Miracle-dz/UniFAR-FTUs huggingface dataset:
CycleGAN-VC: Non-parallel Voice Conversion Using Cycle-Consistent Adversarial Networks

We propose a non-parallel voice-conversion (VC) method that can learn a mapping from source to target speech without relying on parallel data. The proposed method is particularly noteworthy in that it is general purpose and high quality and works without any extra data, modules, or alignment procedure. Our method, called CycleGAN-VC, uses a cycle-consistent adversarial network (CycleGAN) with gated convolutional neural networks (CNNs) and an identity-mapping loss. A CycleGAN learns forward and inverse mappings simultaneously using adversarial and cycle-consistency losses. This makes it possible to find an optimal pseudo pair from non-parallel data. Furthermore, the adversarial loss can bring the converted speech close to the target one on the basis of indistinguishability without explicit density estimation. This allows to avoid over-smoothing caused by statistical averaging, which occurs in many conventional statistical model-based VC methods that represent data distribution explicitly. We configure a CycleGAN with gated CNNs and train it with an identity-mapping loss. This allows the mapping function to capture sequential and hierarchical structures while preserving linguistic information. We evaluated our method on a non-parallel VC task. An objective evaluation showed that the converted feature sequence was near natural in terms of global variance and modulation spectra, which are structural indicators highly correlated with subjective evaluation. A subjective evaluation showed that the quality of the converted speech was comparable to that obtained with a Gaussian mixture model-based parallel VC method even though CycleGAN-VC is trained under disadvantageous conditions (non-parallel and half the amount of data).
Multi-Spectral Fusion and Denoising of RGB and NIR Images Using Multi-Scale Wavelet Analysis

In this paper, we propose multi-spectral fusion and denoising (MFD) of RGB and NIR images using multi-scale wavelet analysis. We formulate MFD of RGB and NIR images as a maximum a posterior (MAP) estimation problem in the wavelet domain. The direct fusion of noisy RGB and NIR image often leads to contrast attenuation due to the discrepancy between RGB and NIR images. Thus, we generate the wavelet scale map for fusion and denoising based on correlation between NIR and RGB wavelet coefficients. To consider local contrast and visibility of NIR data on RGB components, we provide the contrast preservation term for scale map estimation based on the local contrast and visibility. We use the regularization term to select high visibility and contrast of NIR wavelet coefficients in the scale map. Since noise generally appears in the high frequency band, we use gradients of NIR wavelet coefficients as the weight for weighted least square (WLS) smoothing in the scale map. Based on the wavlet scale map, we perform fusion and denoising of RGB and NIR wavelet coefficients. Experimental results show that the proposed method successfully performs fusion of RGB and NIR images with noise reduction and detail preservation as well as outperforms state-of-the-arts in terms of discrete entropy (DE) and feature-based blind image quality evaluator (FBIQE).
Analysis of vulnerabilities in MQTT security using Shodan API and implementation of its countermeasures via authentication and ACLs

Among the technologies evolved in the recent years, a remarkable one is the IoT (Internet of Things), wherein the ‘thing’ in IoT could be smart phones, tablets, PCs and almost anything with a sensor on it like cars, people, machines in production plants, jet engines, oil drills, wearable devices and many more objects. A standardized, light-weight, session layer protocol with publish/subscribe architecture widely used for messaging and information exchange among IoT devices is the MQTT (MQ Telemetry Transport) protocol. In this paper, we identify various security loopholes in MQTT, using Shodan API and implementing an experimental setup on a Raspberry Pi as an MQTT Broker and python programs as publisher/subscriber clients. The experimental results with respect to the security issues in this protocol at packet and topic levels were studied and the corresponding security measures, consisting of authentication and authorization techniques (ACLs) were implemented. As a result, the Broker was then found to be immune to such attacks. This paper is a concise study of security inconsistencies in MQTT and its countermeasures.
Sparse Beamforming for mm Wave Spectrum Sharing Systems

This paper deals with the problem of sparse beamforming for interference mitigation in milimiter wave (mm Wave) frequency bands. Multiantenna solutions in mmWave are generally implemented with phase shifters which are known to have good interference rejection capabilities. However, phase shifters are power-demanding components and, depending on their resolution, they require bulky hardware solutions able to accommodate the control lines. On the other hand, the use of switches leads to a cost-efficient alternative able to provide a sufficiently large interference rejection while substantially reducing the hardware cost and power consumption. This work proposes a beamforming scheme able to maximize the signal-to-interference-plus-noise ratio (SINR) assuming that the beamforming weights can only take 0 and 1 values. The resulting optimization problem is a binary quadratic fractional problem which is a difficult non-convex problem. Two optimization approaches are proposed; namely, the semidefinite relaxation and the penalized convex-concave procedure. We show that both techniques behave well in the considered scenarios and their performance is close to the optimization problem upper bound value.
Modified Reduced Switch Converter for Grid Interfaced Systems

In this work, a modified packed U-cell (MPUC) converter is compared to packed U-cell converter (PUC), which is interfaced to the grid. Here, there are two DC sources one solar photovoltaic (PV) array and another is DC link capacitor. The main difference between PUC and MPUC based converter is that MPUC has reduced DC link voltage value. Here, segregation of each cell is done in three different parts whose structure is in U-shape. Each U-shape cell has two active switches and in total there are six active switches. It is better way than conventional multilevel converters as MPUC requires less count of switches to produce output voltage levels for grid interfaced system. Moreover, MPUC feasibility for proper voltage regulation, make MPUC an appropriate solution for the grid interfaced system, which is cost effective by usage less active switches in order to obtain desired voltage levels. Power quality can be quantified in terms of total harmonic distortion (THD) of grid current and converter voltage under the limits of the IEEE-519 standard.
Novel Computational-Index as a Representative Feature for Non-Intrusive Load Monitoring

This paper presents the development of novel extraction feature used for load disaggregation from aggregated demand profile. Non-intrusive load monitoring tackles the issue of appliances identification inside a residential building. New extraction features used for load identification are based on power, V-I mutual locus and wave-shape features for specific appliances which offers better or generally comparable results in comparison to popular methods. A novel dynamic computational index is developed to achieve a robust feature extraction using V-I mutual locus. Power patterns are used for identification of multi-state devices. Specific screening and classification of extraction features are so developed to get better load disaggregation and lesser computational time. Multi-label classifications approach has been used for the same. The methodology is compared based on precision, classification accuracy, and robustness using experimental data.
A Wasserstein GAN model with the total variational regularization

It is well known that the generative adversarial nets (GANs) are remarkably difficult to train. The recently proposed Wasserstein GAN (WGAN) creates principled research directions towards addressing these issues. But we found in practice that gradient penalty WGANs (GP-WGANs) still suffer from training instability. In this paper, we combine a Total Variational (TV) regularizing term into the WGAN formulation instead of weight clipping or gradient penalty, which implies that the Lipschitz constraint is enforced on the critic network. Our proposed method is more stable at training than GP-WGANs and works well across varied GAN architectures. We also present a method to control the trade-off between image diversity and visual quality. It does not bring any computation burden.
An Efficient Scheme for BBU-RRH Association in C-RAN Architecture for Joint Power Saving and Re-Association Optimization

A key drive towards 5G networks is the Cloud Radio Access Networks architecture (C-RAN). It consists in decoupling two main elements composing the base station: The Base Band Unit (BBU) and the Remote Radio Head (RRH), allowing for centralized processing of BBUs and efficient resource management. The BBU-RRH associations are optimized in this context, where many RRHs are associated to a single BBU when network load conditions are low. Many studies address the BBU-RRH association problem, also known as RRH clustering, for enhancing the energy efficiency. However, rare contributions seek to optimize the BBU-RRH re-association due to the dynamicity of network load conditions. In fact, frequent re-associations between BBUs and RRHs cause a serious degradation in the Quality of Service (QoS) since connected User Equipments (UEs) experience costly handovers (HOs) to different BBUs. This paper tackles such problem by proposing a hybrid algorithm that is divided into two stages: The first is based on a potential game among RRHs to associate to active BBUs, while the second is a centralized scheme, where a central controller decides of the activation/deactivation of BBUs. We compare the results to a previous centralized proposition and to a classical formulation of BBU-RRH association problem as a bin packing optimization. The results show a good tradeoff between power saving and HO rate reduction. In addition, our proposition outperforms the performances of the bin packing problem.
Performance analysis of mobile 4G/LTE networks

Today, the biggest challenge for MNOs (Mobile Network Operators) is to provide broadband services with high performance. The fourth generation mobile network (4G/LTE) has been developed to meet user requirements by offering high-speed data transfer services. The Key Performance Indicators (KPI) are needed to monitor and optimize mobile network performance in order to provide high quality services and to achieve better resource utilization. These indicators are standardized by the Third Generation Partnership Project (3GPP) and typically can be categorizes into following subcategories: accessibility, retainability, mobility, integrity and availability. Some of these indicators are measured at the network level and others at the cell group level (cluster). For this study we have used a case study of Telecom of Kosovo (TK) mobile operator. Measurements and analysis are focused on a 24-cell cluster of 4G/LTE network implemented in TK. In this paper we will try to find out if the performance of the 4G/LTE network performs within the recommended values of 3GPP standard and manufacturers.
Efficiency of Contactless Power Transmission in a Long Distance

This paper describes a power transmission in a long distance of the contactless power transmission by magnetic resonance method. The efficiency of magnetic resonant coupling method is high, however, the efficiency starts to decrease gradually at certain distance. This is because the increase of leakage flux of the sending coil. In this paper, the leakage flux and transmission performance in a long distance are investigated by simulation and experiment.
A Multivalued Emotion Lexicon Created and Evaluated by the Crowd

Sentiment analysis aims to uncover emotions conveyed through information. In its simplest form, it is performed on a polarity basis, where the goal is to classify information with positive or negative emotion. Recent research has explored more nuanced ways to perform emotion analysis. Unsupervised emotion analysis methods require a critical resource: a lexicon that is appropriate for the task at hand, in terms of the emotional range and diversity captured. Emotion analysis lexicons are created manually by domain experts and usually assign one single emotion to each word. We propose an automated workflow for creating and evaluating a multi-valued emotion lexicon created and evaluated through crowdsourcing. We compare the obtained lexicon with established lexicons and appoint expert English Linguists to assess crowd peer-evaluations. The proposed workflow provides a quality lexicon and can be used in a range of text property association tasks.
Access Control Over Secured Data in Public Cloud Server

Data access control has become a very challenging issue in public cloud storage systems. Nowadays Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been adopted. Because in the existing Ciphertext - Policy Attribute-Based Encryption schemes, there exists a single authority and this authority will be responsible for the verification of users, the generation and also the distribution of secret keys to the users. Users will have to wait for a long time to obtain their secret keys which results in the reduction of the efficiency of the system. Even though many multi authority schemes have been suggested, these schemes are still unable to reduce the drawbacks of the existing scheme and also the lower efficiency, because each of the authorities independently manages a separate attribute set. In this paper we propose a framework to eliminate the overload on single authority and enhance the efficiency. This framework aims in providing multiple attribute authorities to share the workload of user legitimacy verification. And we also introduce a central authority in our scheme to distribute the secret keys for the users whose identity is verified. In our scheme, each authority manages the whole attribute set.
Three-Dimensional Pipeline ADC Utilizing TSV/ Design Optimization and Memristor Ratioed Logic

High-performance, low-power-consumption, and high-accuracy analog-to-digital converters (ADCs) with a compact area are necessary for a wide range of current applications. This paper presents a pipeline ADC architecture with a novel 3-D clock distribution network utilizing through-silicon via-induced benefits. It also implements memristor ratioed logic as the basic elements of digital error correction subblock to further decrease the area, delay, and power consumption. In addition, an optimization technique using computational intelligence is applied to maximize the overall system performance. The proposed 3-D pipeline ADC is designed in a 65-nm CMOS technology and shows significant improvement regarding dynamic performance, energy efficiency, area, and clock accuracy compared with that of conventional 2-D ADC designs.
Assembling and Using a Cellular Dataset for Mobile Network Analysis and Planning

In a world of open data and large-scale measurements, it is often feasible to obtain a real-world trace to fit to one's research problem. Feasible, however, does not imply simple. Taking next-generation cellular network planning as a case study, in this paper we describe a large-scale dataset, combining topology, traffic demand from call detail records, and demographic information throughout a whole country. We investigate how these aspects interact, revealing effects that are normally not captured by smaller-scale or synthetic datasets. In addition to making the resulting dataset available for download, we discuss how our experience can be generalized to other scenarios and case studies, i.e., how everyone can construct a similar dataset from publicly available information.
High-Efficiency Dual Output Stator-PM Machine for the Two-Mode Operation of Washing Machines

This paper proposes the novel topology of a high-efficiency dual output stator-permanent magnet (PM) machine (DOSPMM) for two different washing machine operating speeds. The proposed machine consists of two machines integrated into a single machine: Machine I is an outer rotor type Vernier machine with low speed and high torque for washing mode operations. Machine II is an inner rotor type flux-reversal PM (FRPM) machine with high speed and low torque for dehydrating mode operations. Each machine was designed to be highly efficient for the assigned operating mode. Both machines also possess robust rotors due to stator positioned PMs. To demonstrate the superiority of the proposed machine, performance comparisons with conventional machines for general washing machine applications were performed via finite-element method (FEM) analysis. A prototype of the proposed machine was built and experimental results of the prototype machine were presented for a validation of the work.
Measuring the Stability of EHR- and EKG-based Predictive Models

Databases of electronic health records (EHRs) are increasingly used to inform clinical decisions. Machine learning methods can find patterns in EHRs that are predictive of future adverse outcomes. However, statistical models may be built upon patterns of health-seeking behavior that vary across patient subpopulations, leading to poor predictive performance when training on one patient population and predicting on another. This note proposes two tests to better measure and understand model generalization. We use these tests to compare models derived from two data sources: (i) historical medical records, and (ii) electrocardiogram (EKG) waveforms. In a predictive task, we show that EKG-based models can be more stable than EHR-based models across different patient populations.
Endoplasmic reticulum exit of glycosylphosphatidylinositol-anchored proteins in the yeast 'Saccharomyces Cerevisiae'

Lors de ce travail de these, nous avons etudie la sortie du Reticulum Endoplasmique (RE) des proteines a ancrage Glycosyl Phosphatidyl Inositol (GPI), et plus particulierement le phenomene de triage des proteines dans la levure. Apres l'etablissement d'un nouveau protocole de visualisation des proteines a leur sortie du RE par microscopie, nous avons constate que contrairement a la plupart des molecules cargos, la concentration des proteines a ancrage GPI dans les sites de sortie du RE (ERES) est independante du complexe proteique COPII. En revanche le remodelage des chaines lipidiques de l'ancre GPI est un pre-requis a l'incorporation des proteines a ancrage GPI aux ERES. Ces resultats suggerent que l'incorporation des proteines a ancrage GPI dans des domaines specifiques riches en ceramides de la membrane du RE est necessaire a la sortie du RE et au triage des proteines a ancrage GPI.
Image Forensics in Online News

Recognizing fake images in online news is a challenging problem. This is especially true in the case of critical situations, when journalists might insert high-impact images to make a piece of news more appealing to the readers, neglecting to check their authenticity and provenance. Given the importance of this task, in the literature, it is possible to find several attempts to solve the problem from different points of view. This paper faces the specific problem of recognizing images in online news which have been modified or mis-contextualized, i.e. images taken in a different place and/or time with respect to the event to which they are associated. To identify image tampering a number of image forensic techniques were exploited and combined. On the other hand, for mis-contextualization detection, a textual analysis approach is proposed based on the extraction of features from the news the image is associated with, and from textual information retrieved online using the image at stake as pivot. The obtained results are rather satisfactory on laboratory data, with results that in some cases improve the state of the art for image forensics. The method was tested on three datasets, one of which already used in the literature, while the others created ad-hoc to further investigate its performances.
Unsupervised calibration of RGB-NIR capture pairs utilizing dense multimodal image correspondences

In this paper, we propose an unsupervised calibration framework aimed at calibrating RGB plus Near-InfraRed (NIR) capture setups. We favour dense feature matching for the case of multimodal data and utilize the Scale-Invariant Feature Transform (SIFT) flow, previously developed for matching same-category image objects. We develop an optimization procedure that minimizes the global disparity field between the two multimodal images in order to adapt SIFT flow for our calibration needs. The proposed optimization substantially increases the number of inliers and yields more robust and unambiguous calibration results.
PVRNet: Point-View Relation Neural Network for 3D Shape Recognition

Three-dimensional (3D) shape recognition has drawn much research attention in the field of computer vision. The advances of deep learning encourage various deep models for 3D feature representation. For point cloud and multi-view data, two popular 3D data modalities, different models are proposed with remarkable performance. However the relation between point cloud and views has been rarely investigated. In this paper, we introduce Point-View Relation Network (PVRNet), an effective network designed to well fuse the view features and the point cloud feature with a proposed relation score module. More specifically, based on the relation score module, the point-single-view fusion feature is first extracted by fusing the point cloud feature and each single view feature with point-singe-view relation, then the pointmulti- view fusion feature is extracted by fusing the point cloud feature and the features of different number of views with point-multi-view relation. Finally, the point-single-view fusion feature and point-multi-view fusion feature are further combined together to achieve a unified representation for a 3D shape. Our proposed PVRNet has been evaluated on ModelNet40 dataset for 3D shape classification and retrieval. Experimental results indicate our model can achieve significant performance improvement compared with the state-of-the-art models.
Performance Comparison and Analysis of Slowloris, GoldenEye and Xerxes DDoS Attack Tools

DDoS attack has been the most preferred attack by the hackers in the recent years. This is due to its ability to create multitude and variety of problems. A large group of hackers and experts in this field have developed packages and tools that initiate DDoS attack on various type of networks. It is essential to evaluate and compare the strength of DDoS attack launched by these tools to devise efficient countermeasures against it. In this paper, the performance of three DDoS attack tools is compared and analyzed using parameters such as time to successfully launch attack, traffic rate, and packet size. The DDoS tools considered for evaluation are Slowloris, GoldenEye and Xerxes. The experimental results infer that Xerxes outperforms other tools in launching a DDoS attack.