Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Intradialytic Hypotension Prediction in Hemodialysis Patients

Intradialytic hypotension is a common problem during hemodialysis treatment. Despite several clinical variables have been authenticated for associations during dialysis session, the interaction effects between variables has not yet been presented. Our study aimed to investigate clinical factors associated with intradialytic hypotension by deep learning. A total of 279 participants with 780 hemodialysis sessions on an outpatient in a hospital-facilitated hemodialysis center were enrolled in March 2018. Associations between clinical factors and intradialytic hypotension were determined using linear regression method and deep neural network. A full-adjusted model indicated that intradialytic hypotension is positively associated with body mass index (Beta = 0.17, <inline-formula> <tex-math notation="LaTeX">$p =0.028$ </tex-math></inline-formula>), hypertension comorbidity (Beta = 0.17, <inline-formula> <tex-math notation="LaTeX">$p =0.008$ </tex-math></inline-formula>), and ultrafiltration amount (Beta = 0.31, <inline-formula> <tex-math notation="LaTeX">$p < 0.001$ </tex-math></inline-formula>), and is inversely associated with the ultrafiltration rate in a hemodialysis session (Beta = −0.30, <inline-formula> <tex-math notation="LaTeX">$p = 0.001$ </tex-math></inline-formula>). The 4-factor locus obtained by the deep neural network reached the maximum performance metrics evaluation (accuracy =64.97± 0.94; true positive rate = 87.97 ± 2.73; positive predictive value = 66.74 ± 0.98; Matthews correlation coefficient = 0.19 ± 0.03). The prediction model obtained by the deep learning scheme could be a potential tool for the management of intradialytic hypotension.
Neural Embedding & Hybrid ML Models for Text Classification

Representation of knowledge remains a problem for models of machine learning (ML). The Paragraph vector is one of the current methods for embedding the text, where many parameters govern the utility of representation. In this context, we are addressing the effect, on the text classification area, of Paragraph Vector-Distributed Memory (PV-DM) as variant of doc2vec. In comparison, we apply other classification systems focused on doc2vec forms, and a collection of classifiers with current practices in this article. Then, we incorporate hybrid ML methods to improve the quality of classification. The experiments, on benchmarking dataset, prove that the results obtained are excellent, with 99% accuracy in the system based on the PV-DM with average method, and majority voting as a classifier.
A Hybrid Deep Learning-Based Traffic Forecasting Approach Integrating Adjacency Filtering and Frequency Decomposition

Traffic forecasting in urban area has attracted substantial attention in recent years due to its significant assitance for traffic dispatching and trip planning. However, this task is very challenging due to the complex dependencies inherent in the traffic process. In our study, recent and periodic dependencies are identified and used to describe the corresponding near-term and long-term effects of historical traffic data on future traffic states. Subsequently, during the recent dependency modeling, each road is found to correlate with its adjacent roads through traffic flow diffusion, then the correlation intensities are quantified and used to choose strongly correlated roads to build a critical road sequence. While for the periodic dependency modeling, since the historical speed series of a road segment exhibits multi-frequency attributes (i.e., low-frequency daily period and high-frequency stochastic fluctuation), wavelet transform is conducted to decompose the original speed series into low and high-frequency sub-series. On these bases, we propose a hybrid traffic speed forecasting model, flow and wavelet-integrated spatio-temporal network (FW-STN). In the FW-STN, the recent features are captured by the convolutional neural network (CNN) with near-term traffic data from the derived critical road sequence, and the periodic features are captured by the long short-term memory (LSTM) with the low and high-frequency sub-series. Both the recent and periodic features are then fused to conduct the final prediction. Experimental results on real traffic data show that the proposed approach outperforms nine state-of-the-art methods (with improvements of 3% ~15% in mean average percentage error).
Combining Deep Neural Networks for Protein Secondary Structure Prediction

By combining convolutional neural networks (CNN) and long short term memory networks (LSTM) into the learning structure, this paper presents a supervised learning method called combining deep neural networks (CDNN) for protein secondary structure prediction. First, we use multiple convolutional neural networks with different number of layers and different size of filters to extract the protein secondary structure features. Second, we use bidirectional LSTM to extract features continually based on the raw features and the features which extracted by CNNs. Third, a fully connected dense layer is used to map the extracted features by LSTM to the different protein secondary structure classes. CDNN architecture is trained by RMSProp optimizer based on the cross entropy error between protein secondary structure labels and dense layer’s outputs. CDNN not only inherits the abstraction ability of CNN and sequence data process ability of LSTM, but also demonstrates the attractive classification ability for handling protein secondary structure data. The empirical validation on two protein secondary structure prediction datasets demonstrates the effectiveness of CDNN method.
An FPGA-based Hardware Acceleration For Key Steps of Facet Imaging Algorithm

SKA (Square Kilometer Array) is an under-construction radio telescope with the largest observation aperture, and also it is an international science project that requires the participation and cooperation of multiple countries and organizations. SKA scientific data processing is an application with huge amount of data and extremely high computation performance requirement, and has strict energy consumption restrictions. The high performance on data I/O rate determine s cloud computing to be a feasible solution to such a strict computing restriction. However, the traditional processor of cloud computing even if the most sophisticated super computer can not meet the requirements. Therefore, it is necessary to explore a new acceleration structure. FPGA (Field-Programmable G ate Array) is a computation platform that combines the advantages of high-level parallelization and low power consumption. Therefore, heterogeneous cloud computing structure of CPU + FPGA becomes available to solve SKA’s data processing requirements. This paper mainly discusses the high efficiency FP GA-based hardware acceleration of the key algorithm Facet i n SKA. There are two time-consuming parts during the Facet procedures—Phase Rotation and Gridding. Firstly, a pipeline acceleration structure designed for Phase Rotation algorithm is determined. The simulation and tests on FPGA are done to confirm the acceleration result of our design and it shows the performance is 32.07 times of the result running on software. Then determined an efficiency optimized core computation acceleration structure of loop expansion + pipeline for Gridding, and the designed system simulation is done to confirm the performance of our structure. The result shows that the acceleration ratio is 5.48 compared to the result tested on software.
Retinal Vascular Network Segmentation Using Adaptive Thresholding Method Based on LSRV

As digital retina imaging and automatic retinal vascular network analysis continue to find increasing usefulness in the field of biomedicine for the diagnosis, monitoring and management of various forms of human illness like hypertension, retinopathies, glaucoma and cardiovascular diseases, the mitigation of different complications such as nonhomogeneous illumination noise, vessel width variation and very low contrast of the small-width vessels in relation to the retinal fundus background, for an efficient segmentation performance remains a subject of on-going research. This paper investigates the use of an adaptive thresholding method based on local spatial relational variance (LSRV) for the segmentation of the retinal vascular networks in fundus images. An experimental study conducted on DRIVE database shows that the vascular network segmentation results obtained from the investigated method detects large vessels and thin vessels in the retinal fundus images. When compared to some previous methods in the literature, the proposed method achieved higher average accuracy value of 95.04% and average sensitivity value of 76.55%. The proposed method is also computationally fast with a processing time of 4.5 seconds for the segmentation of the retinal vascular networks in each fundus image.
Holistic Descriptors of Omnidirectional Color Images and Their Performance in Estimation of Position and Orientation

The use of visual sensors in robotic navigation tasks is a common approach, and numerous examples can be found in the literature. This work focuses on the problem of map building and localization using omnidirectional images as the only source of information. The main objective of this paper is to present a thorough comparison of global-appearance description techniques including the use of color information in different approaches. Some of the descriptors have been widely tested in previous works using gray-level images. In the present work we concentrate on the role and efficiency of the color information. Other descriptors are presented for the first time. To carry out this study, a database captured in different areas of an office environment is used, including two different datasets: training and test datasets. The experimental results include computational requirements in the map building and localization processes, and the accuracy in the pose estimation of the test images in a topological map, separating both position and orientation. To complete the study, the behavior of the descriptors is tested when the images present noise or occlusions, specially the effect on the color information.
Parkinson’s Disease Identification using KNN and ANN Algorithms based on Voice Disorder

In recent years, speech signal processing has benefited from a lot of attention, because of its widespread application. In this study, we have led a comparative analysis for efficient detection of Parkinson’s disease applied to machine learning classifiers from voice disorder known as dysphonia. To prove robust detection process, we used Artificial Neural Networks (ANN) and K Nearest Neighbors (KNN) algorithms, in the purpose of distinguishing between PD patient and healthy individual. Experimental results show that the ANN classifier achieved higher average performance than the KNN classifier in term of accuracy. The UCI Experiment consists of 31 subjects of which 23 were diagnosed with Parkinson's disease. The established system is able to distinguish healthy people from an acceptable range of people with PD with an accuracy rate of 96.7% by using ANN.
Cluster Scoring Method for Keyword Search with Relevancy-Connected Cluster Model Algorithm

Relevancy-Connected Cluster Model has four steps to be executed in order to search keywords over graphs. On its last step, the outcome clusters are sorted using kernel function. Top ranked cluster covers more diverse input keywords and has small total number of edges. However, there is no specific rule about which of those criteria should be fulfilled first. A cluster which contains more input keywords found is likely to have more edges and vice versa. To tackle that issue, we propose a cluster scoring method which is built upon the combination of the total number of found keyword variations and the total number of edges traced by each core node within clusters. We also examine the r value since it is used on sorting clusters and has significant influence throughout the algorithm.
UGrade: Autograder for Competitive Programming Using Contestant PC as Worker

Competitive programming is a computer science competition where the contestants compete to solve computer science problems by writing a program which satisfies the problem constraints. The autograder is used to grade contestant solutions automatically in real-time. Usually, autograder is deployed in many computers to increase grading performance. In this research, contestants’ computers are used as a worker to run autograder. By using contestants’ computers as a worker, the number of workers will proportional to the number of contestants’ submissions, thus increasing grading performance. Every contestants’ computers have different specification and can affect grading fairness. To keep the fairness of the grading process, contestant’s solution and jury’s solution are executed in contestant’s computer then compared to check whether contestant’s solution satisfies the problem constraints. In order to evaluate the correctness of contestant’s solution, the autograder uses input test-case to execute the contestant’s solution and compares the output with the output test-case. Usually, the test-cases are some big text files generated by the problem setters. In this research, the test-case files are generated in the contestant computer using test-case generator program created by problem setter. In order to improve the security aspect, every contestant submission is graded multiple times. This research was tested by simulating grading process in contestants’ computers. The testing result indicates that using contestants’ computers as a worker gives performance improvement to the grading process. Further research is needed to improve the security aspect of this work. However, this research can be used to organize some competitive programming competition such as ACM-ICPC.
Drop by Drop you break the rock - Exploiting generic vulnerabilities in Lattice-based PKE/KEMs using EM-based Physical Attacks

We report an important implementation vulnerability ex-ploitable through physical attacks for message recovery in five lattice-based public-key encryption schemes (PKE) and Key Encapsulation Mechanisms (KEM) - NewHope, Kyber, Saber, Round5 and LAC that are currently competing in the second round of NIST’s standardization process for post-quantum cryptography. The reported vulnerability exists in the message decoding function which is a fundamental kernel present in lattice-based PKE/KEMs and further analysis of the implementations in the public pqm4 library revealed that the message decoding function is implemented in a similar manner in all the identified schemes and thus they all share the common side-channel vulnerability that leaks individual bits of the secret message. We demonstrate that the identified vulnerability can be exploited through a number of practical electromagnetic side-channel attacks, fault attacks and combined attacks on implementations from the pqm4 library running on the ARM Cortex-M4 microcontroller. As a key contribution, we also demonstrate the first practical EM-based combined side-channel and fault attack on lattice-based PKE/KEMs.
A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. Numerical Weather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon. Novel WS prediction models based on the multivariate empirical mode decomposition (MEMD), random forest (RF) and Kernel Ridge Regression (KRR) were constructed in this paper better accuracy in WS prediction. Particle swarm optimization algorithm (PSO) was employed to optimize the parameters of the hybridized MEMD model with RF (MEMD-PSO-RF) and KRR (MEMD-PSO-KRR) models. Obtained results were compared to those of the standalone RF and KRR models. The proposed methodology is applied for monthly WS prediction at meteorological stations of Iraq, Baghdad (Station1) and Mosul (Station2) for the period 1977-2013. Results showed higher accuracy of MEMD-PSO-RF model in predicting WS at both stations with a correlation coefficient (r) of 0.972 and r = 0.971 during testing phase at Station1 and Station2, respectively. The MEMD-PSO-KRR was found as the second most accurate model followed by Standalone RF and KRR, but all showed a competitive performance to the MEMD-PSO-RF model. The outcomes of this work indicated that the MEMD-PSO-RF model has a remarkable performance in predicting WS and can be considered for practical applications.
A Ubiquitous Machine Learning Accelerator With Automatic Parallelization on FPGA

Machine learning has been widely applied in various emerging data-intensive applications, and has to be optimized and accelerated by powerful engines to process very large scale data. Recently, the instruction set based accelerators on Field Progarmmable Gate Arrays (FPGAs) have been a promising topic for machine learning applications. The customized instructions can be further scheduled to achieve higher instruction-level parallelism. In this article, we design a ubiquitous accelerator with out-of-order automatic parallelization for large-scale data-intensive applications. The accelerator accommodates four representative applications, including clustering algorithms, deep neural networks, genome sequencing, and collaborative filtering. In order to improve the coarse-grained instruction-level parallelism, the accelerator employs an out-of-order scheduling method to enable parallel dataflow computation. We use Colored Petri Net (CPN) tools to analyze the dependences in the applications, and build a hardware prototype on the real FPGA platform. For cluster applications, the accelerator can support four different algorithms, including K-Means, SLINK, PAM, and DBSCAN. For collaborative filtering applications, it accommodates Tanimoto, euclidean, Cosine, and Pearson Correlation as Similarity metrics. For deep learning applications, we implement hardware accelerators for both training process and inference process. Finally, for genome sequencing, we design a hardware accelerator for the BWA-SW algorithm. Experimental results show that the accelerator architecture can reach up to 25X speedup against Intel processors with affordable hardware cost, insignificant power consumption, and high flexibility.
Modeling and Simulating Daily Power Budgets for Sustainable Data Centers

A novel energy-efficient scenario that makes possible to maintain sustainable data centers to feed part of resources through renewable energy sources has emerged. As renewable energies are accumulated in the form of power budgets, data centers must adapt a slice of the processing resources required to meet applications at those limits. This work is modeling and simulating the computing capacity of a data center according to daily power budgets from different sources of renewable energy. The results showed that based on the daily energy harvest of today’s renewable energy sources, intelligent resource orchestration could use such energy so that up to 40% of what is needed to maintain a quality-of-service data center comes from non-polluting sources.
Temporal dynamics of natural communities of marine algal viruses and eukaryotes

The composition of algal virus communities in relation to temperature, salinity, chloro- phyll a (chl a) concentration and eukaryotic community composition was monitored at a single loca- tion for 14 mo. Changes in algal virus and eukaryote communities were determined using poly- merase chain reaction (PCR) and denaturing gradient gel electrophoresis (DGGE) to generate genetic fingerprints. Sequence analysis of bands extracted from denaturing gradient gels revealed the presence of at least 5 distinct viruses as well as temporally dynamic and diverse communities of eukaryotes that included taxa from the viridiplantae, fungi and metazoa. Comparison of algal virus fingerprints with environmental conditions revealed that, at certain times, changes in algal virus community composition were coincident with changes in tide height, salinity or chl a concentration. However, algal virus community changes were not often coupled to eukaryote community changes. The lack of coincidence between changes in virus and eukaryote communities can be explained by the presence of organisms that were not hosts of the detected viruses. It is likely that the uncoupling of 18S and AVS fingerprints was due to succession among non-host eukaryotes. Although algal virus fingerprint patterns were stable throughout most of the study, stable eukaryote fingerprint patterns were observed only during the winter months. Furthermore, specific taxa of algal viruses persisted in fluctuating physical and biological environments. We concluded that the constant production of, and mortality from, some taxa of algal viruses provide further evidence that algal viruses affect phyto- plankton community structure and dynamics.
Frequency Couplings in Impedance Modeling of Voltage Source Converters

A grid-connected voltage source converter (VSC) system may be unstable when the grid is weak. Sequence impedance has proven to be an effective tool for analyzing such problems. As a small signal analysis method, sequence impedance can be obtained by calculating the current response to a voltage perturbation at the same frequency. However due to the phase locked loop (PLL) and dc link voltage controller, the current response at a different frequency from voltage perturbation may be noticeable, which means there exist frequency couplings in the impedance modeling. This paper proposes an impedance model of VSC considering frequency couplings caused by these two factors and analyzes how these factors affect the impedance. The proposed model and analysis results are verified by simulation.
Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network

The growth of the most significant field crops such as rice, wheat, maize, and soybean are influenced because of various pests. And crop production is decreased due to various categories of insects. Deep learning technologies significantly increased the efficiency of identifying and controlling agricultural pests attack. However, agricultural pests images obtained are often obscure and unclear because of the sparse density of cameras deployed in the real farmland. This always makes pests difficult to recognize and monitor. Additionally, the existing classification and segmentation methods are not satisfying for the identification of low-resolution images because they are pre-trained on the clear and high-resolution datasets. Therefore, it is crucial to restore and upscale the obtained low-resolution pest images in order to improve classification accuracy and the recall rate of the instance segmentation. In this paper, we propose a generative adversarial network (GAN) with quadra-attention and residual and dense fusion mechanisms to transform low-resolution pest images. Compared with previous state-of-the-art PSNR-oriented super-resolution methods, our proposed method is more powerful in image reconstruction and achieves the state of the art performance. The experiment results show that after reconstructing with our proposed gan, the recall rate increased by 182.89% and classification accuracy also improved a lot. Besides, our proposed method could decrease the density of the camera layout in the agricultural Internet of Things (IOT) monitor systems and the cost of infrastructure, which is practical for real-world applications.