Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiscale Amplitude Feature and Significance of Enhanced Vocal Tract Information for Emotion Classification

In this paper, a novel multiscale amplitude feature is proposed using multiresolution analysis (MRA) and the significance of the vocal tract is investigated for emotion classification from the speech signal. MRA decomposes the speech signal into number of sub-band signals. The proposed feature is computed by using sinusoidal model on each sub-band signal. Different emotions have different impacts on the vocal tract. As a result, vocal tract responds in a unique way for each emotion. The vocal tract information is enhanced using pre-emphasis. Therefore, emotion information manifested in the vocal tract can be well exploited. This may help in improving the performance of emotion classification. Emotion recognition is performed using German emotional EMODB database, interactive emotional dyadic motion capture database, simulated stressed speech database, and FAU AIBO database with speech signal and speech with enhanced vocal tract information (SEVTI). The performance of the proposed multiscale amplitude feature is compared with three different types of features: 1) the mel frequency cepstral coefficients; 2) the Teager energy operator (TEO)-based feature (TEO-CB-Auto-Env); and 3) the breathinesss feature. The proposed feature outperforms the other features. In terms of recognition rates, the features derived from the SEVTI signal, give better performance compared to the features derived from the speech signal. Combination of the features with SEVTI signal shows average recognition rate of 86.7% using EMODB database.
SaferDrive: An NLG-based behaviour change support system for drivers

Abstract Despite the long history of Natural Language Generation (NLG) research, the potential for influencing real world behaviour through automatically generated texts has not received much attention. In this paper, we present SaferDrive, a behaviour change support system that uses NLG and telematic data in order to create weekly textual feedback for automobile drivers, which is delivered through a smartphone application. Usage-based car insurances use sensors to track driver behaviour. Although the data collected by such insurances could provide detailed feedback about the driving style, they are typically withheld from the driver and used only to calculate insurance premiums. SaferDrive instead provides detailed textual feedback about the driving style, with the intent to help drivers improve their driving habits. We evaluate the system with real drivers and report that the textual feedback generated by our system does have a positive influence on driving habits, especially with regard to speeding.
Scene Categorization Using Deeply Learned Gaze Shifting Kernel

Accurately recognizing sophisticated sceneries from a rich variety of semantic categories is an indispensable component in many intelligent systems, e.g., scene parsing, video surveillance, and autonomous driving. Recently, there have emerged a large quantity of deep architectures for scene categorization, wherein promising performance has been achieved. However, these models cannot explicitly encode human visual perception toward different sceneries, i.e., the sequence of humans sequentially allocates their gazes. To solve this problem, we propose deep gaze shifting kernel to distinguish sceneries from different categories. Specifically, we first project regions from each scenery into the so-called perceptual space, which is established by combining color, texture, and semantic features. Then, a novel non-negative matrix factorization algorithm is developed which decomposes the regions’ feature matrix into the product of the basis matrix and the sparse codes. The sparse codes indicate the saliency level of different regions. In this way, the gaze shifting path from each scenery is derived and an aggregation-based convolutional neural network is designed accordingly to learn its deep representation. Finally, the deep representations of gaze shifting paths from all the scene images are incorporated into an image kernel, which is further fed into a kernel SVM for scene categorization. Comprehensive experiments on six scenery data sets have demonstrated the superiority of our method over a series of shallow/deep recognition models. Besides, eye tracking experiments have shown that our predicted gaze shifting paths are 94.6% consistent with the real human gaze allocations.
A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians

The low-rank tensor factorization (LRTF) technique has received increasing attention in many computer vision applications. Compared with the traditional matrix factorization technique, it can better preserve the intrinsic structure information and thus has a better low-dimensional subspace recovery performance. Basically, the desired low-rank tensor is recovered by minimizing the least square loss between the input data and its factorized representation. Since the least square loss is most optimal when the noise follows a Gaussian distribution, $L_{1}$ -norm-based methods are designed to deal with outliers. Unfortunately, they may lose their effectiveness when dealing with real data, which are often contaminated by complex noise. In this paper, we consider integrating the noise modeling technique into a generalized weighted LRTF (GWLRTF) procedure. This procedure treats the original issue as an LRTF problem and models the noise using a mixture of Gaussians (MoG), a procedure called MoG GWLRTF. To extend the applicability of the model, two typical tensor factorization operations, i.e., CANDECOMP/PARAFAC factorization and Tucker factorization, are incorporated into the LRTF procedure. Its parameters are updated under the expectation–maximization framework. Extensive experiments indicate the respective advantages of these two versions of MoG GWLRTF in various applications and also demonstrate their effectiveness compared with other competing methods.
Protein Domain Level Cancer Drug Targets in the Network of MAPK Pathways

Proteins in the MAPK pathways considered as potential drug targets for cancer treatment. Pathways along with the cross-talks increase their scope to view them as a network of MAPK pathways. Side effect causing targeted domains act as a proxy for drug targets due to its structural similarity and frequent reuse of their variants. We proposed to identify non-repeatable protein domains as the drug targets to disrupt the signal transduction than targeting the whole protein. Network based approach is used to understand the contribution of 52 domains in non-hub, non-essential, and intra-pathway cancerous nodes and to identify potential drug target domains. 34 distinct domains in the cancerous proteins are playing vital roles in making cancer as a complex disease and pose challenges to identify potential drug targets. Distribution of domain families follows the power law in the network. Single promiscuous domains are contributing to the formation of hubs like Pkinease, Pkinease Tyr, and Ras. Hub nodes are positively correlated with the domain coverage and targeting them would disrupt functional properties of the proteins. EIF 4EBP, alpha Kinase, Sel1, ROKNT, and KH 1 are the domains identified as potential domain targets for the disruption of the signaling mechanism involved in cancer.
Detecting 3D Points of Interest Using Multiple Features and Stacked Auto-encoder

Considering the fact that points of interest on 3D shapes can be discriminated from a geometric perspective, it is reasonable to map the geometric signature of a point $p$p to a probability value encoding to what degree $p$p is a point of interest, especially for a specific class of 3D shapes. Based on the observation, we propose a three-phase algorithm for learning and predicting points of interest on 3D shapes by using multiple feature descriptors. Our algorithm requires two separate deep neural networks (stacked auto-encoders) to accomplish the task. During the first phase, we predict the membership of the given 3D shape according to a set of geometric descriptors using a deep neural network. After that, we train the other deep neural network to predict a probability distribution defined on the surface representing the possibility of a point being a point of interest. Finally, we use a manifold clustering technique to extract a set of points of interest as the output. Experimental results show superior detection performance of the proposed method over the previous state-of-the-art approaches.
Consensus Control of a Class of Uncertain Nonlinear Multiagent Systems via Gradient-Based Algorithms

This paper is concerned with the distributed optimization problem for a class of minimum-phase nonlinear multiagent systems with second relative degree. The target is to minimize a cost function composed of a group of convex local functions of the outputs in a cooperative manner. Novel state feedback control laws are first proposed and the consensus subject to the optimization constraint is achieved when the communication graph is directed. By replacing the derivatives of the outputs in the state feedback control laws with their estimations, the output feedback control laws are constructed and proved to achieve exponential consensus under the undirected graph. The effectiveness of the proposed control laws is validated by some simulations.
Transfer Hashing: From Shallow to Deep

One major assumption used in most existing hashing approaches is that the domain of interest (i.e., the target domain) could provide sufficient training data, either labeled or unlabeled. However, this assumption may be violated in practice. To address this so-called data sparsity issue in hashing, a new framework termed transfer hashing with privileged information (THPI) is proposed, which marriages hashing and transfer learning (TL). To show the efficacy of THPI, we propose three variants of the well-known iterative quantization (ITQ) [11] as a showcase. The proposed methods, ITQ+, LapITQ+, and deep transfer hashing (DTH), solve the aforementioned data sparsity issue from different aspects. Specifically, ITQ+ is a shallow model, which makes ITQ achieve hashing in a TL manner. ITQ+ learns a new slack function from the source domain to approximate the quantization error on the target domain given by ITQ. To further improve the performance of ITQ+, LapITQ+ is proposed by embedding the geometric relationship of the source domain into the target domain. Moreover, DTH is proposed to show the generality of our framework by utilizing the powerful representative capacity of deep learning. To the best of our knowledge, this could be one of the first DTH works. Extensive experiments on several popular data sets demonstrate the effectiveness of our shallow and DTH approaches comparing with several state-of-the-art hashing approaches.
STTC design for vehicular communication systems employing fixed-gain AF PLNC over cascaded fading channels

In this study, the authors combine physical layer network coding (PLNC) and space-time trellis coding (STTC) techniques to improve the error performance of vehicle-to-vehicle (V2V) communication systems where all fading channels are assumed to undergo double Rayleigh fading (DRF) which is worse than Rayleigh fading used in cellular communications. In the authors' analysis, they show that the DRF channels can be modelled by using the mixture gamma distribution. After that, pairwise error probability (PEP) analysis is performed for a fixed-gain amplify-and-forward PLNC V2V system employing STTC over the DRF channels, and an upper bound for the PEP is obtained. By examining the behaviour of the upper bound expression in the high SNR region, they derive a new design criterion for STTCs. Finally, using this criterion they propose novel STTCs with 4 and 8 states for V2V systems employing PLNC over the DRF channels.
RSSI based passive detection of persons for waiting lines using Bluetooth Low Energy

Knowledge about the presence of persons in a waiting line can help estimating the waiting time or guiding the decision about opening a second line. However, existing presence detection systems for waiting lines are either mounted at fixed positions, take a long time to deploy, need a power connection or require users to carry devices. Past research on the analysis of the Received Signal Strength Indicator (RSSI) of a radio transmission indicates that it can be used to detect the presence of persons. Here, the accuracy of the detection is directly linked to the quality of the radio link. Radio links based on Bluetooth Low Energy (BLE) offer a stable connection, but implement a mandatory frequency hopping scheme, with the information about the current channel typically not accessible. In this work we extend the concept of passive presence detection to work on BLE radio links. We adapt two RSSI based presence detection techniques and evaluate their performance in experiments. The experimental results indicate that it is possible to achieve a 92% accuracy using BLE when compared to the ground truth.
Dynamic Analysis of Hybrid Impulsive Delayed Neural Networks With Uncertainties

Neural networks (NNs) have emerged as a powerful illustrative diagram for the brain. Unveiling the mechanism of neural-dynamic evolution is one of the crucial steps toward understanding how the brain works and evolves. Inspired by the universal existence of impulses in many real systems, this paper formulates a type of hybrid NNs (HNNs) with impulses, time delays, and interval uncertainties, and studies its global dynamic evolution by a robust interval analysis. The HNNs incorporate both continuous-time implementation and impulsive jump in mutual activations, where time delays and interval uncertainties are represented simultaneously. By constructing a Banach contraction mapping, the existence and uniqueness of the equilibrium of the HNN model are proved and analyzed in detail. Based on nonsmooth Lyapunov functions and delayed impulsive differential equations, new criteria are derived for ensuring the global robust exponential stability of the HNNs. Convergence analysis together with illustrative examples show the effectiveness of the theoretical results.
Active Contours Driven by Local Rayleigh Distribution Fitting Energy for Ultrasound Image Segmentation

The ultrasound image segmentation is a crucial task in many clinical applications. However, the ultrasound image is difficult to segment due to image inhomogeneity caused by the ultrasound imaging technique. In this paper, to deal with image inhomogeneity with considering ultrasound image properties the Local Rayleigh Distribution Fitting (LRDF) energy term is introduced into the traditional level set method newly. While the curve evolution equation is derived for energy minimization, and self-driven uterus contour is achieved on the ultrasound images. The experimental segmentation results on synthetic images and in-vivo ultrasound images present that the proposed approach is effective and accurate, with the Dice Score Coefficient (DSC) of 0.95 ± 0.02. key words: ultrasound image segmentation, Rayleigh mixture model, level set, neighbor information
Pairwise Constraint Propagation-Induced Symmetric Nonnegative Matrix Factorization

As a variant of nonnegative matrix factorization (NMF), symmetric NMF (SNMF) has shown to be effective for capturing the cluster structure embedded in the graph representation. In contrast to the existing SNMF-based clustering methods that empirically construct the similarity matrix and rigidly introduce the supervisory information to the assignment matrix, in this paper, we propose a novel SNMF-based semisupervised clustering method, namely, pairwise constraint propagation-induced SNMF (PCPSNMF). By formulating a single-constrained optimization problem, PCPSNMF is capable of learning the similarity and assignment matrices adaptively and simultaneously, in which a small amount of supervisory information in the form of pairwise constraints is introduced in a flexible way to guide the construction of the similarity matrix, and the two matrices communicate with each other to achieve mutual refinement until convergence. In addition, we propose an efficient alternating iterative algorithm to solve the optimization problem, whose convergence is theoretically proven. Experimental results over several benchmark image data sets demonstrate that PCPSNMF is less sensitive to initialization and produces higher clustering performance, compared with the state-of-the-art methods.
Modelling and mitigating spectrum sensing non-cooperation attack in cognitive radio network

Collaborative spectrum sensing (CSS) is known to improve spectrum sensing performance in Cognitive Radio Network. In CSS, secondary users participate by sharing their local sensing results. They participate in the sensing process at their own cost, i.e., they expend some amount of energy and time for sensing and sharing. But, a selfish user may refrain from collaborating in the spectrum sensing process in order to save up energy, which results in improper sensing. While this problem is widely known, we call this as the spectrum sensing non-cooperation (SSNC) attack for easy reference. In this paper, a collective action prisoner's dilemma game is used to model the SSNC attack. To handle this attack, repeated game punishment mechanisms, namely Tit-for-Tat and Grim strategies are used. In addition, modified Tit-for-Tat and modified Grim strategies are proposed to handle this attack in the presence of reporting channel error.
Optimisation design of systematic LT codes over AWGN multiple access channel

Systematic Luby transform (SLT) codes over the additive white Gaussian noise (AWGN) multiple access channels (MACs) are analysed in this study. The authors introduce a novel optimisation model extending from the point-to-point AWGN channels to design the degree distributions over the AWGN MACs, which dramatically improve the bit-error-rate (BER) performance of SLT codes. First, they extend the Gaussian approximation analysis of SLT codes to the AWGN MACs to analyse the asymptotic BER performance where the symmetric condition of log-likelihood ratio message can no longer be assumed. Second, based on the asymptotic BER analysis over the AWGN MACs, they extend the existing optimisation model from the point-to-point AWGN channels to the AWGN MACs. Simulation results show that the degree distributions obtained in this study outperform other common degree distributions (optimised for single-user AWGN channels) over the two-user AWGN MACs.
Responsiveness of Veterans Affairs Health Care System to Zolpidem Safety Warnings.

STUDY OBJECTIVES Sedative hypnotic medications are routinely prescribed for insomnia treatment, but have been associated with significant risks of morning-after impairment. We evaluated responsiveness in the Veterans Health Administration (VHA) facilities to two drug safety warnings recommending against high-dose zolpidem use-a 2007 Veterans Administration Pharmacy Benefits Management Service warning and a 2013 Food and Drug Administration (FDA) warning. METHODS We used interrupted time-series design to assess how the two warnings influenced prescribing within the VHA in outpatients from 2005 to 2014. We assessed two outcomes: monthly outpatient use of (1) higher-than-recommended dose of zolpidem among zolpidem users and (2) any-dose zolpidem among all VHA users. In sensitivity analyses, we compared zolpidem prescribing to prescribing other sleep medications not subject to safety warnings. RESULTS After the 2007 VHA warning, high-dose zolpidem use decreased significantly among both sexes from approximately 10% to 2%. Following the 2013 FDA warning, high-dose zolpidem use declined again; however, approximately half of women Veterans remained on high doses. Overall zolpidem use nearly quadrupled between the 2007 VHA and 2013 FDA warnings, but the overall use declined after the 2013 FDA warning. Increase in sedating antidepressant use was seen after the FDA warning, suggesting potential substitution. CONCLUSIONS Higher than recommended dose use within the VHA decreased after each zolpidem high dose warning. Although overall use also decreased after the FDA warning, almost 50% of high-dose use among women Veterans is concerning. Different strategies to communicate the warnings should be examined. COMMENTARY A commentary on this article appears in this issue on page 1093.
Left Atrial Appendage Segmentation Using Fully Convolutional Neural Networks and Modified Three-Dimensional Conditional Random Fields

Thrombosis has become a global disease threatening human health. The left atrial appendage (LAA) is a major source of thrombosis in patients with atrial fibrillation (AF). Positive correlation exists between LAA volume and AF risk. LAA morphology has been suggested to influence thromboembolic risk in AF patients and to help predict thromboembolic events in low-risk patient groups. Automatic segmentation of LAA can greatly help physicians diagnose AF. In consideration of the large anatomical variations of the LAA, we proposed a robust method for automatic LAA segmentation on computed tomographic angiography (CTA) data using fully convolutional neural networks with three-dimensional (3–D) conditional random fields (CRFs). After manual localization of ROI of LAA, we adopted the FCN in natural image segmentation and transferred their learned models by fine-tuning the networks to segment each 2–D LAA slice. Subsequently, we used a modified dense 3–D CRF that accounts for the 3–D spatial information and larger contextual information to refine the segmentations of all slices. Our method was evaluated on 150 sets of CTA data using five-fold cross validation. Compared with manual annotation, we obtained a mean dice overlap of $\text{94.76}\%$ and a mean volume overlap of $\text{91.10}\%$ with a computation time of less than 40 s per volume. Experimental results demonstrated the robustness of our method in dealing with large anatomical variations and computational efficiency for adoption in a daily clinical routine. )
Aberrant DNA Methylation of Two Tumor Suppressor Genes, p14ARF and p15INK4b, after Chronic Occupational Exposure to Low Level of Benzene

Background: Exposure to benzene would be associated with many diseases including leukemia. Epigenetic alterations seem to be among the main mechanisms involved. Objective: To determine if chronic occupational exposure to low level of benzene would be associated with DNA methylation. Methods: Global DNA methylation and promoter-specific methylation of the two tumor suppressor genes, p14ARF and p15INK4b, were assessed employing methylation-specific PCR using the DNA extracted from 40 petrochemical workers exposed to ambient benzene levels of <1 ppm, and 31 office workers not exposed to benzene or its derivatives. Results: While an increase in global DNA methylation of 5% in p14ARF (p=0.501) and 28% in p15INK4b (p=0.02) genes was observed in the exposed group, no hypermethylation in either of the studied genes was observed in the unexposed group. No significant association was found between the frequency of aberrant methylation and either of age, work experience, and smoking habit in the exposed group. Conclusion: Chronic occupational exposure to lower than the permissible exposure limit of benzene may still result in DNA methylation of tumor suppressor genes that may ultimately lead to development of cancer.