Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Machine Learning Approach for the Phenotypic Classification of Metagenomic Colon Cancer Reads Based on Kmer Frequency and Biomarker Profiling

Human Microbiome plays a critical role in health and the environment. Colorectal cancer (CRC) is the most common cause of death in many countries, and hence early diagnosis of CRC may help in increasing the survival rate. Tracking changes in the microbiome structure of human gut opens new gates towards the detection and prediction of the risk of CRC. Recently, machine learning became a powerful technique in many bioinformatics fields, one of which is metagenomics. Metagenomics is defined as the study of a collection of microbial genomes isolated directly and sequenced from its natural habitats. Applications of machine learning in metagenomics are numerous, among them are phenotype classification, taxonomic assignment, and sequence annotation. Phenotype classification is assigning a phenotypic class to each sample such as diseased or healthy, according to the available metadata. Phenotype classification in metagenomics is usually done on organism taxonomic units (OTU) tables extracted as a core step in the metagenomic analysis. On the other hand, Natural Language Processing (NLP) methods such as kmer frequency, can provide features for the machine learning model. In this study, we combined a biomarker profiling and a kmer frequency table approaches to classify colorectal cancer from metagenomic data.
Anonymous Communication on the Internet

There are many kinds of systems developed for anonymous communication on the internet. We survey a number of systems and evaluate their security. Among these systems we compare functionalities like Onion Routing, anonymous VPN services, probabilistic anonymity, and deterministic anonymity. Other types of anonymous communication such as messaging, peer-to-peer communication, web use, emailing, and use of other Internet applications are also presented. We follow up by presenting different types of attacks with the purpose of identifying anonymously communicating users. These attacks fall into the following categories: internal/external attacks, passive/active attacks, and static/adaptive attacks. We describe the following attacks as well as known protections against these attacks: predecessor attacks, intersection attacks, timing attacks, and Sybil attacks. Lastly we discuss design choices, operation, and security of the current TOR network – The 2G Onion Router. Access control methods to restrict malicious use of TOR are also proposed. In conclusions the significance of anonymous communication is outlined.
Design of Magnetic Coupling Resonant Wireless Charging System for Cable Tunnel Inspection Robot

A wireless power transfer system for automatically charging the inspection robot in cable tunnels is proposed in this paper. The design procedure of the magnetic coupling resonant wireless charging system is presented. The ZVS condition of the Class-E inverter, and the constant-current charging function are discussed. Also, an infrared module is added to the control system to detect the alignment of the transmitter and receiver coils so as to automatically switch the operation modes between charging and standby. Experimental results verify that the designed wireless charging system can be operated stably and efficiently.
Real-time Learning-based Monitoring System for Water Contamination

It is vital for a city to monitor water quality in real time since the quality of water has profound effect on residents’ health. Unfortunately, it is impossible for human to monitor water’s chemical composition frequently, which is an impossibly demanding task, let alone in real time. Thus, taking advantage of a highly efficient system can be an excellent solution. In this paper, we created a system called Real-time Intelligent Monitoring System for Water Quality, which enables a city to be responsive to potential outbreak of contamination and to protect city residents. The system is capable of processing and classifying the data extracted from visual images to considerably save more money and labor. In this case, two features Fast Fourier Transform (FFT) and Color Layout Descriptor (CLD) are introduced for Saliency features and color features respectively. FFT performs well in extracting saliency features and is not computationally intensive; CLD is able to represent the color features with high effectiveness and efficiency. Additionally, this system utilizes Support Vector Machine (SVM) based on such features that needs small size of training sets, trains very fast and can classify floating rubbish and any other scenarios of water pollution with satisfying efficiency. Till now, the accuracy has reached 75%, which encourages us. While the detection performance can be further improved, the efficient features & classifiers would serve as powerful methods to automatically monitor water pollution.
Orientation-Aware Regression for Oriented Bounding Box Estimation

Oriented bounding box estimation is an essential element for object detection since objects in real images consist of multiple orientations. State-of-the-art bounding box regression utilize linear layers and ReLU activations with an oriented feature as input, however these methods do not directly use the feature orientation. This limits the potential of the feature and causes the estimated bounding box to be sensitive to rotation.In this paper, we propose an Orientation-Aware Regression for oriented bounding box estimation, which conserves the orientation of the feature and regresses the bounding box using more informative features. The orientation-aware regression is robust against rotation and outperforms state-of-the-art regression for oriented object detection.
Discovering Suspicious APT Families Through a Large-Scale Domain Graph in Information-Centric IoT

In recent years, a type of cyberattacks, known as advanced persistent threats, has resulted in very serious losses to various organizations such as governments and enterprises. The APT has the characteristics of long duration, complex attack means, and strong ability to conceal themselves, which make it difficult to detect them. Due to the lack of proper means to protect the Information-centric IoT (ICIoT), the ICIoT devices are extremely vulnerable to APT attacks. Moreover, among the existing APT detection methods, most researchers adopt those that extract the features of different APT attacks, and most of the features extracted are local, which leads to the fact that the related methods have poor scalability, thus reducing the accuracy. Furthermore, the attackers can easily avoid the detection by changing the local features. In this paper, we find that it is inevitable that the infected host will generate C&C communication with the command and control server (C&C server), during the process of APT attacks, and the C&C domain names are the bridge connecting the internal infection with the C&C server. Moreover, a certain APT attack of one attack family, which is the assembly of the same APT attacks, tends to map the C&C domain names to the same IP subnet. Under the assumption that the APT attackers have limited attack resources, the relationship between C&C domain names of APT and IP subnet is inevitable for the APT attackers to get higher attack efficiency, which leads to the effective tracking of APT attack behavior. Therefore, we construct a detection method based on the domain names’ graph structure. This detection method can improve the detection efficiency in the information-centric internet, especially for the IoT devices. And, at the same time, we employ an appropriate pruning strategy and a preprocessing method to reduce the size of data to be processed and improve the computational efficiency. This detection method can also reduce the detection range, increase the detection accuracy, and improve the robustness and scalability of the detection system. In the actual experiment, the data size we process is 257535071 DNS requests and 73136 domain names. The experiment shows that the C&C domain names can be effectively detected even with a small-scale seed domain names.
A Novel Non-Orthogonal Multiple Access Based On Maximum Welch Bound Equality

A novel spreading non-orthogonal multiple access (NOMA) scheme is proposed for the future 5G communication system, which is called maximum Welch-bound spreading multiple access (MWSMA). In the transmitter, each user’s modulated symbols are spread by a complex-valued short spreading sequence and then sent simultaneously on the same frequency spectrum. We design spreading sequences via maximum-Welch-bound-equality (MWBE) to minimize the maximum cross-correlation amplitude. Theoretical analysis on cross-correlation and average mutual information (AMI) proves that the proposed MWSMA scheme is much superior to the current orthogonal frequency division multiple access (OFDMA) and two key NOMA schemes, multi-user shared access (MUSA) and sparse code multiple access (SCMA), especially for high spectral efficiency (SE). Simulation results also verify that MWSMA can achieve significant 0.8dB, 0.8dB and 3.9dB signal-to-noise-ratio (SNR) gain at target block error rate (BLER) 10−1 as compared with MUSA, SCMA and OFDMA, respectively.
TCENR: A Hybrid Neural Recommender for Location Based Social Networks

Point-Of-Interests (POI) recommendation, an important application of location-based social networks (LSBN), has been extensively researched in recent years. This sub-field of recommender systems (RS) poses unique challenges due to high data sparsity and its relative complexity. An emerging technique is the use of deep neural networks to improve the performance of collaborative filtering (CF) based models. Recent works have successfully integrated such networks with external data, such as social networks, locations, categories and written reviews. In this paper, we propose a new method, Textual and Contextual Embedding-based Neural Recommender (TCENR). The suggested algorithm combines two types of neural networks to model the user-POI interactions based on implicit ratings, social networks, geographical locations and natural language reviews. Experiments on the Yelp dataset show that the proposed model is able to learn the complex interaction and enables improved recommendation performance.
Integration of Solar PV Generation with Distribution Grid: Using Novel Adaptive Filter Based Control Technique

In this work, a Novel Adaptive Filter (NAF) is proposed for optimal control of grid-integrated solar PV (Photovoltaic) generating system. The proposed NAF is the improved form of classical adaptive vectorial filter (AVF), where inherent problems of AVF technique, like unable to handle the phase imbalance and phase shift issues, are successfully mitigated. For performance evaluation of NAF based control technique, a single-stage topology of three-phase grid integrated solar PV system is taken, where the loads are connected at the PCC (Point of Common Coupling). The prime objective of proposed NAF based control technique is to meet the active power requirement of loads from generated solar PV power, and after fulfill the load demand, the excess power is supplied to the grid. However, when generated PV power is not sufficient for the load then the NAF based control fulfills the load demand by taking extra required power from the grid. During this process, the power quality of the grid power is improved. The controller action provides, power factor correction, harmonics filtering and mitigation of other power quality issues. Moreover, when the solar irradiation is zero, then the voltage source converter acts as a distribution static compensator (DSTATCOM), which enhances the utilization factor of the system. The proposed techniques are modeled and their performances are verified experimentally on a developed prototype, in solar insolation variation conditions, unbalanced loading, as well as in different grid disturbances such as overvoltage, under-voltage, phase imbalance, harmonics distortion in the grid voltage etc.
Next Generation Train-Centric Communication-Based Train Control System with Train-to-Train (T2T) Communications

High-reliability and low-latency are crucial for urban rail transits. In this paper, we introduce communication strategies for urban rail transit systems using Long Term Evolution for Train-to-Train (LTE-T2T) communication. The transmission latency is set as a system metric to evaluate the performance of urban rail transit system. First, a novel train-centric Communication- Based Train Control (CBTC) model is established using LTE- T2T communications. Then, we introduce a novel sensing-based semi-persistent scheduling method for resource allocation in this proposed system. Based on the introduced method, extensive field test and simulation are executed. The related results show that the transmission latency of safety-critical information in urban rail transit system can be improved using the introduced scheme.
An Adaptive Thermal Equivalent Circuit Model for Estimating the Junction Temperature of IGBTs

Implementation of real-time health assessment and thermal management of insulated gate bipolar transistors (IGBTs) require thermal equivalent circuit models that can be used to predict the junction temperature of the modules. Solder aging in IGBTs has a substantial impact on the accuracy of junction temperatures estimated by the models. This paper proposes an adaptive thermal equivalent circuit model that can estimate the junction temperature of IGBTs with precision under solder aging conditions. First, the solder aging process is monitored in real time by the temperature gradient of the baseplate of the IGBT module, which is easily implemented by placing two thermal sensors at the interface between the baseplate and the cold plate. Then, when the solder aging is detected, the actual junction temperature obtained by an ON-state collector–emitter voltage of the IGBT is utilized to update model parameters based on the thermal behavior of the device. By combining the two stages, the effect of solder aging on the accuracy of the junction temperature estimate is removed in time. Simulation and experimental results are provided to verify the effectiveness of the proposed method.
Antenna Selection and Resource Allocation in Wireless Powered Communication Networks with Self-Energy Recycling

This paper considers an orthogonal frequency division multiplexing (OFDM)-based wireless powered communication network (WPCN) with one hybrid access point (HAP) and multiple users. Each user is assumed to support full-duplex communication with two antennas equipped. The classic "harvest-then-transmit" protocol is adopted for the WPCN, where the time is split into two phases, one for downlink power transfer and the other one for uplink information transmission. Especially, in the uplink information transfer phase, each user selects one antenna for uplink information transmission while executing self-energy recycling by the other antenna. The problem of antenna selection, subcarrier allocations in both downlink and uplink transmissions, downlink and uplink power allocations, and time allocation for the two phases to maximize the sum rate of all the users under the energy causality constraint is investigated. An efficient scheme is proposed to solve the optimization problem. Simulations are performed to verify the effectiveness of the proposed scheme. It is shown that our proposed scheme significantly increases the sum rate of the users compared to the reference schemes.
DS4A: Deep Search System for Algorithms from Full-Text Scholarly Big Data

While information retrieval systems have shown tremendous improvements in searching for relevant scientific literature, there is still a gap to cater users' ever demanding need to search for specific metadata-related information from full-text publications. In this paper, we present a deep learning-based system that enhances the capability of search mechanisms by classifying algorithm-specific metadata, such as accuracy, precision and recall, and further details such as the datasets that they operate on and the time complexity - from full-text publications. Specifically, in contrast to traditional term frequency-inverse document frequency (TF-IDF) based approach that uses frequent terms as in 'bag of words' models, we first generated a synopsis of the full-text document and then enriched it with sentences that classify as algorithm-specific metadata from full-text to improve algorithmic-specific searching capabilities. These sentences were classified from deep learning based bi-directional long short-term memory network (LSTM) model. Our bi-directional LSTM model outperformed Support Vector Machine (SVM) by 9.46 % with 0.81 F-measure in classifying 37,000 algorithm-specific metadata lines, annotated by four human experts. Finally, we present a case study on 21,940 full-text publications downloaded from the full-text repository of the ACL (https://aclweb.org/) to show the advantages of a deep learning-based advanced searching system over conventional TF-IDF-based (Lucene) text-retrieval systems.
Robust Energy-Efficiency Power Allocation in Multicell HetNets with Channel Uncertainties

This paper focuses on a robust power allocation (PA) problem to maximize total energy efficiency of femtocell networks in multicell heterogeneous networks (HetNets). In order to improve system robustness, channel uncertainties are taken into account in advance. In this paper, the robust PA optimization problem is formulated subject to minimum signal-to-interference-noise-ratio (SINR) constraint of each femtocell user, overall transmit power constraint of each femtocell base station (F-BS), and outage-probability constraint of interference links from F-BS to each macrocell receiver. Through Bernstein approximation theory and Dinkelbach method, the original optimization problem with the probabilistic constraint is transformed into a deterministic and convex one that is solved by Lagrangian dual method. Simulation results show the robustness of the proposed algorithm under channel estimation errors.
Estimation of Blood Perfusion and Metabolic Heat Generation of Lung Tumor During Cryosurgery

Cryosurgery applies very cold temperature to freeze tumor cells. For accurate treatment, monitoring the temperature field during this process is a must. In this work, we estimated parameters such as metabolic heat generation and blood perfusion rate which are vital to determine the temperature field during cryosurgery. We applied the Quasi-Newton and the Gauss-Newton inverse algorithms to estimate these parameters. Temperature measurements were taken from sensor output. To estimate the parameters, the one-dimensional Pennes’ bioheat equation was utilized. The parameters were predicted using least square minimization of the difference between sensor output and estimated temperature values. Once the parameters are obtained, the temperature distribution around the lung tumor at any time can easily be determined. Random initial values were given for both algorithms. The result showed that the Gauss-Newton method has faster convergence rate as compared to the Quasi Newton method in estimating the target parameters. The output of the research will help cryosurgeons to monitor the temperature of the cryoprobe during cryosurgery procedures.
A Novel Contactless Capacitive Energy and Data Transfer System

This article presents a novel contactless capacitive energy and data transfer system. The system is used to supply and communicate with low power devices especially for applications with limited space. A typical use case are sensors in electrical machines or in gearboxes. Regarding this, commonly used inductive data and energy transfer systems are spatially too large. Basis of the work is an auto-resonant power oscillator, commonly called royer converter, which provides a frequency shift key modulation by changing the capacitance of its own resonant circuit. To evaluate the novel data transfer, the system is set up and some measurements are presented to show the satisfactory result.
First-Pass Techniques for Very Large Vocabulary Speech Recognition ff Morphologically Rich Languages

In speech recognition of morphologically rich languages, very large vocabulary sizes are required to achieve good error rates. Especially traditional n-gram language models trained over word sequences suffer from data sparsity issues. The language modelling can often be improved by segmenting the words to sequences of subword units that are more frequent. Another solution is to cluster the words into classes and apply a class-based language model. We show that linearly interpolating n-gram models trained over words, subwords, and word classes improves the first-pass speech recognition accuracy in very large vocabulary speech recognition tasks for two morphologically rich and agglutinative languages, Finnish and Estonian. To overcome performance issues, we also introduce a novel language model look-ahead method utilizing a class bigram model. The method improves the results over a unigram look-ahead model with the same recognition speed, the difference increasing for small real-time factors. The improved model combination and look-ahead model are useful in cases where real-time recognition is required or when the improved hypotheses help with further recognition passes. For instance, neural network language models are mostly applied by rescoring the generated hypotheses due to higher computational costs.
Moocart Compatiblety Building Strategy (For the College of Art - University of Baghdad)

The objective of this research is to immediate an efficient strategy, in order to engage in the progression process of Iraqi higher education. The majority of the existing efforts that applied to enhance it, were restricted with the elderly methods of teaching and learning, and did not change the aged traditional framework and methods. The leading objective is to develop our learning methods and reduce the gap with worldwide universities that are progressed and developed learning and education. Executing the MOOCs system, which symbolizes a different appearance by having the qualities of e-learning and lifelong learning, will improve the learning process at the college of Arts at the first university in Iraq, which is the University of Baghdad, and the most significant role is to change it from the procedure of the old traditional learning systems. The plan is to design and perform a complete MOOCs system that competently helps and covers all Iraqi higher education necessities and the UNIVERSITY OF BAGHDAD (COLLEGE of ART), by using the most productive software and methods that minister to our foremost objective. Elameer-Idrus orbital e-education framework was used in the MOOCs designs.
Data-Parallel Training of Generative Adversarial Networks on HPC Systems for HEP Simulations

In the field of High Energy Physics (HEP), simulating the interaction of particle detector materials is a compute-intensive task, that currently uses 50% of the computing resources globally available as part of the Worldwide LCH Computing Grid (WLCG). Since some level of approximation is acceptable, it is possible to implement fast simulation simplified models that have the advantage of being less computationally intensive. In this work, we present a fast simulation approach based on Generative Adversarial Networks (GANs). The model consists of a conditional generative network that describes the detector response and a discriminative network; both networks are trained in adversarial manner. The adversarial training process is computationally intensive and the application of a distributed approach is not straightforward. We rely on the MPI-based Cray Machine Learning Plugin to efficiently train the GAN over multiple nodes and GPGPUs. We report preliminary results on the accuracy of the generated samples and on the scaling of the time to solution. We demonstrate how HPC systems could be utilized to optimize this kind of models, on account of their large computational power and highly efficient interconnect.