Miracle-dz/UniFAR-FTUs huggingface dataset:
Experience of WANG Guo-san in treating epilepsy

According to the theory made by WANG Guo-san, it is critical that the phlegm blocked the heart in the acute stage,kidney asthenia is the fundamental reason of epilepsy’s occurrence out of acute stage.Make summary of his medication experience,the methods of clearing the heart,sweeping phlegm and opening the orifices are very important in acute stage,the herbs of common gardenia fruit,thinleaf milkwort root,mongolian snakegourd fruit and ternate pinellia are commonly used.The methods of subduing,settling and quieting the spirit remain consistent from the start to the very end, the herbs of dragon's bones,common oyster shell,arabia cowry shell,magnetite,dragon's teeth and nacre etc.are commonly used.In the non acute stage, the methods of regulating qi and blood are mainly suggested,the commonly used herbs are different leaves pseudostellaria root tuber, chinese angelica, white peony root, dried longan pulp, chinese arborvilae seed and spine date seed.Meanwhile,the methods of warming yang and invigorating the kidney are emphasized usually,suggested herbs including fructus psoraleae,medicinal morinda root,desertliving cistanche herb,short-horned epimedium herb etc. Doctor WANG also believes that most patients have long disease history,the anti-epileptic have side effect.In avoiding of the methods of soothing liver and regulating qi;extinguishing wind and resolving tetany are abused,the safeguard of healthy qi should be payed more attention now and then.
Artificial Neural Network Inducement for Enhancement of Cloud Computing Security

Cloud computing is a spirited topic in this epoch where IOT introducing the modern lifestyle. These embrace cost-effectiveness, time savings and the actual arrangement of computing properties. Conversely, in Cloud Computing privacy and security is a big issue where there are many kinds of thread and attack are working rapidly. In the context, the best cryptographic method AES algorithm and AI-based method ANN have chosen to work for the enhancement of the security where cloud computing demands the highest priority. In this case, this paper is proposing the highly advanced two-step security layer for cloud computing. Primarily, AES provides for the first layer security of the first stratum. In the AES algorithm, the execution is dependent on the key size of the algorithm when the number of rounds to be accomplished. A MATLAB code is developed for plaintext encryption and cipher text decryption. Experiments are conducted to measure execution times. ANN is a method of computation which is biologically stimulated. These only look like the parallel computation generated which are the basics of human learning by the biological neural network. Iris and Finger recognition is implemented using MATLAB through ANN. The paper demonstrated the great proficiency of the proposed system for the user purpose.
Internet of Things based Air Pollution Penetrating System using GSM and GPRS

In the last decade, level of pollution is indomitable in urban area. Due to this, the quality degrades day by day. Due to this researchers are focused on air pollution monitoring unit with the help of wireless sensor network. These sensor networks give the information of pollution level of the centralized server using internet or telecom network. This paper proposed a reliable and low cost air pollution monitoring system for developing countries. Most of the developing nations where they don’t have fourth generation high speed communication network but they require Air pollution monitoring system with the help of proposed monitoring system they could measure the level carbon mono oxide (Co) and other pollution gas level in the form PPM. The proposed system is based on internet of things, global position system (GPS) and general packet radio servers (GPRS). Sensor collection data in the analog form and send microcontroller unit that is converted into an analog information in digital form and send this digital data to cloud server using GPRS system and store the data on cloud servers and then process this data. After the processing of this collected sensor data represented on http link that is based IP address and also create an APK file App presentation of this data. The proposed pollution measurement system shows low cost and better reliability as compared to other measurement devices that is shown in simulation and result.
Intelligent Path-Finder for The Blind

Eyesight is one of the most useful living organism, but visually impaired people do not realize that sense. They are unable to see the beauty of nature. Not all the problems of the visually impaired people can be solved but with the help of modern technology life can be made easier for them. This project, "Intelligent Path-Finder" tends to make life easier and less problematic. Visually impaired people use general stick for their movement but the sticks are not capable of detecting the obstacles or other objects in front of them automatically; as a result, they need to poke the obstacles to get an understanding of it. To overcome these problems, the "Intelligent Path-Finder" is a remarkable device which can detect various types of obstacles from knee level to ground level, for example liquid, uneven surfaces, etc. In the dark, a special feature is used to prevent accidents with the help of a glowing led light which is triggered by sensing absence of light in the environment, this light can protect the user from moving vehicles and other difficulties by drawing normal people’s attention towards it. If the user is in trouble, he can send his location by pressing an emergency switch. His family can also know the device’s location via SMS. This device consists of a microcontroller, ultrasonic sensor, light dependent resistor, etc. The combination of all these equipment together makes this device relatively accurate, user-friendly, robust and cheap. The device is not a third eye for the blind but it is significantly helpful to them by making their daily lives much easier.
Gnss Passive Radar Imaging Based on Differential Aperture

In this paper, a passive imaging scheme with differential aperture based on multiple GNSS satellites and multiple ground receivers is studied. Firstly, the passive imaging model with differential aperture is established. Then, the factors affecting spatial imaging resolution are analysed. Finally, an inversion method based on point-by-point matched filtering technology is presented. It is seen that the diversity of aperture can be used to compensate for the shortage of narrow bandwidth of GNSS signal, so as to improve the overall spatial resolution of passive radar. Simulation results verify effectiveness of related analysis.
In-motion Adjustment of Beacon-based Observing System with Respect to Trajectory Measurements

The paper discusses the formulation and approaches to the solution of the two-coordinate task of the adjustment (local coordinate binding) of a multi-position system intended for monitoring mobile objects for various special purposes (underwater, surface, air, etc The problem of refining the configuration and spatial orientation for a multiposition observing system during a correction of the solution of a navigation problem by the dead reckoning method is considered. The question of solvability of the problem under finite accuracy of machine computations and measurements is discussed. The results of a numerical experiment with the use of simulation models are presented. These results adequately illustrate the possibility of an efficient solution of the problem.
Software for Predictive Maintenance and Repair of the Enterprise Office Equipment

The article describes the process of maintenance and repair of office equipment at the enterprise, which is carried out by service personnel. The article views the process of developing the software of predictive maintenance and repair of office equipment at the enterprise. Also, it is reviewed about the existing methods for monitoring networked office equipment that can be used by engineers at their workplace, and a review of existing analogs in the market. There are descriptions of the given selected methods with the help of which the solution of the main task will be achieved, the rationale for the selection of specific methods is argued. Algorithms were developed for finding networked office equipment, monitoring and forming maintenance cycles, including algorithms for recording office equipment and cartridges coming in for repair. The main diagrams are constructed showing the relationship between classes and the movement of data within the software. The general conclusions about the effectiveness of the developed algorithms and the methods chosen are given.
Knowledge Graph Embedding Based On Multi-information Fusion

The knowledge graph embedding aims to represent the elements in the knowledge graph as low-dimensional dense distributed representation vectors in the continuous semantic space, which has achieved good results in the knowledge graph completion. However, the existing embedding methods do not make full use of and extract the existing information in the knowledge graph, which will directly affect the quality of knowledge graph embedding. In order to make the embedded model can fuse more reasonable c0onstraints and rich semantic information, in this paper, the entities in the traditional triples are divided into concepts and entities, and through the conceptual constraint information generated by the entity, the structural information of the triple and the entity description text information encoded by the deep learning, the hidden relationship between the existing entities in the knowledge graph is mined, and a more accurate semantic space representation is obtained. Experiments show that the embedded model proposed in this paper has better performance.
Segmentation-based Knowledge Extraction from Chest X-ray Images

Computer-aided detection applications have been extensively used to assist physicians in clinical diagnoses. Extracted information from X-ray, positron emission tomography, and magnetic resonance images enables radiologists and other physicians to identify pathologies, correlate findings with the symptoms, and determine the treatment steps. In this study, we proposed an automatic knowledge extraction methodology from chest X-ray images. The extracted knowledge is obtained from the segmented sections of the images that include pathological findings. We evaluated these segmented images with a) classical machine learning and b) pretrained convolutional neural network (CNN) models. Evaluations were based on areas under the receiver operating characteristic (AUROC) with segmented images using the pretrained CNN and the traditional method models, and they produced the average AUROC scores of 0.96 and 0.52, respectively. Traditional methods yielded lower AUROC scores compared with pretrained CNN methods. However, traditional methods may still be considered as appropriate solutions for disease diagnoses primarily based on their advantages regarding running time and flexibility.
Implementation and Comparison of Different Tropospheric Models to Reduce Error Low-cost Real-time GPS Positioning

The GPS system is one of the widely used methods to determine the real-time position in navigation applications. Using this system requires error compensation which effectively improves the positioning accuracy. One of the errors that needs to be compensated is the tropospheric signal transmission delay from the satellite to the GPS receiver. Tropospheric delay appears as bias in measuring pseudo-range by the GPS receiver and results in a constant deviation in determining final position of the user receiver. Several tropospheric models have been developed and employed to compensate and correct tropospheric delay. In this paper, some of the conventional tropospheric delay models and a combination of several models have been investigated and employed to determine the real-time position. These models are applied to the real-time positioning algorithm of the single frequency GPS receiver and have been compared to each other. This comparison is based on a reduction in the error in estimating the final position of the receiver, and a decrease in the computation time of the real-time positioning. Real-time positioning tests are performed in 6 iterations throughout six days. Positioning results are compared with the triple frequency GPS receiver GINTEC -G10. Comparison results show that real-time positioning of single frequency GPS receiver has sufficient and better accuracy using "Modified Saastamoinen" model and "Modified Hopfield" model have enough accuracy for low cost real-time positioning in most iterations of the experiment, and "Modified Hopfield" model is more suitable for low-cost real-time positioning, because its computation time is shorter.
Unstructured–PEEC Method for Thin Electromagnetic Media

In this article, an unstructured–partial element equivalent circuit (PEEC) method for modeling electromagnetic thin regions is proposed. Two coupled circuits’ representations are used for solving both electric and/or magnetic effects in thin regions discretized by a finite-element surface mesh. Dynamic effects across the thickness of the sheet are modeled by equivalent complex conductivity and reluctivity. Nonsimply connected regions are treated with fundamental branch independent loop matrices coming from the circuit representation. The formulation enables the computation of eddy current losses and can be coupled with external circuits, PEEC cables or coils, thanks to the circuit representation.
Sentiment Analysis using Novel Distributed Word Embedding for Movie Reviews

Sentiment analysis is the recent trend explored to provide the insight of public and consumer’s emotions, opinion and attitude to product developers, governments, marketing organizations, political organizations etc. Feature engineering is an essential requirement for Machine Learning (ML) based sentiment analysis. Word embedding is set of techniques to map the row text word as a real-valued vector which can be utilized as feature vector in sentiment analysis. Word2vec is a powerful and efficient word embedding technique to preserve the semantic relationship of words in low-dimensional embedding space. It can effectively handle small text corpus with few millions of unique words (also called as vocabulary). Whilst, real-life applications like social media, search engine, etc. yield voluminous text data consisting of large vocabulary. Word2vec has demonstrated poor performance due to in-memory processing of entire vocabulary and associated word embedding. It also demands the huge computation capability due to numerous mathematical calculations. Scalability and processing complexity make the commodity hardware incapable for word2vec of voluminous text data. This paper presents novel and scalable technique namely MapReduce framework for Word2Vec (MRW2V), amalgamates the computational resources from cluster of commodity machines to accomplish the above essentials. MRW2V explores the Hadoop distributed environment to generate the feature vectors from IMDB movie review dataset. Various ML algorithms are applied over the generated feature vectors to learn the sentiment models. It is observed from experiments that MRW2V is well suited word embedding framework for voluminous data which also sustaining the admissible performance.
A Novel Multi-Sensor Image Matching Algorithm Based on Adaptive Multiscale Structure Orientation

Automatic and reliable multi-sensor image matching is a very challenging task due to the significant nonlinear radiometric differences between multi-sensor images. In this paper, a novel dense descriptor based on adaptive multiscale structure orientation is proposed for capturing the geometrical structure information of an image. The dense descriptor of the proposed matching algorithm is not only illumination and contrast invariant but also robust against the image noise. Further, an improved similarity measurement is introduced for adapting the orientation reversal caused by the intensity inversion between multi-sensor images. Based on the robust dense descriptor and the improved similarity measurement, we developed a novel and practical template matching algorithm to match multi-sensor images reliably. We evaluate the proposed matching algorithm by comparing it with other state-of-the-art algorithms. The experimental results show the proposed algorithm has a significant advantage on matching accuracy.
Stochastic Models and Calculations of Distributed Computer Systems Reserve Size

Robustness scalable computer systems (CS) are considered. CS functioning is described by stochastic methods Using elements of Queuing theory, mathematical models of CS with a reserve for a finite number of states are constructed. Analytical solutions for the probabilities of the states and the size of the CS reserve are found to maintain CS high performance. A system of differential equations to derive the analytical dependence of the efficiency indices characterizing the reliability of CS is obtained.
A Concentric Double Hole Shape Microstrip Patch Antenna at 3.12 GHz Resonant Frequency

In this paper, a co-centric double hole shape patch antenna is designed and simulated in CST software. A textile material named as “Jeans” is used as a substrate in which presented antenna is simulated. The resonant frequency of co-centric double hole shape antenna is 3.12 GHz having long bandwidth from 2.44 GHz to 12.34 GHz. The design is simulated on Partial Ground having rectangular in shape. The size of substrate is 61 x 31 mm having thickness of 1 mm and dielectric constant is 1.7. This type of textile antenna can be wearable and can be used for single band applications.
An Optimized Positive-Unlabeled Learning Method for Detecting a Large Scale of Malware Variants

Malicious softwares (Malware) are able to quickly evolve into many different variants and evade existing detection mechanisms, rendering the ineffectiveness of traditional signature-based malware detection systems. Many researchers have proposed advanced malware detection techniques by using Machine Learning. Although the machine learning based techniques perform well in detecting a wide range of malware variants, there still remain some problems when meeting the real scene in the industry. Since the volume of new malware variants grows fast and labelling data is expensive and takes a lot of labor, companies cannot label every one of those samples. They tend to label a small part of the malware samples and treat the rest of the unlabeled samples as benign samples in which the original malware samples are treated as mislabeled. This causes a bias of decision boundary which severely limits the accuracy. To address such a problem, in this paper, we propose a cost-sensitive boosting method to train an unbiased detection model with the malicious-unlabeled executables to improve the accuracy. Along with that, in order to detect malware variants efficiently, we propose a byte co-occurrence matrix as a representation of byte streams of executables to detect malware variants directly. Experimental results show that the machine learning methods optimized by our approach can achieve 80% to 90% accuracy while the original machine learning methods can only achieve 50% to 85% accuracy when the unlabeled data contain different rates of mislabeled positive data.
Anomalous Communications Detection in IoT Networks Using Sparse Autoencoders

Nowadays, IoT devices have been widely deployed for enabling various smart services, such as, smart home or e-healthcare. However, security remains as one of the paramount concern as many IoT devices are vulnerable. Moreover, IoT malware are constantly evolving and getting more sophisticated. IoT devices are intended to perform very specific tasks, so their networking behavior is expected to be reasonably stable and predictable. Any significant behavioral deviation from the normal patterns would indicate anomalous events. In this paper, we present a method to detect anomalous network communications in IoT networks using a set of sparse autoencoders. The proposed approach allows us to differentiate malicious communications from legitimate ones. So that, if a device is compromised only malicious communications can be dropped while the service provided by the device is not totally interrupted. To characterize network behavior, bidirectional TCP flows are extracted and described using statistics on the size of the first N packets sent and received, along with statistics on the corresponding inter-arrival times between packets. A set of sparse autoencoders is then trained to learn the profile of the legitimate communications generated by an experimental smart home network. Depending on the value of $N$, the developed model achieves attack detection rates ranging from 86.9% to 91.2%, and false positive rates ranging from 0.1% to 0.5%.
Electrical degradation behavior in metal oxide thin film transistor under negative bias-illumination stress

The electrical degradation behavior in metal oxide thin film transistor (TFT) under negative bias-illumination stress (NBIS) is one of the biggest obstacles for its applications in flat panel display. In order to solve this issue, Sr and N are doped in In2O3 thin film and significantly improve the NBIS stability of In2O3 TFT. In addition, the doping of N can also reduce the degradation in electrical performance caused by Sr doping. The XPS of SrInON thin film is tested to prove the reduction of the oxygen vacancy.
A Comparative Study of Walking Assistance Tools Developed for the Visually Impaired People

The number of visually impaired people is growing with the fast increase of population. The visually impaired people face much difficulty in their day-to-day lives due to losing their vision. This paper analyzed the performances of the raspberry pi and Arduino for the obstacle detected to assist the visually impaired people. We have used raspberry pi, Arduino and ultrasonic sensor as hardware components in this paper. The position of the obstacles is varied from the sensor and the data is measured corresponding to actual distance. The average accuracy and error rate are obtained 98.47% and 1.53% from the raspberry pi. In addition, Arduino achieved the average accuracy and error rate of 95.22% and 4.78%. Raspberry pi obtained more accuracy and lower error rate than Arduino.
Hybrid-core microstructure fiber for chemical identification in terahertz regime

In this paper, a new kind of hybrid-core porous hexagonal microstructure fiber is presented for different chemical identification by using terahertz signal. Comsol V5.3a software is employed to collect the propagating characteristics of the new kind of sensor. By filling the core air holes with aqueous analytes (Benzene and Ethanol) and tuning various designing parameters, maximum relative sensitivity of 79.2% and 78.9% is achieved at 1.5 THz for benzene and ethanol respectively. Besides the high relative sensitivity, this reported chemical sensor provides very low confinement loss and bending loss of 7.4×10-11 cm-1 and 4.9×10-36 cm-1 at optimal condition for benzene respectively. The offered photonic crystal fiber based sensor is very much feasible and can be produced in the laboratory without any complexity. The excellent numerical results may crate the proposed sensor popular in different industrial applications.