Miracle-dz/UniFAR-FTUs huggingface dataset:
Parametric study of the control system in the TCP network

Self-oscillating modes in control systems of computer networks quite negatively affect the characteristics of these networks so the investigation of parameters of self-oscillations as well as self-oscillations areas is actual. But due to the non-linear nature of usually constructed mathematical models the study of self-oscillations areas and parameters are extremely labor-intensive. It is of interest to obtain a so-called parametric portrait describing the zones of occurrence of self-oscillations depending on the value of the parameters: one parameter (two-dimensional graph), two parameters (three-dimensional graph), and so on. Such a parametric portrait allows us to purposefully manage the characteristics of the investigated control system. The paper describes a parametric study technique based on the method of harmonic linearization because in the standard mathematical model based on ordinary linearization by Taylor expansion a self-oscillation regime disappears (due to Taylor expansion linearization). To verify the theoretical results obtained, simulation is used. In addition, it is proposed to use the computer algebra system for analytical calculations. For this, the criteria for choosing software were formulated. Based on these criteria, a set of software for analytical and numerical calculations was proposed.
Local Field Potential Functional Network Analysis of the Left and Right Hippocampus of Pigeons in Goal-Directed Task

Pigeons have excellent spatial cognition and navigation ability. The hippocampus $(\text{Hp})$ is the key brain area for navigation behavior. However, the difference of the neural cluster electrical activity in the goal-directed task in the left and right hippocampus of the pigeon brain is still unclear. In this paper, the function network of the left hemisphere and the right hemisphere local field potential (LFP)signals is constructed using Pearson correlation method, the LFP functional networks and their topological characteristics of different Hp are analyzed in different task areas. The results show that the connection density of LFP functional networks in different areas is different, the clustering coefficient (CC)and global efficiency (E)of the pigeons in the straight area(SA)are higher than the waiting area(WA)and the turning area(TA)$(\mathrm{P} < 0.05)$, at the same time, the topological characteristics of the LFP functional network in the left Hp were significantly higher than that in the right Hp $(\mathrm{P} < 0.05)$. Further research also found it is not affected by the goal location $(\mathrm{P} < 0.01)$. This study further analyzes the role of the hippocampus functional network in pigeon navigation behavior, and makes a useful attempt to analyze the brain neural mechanisms of bird spatial cognition and navigation.
Analysis of Fe(Se,Te) Films Deposited On Unbuffered Invar 36

In this paper, the feasibility of producing Fe(Se,Te)-coated conductors on cost-effective, unbuffered Invar 36 substrates is studied. A suitable substrate with a sharp {001}<100> texture and a misorientation angle lower than 10° was developed, and the possibility of growing epitaxial thin films without any buffer layer was demonstrated. It was also shown that the presence of a Fe(Se,Te) seed layer can improve the in- and out-of-plane orientation of the film. A high-resolution scanning electron microscopy study was performed, and the absence of a superconducting transition has been attributed to Ni poisoning based on X-ray microanalysis.
Balanced-to-Balanced and Balanced-to-Unbalanced Power Dividers With Ultra-Wideband Common-Mode Rejection and Absorption Based on Mode-Conversion Approach

In this paper, for the first time, a balanced-to-balanced (B–B) power divider (PD) and a balanced-to-unbalanced (B–UB) PD with ultra-wideband common-mode (CM) rejection and absorption characteristics are introduced based on a new design approach. The design of B–B and B–UB PD structures is using one core circuit to provide power dividing/combining and differential-mode-to-common-mode (DM–CM) converters to realize mode conversion at the balanced ports. With the use of the DM–CM converter, the boundary condition on the symmetric plane of the core circuit is then reversed. Specifically, a perfect-electric wall will be formed along the symmetric plane of the core circuit under the CM operation, with a perfect-magnetic wall under the DM operation. Based on this characteristic, the proposed B–B and B–UB PDs can then achieve wideband CM suppression and absorption with some proper designs. The measured results show that the transmission coefficients of CM operation for the proposed B–B and B–UB PDs are all less than −15 dB with a fractional bandwidth about 200%. In addition, the measured CM absorption rate is higher than 85% with a fractional bandwidth about 71.2% and 116% for the proposed B–B and B–UB PDs, respectively.
Image Semantic Analysis and Application Based on Knowledge Graph

The semantic analysis technology of image has always been a difficult point in image field. As an intelligent and efficient way of organizing, knowledge graph can help users accurately query the information. This paper firstly puts forward an image semantic analysis process based on knowledge graph, then adopts deep learning model to describe image's features. Image semantic knowledge fusion and processing are studied on this basis. A multilevel image semantic model is constructed to manage entity triples and support automatic construction. Finally, applications for semantic retrieval and visualization are analyzed which has some guiding significance for information organization and knowledge management of media semantic.
What Can Be Learned from Bigrams Analysis of Messages in Social Network?

In this paper we investigate if the bigram graph-based analysis can be applied to identify the most common subjects of discussion in English - spoken social media. We will present the construction of directed graph of bigrams, data filtration model and we will perform validation on three real (not artificially created)datasets, each containing 1 000 000 microblogging posts from Twitter platform. We will compare our findings with our earlier researches that used hashtag - based analysis and simple NLP method namely lemmatization. Those methods were evaluated on the same dataset as we use in this paper. With bigrams network it is possible to obtain some limited information about subjects the users are talking about however less than when the analysis uses hashtags. Bigrams also work better than simple lemmatization that in our previous experiment did not produce any useful data. The approach that supplies us with most useful data describing content of microblogging posts seems to be hashtag filtration. However this last type of analysis requires the presence of additional annotation data in user messages. Terms generated by bigrams and hashtag analysis might not be identical and sometimes one of them might be valuable complement of the other. Bigrams network might show the general idea of the content of the information gathered in microblogging posts. It is recommended to visualize a filtered part of this graph (for example limited by weights of edges)to see the general idea of data consisted in this network.
Acoustic Characteristics of Emotional Speech Using Spectrogram Image Classification

One of the problems limiting the accuracy of speech emotion recognition (SER) is difficulty in the differentiation between acoustically-similar emotions. Since it is not clear how emotions differ in acoustic terms, it is difficult to design new, more efficient SER strategies. In this study, amplitude-frequency analysis of emotional speech was performed to determine relative differences between seven emotional categories of speech in the Berlin Emotional Speech (EMO-DB) database. The analysis transformed short J-second blocks of speech into RGB images of spectrograms using four different frequency scales. The images were used to train a convolutional neural network (CNN) to recognize emotions. By training the network with different combinations of frequency scales and color components of the RGB images that emphasized different frequency and spectral amplitude values, links between different emotions and corresponding amplitude-frequency characteristics of speech were determined.
An Adaptive Nonunitary Joint Diagonalization Algorithm of High-Order Tensors

Joint diagonalization (JD)of high-order tensors is a generalization of an approximate JD algorithm of a series of target matrices. Considering the number of target tensors may increase with time, we present an adaptive nonunitary JD algorithm of high-order tensors. The algorithm recursively minimizes a least squares criterion and updates diagonalizer matrices by the online algorithm and tensor calculations. The complexity of our algorithm is lower than the iterative algorithms. Simulation results demonstrate that the proposed algorithm is efficient for JD of high-order tensors.
Pulsewidth Modulation Control Algorithm for a Six-Phase PMSM: Reducing the Current in the Inverter Capacitor and Current Sensing With Resistors

Motors and inverters are increasingly being used in vehicles. As more vehicle components are being adopted, requirement for safety and installation space are increasing. Growing attention is being paid to the use of six-phase permanent magnet synchronous motors (PMSMs) and their parallel control by two inverters, because of their reduced size and enhanced safety. This paper proposes a pulsewidth modulation (PWM) control algorithm for a six-phase PMSM. In low-voltage vehicle components such as electric power steering, the current is usually acquired with resistors placed under the low-side switching devices. In the proposed control algorithm, the neutral voltage of the two inverters is shifted to either a lower or higher voltage within a certain range to allow for the signal conditioning time delay after the switching device turns on or off, and the time taken for the phase current to rise or settle. The paper proposes a novel PWM control algorithm that ensures that the on-time of the low-side switching devices satisfies the restriction on current sensing with resistors and reduces the current in the capacitor at the same time. The work contributes to reducing the current in a practical configuration involving low-voltage vehicle components.
Adaptive Threshold for Outlier Detection on Data Streams

As the distribution of a data stream evolves over time, a learner must adapt to its distributional shifts in order to make accurate predictions. In the context of anomaly detection, it is crucial for the learner to distinguish between natural changes in distribution and true anomalies in the data stream. This is the problem we focus on in this study which considers the situation where only normal data are available for initial training, but subsequent data can be either normal or anomalous. In that context, it is necessary to train a one-class learning anomaly detection system on the normal data and let the system output a score representing the degree of normalcy or outlierness that each data point in the subsequent data stream exhibits. The system then uses a threshold to discriminate between normal and anomalous instances. In the case of data stream, the data distribution may shift overtime, and a fixed threshold could develop a high false alarm rate or a low outlier detection rate in case of concept drift. To this end, we designed an adaptive sliding window approach which updates the threshold when necessary based on the scores distribution. Experimental results show that our method improves the performance of base anomaly detectors by dynamically updating the threshold of the scores when needed rather than using a fixed threshold or an adaptive threshold with fixed window sizes.
Effect of Steering Vector Estimation on MVDR Beamformer for Noisy Speech Recognition

The minimum variance distortionless response (MV-DR) beamformer is a widely used beamforming technique that extracts sound components coming from a direction specified by a steering vector. In this paper, we present four different steering vector estimation methods and analyze their influence on the MVDR beamformer in speech recognition. The first one is based on the direction of arrival under the plane wave propagation assumption with the prior knowledge of microphone array geometry. The other three methods are based on the decomposition of the observed speech covariance matrix, including the covariance subtraction based method, the eigenvalue decomposition based method, and the generalized eigenvalue decomposition (GEVD) based method. We theoretically prove that the three decomposition based methods are equivalent under the narrowband approximation or after the rank -1 speech covariance matrix approximation. The speech recognition experiments conducted on the CHiME-3 dataset shows that the MVDR beamformer using GEVD-based steering vector estimation achieves the best performance, and word error rates can be further reduced with the rank -1 approximation.
Facial Expressions of Sentence Comprehension

Understanding facial expressions allows access to one’s intentional and affective states. Using the findings in psychology and neuroscience, in which physical behaviors of the face are linked to emotional states, this paper aims to study sentence comprehension shown by facial expressions. In our experiments, participants took part in a roughly 30-minute computer mediated task, where they were asked to answer either “true” or “false” to knowledge-based questions, then immediately given feedback of “correct” or “incorrect”. Their faces, which were recorded during the task using the Kinect v2 device, are later used to identify the level of comprehension shown by their expressions. To achieve this, the SVM and Random Forest classifiers with facial appearance information extracted using a spatiotemporal local descriptor, named LPQ-TOP, are employed. Results of online sentence comprehension show that facial dynamics are promising to help understand cognitive states of the mind.
Facial Expression Region Segmentation Based Approach to Emotion Recognition Using 2D Gabor Filter and Multiclass Support Vector Machine

Facial expressions have been studied extensively for the analysis of human sentiment properly. A human emotion recognition system through recognizing human facial expression is proposed in this paper. After preprocessing, segmentation of the facial expression regions is done in a unique yet effective and easy way to segment the left eye, right eye, nose, mouth properly from the facial region. 2D Gabor filter is used for the extraction of features from the expression regions. For reducing the dimension of the extracted features, downsampling and Principal Component Analysis (PCA) is used. For carrying out the classification task multiclass Support Vector Machine (SVM) is used for its ability to handle complex problems in high dimensional spaces. Three publicly available facial expression dataset was used to evaluate the performance of the proposed system. Finally, performance on these datasets by the proposed method is compared to previously attained performance by different methods which indicate that the proposed method attains state-of-the-art performance.
Towards Efficient Closed Infrequent Itemset Mining Using Bi-Directional Traversing

In this work, we investigate the opposite question of frequent itemset mining: what patterns occurred less than a given minimum support in a transactional database? This question, known as infrequent itemset mining, is important in fields such as medical science, security, finance and scientific research. Frequent patterns represent expected or obvious information while infrequent patterns are those unexpected behaviors and are more interesting in some applications. For example, health-care needs to identify sporadic but lethal crossover effects. Security agents have to uncover infrequent associative fraud indicators. Existing infrequent itemset mining approaches are time-consuming. Furthermore, extracting all infrequent patterns might suffer from the redundant problem. In this paper, we study the two factors that affect the performance of itemset mining tasks. The concept of closed itemset is applied for infrequent patterns to reduce the number of returned patterns. An efficient closed infrequent itemset mining approach is proposed which combines both bottom-up and top-down traversing strategies. Extensive experimental results show that a simple algorithm based on our framework, without using advanced data structure or pruning techniques, can still be significantly more efficient when compared with other approaches.
The Early Stage Lung Cancer Prognosis Prediction Model based on Support Vector Machine

According to the annual statistics of the American Cancer Society, lung cancer has become the leading cause of death for cancer patients. It is therefore vital to research lung cancer prognosis prediction model. From the characteristics of cancer data samples, we consider the unbalanced category data. Due to the small number of samples, one of the commonly used over-sampling techniques is selected, which is an improved Synthetic Minority Over-sampling Technique (Borderline-SMOTE) to expand a few types of samples. For labeling the dataset by 5-year survival time, support vector machines (SVM) and Cox-proportional hazard regression model (COX) were used for training and calculating, respectively. The results show that the performance of the proposed prognosis model based on SVM is better. Similarly, 2-year survival time as the standard for labeling the dataset, the experimental results also show that the performance of the proposed model is better, which verifies the validity and reliability of the designed model.
SIMULATION OPTIMIZATION FOR PLANNING PRODUCT TRANSITIONS IN SEMICONDUCTOR MANUFACTURING FACILITIES

The introduction of a new product into a semiconductor manufacturing facility can have significant adverse effects on both current and new products. We combine a simulation model of a simplified production system and a learning model to optimize releases of old and new products using simulation optimization. Our results show that the simulation optimization model yields significant improvements in performance over simple alternatives, and provides insights into the structure of optimal release policies.
Non-Immersive Virtual Cardiac Auscultation Interactions Employing a 3D Printed Stethoscope

Cardiac auscultation is an examination procedure that allows a medical professional to listen to heart murmurs while employing a stethoscope. Current auscultation training is seeing a trend in favour of employing diagnostics equipment, which in comparison to a stethoscope-based examination, provides a comprehensive visualization of the heart. However, despite employing advance auscultation equipment, heart diagnosis skills with the stethoscope are declining. The loss of such skills has caused concern amongst physicians, as the stethoscope remains widely available and affordable worldwide, unlike other specialized tools. Currently, simulation is being employed to improve the diagnosis of heart conditions since mastering cardiac auscultation skills requires extensive training due to the complexity of the heart murmurs. Moreover, high fidelity medical simulators provide realism and accuracy at a high cost, thus providing suitable training but limited practices depending on the number of trainees. In this paper, we present a 3D printed stethoscope user input device to be used in a non-immersive virtual cardiac auscultation examination training mobile application. We also describe the results of a preliminary usability study that involved participants using a 3D printed stethoscope, touch screen interactions, and a simulation manikin to examine a virtual patient.
Hearing loss identification via wavelet entropy and combination of Tabu search and particle swarm optimization

Sensorineural hearing loss is correlated to massive neurological or psychiatric disease. We treated a three-class classification problem: HC, LHL, and RHL, and checked three different orientation images: coronal, axial, and sagittal. Different methods are compared with 10x6-fold cross validation. The results show that our proposed system shows better performance in detecting hearing loss.
Malware-Detection Model Using Learning-Based Discovery of Static Features

The proliferation of malware poses a severe threat to enterprises and individuals. Therefore, there has been a spurt in the studies related to discovering learning-based malware-detection methods. The previous studies are mostly concentrated on the performance of learning model, while our targets are two folds: optimizing the machine-learning model, and exploring the application of finalized learning model to malware activities. We adopt optimizing methods for the machine-learning workflow, which includes noise reduction, initial triage, feature engineering, and ensemble learning. We train two different malware-detection models for packed and non-packed executable datasets through the learning stage. Besides experimental validation of the superior performance of the proposed optimizing methods, we analyzed the results by comparative analyses. The performance was measured using three evaluation metrics: the receiver operating characteristic curve, the classification accuracy, and the classification report. When the variance threshold was used along with the random forest algorithm, we obtained the best performance for non-packed malware detection, whereas when feature importance was used along with the random forest algorithm, we obtained the best performance for packed malware detection. The finalized models were implemented in the detection stage.
An Indoor Positioning Error Correction Method of Pedestrian Multi-Motions Recognized by Hybrid-Orders Fraction Domain Transformation

Aiming at the inherent problems of heading divergence and error accumulation in indoor inertial positioning of pedestrian field, we proposed a new indoor positioning error correction method for pedestrian multi-motions recognition. This method is aimed at pedestrians’ seven common indoor movements. Periodically, divide the motion data of the accelerometer in the sensitive-axis-direction (vertical geodetic direction) of MEMS-IMU worn on the pedestrian’s waist. And acquire the feature vector through feature extraction by the hybrid-orders fraction domain transformation. The effective features with high identification degree under the different optimal order transformations are mixed and matched. Then, these are sent to each sub-classifier to complete the classification process by the dichotomy and finish the processes of subsequent machine learning. The error correction of the heading angle and positioning is carried out combining with the improved HDE algorithm, the innovative floor constraints’ method, and the motion states’ transition correction technique. The final indoor positioning experiment results show that the classification effect of this motion detection method is better than the traditional methods’. The average classification accuracy can reach up to 97%. And the method reduces the computing requirements for hardware. By using the floor constraint method, the vertical height difference can achieve the effect of complete reset of the origin. The trajectory lines of the movement in the same floor are well displayed. At the same time, the best horizontal error positioning result is only 1.72 m during the total travel distance of 412.40 m (TTD ≈ 0.42%).