Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Using Cepstrogram for Countermeasure Against Replay Attacks

This study investigated the cepstrogram properties and demon-strated their effectiveness as powerful countermeasures against replay attacks. A cepstrum analysis of replay attacks suggests that crucial information for anti-spooﬁng against replay attacks may be retained in the cepstrogram. When building countermeasures against replay attacks, experiments on the ASVspoof 2019 physical access database demonstrate that the cepstrogram is more effective than other features in both single and fusion systems. Our LCNN-based single and fusion systems with the cepstrogram feature outperformed the corresponding LCNN-based systems without the cepstrogram feature and several state-of-the-art single and fusion systems in the literature.
Quantile analysis of factors influencing the time taken to clear road traffic incidents

The time it takes to clear road traffic incidents is an important performance measure in highway management. Understanding the effects of explanatory variables on incident clearance time is a necessary step in developing incident management strategies. Previously, hazard-based duration models have been used extensively in identifying the influential factors in incident duration. Quantile regression models have been introduced in survival analysis, and these offer a more flexible approach to analyse incident duration data. The advantage offered by quantile regression lies in the ability to make inferences about the effect of explanatory variables on different quantiles of the incident duration distribution. In this work, quantile regression was applied to an incident clearance time dataset collected on freeway sections in Seattle, USA. Based on the estimation results from quantile regression, four influential factors were identified to have a stronger impact at the upper tail of the clearance time distribu.
Predistortion for Nonlinear Power Amplifiers with Memory

The fusion of voice and data applications, along with the demand for high data-rate applications such as video-on-demand, is making radio frequency (RF) spectrum an increasingly expensive commodity for current and future communications. Although bandwidth-efficient digital modulation alleviates part of the problem by requiring a minimal use of spectral resources, they put an extra design burden on RF engineers. RF transmitters and power amplifiers account for more than half the total maintenance cost of a base-station, and occupy nearly the same portion of space. Therefore, power amplifiers become a bottleneck for digital systems in terms of space and power consumption. However, power-efficient use of the amplifiers, although desirable, is extremely detrimental to end-to-end performance due to the very high peak-to-average power ratios of modulations that are used today. In order to reduce distortion while maintaining high power conversion efficiency in a power amplifier, linearization schemes are needed. In addition, significant frequency-dependent Memory Effects result in high power amplifiers operating on wideband signals. Therefore, these effects need to be considered during any attempt to minimize amplifier distortion. In this thesis, we present two schemes to cancel nonlinear distortion of a power amplifier, along with its memory effects and results for one of the schemes. The results highlight the fact that in the presence of significant memory effects, cancellation of these effects is necessary to achieve reasonable improvement in performance through linearization. We focus on predistortive schemes due to their digital-friendly structure and simple implementation. The operating environment consists of a multi-carrier W-CDMA signal. All of the studies are performed using numerical simulation on MATLAB and Agilent’s Advanced Design System (ADS).
A Study on Designing and Realizing MQSPT-based Open API SMS Architecture

The Enterprise Management System is the representative system supporting information about important decision making of a company's information system management. SMS (System Management System), which manages the various system status in the information system, uses TCP/IP communication-based SNMP (Simple Network Management Protocol) to collect, process, and save different system information, and provide information for users to make decisions. However, SNMP cannot guarantee the data transmission reliability in regard to system circuit error, delay, and short that may occur during the information collection. Consequently, it involves the issue of inaccuracy of the SMS information. In addition, it is difficult to guarantee the data transmission reliability due to different system problems in the TCP/IP communication. In this study, we applied the SPT (Safe Proper Time) technology to guarantee the reliability of TCP/IP communication, safely linked the intranet system information to the internet network, using the MQSPT-based Open API technology. We designed it to be able to manage the system in the intranet environment efficiently even on the internet network. This study suggests designing and constructing the MQSPT-based Open API SMS architecture.
NB-IoT Based Waste Monitoring and Collection Planning System

Increasing Municipal Solid Waste (MSW) generation has become a major challenge in Namibia particularly in the capital city of Windhoek. Lack of finances and sufficient data in waste management system is a limiting factor to improving the efficiency of MSW management system in Windhoek city. IoT technology provides cost saving solutions that enables municipal authorities and companies to implement solution using less finances. Moreover, IoT enable the municipal and responsible authorities to capture relevant data that provides more insight into the waste management system i.e. in planning an efficient waste collection process. In this paper, the author proposes a waste monitoring and collection planning system based on narrow-band Internet of Things (NB-IoT). NB-IoT communication technology provides competitive advantage in flexibility and coverage while ensuring scalability and reliable service. The proposed solution will design and implement a smart bin prototype and a web-based waste monitoring application, capable of performing real time monitoring of waste fill level and detecting foul smell from the garbage bin. Moreover, the system provides effective decision-making support in waste collection planning. This will allow the municipal City of Windhoek to collect waste materials on time in order to avoid garbage bins from overflowing and keep the city clean.
Implementation and Evaluation of Image Transmission Protocol in Wireless Multimedia Sensor Networks

In wireless multimedia sensor networks (WMSNs), a multimedia sensor node captures an image and sends it to a server via multiple wireless hops. In this process, an image transmission protocol has some stringent requirements such as reliability in data transmission, energy consumption of nodes, and end-to-end delay. This paper implements an image transmission protocol called the pipelined cooperative transmission (PCT) protocol on WMSNs and evaluates its suitability in the viewpoint of the requirements. According to experiments, the protocol could deliver an image of 34 Kbytes within less than 0.4 seconds without any loss of packet and also about 94% of totally generated images, even with a long-distance multi-hop topology and the high interference of WiFi signals.
Energy-Efficient Resource Allocation for Relay-Assisted Backscatter Communication Systems

In this paper, we investigate energy-efficient resource allocation in relay-assisted backscatter communication systems. We employed a backscatter user device, and a relay device that supported to operate in different modes, i.e., the backscatter communication (BackCom) mode and the harvest-then transmit (HTT) mode, respectively. We formulate the system energy efficiency (EE) maximization problem by jointly optimizing the power and time allocations. A dual-layer iterative resource allocation algorithm that separates power allocation from time allocation is presented. The inner-layer is responsible for optimizing power allocation, while the outer-layer determines the time allocation strategy. Finally, an algorithm is proposed for jointly determining the optimal solution. Simulation results show that the proposed resource allocation algorithm can increase the system EE.
Producing Artificial Male Voices with Maternal Features for Relaxation

Maternal voices are fundamental for babies and children in developmental years, responsible for emotional containment as guidance through intense emotional experiences when relating to others and the world, and ultimately teaching us how to self-manage and properly contain emotions at later stages of life. States of anxiety and depression can be the chronic effect of overwhelming emotions in particular events, e.g., trauma, and their treatment can be supported with relaxation exercises, e.g., breathing techniques. Using voices with maternal characteristics could aid relaxation via digital tools. In this work, we study the application of maternal female voice features to male artificial voices for relaxation. Starting from a natural maternal voice, we analyze its Bio-Informational Dimensions (BID) and produce maternal male voices to be evaluated for calming purposes.
Seismic Risk Assessment of New Zealand Unreinforced Masonry Churches using Statistical Procedures

ABSTRACT The 2010–2011 Canterbury earthquake sequence provided extensive evidence of the significant seismic vulnerability of New Zealand unreinforced masonry (URM) churches. Given the high seismicity of the country, the exposure of human lives and the societal significance of ecclesiastic buildings, for both historical and religious reasons, the reduction in seismic vulnerability of this building type is of primary importance. By analyzing the seismic performance of a sample of 80 affected buildings, regression models correlating mean damage levels against ground-motion parameters were developed for observed collapse mechanisms, accounting for vulnerability modifiers whose influence was estimated via statistical procedures. Considering the homogeneity of New Zealand URM churches, the vulnerability models developed for the Canterbury region were extended to the whole country inventory, and a synthetic index was proposed to summarise damage related to several mechanisms. Territorial scale assessment of the seismic vulnerability of churches can assist emergency management efforts and facilitate the identification of priorities for more in-depth analysis of individual buildings. After proper calibration, the proposed approach can be applied to other countries with similar building heritage.
A Survey On Fault Atacks On Symmetric Key Cryptosystems

Fault attacks are among the well-studied topics in the area of cryptography. These attacks constitute a powerful tool to recover the secret key used in the encryption process. Fault attacks work by forcing a device to work under non-ideal environmental conditions (such as high temperature) or external disturbances (such as glitch in the power supply) while performing a cryptographic operation. The recent trend shows that the amount of research in this direction; which ranges from attacking a particular primitive, proposing a fault countermeasure, to attacking countermeasures; has grown up substantially and going to stay as an active research interest for a foreseeable future. Hence, it becomes apparent to have a comprehensive yet compact study of the (major) works. This work, which covers a wide spectrum in the present day research on fault attacks that fall under the purview of the symmetric key cryptography, aims at fulilling the absence of an up-to-date survey. We present mostly all aspects of the topic in a way which is not only understandable for a non-expert reader, but also helpful for an expert as a reference.
EEG Spatial Analysis based on Brain thermogram Image Recognition

EEG feature recognition is an important research issue for understanding human brain activities. However, the invasive solution for brain informatics research is causing concerns on its safety and reliability. Therefore, current research in brain informatics field pays more attention on non-invasive brain analysis. In this paper, we introduce a novel solution, which combines EEG signal analysis with EEG heat spots image recognition based on the Convolutional Neural Network (CNN) method. The experimental results demonstrate our solution can enhance the EEG feature recognition.
Classification of Corn Leaf Disease Using the Optimized DenseNet-169 Model

Corn is the second main commodity after rice in Indonesia. Meanwhile, in its cultivation, there are main obstacles, namely pests and diseases. Diseases in plants usually occur in different parts, such as roots, leaves, and stems. However, the leaves are the most common parts to detect the disease because of the differences in size, shape, and color of the leaves. This makes it a major challenge to identify and classify diseases. Classification of leaf diseases of corn is one way to increase the accuracy of diagnosis by utilizing the symptoms and signs found on the leaves of corn plants. This paper presents one of the Deep Convolutional Neural Network (CNN) models, namely DenseNet-169 optimized. Applied models trained with an open dataset from the plant village dataset and primary data obtained from four districts in Madura, Indonesia. Because the amount of primary data is not much, data augmentation is carried out, namely rotate range 90⁰, flip horizontal, flip vertical, brightness random 0.6 to 2.0, zoom range 0.65 to 0.95. To evaluate the model's performance, different optimization parameters were included, namely, Stochastic Gradient Descent (SGD) optimization compared to Adam optimization. The implemented model achieves 62.3%, 75.66%, 98.08% and 99.32% accuracy of corn leaf disease classification for the original primary dataset, the augmented primary dataset, the original secondary dataset and the augmented secondary dataset for the SGD optimizer. As for the Adam optimizer, this model produces a classification accuracy of corn leaf disease of 67.78%, 83.5%, 99% and 99.32% with the same conditions. The accuracy results show that the DenseNet-169 model with Adam optimizer is more hopeful and can significantly affect the efficient recognition of diseases. This makes it possible to have the potential to detect disease in real-time farming systems.
Time-aware Service Recommendation Algorithm Based on K-means and Low-rank Matrix Factorization

∗ In recent years, with the emergence of more and more similar Web services, how to recommend high-quality Web services to consumers has become a challenging task. Traditional service recommendation has the problems of cold start and data sparseness, resulting in low accuracy of service recommendation through similar users of the target user. In response to the above problems, this paper proposes a time-aware service recommendation algorithm based on K-means and low-rank matrix factorization (K-LMF). First, this paper uses the K-means clustering algorithm to gather data with similar characteristics into the same cluster to improve the efficiency and accuracy of service recommendation. In order to prevent data sparseness from causing unpredictability, this paper divides the data from multiple dimensions, and then uses the low-rank matrix decomposition algorithm of the L1 paradigm to complete the quality of service (QoS) matrix to predict the appropriate service for the target user. Finally, experiments are carried out on the WS-DREAM data set to verify the effectiveness and feasibility of this scheme.
Research on Modelling, Finite Element Analysis and Experiment of Gravity Valve

In this paper, two kinds of gravity valves are designed: copper bead gravity valve and hose gravity valve. Firstly, the stress distribution on the substrate of the gravity valve is simulated when the copper bead and the substrate of the gravity valve is in the non-touched, half-touched or full-touched states respectively, and the fluid leakage laws are also analyzed in the different states, by ABAQUS 6.16. The flow rate control effects of the copper bead gravity valve with different cone angle are measured and analyzed experimentally, and the optimal cone angle are obtained. Secondly, the cross-section shapes of the hose are compared and analyzed by FEA (Finite Element Analysis) for several kinds of hose gravity valve. It is concluded that the shuttle hose has the best performance. Finally, the shuttle hose gravity valve is made and its flow characteristics are measured and analyzed with different length of the hose.
KIDNet: A Knowledge-Aware Neural Network Model for Academic Performance Prediction

Academic performance prediction and analysis in educational data mining is meaningful for instructors to know the student’s ongoing learning status, and also provide appropriate help to students as early as possible if academic difficulties appear. In this paper, we first collect the basic information of students and courses as features. Then, we propose a novel knowledge extraction framework to obtain course knowledge features to reinforce feature groups. The comparative analyses of the knowledge similarity and average grades of the courses in all terms demonstrate a strong correlation between them. Furthermore, we build the Knowledge Interaction Discovery Network (KIDNet) model, based on factorization machine (FM) and deep neural network (DNN) algorithms. This model uses FM to model lower-order interactions of sparse features and employs DNN to model higher-order interactions of both dense and sparse features. The effectiveness of KIDNet has been validated by conducting experiments based on a real-world dataset.
Corpus Annotation Research on Ancient Chinese Materia Medica Oriented to the System of Textual Knowledge Graph of Ancient Chinese Medical Literatures—Taking Shen Nong Ben Jing Jiao Zhu as An Example

The ancient Chinese medical literatures were written in ancient Chinese, with semantic ambiguity and high academics. Corpus annotation is the fundamental work for constructing the textual knowledge graph of the ancient Chinese medical literatures. Whether the corpus annotation is good or not determines the utilization degree of knowledge graph. In this paper, we select the Shen Nong Ben Jing Jiao Zhu as the objective for corpus annotation to analyze the results and propose further solutions by comprehensively considering the content and style of ancient Chinese Materia Medica, its academic thought and academic influence, in the hope that a suitable corpus could be provided for constructing the textual knowledge graph of the ancient Chinese medical literatures.
Proposal for a Mathematical Model of the Hit Phenomenon Using Cumulative Values of Data

In the conventional mathematical model of the hit phenomenon, the strength of influence is assumed to be constant for each interval, but it is natural to assume that it changes with time. In order to express the strength of influence over time, we devised a mathematical model of the hit phenomenon that uses the cumulative number of twitter posts. By setting a range of values that can be taken at each time when calculating the strength of influence, the model is more accurate than the conventional mathematical model of the hit phenomenon, and it is possible to see the strength of influence in detail. However, the setting of the range of influence values is not sufficient, and further improvement is needed.
Investigating the Acceptance Model of Cloud Learning: MOOCs for Sustainability Usage in Higher Education Institutes

∗ This study’s objective was to understand better the psychological mechanisms underlying long-term participation in massive open online courses (MOOCs). The researchers proposed a research model that includes nine variables: content quality, cloud system quality, collaborative learning, knowledge assessment, perceived ease of use, perceived usefulness, satisfaction, habits, and sustainability usage, all of which have been extensively studied in the relevant literature. The study was undertaken in Thai MOOCs. The literature review was conducted using various online databases for the digital library. According to this study, MOOCs will be successful if the above conditions are followed. Furthermore, this research can assist MOOC instructors in improving the student experience in this virtual environment, MOOC providers in maintaining MOOCs, and universities in preparing MOOCs for use in blended classrooms.
A Water Quality Prediction Model Based on Knowledge- enhanced Deep Adversarial Network

Water quality prediction is an important means to control and reduce water pollution. Exist water quality prediction models are mainly data driven and only depend on various sensor data. At present, the integration of machine learning, especially deep learning, and the knowledge graph (KG) has become a research hotspot. Many studies have proved that the introduction of domain knowledge can effectively improve the data-driven models. This paper proposes a water quality prediction model integrating KG and deep adversarial network. The KG of water quality is extracted by the joint extraction of entities and relations, and introduced into prediction modeling as prior knowledge for parameter importance learning. A FreeAT-based adversarial learning framework is combined with the deep prediction model to improve the generalization ability of model in a few-slot learning scenario. The experimental results on monitoring data from the Juhe River show that the proposed model can greatly improve the robustness of the model and reduce the prediction error.
Noise and Codification Effect on Emotional Speech Classification Systems

Emotions are reactions that all human beings experience daily, e.g., joy, sadness, fear, anger. They can be manifested through speech: when we speak, words are always accompanied by our emotions. This fact affects the muscular movements of the respiratory system and the larynx, thus modifying the timbre, tone, intensity, and intonation of what we say. There are many acoustic databases labeled with emotions available in the public domain. However, most of them were created under non-real-world circumstances, i.e., actors recreated emotions, and emotions were labeled under fictitious conditions where noise is absent. Another drawback of the design of emotion recognition systems is the lack of enough patterns in the available databases, thus driving to generalization problems and leading to overfitting. In this paper, a system is developed in order to verify the noise and the codification effect. Results have shown a performance deterioration in both cases, increasing the error probability from 28.54% to 65.29% in the first case and from 28.54% to 40.09% in the second case. Furthermore, an enlargement of the training set is proposed, creating new virtual patterns consisting of the original patterns with the addition of different values of signal-to-noise ratio and its effect in the design of an emotion classification system, confirming an improvement of the test error probability.