Miracle-dz/UniFAR-FTUs huggingface dataset:
Acoustic Scene Classification Using A Deeper Training Method for Convolution Neural Network

In this paper, we present a deep learning framework applied for acoustic scene classification (ASC) recognizing the environmental sounds. Since an audio scene related to a given location potentially contains numerous sound events, only few of these events supply helpful information on the scene, which makes the acoustic scene classification task become a very complex problem. To confront this challenge, we suggest a novel architecture consisting of two basic processes. The front-end process approaches a spectrogram feature, using Gammatone filters. Regarding the back-end classification, we propose a novel convolutional neural network (CNN) architecture that enforces the network deeply learning middle convolutional layers. Our experiments conducted over DCASE2016 task 1A dataset offer the highest classification accuracy of 84.4% as compared to 72.5% of DCASE2016 baseline.
A Novel PWM Modulation Based on Model Predictive Control for PMSM

PWM predictive control of permanent magnet synchronous motor (PMSM) based on motor prediction model and SVPWM modulation technology. In traditional SVPWM modulation, triangle carrier and Seven-segment vector distribution are adopted. Among them, the action time of zero vector and effective vector is symmetrical and equally distributed, and it still has space for further improvement. Symmetrical duty cycle modulation also brings problems such as current harmonics and noise pollution. In this paper, based on the model predictive control of permanent magnet synchronous motor, a novel PWM modulation method is proposed. A double-sampling and double-update strategy is introduced. And the basic vector’s action time in seven segments is calculated separately. The action time of zero vector and effective vector which minimizes the current ripple are derived by the prediction model, and the optimal asymmetric duty cycle is obtained. The proposed method in this paper compared with the traditional SVPWM can further reduce the motor control current ripple and the current harmonic content, without increasing the switching frequency. Simulation results verified the effectiveness of the proposed method.
Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks

The unpredictability of seizures is often considered by patients to be the most problematic aspect of epilepsy, so this work aims to develop an accurate epilepsy seizure predictor, making it possible to enable devices to warn patients of impeding seizures. To develop a model for seizure prediction, most studies relied on Electroencephalograms (EEGs) to capture physiological measurements of epilepsy. This work uses the two domains of EEGs, including frequency domain and time domain, to provide two different views for the same data source. Subsequently, this work proposes a multi-view convolutional neural network framework to predict the occurrence of epilepsy seizures with the goal of acquiring a shared representation of time-domain and frequency-domain features. By conducting experiments on Kaggle data set, we demonstrated that the proposed method outperforms all methods listed in the Kaggle leader board. Additionally, our proposed model achieves average area under the curve (AUCs) of 0.82 and 0.89 on two subjects of CHB-MIT scalp EEG data set. This work serves as an effective paradigm for applying deep learning approaches to the crucial topic of risk prediction in health domains.
HOMEOPATHY EFFECT ON HIGH BLOOD PRESSURE

There are many problems in the treatment of hypertension, so we use alternative medicine to achieve better results to solve these problems. In this article we describe a hypertensive patient resisted to conventional drugs and she had some other somatic and psychotic symptoms and was treated by homeopathy, a method of alternative medicine. Keywords: hypertension, homeopathy.
A Multilevel Approach for Building Location-Based Social Network by using Stay Points

The main challenge of building Location-Based Social Network (LBSN) using stay points is the definition of local-vertices. Stay points are extracted from a GPS log and indicate that a user has remained in that place for a significant time. Usually, clustering techniques are employed to transform stay points into local-vertices. LBSNs built using stay points are richer in information since GPS logs have more user mobility information. It is usual building LBSN by using stay points, but there is room to discuss the approaches for construction of these networks. This article addresses this gap and presents a novel approach that uses the coarsening stage of a multilevel optimization scheme to build LBSNs by using stay points. The experimental evaluation carried out indicates that our approach has advantages when compared to usual clustering methods to representing real-world features.
Indoor State Estimation for Multirotors using a Loosely-Coupled Integration of Inertial Navigation*

In indoor applications of unmanned multirotors, absolute localization with high availability is necessary to enable autonomous flights. Due to the fast dynamics of multirotors, (smooth) estimates of position, velocity and orientation are required in real-time and at high update rates. This paper addresses the indoor state estimation problem by means of a loosely-coupled integration of an inertial navigation scheme with position measurements of a localization system. State propagation is performed using a Cartesian implementation of the strapdown integration of inertial measurements. An error-state extended Kalman filter is then applied, compensating the delay in availability of position measurements. Equations are derived in detail, and parameters for initialization and parametrization are given. For performance discussion, experiments are performed and discussed using different filter parameters, variation of the assumed measurement delay, and artificial outages in position measurements. Results demonstrate the suitability of the proposed method, showing mean errors in position estimates below 5 cm, and realizing an error reduction of up to 50% by consideration of delays.
A Sampling-Based Framework for Transductive Classification in Information Networks

Knowledge extraction from large information networks has received increasing attention in recent years. Among existing methods for knowledge extraction, transductive classification is a well-known semi-supervised learning method, where both labeled and unlabeled vertices are used in the learning process. However, transductive classification tasks become impractical in large information networks and the use of network sampling techniques in the transductive classification setting is not a trivial task, since it is required that all the vertices of the original network be classified during the transductive learning – and not only the vertices of the sample. In this paper, we present a framework called TCSN (Transductive Classification for Sampled Networks). TCSN allows the use of various network sampling techniques, as well as enables the use of various methods of transductive classification for information networks. We present a variation of the Chernoff Bounds method to calculate the minimum size of a sampled network, thereby bounding sampling error within a pre-specified tolerance level. Moreover, TCSN extends the concept of evidence accumulation to combine the results of several rounds of transductive classification into a final classification. Experimental results from different information networks reveals that TCSN statistically outperformed the classification performance in the whole original network. These promising results show that the TCSN enables transductive classification in large information networks without loss of quality in the knowledge extraction process.
A New Type of Curved Coupling Coil for Wireless Power Transmission

With the development of wireless charging of electronic devices and vehicles, research on wireless energy transmission has received more and more attention. In the case of cylindrical power receiving objects, the planar coils can’t meet the requirements, so a curved coil model is proposed. Firstly, this paper deduces the influencing factors of system output power and system efficiency under SS compensation structure. Secondly, magnetic simulation is performed in Maxwell software. The coil and core structure were designed based on magnetic simulation to determine various parameters. Thirdly, we design and build a hardware platform. Finally, the transmission characteristics of the system is explored, so do the variable distance experiments and the trend of output power and system efficiency at different distances.1
Bi-level Proximal Policy optimization for Stochastic Coordination of EV Charging Load with Uncertain Wind Power

Energy and environmental issues are one of the biggest technological challenges facing human society in the new century, electrical vehicle (EV) and wind power are good solutions for both ends of demand and supply. It is of great importance to schedule the EV charging load to match the uncertain wind power supply both for economic reasons and for sustainable development. However, many classical methods are hard to apply for solving this problem due to its huge state space, the high uncertainty and the complex dynamic evolution of the system. Proximal policy optimization (PPO) shows us a new way to solve large scale stochastic optimization problems. In this paper, we make the following major contributions. First, using the structure of different spatial scales of this problem, we formulate the system as a bi-level Markov decision process (MDP). EV aggregators (EVAs) are applied to gather EVs according to their parking regions. On the upper level, based on some brief state information, the independent system operator (ISO) only decides how much power to buy from the state grid, and how much power will be allocated to each EVA. On the lower level, each EVA will finally decide every EV’s charging process based on the dispatched power and then the system state starts to shift. Second, we develop a bi-level proximal policy optimization (BPPO) algorithm to solve this bilevel MDP where the upper network and lower level network are interrelated. Third, the numerical results show that our algorithm outperforms three other algorithms on reducing the total charging cost.
Robust Control for Over-Actuated Vehicles*

Many car manufacturers are equipping their vehicles with advanced systems to win the race towards autonomous vehicles. In order to ensure multiple objectives, different embedded systems influencing differently the same physical variable are often implemented in the same vehicle. Most of car manufacturers tend to activate one system at a time, especially when the low-level control of actuators is developed by different equipment suppliers, making the inner dynamics uncertain. However, this limits the potential of the vehicle. This paper discusses the feasibility of coordinating these systems when activated at the same time, while ensuring an acceptable robustness regarding the uncertain dynamics. Results showed good performance in severe maneuvers when combining robust control synthesis and optimization-based control allocation algorithms. Therefore, several embedded systems may be activated at the same time to expand the overall potential of the vehicle and deal with more difficult situations.
Recent Advances in Vision-Based Lane Detection Solutions for Automotive Applications

In recent years, autonomous vehicles have become increasingly popular. Different Advanced Driver Assistance Systems (ADAS) are implemented in today’s vehicles and one of them is the system for lane detection and tracking (LDaT). It can be used either in assistance to the driver, or in the process of autonomous vehicle driving. In this paper, we analyze and explore the advancements in the field of LDaT throughout the recent years and provide a brief review of existing solutions. We categorize the existing solutions, compare them, look for limitations and finally bring a conclusion on where the advancements might be heading in the future.
The Use of Deep Learning to Support Math Teaching and Learning for the Blind

In 2010, the World Health Organization (WHO) estimated that 19 million children under the age of 15 were visually impaired, with about 39 million lind and 246 million people with severe or moderate vision loss. This work presents a step towards bridging a technological gap with the design and validation of a system of artificial neural networks (DNNs), capable of identifying the main Cartesian curves of the mathematics curriculum. The Cartesian set explored in this work comprises 6 degrees of rational lgebraic curves, in addition to a total of 42 conic curves. The development of this gave some methodological steps. First, relevant artificial neural networks were investigated considering the needs of the user and the pace of the problem - the temporal, computer efficiency and precision of the generated models used as selection metrics for each neural network ested. The selected network models were InceptionV3, MobileNETV2, VGG16, and VGG19. With average accuracy of 94% and 20% of mean loss, the selected network for the application was VGG16.
Image Regularization Using Total Variation and Morphological Gradient Priors with Optimization of Structuring Element

As an image prior for image restoration, the sum of morphological gradients for an image has previously been proposed. Optimization of the structuring element (SE) used for this morphological gradient using a genetic algorithm (GA) has also been proposed. This method uses the minimized value of the objective function of the restoration problem as the fitness of the GA. However, this value does not necessarily coincide with an objective evaluation such as the mean square error. Therefore, in this paper, we formulate the objective function using the morphological gradient and total variation as a new image prior for an image restoration problem. The proposed objective function makes it possible to almost match the fitness to the objective evaluation and can improve the restoration accuracy. It also solves the problem of the artifact due to the unsuitability of the SE for the image. An experiment shows the effectiveness of the proposed image restoration method.
Research on Fractional Slot Permanent Magnet Synchronous Motors with Asymmetric Winding

With the development of research, asymmetric winding motors (AWM) have been found taking advantage of lower cogging torque and higher torque density in some cases. In this paper, excellent characteristics of fractional slot motor with asymmetric concentrated winding are revealed by combining theoretical derivation and simulation computation. In addition, some correlation coefficients which represent the adverse effects of AWM are introduced, and provide references for the comprehensive design of AWM. Moreover, the 36-pole three-phase permanent magnet synchronous motor(PMSM) is taken as an example to compare the design of AWM and conventional motor. The feasibility of AWM scheme and its advantages in cogging torque and torque density are verified by the finite element modeling.
A test case for the use of DNA barcoding using Berosus (Order: Coleoptera)

Most identification of aquatic invertebrate specimens is currently achieved using morphological characters. However, often the diagnostic features required for species level identification only occur in a particular gender or life-history stage, and so achieving the lowest taxonomic resolution is not always possible. DNA barcoding, a technique used to describe identification of an organism by sequencing a section of DNA (~648 base pairs) for the Cytochrome Oxidase I mitochondrial gene (COI), provides a valuable tool to assist with these problem areas in species-level identification. During a recent survey of wetlands in the Goldfields, aquatic beetles in the genus Berosus (Family Hydrophilidae) were collected from 11 of the 14 main survey locations and opportunistically hand-collected from four additional locations. However, at two-thirds of these sites (10 of the 15 locations) only larvae were collected and therefore no species level identifications were possible. The aim of undertaking molecular work on this group of aquatic beetles was to investigate whether DNA could readily be extracted from collected specimens, and if so, could DNA barcoding assist with species level identification of unknown Berosus larvae.
CBFD: A Count-Based Fault Detection Scheme for Memory Arrays

The performance-cost benefits enjoyed for decades due to the scaling of device area are challenged by power and reliability constraints. Fixed power envelopes and increases in static and dynamic variations have lead to higher probability of parametric and wear-out failures. This is particularly true for processor memory arrays, such as caches, that dominate the area of modern processors and are built with minimum sized SRAM cells that are prone to failure. It is, therefore, becoming essential to develop scalable cost-effective fault-tolerant techniques for processor memory arrays. Our attempt, presented in this paper, to address scalable memory array fault tolerance is a novel error detection scheme that relies on a count-based method, called CBFD (count-basedfault-detection), that provides the number of 1s(0s) in an array at any given time. The state overhead of the method is (log2k) + 1 bits for an array with k bits. For fault-free array operation a basic invariance is maintained by the method: the number of 1s(0s) never exceeds the array size or becomes negative. This invariance can be violated when there is a fault. The paper introduces the count-based method and explain its detection latency, overheads, and fault coverage. It also discusses how it can be used to detect soft and hard errors both in array cells and peripheral logic.
Attacks in Program Data Dimension

Computer systems are playing essential roles in all aspects of our daily life. Protecting these systems against aacks has been one of the most indispensable tasks. However, due to the ever-increasing demand on performance and functionality, operating systems and popular applications are still built on top of unsafe languages, like C/C++. Consequently, aacks stemmed from memory safety issues have led to billions of dollars lost and will continuously plague end-users. My research aims to secure computer systems by discovering severe aack vectors and developing comprehensive defenses. My research philosophy is to break up complicated systems into smaller, manageable components; with a systematic understanding of the characteristic of each component, I can identify hidden weaknesses and develop cross-boundary solutions. In my previous work, I divide the program memory into two general dimensions: the control dimension that determines the control-ow of the execution, and the data dimension that hosts the user-inputs and saves calculation results. With a systematic analysis of each dimension, my work demonstrated that: data-oriented aacks are generic, severe and expressive for aackers to bypass all existing practical defenses [6, 7, 9]; with the information from the data dimension, we can construct comprehensive protections against predominant aacks in the control dimension [2, 5]; user-inputs provide practical guidance to identify necessary functionalities and help customize the code to reduce the aack surface [3, 10]. My research has received the Best Paper Awards from CCS 2019 and ICECCS 2014, and has led to many real-world impacts. TypeDive [2] detects 35 new bugs in the Linux kernel that either leak sensitive information or allow excessive permissions. Data-oriented aacks on Chrome [6] got the acknowledgment from Google and they have implemented site isolation to eliminate the reported threat. I have submied two proposals as Co-PI. e one on detecting weird machines has been awarded $805,070 dollars by DARPA from January 2020 to June 2021. I led the proposal preparation and my research work [7, 9] on data-oriented aacks become the foundation of this grant. Another proposal on automatically selecting fuzzing parameters is under review. Now I am leading the development of two ONR fundings about program debloating, where Razor [3] is the rst outcome of these two projects. Other than soware security, my research also spans the following areas of computer science: compilers [2, 4, 5, 7]; binary analysis [8, 9]; systems [1, 6, 12]; trusted computing [10, 11].
ZAM ZAM WATER A NEW MODALITY FOR FAILED REPEATED INTRA CYTOPLASMIC SPERM INJECTION

Objective: is to test the value of ZAM ZAM water in the treatment of failed repeated intracytoplasmic sperm injection. Material and Method: 10 patients complaining of failed repeated intracytoplasmic sperm injection were enrolled in the study. Intrauterine injection of sterile 15 ml of ZAM ZAM water by embryonal catheter at the time of ovum pickup and 15 ml of sterile ZAM ZAM water at the time of embryo transfer. Results: Live birth rate 70%, mean birth weight 2.99 + 100.11, method of delivery by cesarean section. No fetal and maternal complication. Conclusion: ZAM ZAM water is a new modality for repeated failed intracytoplasmic sperm injection.
Comparison between CEA, CA 19-9 and CA 72-4 in Patients with Colon Cancer

Background: In Egypt, colorectal cancer (CRC) is one of the most common malignancies and represents 6.5% of cancers. The most important currently available tumor markers in CRC that provide diagnostic information to reduce mortality and morbidity are carcinoembryonic-antigen (CEA), carbohydrate antigen 19-9 (CA19-9) and CA 72-4. The main objectives of the present work were to compare the serological tumor markers CEA, CA19-9 and CA 72-4 and to verify the effectiveness of each marker as a potential routine diagnostic test in CRC. Subjects and methods: Blood samples were collected from twenty five CRC patients then compared with thirty five normal blood samples from apparently healthy persons who were considered as controls. Serum of each sample was evaluated for the three tumor markers and assessed by ELISA technique Results: CA72-4 and CEA showed high statistically significant difference between CRC and controls, on the contrary CA19-9 showed a statistically insignificant between the studied groups. As for the results of the ROC curve, the sensitivity for using CA 72-4 parameter was 82.86%, the specificity was 100% and the highest AUC for CA72-4 denoting its performance as the preferred diagnostic routinetest among other markers mentioned above. Conclusions: Based on our findings, the results of this study indicated that the serum CEA is not a much more sensitive tumor marker than CA72-4. As well as the serum CA72-4 levels can be used in diagnosis of colon cancer and in need to be applied as a routine tumor marker. Keywords Colorectal cancer (CRC), Carcinoembryonic antigen (CEA),Cancer antigen 19-9 (CA 19-9), Cancer antigen 72-4 (CA72-4), Area undercurve (AUC)
4-Chloro-N-[(E)-(3,4-dimethoxyphenyl)methylidene]aniline

The asymmetric unit of the title compound, C15H14ClNO2, contains two molecules with significantly different conformations: the dihedral angles between the 4-chloroaniline and 3,4-dimethoxyphenyl (excluding C atoms) moieties are 19.68 (7) and 45.54 (4)°. In the crystal, the molecules are linked by C—H⋯O hydrogen bonds and weak C—H⋯π interactions.
(Z)-3-(9-Anthryl)-1-(2-thienyl)prop-2-en-1-one1

There are two crystallographically independent molecules in the asymmetric unit of the title heteroaryl chalcone, C21H14OS: the dihedral angle between the thiophene and anthracene rings is 75.07 (17)° in one molecule and 76.32 (17)° in the other. The crystal structure is consolidated by short C⋯O [3.348 (5)–3.394 (5) Å], C⋯S [3.607 (5)–3.666 (5) Å] and S⋯O [2.926 (3) Å] contacts, as well as by C—H⋯π and π–π interactions [Cg⋯Cg = 3.745 (3) Å].