Miracle-dz/UniFAR-FTUs huggingface dataset:
University of Birmingham Resample-based ensemble framework for drifting imbalanced data streams

Machine learning in real-world scenarios is often challenged by concept drift and class imbalance. This paper proposes a Resample-based Ensemble Framework for Drifting Imbalanced Stream (RE-DI). The ensemble framework consists of a long-term static classiﬁer to handle gradual and multiple dynamic classiﬁers to handle sudden concept drift. The weights of the ensemble classiﬁer are adjusted from two aspects. First, a time-decayed strategy decreases the weights of the dynamic classiﬁers to make the ensemble classiﬁer focus more on the new concept of the data stream. Second, a novel reinforcement mechanism is proposed to increase the weights of the base classiﬁers that perform better on the minority class and decrease the weights of the classiﬁers that perform worse. A resampling buffer is used for storing the instances of the minority class to balance the imbalanced distribution over time. In our experiment, we compare the proposed method with other state-of-the-art algorithms on both real-world and synthetic data streams. The results show that the proposed method achieves the best performance in terms of both Prequential AUC and accuracy.
Active Contours Driven by Visual Saliency Fitting Energy for Image Segmentation in SAR Images

Image segmentation actually could be regarded as a computer vision pre-processing step of cloud computing and big data analysis. Active contour model is a new research direction for images segmentation in synthetic aperture radar(SAR) images. However, due to the SAR images have speckle noise and low signal-to-clutter ratio, active contour models cannot produce satisfied segmentation results. To solve these issues, active contours driven by visual saliency fitting energy is proposed in this paper. First, a saliency map is calculated by improved local contrast measure. Then, a visual saliency fitting energy is proposed based on saliency map and local binary fitting energy. The saliency map has a higher signal-to-clutter ratio and the local binary fitting energy is an active contour model that can effectively segment images with intensity inhomogeneity. In this way, the proposed method has a better segmentation result. The experiments on ship chips in SAR images demonstrate that the proposed method is robust to speckle noise and the segmentation result has good connectivity and integrity for entire ship targets.
New Approach to Set Representation and Practical Private Set-Intersection Protocols

Private set intersection (PSI) is a fundamental cryptographic protocol, which has many important applications, such as personal properties matching, data sharing, or data mining. PSI has been widely studied in the literature, and many PSI protocols have been presented; however, today, many real-world applications do not use a secure PSI protocol, mainly because current PSI protocols have two issues: the insufficient efficiency and not considering the secure storage of users’ datasets. Without using the PSI protocols, users of the real-world applications will sacrifice their privacy. In this paper, we propose a new approach for sets representation, which denotes sets by bit vectors and naturally hides the cardinality of a set. The new approach is particularly suitable to cloud computing environments. Then, we present two practical PSI and PSI cardinality protocols based on an additive homomorphic public-key cryptosystem (PKC). The new protocols enjoy two main advantages: 1) they are more efficient than other related protocols, especially when the set size is less than 212 and 2) the approach used in our protocols provides a good solution to securely store users’ datasets, and the encrypted datasets could be used as protocols’ messages directly without any additional computations. Finally, we implement our PSI and PSI cardinality protocols with Paillier PKC and ElGamal PKC in Java.
Longitudinal Velocity Control of Robotic Vehicle with Articulated Suspension

Evolution of virtual prototype technology has a paramount importance in advance mechanical system development and exploration. In this paper skid steered RVAS (Robotic Vehicle with Articulated Suspension) virtual prototype model was designed and co-simulation method was presented to integrate dynamic system model with a designed velocity tracking controller. The vehicle modelling was carried out in ADAMS/View. In addition, PID (Proportional Integral and Derivative) control strategy was employed to achieve desired velocity response along with efficient PID tuning technique in MATLAB/SIMULINK. It was observed that cosimulation was performed successfully and desired longitudinal velocity was achieved by the controller in an effective way.
English Spoken Digits Database under noise conditions for research: SDDN

In this paper, we introduce a modified database for English spoken digits under all types of noise conditions (SDDN). This database was designed for use in scientific research, especially in the field of speech enhancement, nois robustness, background noise, speech recognition, noise reduction, signal processing. It was synthesized by a set of open source spoken numbers of the public on the network, in particular from Speech Commands Dataset v0.02 released by Google's TensorFlow and AIYteams. Various types of real and artificial noise were added for a SNR from 10 dB to −10 dB to make them more suitable for reality. The AURORA, CHiME3 and NOISEX-92 databases were used to select noise types.
Improving Children's Speech Recognition Through Time Scale Modification Based Speaking Rate Adaptation

In the work presented in this paper, we have explored the effect of speaking-rate adaptation on children's speech recognition using acoustic models trained on adults' speech. It is well known that, the shape of the vocal organs, pitch and speaking-rates are significantly different for adult and child speakers. Consequently, the recognition performance for children's speech in such mismatched setup is reported to be extremely poor. To address the acoustic mismatch resulting from the differences in pitch and vocal-tract geometry, a large number of studies have been reported that have presented a myriad of techniques. But, only a few works have studied the role of speaking-rate adaptation on children's speech recognition. Furthermore, those studies were performed on systems employing Gaussian mixture models. Motivated by these facts, we have explored speaking-rate adaptation in the context of systems employing deep neural network based acoustic modeling. Timescale modification using an approach based on phase-independent iterative spectrogram inversion is employed for speaking-rate adaptation. Significant reductions in errors are noted by adapting the speaking-rates. Furthermore, the effect of combining speaking-rate adaptation with vocal-tract length normalization and pitch scaling is also studied. Additive improvements are obtained by combining the explored techniques with speaking-rate adaptation.
A Novel Built-In Self-Repair Scheme for 3D Memory

Three-dimensional (3D) memory products based on through silicon via (TSV) are widely developed to fulfill the ever-increasing demands of per unit area storage capacity. The yield is still one of the critical challenges for 3D memory. Redundancy technique is now widely used in industry to improve yield. How to reduce the overhead of redundancy by improving the utilization of redundancy is important to 3D memory. In this paper, we propose a row/column block-based mapping technique for 3D memory built-in self-repair scheme to improve the utilization of redundancy and low hardware overhead. Each row/column is divided into row/column block and the mapping can be performed at row/column-block level instead of the original row/column level. Therefore, more faulty cells can be clustered into the same row/column. Based on the proposed technology, a 3D-essential spare pivoting (ESP) algorithm is also proposed for the allocation of redundant rows and columns, and the area overhead of this algorithm is particularly low. The experimental results show that on an average the repair ratio of our proposed scheme is much better than the fault clustering technique by 12% and the redundancy-cost can reduce 23%.
A Hierarchical Time Order Petri Nets Modeling Method

Aiming at the difficulty of complex system modeling by using normal Petri Nets, Hierarchical Time Order Petri Nets was put forward, and gave its definition. Taking the process of space vehicle technical preparation for example, established the Hierarchical Time Order Petri Nets, verified the model by using relational matrix. It could conclude that the order time of complex system could be exhibited strictly, the system could be grasped on the macro and micro, the verification and simulation of model would become simple.
Semi-Supervised K-Means DDoS Detection Method Using Hybrid Feature Selection Algorithm

Distributed denial of service (DDoS) attack is an attempt to make an online service unavailable by overwhelming it with traffic from multiple sources. Therefore, it is necessary to propose an effective method to detect DDoS attack from massive data traffics. However, the existing schemes have some limitations, including that supervised learning methods, need large numbers of labeled data and unsupervised learning algorithms have relatively low detection rate and high false positive rate. In order to tackle these issues, this paper presents a semi-supervised weighted k-means detection method. Specifically, we firstly present a Hadoop-based hybrid feature selection algorithm to find the most effective feature sets and propose an improved density-based initial cluster centers selection algorithm to solve the problem of outliers and local optimal. Then, we provide the Semi-supervised K-means algorithm using hybrid feature selection (SKM-HFS) to detect attacks. Finally, we exploit DARPA DDoS dataset, CAIDA “DDoS attack 2007” dataset, CICIDS “DDoS attack 2017” dataset and real-world dataset to carry out the verification experiment. The experiment results have demonstrated that the proposed method outperforms the benchmark in the respect of detection performance and technique for order preference by similarity to an ideal solution (TOPSIS) evaluation factor.
A Multimodal Malware Detection Technique for Android IoT Devices Using Various Features

Internet of things (IoT) is revolutionizing this world with its evolving applications in various aspects of life such as sensing, healthcare, remote monitoring, and so on. Android devices and applications are working hand to hand to realize dreams of the IoT. Recently, there is a rapid increase in threats and malware attacks on Android-based devices. Moreover, due to extensive exploitation of the Android platform in the IoT devices creates a task challenging of securing such kind of malware activities. This paper presents a novel framework that combines the advantages of both machine learning techniques and blockchain technology to improve the malware detection for Android IoT devices. The proposed technique is implemented using a sequential approach, which includes clustering, classification, and blockchain. Machine learning automatically extracts the malware information using clustering and classification technique and store the information into the blockchain. Thereby, all malware information stored in the blockchain history can be communicated through the network, and therefore any latest malware can be detected effectively. The implementation of the clustering technique includes calculation of weights for each feature set, the development of parametric study for optimization and simultaneously iterative reduction of unnecessary features having small weights. The classification algorithm is implemented to extract the various features of Android malware using naive Bayes classifier. Moreover, the naive Bayes classifier is based on decision trees for extracting more important features to provide classification and regression for achieving high accuracy and robustness. Finally, our proposed framework uses the permissioned blockchain to store authentic information of extracted features in a distributed malware database blocks to increase the run-time detection of malware with more speed and accuracy, and further to announce malware information for all users.
SSQA: Speech Signal Quality Assessment Method using Spectrogram and 2-D Convolutional Neural Networks for Improving Efficiency of ASR Devices

Most on-device and cloud processing based automatic speech recognition (ASR) systems had poor recognition performance due to the noisy speech signals corrupted by various kinds of background noises such as vehicle, train, aircraft, fan, wind, rain, air-conditioner, and machinery noises which are unavoidable realistic scenarios. In this paper, we propose a novel speech signal quality assessment (SSQA) method for automatically assessing the quality of a recorded speech signal before processing on-device and sending the recorded data to the cloud server. The proposed method is based on the spectrogram feature and two-dimensional convolutional neural networks (2D-CNNs). The proposed SSQA method is evaluated using a large scale of noise-free speech and noisy speech signals which are corrupted with various kinds of noises with different noise levels. Results show that the 2D-CNN based method had an average Se=90.92%, Sp=98.44% and OA =96.44%. The method had better results in detecting the noisy speech segments. Results showed that there is confusion in performing the manual labelling of noise-free and noisy speech segments. Therefore, the noise-free and noisy speech signals are given to the publicly available ASR system to obtain the corresponding text. Then the word error rate (WER) and character error rate (CER) metrics were used to know the level of noise wherein the ASR system fails to correctly recognize its text. In this way, the noise level is determined for each of the noises to label the recorded speech signal into acceptable and unacceptable speech segments. The proposed quality-aware ASR system has great potential in improving the lifetime of the battery of the portable ASR devices and reducing the bandwidth and speech recognition software utilization costs in the case of cloud processing based ASR system.
Radio Resource Scheduling for Narrowband Internet of Things Systems: A Performance Study

In the context of the IoT, mMTC technology in 5G enables a range of new applications. The 3GPP NB-IoT introduced in Release 13 is a major enhancement toward mMTC to support devices requiring low data rate, low cost, and long battery life. Previous works suggested that NB-IoT physical channel characteristics have potential to fulfill design requirements. From the system point of view, the interaction among UEs and application data transmission procedures have to be further evaluated considering radio resource scheduling. In this article, we identify radio resource scheduling issues for NB-IoT systems and provide a comprehensive performance evaluation. We consider control plane optimization procedures with a massive number of UEs in mixed CE levels. Using a scheduling architecture verified with derived effective data rates, we show the effects of essential parameters on control channel allocation and data scheduling. Given our scheduling settings in anchor carriers, the number of transmitted data packets achieves about 65,000 per cell per hour with mixed CE levels. The results satisfy the design goal of NB-IoT data transmission in 3GPP TR 45.820.
State-of-the-Art to Measure the TPACK Level of Trainees in Higher Education to Increase the Learnability of the Train-The-Trainer (TTT) Sessions

With the increased responsibilities in preparing students for the competitive job market and affecting success of their universities in the rankings of world-class universities, lecturers in higher education play an essential role. As one of the academic professional development methods, Train-the-Trainer (TTT) programs presented by SAP University Competence Center (UCC) at the Technical University of Munich offer an extensive knowledge and skills gain for the lecturers. Adopting the guided-learning methodology in its curriculum, TTT programs focus on teaching business processes through case studies. Considering lecturers’ background and different learning styles, TTT programs might show a gap in improving any of these three knowledge domains: technology, pedagogy and content. Aiming to design an ideal modularized curriculum, the authors identify the state-of-the-art teaching methods that can improve technological pedagogical content knowledge (TPACK) level of educators based on insights from literature review. Then they evaluate the TPACK level of thirty-two lecturers working in higher education both quantitatively and qualitatively. Finally, aiming to create a blended learning environment together with face-to-face trainings, the authors present the design of a modularized curriculum. This research study sheds light on the measurement of TPACK level of trainees in higher education, extends the knowledge base and facilitates the ground to improve SAP UCC trainings and the curriculum.
Exploring the Opportunities of Cisco Packet Tracer For Hands-on Security Courses on Firewalls

Along with teaching the fundamental and theoretical information security concepts, nowadays, hands-on lab activities are key to any information security education program. Moreover, the use of a hands-on approach that is based on a virtual or simulated lab environment is important. Such an environment offers a safer, more secure, and affordable solution to build security laboratories where students can experiment with offensive and defensive techniques. This is especially beneficial for academic institutions with limited lab budgets. Cisco Packet Tracer is an educational simulation tool that is designed to simulate the creation and testing of network architectures based on Cisco networking devices. This paper explores the opportunities of using this tool for hands-on information security education, along with teaching networking concepts. Practically, the capabilities of Cisco Packet Tracer for the development of hands-on lab activities on firewall concepts (Network traffic filtering) will be demonstrated. The goal is to use Cisco Packet Tracer to allow students to better understand the basic concepts of firewalls through experiments on in a virtual laboratory. This paper also discusses the impact of offering Cisco Packet Tracer to the students and the instructors’ opinions on the tool. Using the tool was found to help students understand course material easier at a lower cost but affected the amount of material that could be covered by the instructor.
Novel 0.064us Settling Time CMOS OP-AMP with 0.62 mW Power Consumption

We present a new analog design of an operational amplifier (OP-AMP), operating in the temperature range from - 55 °C to 225 °C due to the used technology process and its ability to support the temperature changes. The 0.064us settling time amplifier is developed in the standard process of 1um (Fully Depleted Silicon On Insulator) FDSOI CMOS and can be used in designing high-speed applications such as aerospace and automotive embedded applications. The proposed OP-AMP achieved 41.12 dB CMRR, the phase margin is more than 100 degrees for the DC gain of 13.97dB, which is a reasonable margin when temperature range increases. The OP-AMP biasing current of 70uA while consuming only 0.62 mW in a sub-threshold region with 3.75 V supply voltage including the cascode bias circuitry
A Novel Truncation Rule for the EMS Decoding of Non-binary LDPC Codes

This paper presents a novel truncation rule for the extended min-sum (EMS) decoding of non-binary low-density parity-check (NB-LDPC) codes. The conventional NB-LDPC decoders involve vector-messages of length equal to the field size $q$. The complexity of these decoders increases significantly as $q$ increases. In the EMS algorithm, the vector-messages are truncated by discarding the less-likely symbols. In this paper, we propose a truncation rule so that the lengths of the vector-messages are reduced to the so-far best limit without compromising on the error correction capability. We devise a proximity matrix which helps to identify the essential components of a vector-message for truncation. The proposed truncation rule has been applied to several NB-LDPC codes of different field sizes. Simulation results for these codes show that the proposed rule can achieve the maximum truncation with negligible performance loss.
A 31-level MLI Topology with Various Level-Shift PWM Techniques and its Comparative Analysis

This paper highlights the comparative analysis of carrier wave based modulation techniques on a single phase extendable reduced switch topology. The topology is implemented using level shift PWM (pulse width modulation) and a detailed analysis of voltage and current total harmonic distortion (THD) for each pulse width modulation (PWM) control strategy is carried out. The extendable topology uses 4 dc sources and 10 switching devices which helps to minimize the complexity of the circuit and overall cost. The performance of the switching strategies for 31-level is carried out in Matlab/Simulink.
A new FPGA-based Detection Method for Spurious Variations in PCBA Power Distribution Network

Nowadays, increasing demand for High-Performance Systems produces significant growth in usage of Field Programmable Gate Arrays (FPGAs) for different applications thanks to their flexibility and high level of parallelism. Such systems rely on complex multi-layer Printed Circuit Board Assemblies (PCBA)with a few dozens of hidden layers, stacked microvias and high-density interconnects. Along with creating new test challenges, the increasing PCBA complexity elevates the criticality of defects in various subsystems. One of such sub-systems is a Power-Delivery-Network (PDN) with operating margin progressively reduced due to increasingly strict requirements of High-Performance applications. As a consequence, Marginal Defects and process variations in a PDN may create latent problems that will manifest in a particular condition thus compromising the overall system performance and causing malfunctions. In this paper we propose a new FPGA-based non-intrusive method to detect Marginal Defects in a PCBA PDN. The method is based on a monitoring circuit that measures signal delays caused by PDN variations and thus detects relevant anomalies. Additional ad-hoc PDN stress circuits have been developed to validate the measurement technique. Experimental results demonstrating the consistency of the proposed approach are obtained by comparing stress and non-stress scenarios.
Evaluating Reasoning in Factoid based Question Answering System by Using Machine Learning Approach

in this paper; we present a closed domain factoid question answering system focusing on a why type Questions that can generate the answers of subjective type questions. The reasons we work on this research is that we want to show the system on what bases the why type Answers are generated this is because the strong requirement to satisfied in the early phase of system development. we follow the knowledge-based approach using TF-IDF algorithm and TF-IDF Vectorizer module we find that using the four parameter on the bases of because, neither, either, thoughts, and thus parameter is considered and lastly we present an evaluation of our system by giving precision, recall, & Accuracy of our system for why type questions.