Miracle-dz/UniFAR-FTUs huggingface dataset:
Spoken Language Identification Based on I-vectors and Conditional Random Fields

The task of an automatic language identification (LID) system is to automatically identify the language in a spoken utterance. Language identification can be applied as front-end to speech-to-speech translation systems, in speaker diarization, and at call centers to automatically route incoming calls to appropriate native speaker operators. In the current study, a method for automatic language identification based on i-vector paradigm and conditional random fields (CRF) is presented. CRF belong to discriminative classifiers and use an exponential distribution to model a sequence given the observation sequence. This allows non-independent observations, and allows also non-local dependencies between state and observation. When the proposed method is evaluated on the NIST 2015 i-vector Machine Learning Challenge task for the recognition of 50 in-set languages, a 3.7% equal error rate (EER) (i.e., miss probability equal to false alarms) is achieved. Using support vector machines (SVM) a 5.2% EER, using probabilistic linear discriminant analysis (PLDA) a 6.7% EER, and when using convolutional neural networks (CNN) a 4.2% EER are achieved.
ThingsGame: when sending data rate depends on the data usefulness in IoT networks

Internet of Things (IoT) is an emerging paradigm that aims at making objects in the world to be connected through Internet. IPv6 over Low-power Wireless Personal Area Networks (6LoWPAN) is considered as one of the common protocol stack suite for IoT applications. The 6LoWPAN network is implemented on the top of IEEE 802.15. 4 standard in order to alleviate the challenges of connecting resource constrained objects to the Internet. In such a network, nodes are competing to send their sensed data as high as possible in a selfish way. However, high network data traffic degrades network performance and quality of service aspects, e.g., data sending rate, network latency and reliability, and energy consumption. In this paper, we formulate the sending rate adjustments as a non-cooperative game where each node is modeled as a player in the game and demands high data sending rate in a selfish way. The basic idea of our scheme is to adjust the data sending rate according to the preferences of nodes to send high data rate, the quality of data in terms of similarity, and nodes priorities in the targeted IoT application. We then prove the existence and uniqueness of Nash equilibrium before computing the optimal sending rate using Lagrange multipliers and KarushKuhnTucker (KKT) conditions. We called our game-based scheme ThingsGame. We validate and evaluate ThingsGame scheme in the IoT operating system Contiki OS using Cooja simulator. Simulation results show that ThingsGame improves significantly network performance in terms of overall throughput, energy consumption, number of lost packets, as compared to the Selfish way scheme.
Flexible Signals and Images Lossless Compression Chip Design for IoT and Industry 4.0

This paper presents a lossless compression algorithm and its chip design for signals and images applied to Internet of Things (IoT) and industry 4.0. Since the sensing signals, such as thermal, photo, gas, etc., are important for IoT and industry 4.0, a lossless signal compression technique based on an adaptive prediction and a hybrid entropy coding is proposed in this study. In addition, since applications of images are more and more important for IoT and industry 4.0, a flexible lossless image compression technique based on pixel restoration, median edge detection, and a hybrid entropy coding was developed for the proposed algorithm. The proposed algorithm can provide lossless compression for images in different types which includes grayscale, color (RGB), color filter array (CFA), and infrared images. In order to meet the demands of tiny and low-power-consumption for IoT, the proposed algorithm was realized by VLSI technique. A hardware sharing and pipeline scheduling techniques were used to reduce hardware cost and improve the operating frequency of the proposed design. The proposed design was synthesized by using a TSMC 0.18-$\mu \text{m}$ CMOS process and it contained 5.46 k gate counts. Compared with previous JPEG-LS based designs, this work has benefits of better compression rates, lower cost, lower memory requirement and more lossless compression functions than previous designs. It is suitable for development of IoT and industry 4.0.
Tight MMSE Bounds for the AGN Channel Under KL Divergence Constraints on the Input Distribution

Tight bounds on the minimum mean square error for the additive Gaussian noise channel are derived, when the input distribution is constrained to be ε-close to a Gaussian reference distribution in terms of the Kullback-Leibler divergence. The distributions that attain the bounds are shown be Gaussian whose means are identical to that of the reference distribution and whose covariance matrices are defined implicitly via systems of matrix equations. The estimator that attains the upper bound is identified as a minimax optimal estimator that is robust against deviations from the assumed prior. The lower bound is shown to provide a potentially tighter alternative to the Cramér-Rao bound. Both properties are illustrated with numerical examples.
Rotor Thermal Design for Electric Machines: Challenges and Opportunities

As the power density of electric machines increases, rotor thermal analysis becomes critical as losses caused by higher fundamental frequencies increase. The rotor often has poor thermal communication with the stator cooling medium, and therefore alternative cooling methods may be required in high power density machines to maintain safe operating temperature, especially in rotor mounted permanent magnet motors. In this paper, a review of rotor cooling methods and thermal design considerations is discussed, along with key tools used in thermal analysis of rotors for electric machines.
Application of Active Learning for Churn Prediction with Class Imbalance

Churn prediction is a major focus that all the companies need to concern. Many studies have shown that class imbalance has a significant impact on churn prediction, but there is still no consensus on which technique is the best to cope with this issue. Recently, active learning has proved to be effective for imbalance learning. We try to apply it to churn prediction in this paper. In order to verify its effectiveness, we carry out experiments on six real-world data sets from the telecommunication industry and compare its performance with the other three benchmark resampling methods. Besides using the AUC to measure the accuracy of classification, we take the profit-based measure --- expected maximum profit (EMP) into account which measures the real cost and benefit produced by the models. The experimental results show that active learning is a good choice in dealing with the class imbalance problem in churn prediction.
DC-DC Converter Based Impedance Matching for Maximum Power Transfer of CPT System with High Efficiency

It is well known that a DC or fully tuned inductive power transfer (IPT) system operating at its maximum power output point results in a low power efficiency being equal or below 50%. Based on the analyses of the maximum power transfer condition against the detuning factor of an LC resonant circuit, this paper proposes a Capacitive Power Transfer (CPT) system which can track the maximum power output using a DC-DC converter, at the same time achieve a high-power efficiency when the circuit is not fully tuned. The method is applied to a voltage-fed inverter at series tuned off-resonance operation for reduced power losses. A prototype system is built with a half-bridge inverter to drive the capacitively coupling circuit, and a buck-boost converter is designed for impedance matching. A perturb and observe algorithm is developed for controlling the duty cycle of the DC-DC converter to track the maximum power against load variations. It is found that under a fixed electric field coupling condition, the system can maintain the maximum power transfer operation at 10W and achieve a power efficiency of about 70% over a wide range of load variation from $5\mathbf{\Omega}$ to $500\mathbf{\Omega}$.
Gaussian Residual Bidding Based Coalition for Two-Settlement Renewable Energy Market

To stabilize the output variability of renewable energy sources (RESs), distributed RESs can be aggregated and treated as any other conventional generators in the existing electricity markets. This paper proposes a coalitional framework to cope with the uncertainty of distributed RESs. We consider the case when RES owners participate in a two-settlement wholesale market, and a market operator financially penalizes the deviation of real-time generation from the day-ahead contract. Under the two-settlement market, we show that the grand coalition is stable and the core is non-empty so that there exists a revenue allocation vector that incentivizes the RES owners to stay in the grand coalition. Then, we propose a bidding strategy called Gaussian residual bidding (GRB) to maximize the coalition gain under different price-penalty ratio in the two-settlement process. We show that the GRB strategy makes the game a convex cooperative game. Thus the Shapley value lies within the core and can be used for fair revenue allocation. Our extensive simulations with real data demonstrate that the GRB strategy combined with the coalitional framework substantially increases both social welfare and individual revenue under various market scenarios compared to existing empirical quantile bidding and the forecast bidding strategies.
On Approximate Nonlinear Gaussian Message Passing on Factor Graphs

Factor graphs have recently gained increasing attention as a unified framework for representing and constructing algorithms for signal processing, estimation, and control. One capability that does not seem to be well explored within the factor graph tool kit is the ability to handle deterministic nonlinear transformations, such as those occuring in nonlinear filtering and smoothing problems, using tabulated message passing rules. In this contribution, we provide general forward (filtering) and backward (smoothing) approximate Gaussian message passing rules for deterministic nonlinear transformation nodes in arbitrary factor graphs fulfilling a Markov property, based on numerical quadrature procedures for the forward pass and a Rauch-Tung-Striebel-type approximation of the backward pass. These message passing rules can be employed for deriving many algorithms for solving nonlinear problems using factor graphs, as is illustrated by the proposition of a nonlinear modified Bryson-Frazier (MBF) smoother based on the presented message passing rules.
Investigating the Reason for Increased Postural Instability in Virtual Reality for Persons with Balance Impairments

The objective of this study is to investigate how different visual components of Virtual Reality (VR), such as field of view, frame rate, and display resolution affect postural stability in VR. Although previous studies identified these visual components as some of the primary factors that differ significantly in VR from reality, the effect of each component on postural stability in VR is yet unknown. While most people experience postural instability in VR, it is worse for people with balance impairments (BIs). This is likely because they depend more on their visual cues to maintain postural stability. Therefore, we conducted a within-subject study with ten people with balance impairments due to Multiple Sclerosis (MS). In each condition, we varied one component and kept all other components fixed. Each participant explored the virtual environment (VE) in a controlled fashion to make sure that the effect of the visual components was consistent for all participants. Results from our study suggest that decreased field of view and frame rate have significant effects on postural stability, but the effect of display resolution is inconclusive. Therefore, VR systems targeting people with balance impairments should focus on improving field of view and frame rate rather than display resolution.
An FPGA-Based Transceiver for Human Body Channel Communication Using Walsh Codes

In this paper, a body channel communication (BCC) transceiver with Walsh codes modulation is presented. In the transmitter part, data are encoded in Walsh code format, and the chosen Walsh codes restrict the maximum consecutive identical digit (CID) to increase the data transitions. In the receiver part, we use an analog front-end (AFE) circuit board to amplify the attenuated signal from the transmitter and restore the signal to the digital waveform. After the $\mathbf{8x}$ oversampling sampler and vote integrator recovery the clock and data, the Walsh code demodulator demodulates the original data. The proposed BCC transceiver has higher data reliability because of the orthogonal characteristic of Walsh codes. Moreover, the proposed Walsh code concatenated method strengthens the jitter tolerance and improve the code rate. The proposed BCC transceiver was verified on a field-programmable gate array (FPGA) board. The chip rate of the proposed transceiver ranges from 1.56 Mcps to 12.5 Mcps. Also, the bit error rate is $< \mathbf{10}^{-\mathbf{8}}$ and $< \mathbf{10}^{-\mathbf{5}}$ at 6.25 Mcps and 12.5 Mcps, respectively.
People Trajectory Forecasting and Collision Avoidance in First-Person Viewpoint

we propose a new collision avoidance system for first-person viewpoint, to show trajectory of people and to predict the future location of them. Then, we can determine the predicted location to avoid collision. We use deep learning to detect pedestrians and plot out coordinates of the trajectory. We predict future location of the target according to the law of inertia. In the first-person screen, this system can show whether possible collision occurs. Experimental results show that our method is feasible and outperforms state-of-the-art.
Negotiation Algorithms for Peer-to-Peer Electricity Markets: Computational Properties

Building on the concepts of trans active energy and consumer-centric electricity markets, the interest in community-based and peer-to-peer structures to energy transactions and management has substantially increased over the last few years. However, several computational challenges are to be tackled in order for these approaches to be deployed in real-world applications. Our aim here is to identify and analyze these challenges, by comparing distributed community-based market approaches to decentralized and distributed versions of peer-to-peer electricity markets. We show convergence trends of the investigated algorithms as well as how they respond to larger number of participants and presence of asynchronicities. Our findings highlight the practical challenges to face with these setups, in particular with peer-to-peer markets, justifying the further proposal of hybrid approaches and of sparsification of negotiation processes.
Minimax Lower Bounds for Nonnegative Matrix Factorization

The non-negative matrix factorization (NMF) problem consists in modeling data samples as non-negative linear combinations of non-negative dictionary vectors. While many algorithms for NMF have been proposed, fundamental performance limits of these algorithms are currently not available. This paper plugs this gap by providing lower bounds on the minimax risk (the minimum achievable worst case mean squared error) of estimating the non-negative dictionary matrix under a set of locality and statistical assumptions.
Quantifying the Cost of Cooling in Qatar

Over the last decade, the electricity demand in Qatar has grown more than twofold. The residential sector is predicted to be responsible for two-third of the entire demand while air conditioning represents a substantial portion of the domestic electricity consumption. One of the grand challenges in Qatar, as in the GCC as well, is to reduce the electricity consumption and to improve the energy efficiency. Therefore, it is crucial to analyze and quantify the consumption patterns for different activities. In this paper, we estimate the electricity cost of air conditioning in Qatar based on the comparison of seasonal electricity consumption data. First, we collect high-resolution electricity data for one year (December 2015 - November 2016). Next, by following the literature we determine the cooling degree days (CDD) in which the average temperatures are higher than 18.3°C and air conditioning is assumed to be running. Then, the load curves for cooling demands are obtained by subtracting the load curves of the average of noncooling days (nCDD) from the CDD load curves. Next, we calculate the correlation between the heat index and cooling demand as 95%. The high correlation confirms that the resultant curves represent the cooling load with a reasonably high accuracy. Moreover, we calculate the annual demand for cooling as 14.675 TWh, which constitutes around 36 % of total annual electricity consumption. This, in turn, translates into a total cost of 1.467 billion USD per year using the average cost of electricity assumptions. Such results are critical inputs to design right policies for demand-side management and energy efficiency programs to and curb the undesired effects of energy generation, distribution and usage including hazardous emissions, air quality degradation, public health expenses, and lost economic opportunities.
Prediction of RNA-protein interactions with distributed feature representations and a hybrid deep model

The interactions between proteins and RNAs play essential roles in many important biological processes. While the detection of protein-RNA interaction by biological experiments is a laborious and time-consuming task, computational prediction tools are highly in need. The prediction performance of the computational tools rely on two factors, namely feature representation of RNA sequences and classification models. In the existing methods, statistical features or one-hot vectors are adopted, and most of the classifiers are traditional machine learning models, while the distributed representation and flexible deep learning architectures have not been exploited. Therefore, in this study, we represent RNA sequences by continuous distributed features, and propose a hybrid deep learning architecture, which combines both CNN and RNN. The experiments are conducted on 31 benchmark datasets, corresponding to 31 RNA-binding-proteins. The results show that the new method achieves obvious advantages against the existing methods on most of the datasets.
Pharmacological c-Jun NH2-Terminal Kinase (JNK) Pathway Inhibition Reduces Severity of Spinal Muscular Atrophy Disease in Mice

Spinal muscular atrophy (SMA) is a severe neurodegenerative disorder that occurs in early childhood. The disease is caused by the deletion/mutation of the survival motor neuron 1 (SMN1) gene resulting in progressive skeletal muscle atrophy and paralysis, due to the degeneration of spinal motor neurons (MNs). Currently, the cellular and molecular mechanisms underlying MN death are only partly known, although recently it has been shown that the c-Jun NH2-terminal kinase (JNK)-signaling pathway might be involved in the SMA pathogenesis. After confirming the activation of JNK in our SMA mouse model (SMN2+/+; SMNΔ7+/+; Smn−/−), we tested a specific JNK-inhibitor peptide (D-JNKI1) on these mice, by chronic administration from postnatal day 1 to 10, and histologically analyzed the spinal cord and quadriceps muscle at age P12. We observed that D-JNKI1 administration delayed MN death and decreased inflammation in spinal cord. Moreover, the inhibition of JNK pathway improved the trophism of SMA muscular fibers and the size of the neuromuscular junctions (NMJs), leading to an ameliorated innervation of the muscles that resulted in improved motor performances and hind-limb muscular tone. Finally, D-JNKI1 treatment slightly, but significantly increased lifespan in SMA mice. Thus, our results identify JNK as a promising target to reduce MN cell death and progressive skeletal muscle atrophy, providing insight into the role of JNK-pathway for developing alternative pharmacological strategies for the treatment of SMA.
Constructing Prosumer Coalitions for Energy Cost Savings Using Cooperative Game Theory

Distributed energy storage (ES) is widely regarded as a tool to mitigate the supply-demand imbalance caused by intermittent generations and variable loads in a distribution network. Because a retail supplier generally offers a lower electricity sell price (e.g. feed-in tariff) than its buy price, ES owners are monetarily incentivized to store excess generation for later usage. However, since each ES owner typically optimizes the ES operation based on their individual usage profile, the joint load balancing effect of multiple ES systems becomes insignificant. This paper proposes an energy coalition, in which ES system owners operate collaboratively to minimize the total coalitional energy cost. Using cooperative game theory, the nucleolus and Shapley value are developed as methods to reward players. Finally, selected case studies compare the load balancing effectiveness of noncooperative and cooperative ES operations, and empirically demonstrate the nucleolus's stablizing characteristic, which incentivizes prosumers to stay within the grand coalition.
On The Non-Detectability of Spiked Large Random Tensors

This paper addresses the detection of a low rank high-dimensional tensor corrupted by an additive complex Gaussian noise. In the asymptotic regime where all the dimensions of the tensor converge towards $+\infty $ at the same rate, existing results devoted to rank 1 tensors are extended. It is proved that if a certain parameter depending explicitly on the low rank tensor is below a threshold, then the null hypothesis and the presence of the low rank tensor are undistinguishable hypotheses in the sense that no test performs better than a random choice.
Estimation of the Regularization Parameter of an On-Line NMF with Minimum Volume Constraint

In this paper, the estimation of the regularization parameter of the on-line Non-negative Matrix Factorization (NMF) with minimum volume constraint on sources is addressed. Adding a volume constraint in the model is important to ensure uniqueness of the solution and good data representation. However, the effectiveness of this approach is hampered by the optimal determination of the strength of minimum volume term. To solve this problem, we formulate it as a bi-objective optimization problem and three Minimum Distance Criterion (MDC) strategies are proposed and evaluated. The three strategies yield similar results but one of them in particular yields an interesting tradeoff between accuracy and computation time.
Automatic makeup based on generative adversarial nets

Automatic makeup aims to compose and synthesize makeup on human face through computer algorithm, which belongs to the field of face image analysis. It plays an important role in interactive entertainment applications, image and video editing and face recognition assistance. However, as a face editing task, automatic makeup has several challenges as following: 1) It is usually difficult to meet the editing requirements while ensuring that makeup results are naturally looking without obvious artifacts. 2) Designed network should preserve the content (human identity) of input images but only edits the makeup-related parts of input images such as lip gloss and eye shadow. 3) Number of face makeup dataset is limited. To address these difficulties, we propose an automatic makeup generative adversarial network, a novel approach that can perform makeup attribute editing for multiple makeup styles. The network is an end-to-end model with several specially designed skip layers in generator network and allows simultaneous training of multiple datasets in addition to makeup datasets. Finally, the performance of proposed method was compared with state-of-art method qualitatively and quantitatively.