decreases monotonically with the frequency of winning the competition. Neurons that do not frequently win have high variance, and thus a better chance of winning the competition. Simulation results are presented which demonstrate the effectiveness of the proposed stochastic competitive learning scheme. It achieves better neuron utilization than conventional competitive learning does, resulting in lower distortion rates in clustering and vector quantization applications.