Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech Segregation in Background Noise Based on Deep Learning

The most important way several people communicate is through speech. Speech is used to convey other information such as speaker communication, emotion, and attitude. Therefore, it is the most convenient and natural means of communication. The concept of speech segregation or processing involves sorting out wanted speech from noises in the background. Recently, a supervised learning approach was formulated for speech segregation problems. The latest trend in speech processing comprises the utilization of deep learning systems to increase the computational speed and performance of speech processing tasks. Hence, this study employed the use of a convolutional neural network to segregate speech in background noise. The convolutional neural network was used to explain the features of presenter auditory and consecutive subtleties. An unadapted speaker model was originally utilized to separate the two vocalizations gestures; they were then applied to the assessed signal-to-noise ratio (SNR) participation. The participation of SNR was thereafter applied to modify the speaker prototypes for re-estimating the speech signals that iterated twice before convergence. The developed method was tested on the TIMIT dataset. The results showed the strength of the developed method for speech segregation in background noise. Also, the findings of the study suggested that the method enhanced isolation performance and congregated reasonably fast. It was deduced that the system is simple and performs better in comparison to ultramodern speech processing methods in some input SNR conditions.
Learning Immersion Assessment Model Based on Multi-dimensional Physiological Characteristics

The wisdom classroom full of immersion experience could create an excellent sense of learning experience for learners to improve their learning effect, and the level of learning immersion has become one of the indicators to assess students' learning state. However, the traditional way to evaluate learning immersion is greatly influenced by the subjective factors of individuals. This study proposes a novel method to assess learning immersion based on physiological characteristics: by constructing two learning scenes with different immersion senses: VR video watching (for high immersion) and normal document reading (for low immersion) to induce immersion. During the learning process, the subjects' PPG signals and EEG signals were collected for later preprocessing and extracting. By entering different compositions of feature vectors to train SVM classifier model and comparing their prediction accuracy and training time, the results show that the most suitable feature vectors to assess learning immersion are: pulse rate, the ratio of attention score and relaxation score, high alpha wave of EEG. The precision of the model reaches 88.93%.
Study of the Dynamics of Spin-Polarized Vertical Cavity Surface Emitting Lasers Using Largest Lyapunov Exponent

Polarization dynamics of spin polarized Vertical Cavity Surface Emitting Lasers (VCSELs) has been the subject of intense research work. Apart from the interesting physics involved, polarization dynamics of VCSELs offer a platform for a wide range of applications. Recently, the potential of the use of Quantum Dot (QD) VCSELs has been highlighted. Regardless of the active material, reliable resolution of the dynamics is important. Two methods are primarily used, namely Bifurcation Theory (BT) and the Largest Lyapunov Exponent (LLE). Among the two, BT is more popular within the optoelectronics community by virtue of its simplicity. The method can determine in a fast and trackable manner the kind of the nonlinear dynamics. However, it cannot quantify the strength of nonlinear dynamics. This is possible with the LLE method which can be beneficial in some cases where an asymmetry in the dynamics of the system might exist. Our recent work on the dynamics of spin polarized QD-VCSELs that accounts for both the effects of Ground States (GS) and Excited States (ES) has concluded that the time evolution of the ES and GS is asymmetric. This is studied here by means of LLE.
Transposon Insertion in the purL Gene Induces Biofilm Depletion in Escherichia coli ATCC 25922

Current Escherichia coli antibiofilm treatments comprise a combination of antibiotics commonly used against planktonic cells, leading to treatment failure. A better understanding of the genes involved in biofilm formation could facilitate the development of efficient and specific new antibiofilm treatments. A total of 2578 E. coli mutants were generated by transposon insertion, of which 536 were analysed in this study. After sequencing, Tn263 mutant, classified as low biofilm-former (LF) compared to the wild-type (wt) strain (ATCC 25922), showed an interruption in the purL gene, involved in the de novo purine biosynthesis pathway. To elucidate the role of purL in biofilm formation, a knockout was generated showing reduced production of curli fibres, leading to an impaired biofilm formation. These conditions were restored by complementation of the strain or addition of exogenous inosine. Proteomic and transcriptional analyses were performed to characterise the differences caused by purL alterations. Thirteen proteins were altered compared to wt. The corresponding genes were analysed by qRT-PCR not only in the Tn263 and wt, but also in clinical strains with different biofilm activity. Overall, this study suggests that purL is essential for biofilm formation in E. coli and can be considered as a potential antibiofilm target.
A Malware Detection Method of Code Texture Visualization Based on an Improved Faster RCNN Combining Transfer Learning

Today, with the continuous promotion and development of IoT and 5G technology, Cyberspace has become an important pillar of economic and social development, and also a foundational domain of national security. Cyberspace security is attracting more and more attention. Therefore, detecting malware and its variants is of great significance to Cyberspace. However, the increasing sophistication of malicious variants, such as encryption, polymorphism and obfuscation, makes it more difficult to identified malware effectively. In this article, a malware detection method of code texture visualization based on an improved Faster RCNN (Region-Convolutional Neural Networks) combining transfer learning is proposed. We utilize visualization technology to map malicious code into corresponding images with typical texture features, and realize the classification of malware. Firstly, in order to quickly acquire and locate the representative texture of malware, we adopt CNN to extract the global and deeper features of malicious code images. Then with RPN (Region Proposal Network) we generate the target image frame, which is used to locate the core texture of malware file (.text file), to realize the accurate positioning of malicious features. Secondly, we preprocess and train Faster RCNN model with ImageNet set, and then transfer the model to the malware classification model to accelerate the convergence of the first model and promote generation performance. Thirdly, we construct an improved objective function in which a novel multi-label of classification proportion is added to solve the problem that the texture change of “. text” section and other sections in malicious code image is not obvious after transfer learning. We collect code samples of six malware families from Kaggle platform, and compared the experimental results before and after transfer. The results show that the novel method can accelerate the convergence of loss function, and obtain higher accuracy (92.8%), lower FPR (6.8%) and better P-R (precision-recall) curve.
Communication Cost of Quantum Processes

A common scenario in distributed computing involves a client who asks a server to perform a computation on a remote computer. An important problem is to determine the minimum amount of communication needed to specify the desired computation. Here we extend this problem to the quantum domain, analyzing the total amount of (classical and quantum) communication needed by a server in order to accurately execute a quantum process chosen by a client from a parametric family of quantum processes. We derive a general lower bound on the communication cost, establishing a relation with the precision limits of quantum metrology: if a <inline-formula> <tex-math notation="LaTeX">$v$ </tex-math></inline-formula>-dimensional family of processes can be estimated with mean squared error <inline-formula> <tex-math notation="LaTeX">$n^{-\beta }$ </tex-math></inline-formula> by using <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> parallel queries, then the communication cost for <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> parallel executions of a process in the family is at least <inline-formula> <tex-math notation="LaTeX">$(\beta \, v /2-\epsilon) \, \log n$ </tex-math></inline-formula> qubits at the leading order in <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, for every <inline-formula> <tex-math notation="LaTeX">$\epsilon >0$ </tex-math></inline-formula>. For a class of quantum processes satisfying the standard quantum limit (<inline-formula> <tex-math notation="LaTeX">$\beta = 1$ </tex-math></inline-formula>), we show that the bound can be attained by transmitting an approximate classical description of the desired process. For quantum processes satisfying the Heisenberg limit (<inline-formula> <tex-math notation="LaTeX">$\beta =2$ </tex-math></inline-formula>), our bound shows that the communication cost is at least twice as the cost of communicating standard quantum limited processes with the same number of parameters.
Public awareness, emotional reactions and human mobility in response to the COVID-19 outbreak in China – a population-based ecological study

Abstract Background The outbreak of COVID-19 generated severe emotional reactions, and restricted mobility was a crucial measure to reduce the spread of the virus. This study describes the changes in public emotional reactions and mobility patterns in the Chinese population during the COVID-19 outbreak. Methods We collected data on public emotional reactions in response to the outbreak through Weibo, the Chinese Twitter, between 1st January and 31st March 2020. Using anonymized location-tracking information, we analyzed the daily mobility patterns of approximately 90% of Sichuan residents. Results There were three distinct phases of the emotional and behavioral reactions to the COVID-19 outbreak. The alarm phase (19th–26th January) was a restriction-free period, characterized by few new daily cases, but a large amount public negative emotions [the number of negative comments per Weibo post increased by 246.9 per day, 95% confidence interval (CI) 122.5–371.3], and a substantial increase in self-limiting mobility (from 45.6% to 54.5%, changing by 1.5% per day, 95% CI 0.7%–2.3%). The epidemic phase (27th January–15th February) exhibited rapidly increasing numbers of new daily cases, decreasing expression of negative emotions (a decrease of 27.3 negative comments per post per day, 95% CI −40.4 to −14.2), and a stabilized level of self-limiting mobility. The relief phase (16th February–31st March) had a steady decline in new daily cases and decreasing levels of negative emotion and self-limiting mobility. Conclusions During the COVID-19 outbreak in China, the public's emotional reaction was strongest before the actual peak of the outbreak and declined thereafter. The change in human mobility patterns occurred before the implementation of restriction orders, suggesting a possible link between emotion and behavior.
Short-Term Highway Traffic Flow Forecasting Based on XGBoost

With the rapid development of urban intelligent transportation system, the prediction short-term of traffic flow attracts more and more attention. As the lack of the characteristics of traffic flow and appropriate models, the accurate predction of the traffic flow are facing a big challenge. A short-term traffic flow prediction model based on extreme gradient rise is proposed in this paper. The experiment results reveal the superiority of the modle by comparing with the traditional prediction model.
A Tangible-Augmented Concept Inventory to Identify Novices’ Misconceptions in Programming

Introductory programming courses are a challenge for any teacher and a barrier for undergraduate students. A tangible-augmented concept inventory (TACI) is developed according to a design-oriented methodology in close collaboration with future users (students, teachers). It aims to help teachers to become aware of the misconceptions their students have and to rectify them appropriately. Tests conducted with 8 experts and 9 students show promising results and lead to improvement of the TACI.
A Breast Image Classification Method based on Deep Convolutional Neural Network

Breast cancer is a serious threat to women’s physical and mental health. Computer-based breast cancer assistant diagnosis system can help radiologists reduce workload, reduce missed diagnosis rate, and improve accuracy to a certain extent, but the performance still needs to be further improved to meet clinical needs. Aided diagnosis systems based on traditional machine learning and pattern recognition methods require manual design and feature extraction, which not only requires rich medical expertise, but also difficult to extract deep features of the image, causing a bottleneck in system performance. In this paper, a model based on deep convolutional neural network is designed and used for breast cancer assistant diagnosis. Experimental results show that the area under the ROC curve can reach 0.896, which has achieved good performance.
Product Pre-Launch Prediction From Resilient Distributed e-WOM Data

Pre-launch success prediction of a product is a challenge in today’s electronic world. Based on this prediction, industries can avoid huge losses by deciding on whether to launch or not to launch a product into the market. We have implemented a Multithreaded Hash join Resilient Distributed Dataset (MHRDD) with a prediction classifier for pre-launch prediction. MHRDD helps to remove the redundancy in the input dataset and improves the performance of the prediction model. Large volume of e-Word of Mouth (e-WOM) data like product reviews, comments and ratings available on internet about products can be used for pre-launch product prediction. In MHRDD, to identify features a distance similarity score is used. In order to remove duplicates, a hash key and join operations are used to create a hash table of significant features. With in-memory computations and hashing on the join operations, this model reduces redundancy of data. This model is scalable and can handle large datasets with good prediction accuracy. This paper presents a novel big data processing method that predicts product success before its launch in the market. Proposed method helps to identify features that are significant for the product to be successful. Based on the pre-launch prediction, companies can reduce cost, effort and time with improved product success.
Object detection method for industrial scene based on MobileNet

The application of machine vision in the industrial field is of great significance for improving product quality and production efficiency. Aiming at the problem of product quality inspection and assembly manufacturing process object detection in industrial scenes, a vision detection method based on MobileNet convolutional neural network is proposed. By collecting images in industrial scenes to establish dataset for network training, the training results are implanted into wearable devices based on Android system to identify target scenes. The experimental results show that the method has the characteristics of fast speed, high precision and portability, and is suitable for the detection and recognition of target objects in industrial scene.
The TRANSFAC System on Gene Regulation

ject to a number of control mechanisms. Thus, (parts of) the genomic DNA of eukaryotes can not only be amplified to provide a higher copy number of certain genes, but its three-dimensional organization in the chromatin structure also undergoes a number of changes. These are reflected by DNA methylation events, by alteration of the nucleosomal structure and by remodeling chromatin packing within individual chromatin domains. The RNA is subject to processing such as splicing or editing, and individual RNA molecules may greatly differ in their half-life. Proteins also have different turn-over rates as well as they are posttranslationally modified (phosphorylated,
Enhanced Solar PV Power Generation Under PSCs Using Shade Dispersion

The solar photovoltaic (PV) array generates the power below the rated value due to the shading intensity and shading pattern losses under partial shading conditions (PSCs). The different levels of solar irradiance are received by the modules of PV array under PSCs; it causes the mismatch between the generated module currents, leading unfortunate impacts such as hot-spot and therefore total PV power generation reduces. Resulting, the efficiency of solar PV array decreases. This article presents the optimal energy harvesting using physical relocation and fixed column position of modules with fixed electrical connection (PRFCPM-FEC) under different shading conditions. The proposed algorithm is presented for ${m} \times {n}$ modules under different shading conditions. The locations of modules are changed according to the proposed configuration without removing the electrical connections. A comparative analysis has also been presented between proposed pattern configuration, total cross tied (TCT), and physical relocation of the modules with a fixed electrical connection (PRM-FEC) configurations. The results demonstrated that the proposed array configuration produced the improved performance under PSCs.
Molecular Genetic Diversity in the Turkish National Melon Collection and Selection of a Preliminary Core Set

Turkey is a secondary center of diversity for melon (Cucumis melo) and is home to a variety of regional morphotypes. This diversity is housed in a national germplasm repository with more than 500 accessions. Molecular genetic variability of 209 melon genotypes from 115 accessions of this collection was characterized using amplified fragment length polymorphisms (AFLPs). Ten AFLP primer combinations yielded 279 reproducible fragments, which were used for dendrogram and principal coordinate analyses. These analyses showed two major clusters of Turkish melons: one group contained highly similar genotypes (maximum Dice dissimilarity coefficient of 0.18), whereas the other group was genetically more diverse (maximum dissimilarity 0.41). Although average dissimilarity was low (0.13), a broad range of genetic diversity was observed in the collection. A marker allele richness strategy was used to select a core set of 20 genotypes representing the allelic diversity of the AFLP data. The core set had double the average diversity (0.26) of the entire set and represented the major morphotypes present in the collection. Molecular genetic diversity of the core set was further validated using simple sequence repeat marker data (116 polymorphic fragments), which confirmed that the selected core set retained high levels of molecular genetic diversity.
Modeling and Optimization of Moisture Diffusion and Integrated Thermo-Hygro-Vapor Stress for PBGA Package

PBGA packages are very sensitive to humidity, moisture-induced failures such as popcorn crack and delamination during moisture preconditioning and reflow soldering are major concerns for the lifetime and reliability of PBGA packages. In this paper, a continuous experiment up to 1500h at JEDEC Level 1 was executed in a constant temperature-humidity chamber to characterize the moisture absorption behavior of EMC (Epoxy molding compound). At the same time, an approximate fitting method called Dual-Fickian was applied to better describe and simulate the moisture diffusion process during moisture preconditioning. Besides, the CHS (coefficients of hygroscopic swelling) of five EMC materials at four different temperatures were characterized. The correlation of CHS with temperature and its relationship with the Tg (Glass Transition Temperature) were examined. On this basis, a simplified PBGA package model was established and the coupling effects of thermal stress, hygroscopic swelling stress, and vapor pressure on this package in the reflow environment were quantitatively analyzed with FEM simulation. Furthermore, a tentative optimal structure design was proposed to release the layered stress and improve the reliability of the package.
A Misalignment Adaptive WPT System Ensuring Maximum Power Transfer under Asymmetric Frequency Splitting Conditions

One of the major issues is that the output power and power transfer efficiency are dependent on the coupling coefficient (k) between the magnetic couplers. Any deviations in optimal distance, misalignment between the couplers or un-modeled dynamics such as temperature dependent variation in components lead to a shift in system’s resonance frequency. Also, asymmetric frequency splitting phenomenon occurs decreasing the output power and the power transfer efficiency of the system. Non-metaheuristic optimization methods are not capable of distinguishing the global maximum power (GMP) transfer frequency under frequency splitting conditions. This study presents an adaptive wireless power transfer (WPT) system with a Cuckoo Search Algorithm (CSA) based frequency-tracking method. GMP transfer frequencies determined by experimental frequency scanning method. The method is implemented under asymmetric splitting conditions for the first time. The suitability of the algorithm is tested in an experimental WPT setup. In experimental studies, wireless power transfer was provided through flux pipe couplers with three sub-coils which exhibit higher coupling coefficient as compared to traditional flux pipe couplers with two sub-coils. Results show that the CSA based controller can accurately find the optimal operation frequency under various misalignment and distance between the couplers.
Chinese Developers’ Information Behaviors When Using Technical Documentation

This paper was presented at the Invited Panel session “Technical Communication in China”. Technical documentation quality is an essential part of product help center quality. As user information behavior gains increasing attention in user-interface design, for help centers, understanding user’s information behavior of documentation provides insights to better develop product resources. To improve help center quality, we focus on typical technical documentation users—developers and their information behavior. We used combined sociological approaches and conducted a two-phase user research: an in-depth interview was first performed to gain basic idea of developers’ documentation acquiring and using habits; then, a questionnaire survey was conducted to validate former result and further explore the factors influence those behaviors. This paper present how developers need, acquire and use technical documentations, and discussed the related factors. According to the result, we found that developers have preference in documentation acquiring methods, besides, quickly locating target information is their common way to start using a documentation. Furthermore, gender and user documentation dependency both have impact on their information behaviors and documentation using experience. In conclusion, developers’ information behaviors of technical documentation have patterns, and these patterns should be carefully considered in help centers’ future development.