Miracle-dz/UniFAR-FTUs huggingface dataset:
An Attempt to Create Speech Synthesis Model That Retains Lombard Effect Characteristics

The speech with the Lombard effect has been extensively studied in the context of speech recognition or speech enhancement. However, few studies have investigated the Lombard effect in the context of speech synthesis. The aim of this paper is to create a mathematical model that allows for retaining the Lombard effect. These models could be used as a basis of a formant speech synthesizer. The proposed models are based on dividing the speech signal into harmonics and modeling them as the output of a SISO system whose transfer function poles are multiple, and inputs vary in time. An analysis of the Lombard effect of the synthesized signal is performed on the noise residual. The synthesized signal residual is described by vectors of acoustic parameters related to the Lombard effect. For testing the performance of the created models in various noise conditions two classifiers are employed, namely kNN and Naive Bayes. For comparison of results, we created models of sinusoids based on frequency tracks. The results show that a model based on the residual sinewave sum demonstrates the possibility of retaining the Lombard effect. Finally, future work directions are outlined
Beyond De-Identification Record Falsification to Disarm Expropriated Data-Sets

The wild enthusiasm for big data and open data has brought with it the assumptions that the utility of data-sets is what matters, and that privacy interests are to be sacrificed for the greater good. As a result, techniques have been devised to reduce the identifiability of expropriated data-records, on the assumption that privacy is to be compromised to the extent necessary. This paper argues for and adopts data privacy as the objective, and treats data utility for secondary purposes as the constraint. The inadequacies of both the concept and the implementation of deidentification are underlined. Synthetic data and Known Irreversible Record Falsification (KIRF) are identified as the appropriate techniques to protect against harm arising from expropriated data-sets.
Power Control Identification: A Novel Sybil Attack Detection Scheme in VANETs Using RSSI

Vehicular ad hoc networks (VANETs) have far-reaching application potentials in the intelligent transportation system (ITS) such as traffic management, accident avoidance and in-car infotainment. However, security has always been a challenge to VANETs, which may cause severe harm to the ITS. Sybil attack is considered as a serious security threat to VANETs since the adversary can disseminate false messages with multiple forged identities to attack various applications in the ITS. RSSI-based Sybil nodes detection is an efficient scheme against Sybil attacks, which adopts position estimation, distribution verification or similarity comparison to identify Sybil nodes. However, when Sybil nodes conduct power control to deliberately change transmission powers, the received RSSI values would change correspondingly, which leads to inaccurate localization or different RSSI time series of these Sybil nodes. Thus, it is very difficult to differentiate Sybil nodes from normal nodes via conventional RSSI-based methods. This paper first discusses potential power control models (PCMs) for launching Sybil attacks in VANETs, then presents two simple Sybil attack models and three sophisticated Sybil attack ones with or without power control in detail, finally proposes a power control identification Sybil attack detection (PCISAD) scheme to find anomalous variations in RSSI time series, which are then used to identify Sybil nodes via a linear SVM classifier. Extensive simulations and real-world experiments prove that the proposed scheme can effectively deal with Sybil attacks with power control.
A survey on access control mechanisms for cloud computing

Cloud computing is an Internet‐based computing where the information technology resources are provided to end users following their request. With this technology, users and businesses can access programs, storage, and application development platforms through the Internet and via the services offered by the cloud service providers (CSPs). One of the biggest obstructions in the cloud computing environment is data security. Actually, the data are dispersed across multiple machines and storage devices such as servers, computers, and various mobile devices. The uncontrolled access to these resources and data leads to many important data security risks for the end users. In this way, and in order to ensure the reliability of the cloud and the trust of the users regarding this environment, controlling access to data and resources as well as protecting and ensuring their security becomes a critical task for CSPs. In this work, we present a comprehensive review of existing access control mechanisms used in the cloud computing environment. The advantages and disadvantages of each of these models are discussed and presented along with their analysis. Also, we study the cloud requirements of these models, and we evaluate existing control mechanisms against these requirements.
Adaptive sliding mode control for multi-UAV systems with parametric uncertainties

This paper proposes a parametric uncertain adaptive sliding mode control method for multi-UAVs formation. The tracking effect is well under the condition of uncertain inertial matrix parameters and external interference. An adaptive law is designed to estimate the inertial matrix parameters of each UAV in the formation on-line, and the estimated values are used in the sliding mode controllers, which guarantee that the tracking error is uniformly asymptotically converged to zero. Then the adaptive sliding mode control the stability performance of is proved by Lyapunov stability theory. Finally, simulation illustrated that under the uncertain conditions and external disturbances in the system, the method can achieve the Multi-UAVs formation with a stable attitude and speed synchronization.
AN INTELLIGENT AGENT FOR MONITORING AND DETECTING COUNTERFEIT PRODUCTS

In the world global market today, there is an increasing threat that product are not what consumer's and end users think they are, industries, manufacturing Companies as well as commercial Companies which run on a web based platform-face the challenges of faking their products, therefore causing downstream in sales and quality of products in the market. The aim of this research is to design and develop an intelligent agent for monitoring and detecting counterfeit products. In this project, Barcode technology was used to develop a web based intelligent agent for monitoring and detecting counterfeit products, Qualitative and quantitative research methodology was adopted in this research in which questionnaire was used to determine the impact of counterfeit products on consumers and business and also to ascertain the ability of the consumers to identify counterfeit products. Based on the findings from this research, it is shown that the application of this system in t he global market can help minimize and if possible eradicate counterfeit products from the global markets
Identifying Expressive Semantics in Orchestral Conducting Kinematics

Existing kinematic research on orchestral conducting movement contributes to beat-tracking and the delivery of performance dynamics. Methodologically, such movement cues have been treated as distinct, isolated events. Yet as practicing musicians and music pedagogues know, conductors’ expressive instructions are highly ﬂexible and dependent on the musical context. We seek to demonstrate an approach to search for effective descriptors to express musical features in conducting movement in a valid music context, and to extract complex expressive semantics from elementary conducting kinematic variations. This study therefore proposes a multi-task learning model to jointly identify dynamic, articulation, and phrasing cues from conducting kinematics. A professional conducting movement dataset is compiled using a high-resolution motion capture system. The ReliefF algorithm is applied to select signiﬁcant features from conducting movement, and recurrent neural network (RNN) is implemented to identify multiple movement cues. The experimental results disclose key elements in conducting movement which communicate musical expressiveness; the results also highlight the advantage of multi-task learning in the complete musical context over single-task learning. To the best of our knowledge, this is the ﬁrst attempt to use recurrent neural network to explore multiple semantic expressive cuing in conducting movement kinematics.
ESTIMATION OF INTER-CHANNEL PHASE SYNCHRONIZATION OF EEG SIGNALS IN THE RIDGES OF THEIR WAVELET SPECTROGRAMS IN PATIENTS WITH TRAUMATIC BRAIN INJURY

Identical inter-channel phase coherency of electroencephalogram (EEG) signals is determined for healthy subjects during cognitive and motor tests. EEG signal phase is evaluated at the points of it wavelet-spectrogram ridge. Areas of interest of the cortex at cognitive and motor tests for group of healthy subjects are determined. Inter-channel EEG phase coherency for patients after a traumatic brain injury are represented.
PENERAPAN METODE PROTOTYPE DALAM PERANCANGAN APLIKASI E-LAPOR BENCANA BPBD SUMEDANG BERBASIS ANDROID

The disaster that occurred in Sumedang district was the main concern of the services provided by the BPBD of Sumedang Regency, the handling of disasters that was fast and precise became the motto of BPBD. But in practice there are many obstacles, one of which is the lack of community participation in disaster reporting and many other technical constraints. Whereas with this reporting BPBD can quickly and responsively handle the disaster. E-disaster report based on Android BPBD Sumedang district is present as an electronic disaster reporting service to overcome problems in the BPBD of Sumedang district. This e-disaster report was built using a prototype software design methodology. This methodology is very suitable for software that continues to grow such as Android-based software. With this disaster report, people can easily report disasters. The location of the disaster can also be detected with the GPS feature on the Android smartphone. The community only needs to attach a photo of the event and the description of the incident and the report will be processed immediately by the investigating officer. With this application, Sumedang District people can participate in disaster reporting, and the BPBD of Sumedang District can be helped in carrying out the handling and handling of disasters quickly and precisely in Sumedang District.
Neural network learns from mock-up operation experience:implementing on a solar energy community distribution

Inspired by Imitation Learning, this paper trained a LSTM network by a mock-up operation experience of a solar energy community distribution system. Unlike the conventional method that implements LSTM only to predict features for the control programme to calculate an operation action according to a strategy, the LSTM of the proposed model integrates the strategy into its structure and thus can outputs actions directly. To examine whether the proposed model outperforms the conventional model, this paper first describes an operation strategy, adopted by both models, that aims to decrease total operation cost. Since the strategy needs accurate predictions to work effectively, an expert who can perfectly predict the future is created by historical data. The behaviours of the expert that follows the strategy are used as the training data of the LSTM in the proposed model. During simulation, the proposed model has better performance and computation efficiency than the conventional LSTM model by 25% higher and 75 times faster. Many researches have proposed control models for different systems and implemented LSTM only to predict key uncertainty in those models. To these researches, this paper demonstrates a promising result that the performance of a control model can be improved by integrating the strategy of that model into a neural network with mock-up operation experience.
High-performance, platform-independent DDoS detection for IoT ecosystems

Most Distributed Denial of Service (DDoS) detection and mitigation strategies for Internet of Things (IoT) are based on a remote cloud server or purpose-built middlebox executing complex intrusion detection methods, that impose stringent scalability and performance requirements on the IoT due to the vast amounts of traffic and devices to be handled. In this paper, we present an edge-based detection scheme using BPFabric, a high-speed, programmable data-plane switch architecture, and lightweight network functions to execute upstream anomaly detection. The proposed detection scheme ensures fast detection of DDoS attacks originated from IoT devices, while guaranteeing minimum resource usage and processing overhead. Our solution was compared against two widespread coarse-grained detection techniques, showing detection delays under 5ms, an overall accuracy of 93 − 95% and a bandwidth overhead of less than 1%.
Sparsely Self-Supervised Generative Adversarial Nets for Radio Frequency Estimation

Radio frequency (RF) estimation plays a significant role in cellular network’s planning and optimization. The conventional methods for RF estimation are mainly based on radio propagation models, which suffer from low accuracy and coarse granularity. Distinguished from existing methods, we propose sparsely self-supervised generative adversarial nets (SS-GAN), a novel data-driven model to generate the RF maps of an area from irregularly distributed measurement samples. SS-GAN meticulously adopts the standard GAN framework, where the generator learns the distribution of true observations under the guidance of the discriminator that discriminates whether the input data is from real samples or from generated outputs. Competition in the minmax game between generator and discriminator drives them to improve their capability, until the generator is indistinguishable from the true RF distribution. Specifically, on top of the GAN framework, SS-GAN carries out a variety of operations to enhance the estimation: (1) In addition to observations of the measured RF coverage and RF interference, SS-GAN also employs a collection of crucial auxiliary information (e.g., geographic data) as additional input features to the GAN framework so as to precisely characterize the RF environment; (2) To dampen the training instability, a new lightweight reconstruction loss is introduced to the objective function of SS-GAN rather than solely using the adversarial loss, which aims to impose a weak supervision on the generated RF maps according to estimation accuracy; (3) Moreover, SS-GAN designs an innovative sparsely self-supervised ( ${\mathcal {SS}}$ ) learning mechanism that facilitates the validation of the estimated results for a model lacking direct ground truth knowledge. Extensive experiments on a real-world 4G LTE dataset demonstrate that SS-GAN can substantially improve the estimation accuracy over the state-of-the-art baselines. Comparison results are presented through visualized case studies and quantitative statistics.
An Inductorless 20-Gb/s CDR With High Jitter Tolerance

A full-rate clock and data recovery loop employs a three-stage ring voltage-controlled oscillator, a master–slave passive sampler as both a phase detector and a filter, and a new flip-flop to achieve a loop bandwidth of 170 MHz. Implemented in 45-nm CMOS technology, the circuit occupies an area of 14 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}\,\,\times $ </tex-math></inline-formula> 26 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> and exhibits a jitter tolerance of 2 UI at 5 MHz and a recovered clock jitter of 459 fs with 2<sup>31</sup> – 1 pseudorandom bit sequence.
Image retrieval based on convolutional neural network and linear discriminate analysis

Image retrieval is a hot research topic in the field of computer vision image processing, and the user queries the image database for similar images and produces a list of recommendations. The paper firstly sets forth the research status of image retrieval, then the convolution neural network is briefly introduced. Due to the traditional image retrieval and recommendation system use manual extraction of image features is relatively cumbersome, and the retrieval accuracy is not high research status, the paper proposes an image retrieval method based on the improved convolutional neural network and linear discriminate analysis. Caltech256 and CIFAR-10 datasets were trained using the model in this paper, experimental, results show that the proposed method can effectively improve the performance of retrieval.
Aptamers for Targeted Delivery: Current Challenges and Future Opportunities

Aptamers are synthetic ssDNA/RNA molecules that are emerging as novel tools for the development of therapeutics, especially for targeted delivery. Aptamers are comparable to monoclonal antibodies, which are well-established therapeutic molecules, in terms of specificity and affinity to their target. The advantage of aptamers over antibodies includes their high stability, ease of synthesis, less batch-to-batch variation, easy chemical modifications that allow different conjugation chemistries, small size for better tissue penetration and low immunogenicity. These advantages make aptamers an important tool for use in therapeutics for targeted delivery. However, aptamers do have some limitations that have hindered their widespread clinical use as a therapeutic agent. Some of their common limitations include serum stability, renal filtration and endocytic escape. Other limitations that are more specific to aptamers include lack of diversity in the aptamer library, nuclease susceptibility and claims of aptamer specificity as well. This book chapter sheds light on these challenges, and using examples, it explains the scientific advancements that have been achieved in overcoming these limitations. We will end this chapter by discussing the use of high-throughput technology, which is the only way of truly industrializing the aptamer technology akin to the development of small molecule drugs. levels in vitro and in vivo , both. Findings from this work suggest that properties of RNA nanoparticles may be tuned for safe
Advanced Statistical Inference

Content This course covers topics in statistical theory essential for graduate students. The course introduces some fundamental concepts in statistical decision theory and inference, and contains detailed studies on some important topics: unbiased estimation, parametric estimation, nonparametric estimation, hypothesis testing, and confidence sets. In addition to the classical results, some topics in modern statistical theory are also introduced; including Markov chain Monte Carlo, quasi-likelihoods, empirical likelihoods, semiparametric models, statistical functionals, generalized estimation equations, the jackknife, and the bootstrap, together with a large number of exercises which includes many additional results.
OPTIMAL DESIGN OF HIGH PERFORMANCE INTERIOR PM MOTOR FOR ELECTRIC VEHICLE

Interior permanent magnet synchronous motors (IPMSMs) are widely used in electric vehicles (EVs) due to their electrical and mechanical characteristics. In addition to the magnetic torque generated by the magnets in the IPMSM motors, a higher torque density is achieved by generating reluctance torque resulting from the inductance variation of the rotor on the d-q axes. Therefore, rotor and magnet geometry have significant effects on the motor performance. In an IPM design, it is often desirable to have maximum torque and flux weakening capability, minimum torque ripple, and low rotor harmonic losses. For this reason, rotor geometry is the most important part that affects the overall performance of the motor. In this study, a 100kW, single layer design V-shape structure IPMSM has been designed. The motor is optimized by considering important geometric data such as rotor geometry, magnet dimensions, bridge and flux barrier. Thus, it is aimed to design an optimum IPM traction motor considering efficiency and cost criteria. Also, the study provides design criteria that can meet low leakage flux and low cogging torque requirements for rotor topologies with V-shaped structures. 2D finite element analyzes have been used to verify the theoretically optimized designs.
Performing Stochastic Computation Deterministically

Stochastic logic performs computation on data represented by random bit-streams. The representation allows complex arithmetic to be performed with very simple logic, but it suffers from high latency and poor precision. Furthermore, the results are always somewhat inaccurate due to random fluctuations. In this paper, we show that randomness is <italic>not</italic> a requirement for this computational paradigm. If properly structured, the same arithmetical constructs can operate on <italic>deterministic</italic> bit-streams, with the data represented uniformly by the fraction of 1’s versus 0’s. This paper presents three approaches for the computation: relatively prime stream lengths, rotation, and clock division. Unlike stochastic methods, all three of our deterministic methods produce completely accurate results. The cost of generating the deterministic streams is a small fraction of the cost of generating streams from random/pseudorandom sources. Most importantly, the latency is reduced by a factor of <inline-formula> <tex-math notation="LaTeX">$({1}/{2^{n}})$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the equivalent number of bits of precision. When computing in unary, the bit-stream length increases with each level of logic. This is an inevitable consequence of the representation, but it can result in unmanageable bit-stream lengths. We discuss two methods for maintaining constant bit-streams lengths via approximations, based on low-discrepancy sequences. These methods provide the best accuracy and area <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> delay product. They are fast-converging and therefore offer progressive precision.
Cloneless: Code Clone Detection via Program Dependence Graphs with Relaxed Constraints

Cloneless: Code Clone Detection via Program Dependence Graphs with Relaxed Constraints Thomas Simko Code clones are pieces of code that have the same functionality. While some clones may structurally match one another, others may look drastically different. The inclusion of code clones clutters a code base, leading to increased costs through maintenance. Duplicate code is introduced through a variety of means, such as copy-pasting, code generated by tools, or developers unintentionally writing similar pieces of code. While manual clone identification may be more accurate than automated detection, it is infeasible due to the extensive size of many code bases. Software code clone detection methods have differing degree of success based on the analysis performed. This thesis outlines a method of detecting clones using a program dependence graph and subgraph isomorphism to identify similar subgraphs, ultimately illuminating clones. The project imposes few constraints when comparing code segments to potentially reveal more clones.