Reviewerly/exHarmony huggingface dataset:
Robust Localization Using IMM Filter Based on Skew Gaussian-Gamma Mixture Distribution in Mixed LOS/NLOS Condition

This article proposes a new skewed outlier-robust localization algorithm that is based on time-difference of arrival (TDOA) measurements at an airport. A new outlier-robust filtering framework is derived based on the skew Gaussian-gamma mixture (SGGM) distribution, where the state, a mixing parameter, a shape parameter, a scale matrix, and the degrees of freedom (DOFs) are inferred simultaneously using variational Bayesian (VB) approach. An interacting multiple-model (IMM) filter with different kinematic system models is implemented to handle the multimodal dynamics of the vehicle, yielding the IMM-SGGM algorithm. In particular, a new measurement likelihood based on the SGGM distribution is derived utilizing VB inference for the combination procedure in the proposed IMM-SGGM algorithm. Car-mounted experiments using TDOA measurements at an airport were conducted to verify the effectiveness of the proposed algorithm. The performance of the proposed IMM-SGGM algorithm is evaluated through comparisons with the state-of-the-art approaches. The experimental results demonstrate that the proposed IMM-SGGM algorithm has better localization accuracy and robustness to skewed outlier measurements than the state-of-the-art approaches.
Hallucinating Agnostic Images to Generalize Across Domains

The ability to generalize across visual domains is crucial for the robustness of artificial recognition systems. Although many training sources may be available in real contexts, the access to even unlabeled target samples cannot be taken for granted, which makes standard unsupervised domain adaptation methods inapplicable in the wild. In this work we investigate how to exploit multiple sources by hallucinating a deep visual domain composed of images, possibly unrealistic, able to maintain categorical knowledge while discarding specific source styles. The produced agnostic images are the result of a deep architecture that applies pixel adaptation on the original source data guided by two adversarial domain classifier branches at image and feature level. Our approach is conceived to learn only from source data, but it seamlessly extends to the use of unlabeled target samples. Remarkable results for both multi-source domain adaptation and domain generalization support the power of hallucinating agnostic images in this framework.
Big Data Analytics for Event Detection in the IoT-Multicriteria Approach

Security requirements applicable to the Internet of Things (IoT) should aim to ensure integrity, authenticity and authorization, confidentiality/privacy, nonrepudiation, and last but not least, availability. Classic data analysis algorithms are no longer valid for assuring security at all levels and a new approach to data sciences is required, which would consider the complex heterogeneous nature of the IoT, taking also into consideration, its potential to deploy cross layer for security assessment mechanisms. Furthermore, data collected from sensors should be processed and analyzed nearly in real time. The classical algorithms have two main drawbacks: 1) they deal with unidimensional data and 2) they fail to assume limited information available in the stream data processing. In this article, new solutions are discussed and presented that detect anomalies in data streams nearly in real time. Specifically, we propose: 1) an event detection method used in unidimensional data streams and relying on the event strength function, which is an extension of the typical “True or False” decision-making scheme; 2) multiple-criteria event detection approaches based on the Dynamic Pareto Set, introducing a time-depending decision set; and 3) an anomaly detection method based on multicriteria temporal graphs, combining the dynamics of decision making and multicriteria. All the proposed algorithms are presented by means of their formal description and are illustrated with examples.
TNAM: A tag-aware neural attention model for Top-N recommendation

Recent work shows that incorporating tag information to recommender systems is promising for improving the recommendation accuracy in social systems. However, existing approaches suffer from less reasonable assignment of tag weights when constructing the user profiles and item characteristics in real-world scenarios, resulting in decreased accuracy in making recommendations. The above issue is specifically summarized into two aspects: 1) the weight of a target item is mainly determined by number of one certain type of tags, and 2) users place equal focus on the same tag for different items. To tackle these problems, we propose a novel model named TNAM, a Tag-aware Neural Attention Model, which accurately captures users’ special attention to tags of items. In the proposed model, we design a tag-based neural attention network by extracting potential tag information to overcome the difficulty of assigning tag weights for personalized users. We combine user-item interactions with tag information to map sparse data to dense vectors in higher-order space. In this way, TNAM acquires more interrelations between users and items to make recommendations more accurate. Extensive experiments of our model on three publicly implicit feedback datasets reveal significant improvements on the metrics of HR and NDCG in Top-N recommendation tasks over several state-of-the-art approaches.
Towards feature representation for steganalysis of spatial steganography

Feature separability analysis supports the feature selection and reduction and also guides the feature construction. This paper addresses the feature representation for steganalysis of spatial steganography. The paper analyzes the effects of spatial steganography on image residual histogram, and gives the feature separability of histogram features represented in both spatial and frequential domains, which explains the effectiveness of the typical spatial features, such as SRM and TLBP features. Based on the feature separability analysis, TLBP feature is improved by using a new texture pattern mapping to replace the 'riu2' mapping in the feature construction. Besides, features in the frequency domain are constructed by applying DFT to the submodels of SRM. Experimental results show that both the two schemes improved the steganalysis performances. Furthermore, features in spatial and frequency domains are merged, which obtains better results than the corresponding single type features for steganalysis of S-UNIWARD, HILL, and MiPOD. For example, using HILL for information embedding with payload 0.4 bpp on BOSSbase dataset, the detection accuracy of the best merged feature is 3.64% and 1.13% higher than the corresponding single type features.
A New Correlation Coefficient Based on Generalized Information Quality

How to manage conflict is still an open issue in Dempster-Shafer (D-S) evidence theory. The conflict coefficient k in D-S evidence theory cannot represent conflict reasonably, especially sometimes two Basic Probability Assignments (BPAs) are identical but k is not zero. Generalized information quality, as a concept of data fusion, can effectively measure the uncertainty of BPA. However, how to use the generalized information quality to manage conflicts is still an open question. Therefore, this paper proposed a new correlation coefficient based on generalized information quality to manage conflict. This method can better reflect the correlation coefficient between BPA. Numerical examples are used to demonstrate the effectiveness of the correlation coefficient based on generalized information quality. In addition, a weighted average combination rule based on the new correlation coefficient is presented. A numerical example in target recognition is illustrated to show its validity in combining conflicting evidence.
Short-Term Human–Robot Interaction Adaptability in Real-World Environments

In recent years there has been an increasing interest in deploying robotic systems in public environments able to effectively interact with people. To properly work in the wild, such systems should be robust and be able to deal with complex and unpredictable events that seldom happen in controlled laboratory conditions. Moreover, having to deal with untrained users adds further complexity to the problem and makes the task of defining effective interactions especially difficult. In this work, a Cognitive System that relies on planning is extended with adaptive capabilities and embedded in a Tiago robot. The result is a system able to help a person to complete a predefined game by offering various degrees of assistance. The robot may decide to change the level of assistance depending on factors such as the state of the game or the user performance at a given time. We conducted two days of experiments during a public fair. We selected random users to interact with the robot and only for one time. We show that, despite the short-term nature of human–robot interactions, the robot can effectively adapt its way of providing help, leading to better user performances as compared to a robot not providing this degree of flexibility.
Novel Feature Selection Method Using Bhattacharyya Distance for Neural Networks Based Automatic Modulation Classification

In the context of Automatic Modulation Classification (AMC), some recent works have utilized multiple features to train the neural network. With an ultimate aim to develop a systematic approach to select the most diverse and unique features, we propose and demonstrate a novel method to select the most diverse ( <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) features from a larger feature set. Bhattacharyya distance metric for the dissimilarity between two probability distributions is utilized to select the features with the highest distance for all modulation pairs within a test pool. The proposed approach is analyzed for three different neural networks based classifiers, amidst AWGN and frequency-selective fading channels. A substantial reduction in computational complexity is achieved with an acceptable compromise on the classification performance.
JET-Net: Real-Time Object Detection for Mobile Robots

In most applications for autonomous robots, the detection of objects in their environment is of significant importance. As many robots are equipped with cameras, this task is often solved by image processing techniques. However, due to limited computational resources on mobile systems, it is common to use specialized algorithms that are highly adapted to the respective scenario. Sophisticated approaches such as Deep Neural Networks, which recently demonstrated a high performance in many object detection tasks, are often difficult to apply. In this paper, we present JET-Net (Just Enough Time), a model frame for efficient object detection based on Convolutional Neural Networks. JET-Net is able to perform real-time robot detection on a NAO V5 robot in a robot football environment. Experiments show that this system is able to reliably detect other robots in various situations. Moreover, we present a technique that reuses the learned features to obtain more information about the detected objects. Since the additional information can entirely be learned from simulation data, it is called Simulation Transfer Learning.
Two-stage data encryption using chaotic neural networks

Securing a wireless sensor system is a hard task for researchers today. Strengthening the authentication system before connection establishment is the right way to enhance the architecture and also to provide secured communication from eavesdroppers. In this paper, we present a novel algorithm to enhance the security of data using a hybrid model which uses an adaptive encoding technique alongside Chaotic Hopfield Neural Network. The proposed computation upgrades the security of a key shared between any nodes. Our experimental results show that the security of transmitted data is better than traditional algorithms. Moreover, we also show that the computational time for the proposed algorithm is less than known traditional algorithms.
Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images

Noise reduction is one the most important and still active research topic in low-level image processing due to its high impact on object detection and scene understanding for computer vision systems. Recently, we can observe a substantial increase of interest in the application of deep learning algorithms in many computer vision problems due to its impressive capability of automatic feature extraction and classification. These methods have been also successfully applied in image denoising, significantly improving the performance, but most of the proposed approaches were designed for Gaussian noise suppression. In this paper, we present a switching filtering design intended for impulsive noise removal using deep learning. In the proposed method, the impulses are identified using a novel deep neural network architecture and noisy pixels are restored using the fast adaptive mean filter. The performed experiments show that the proposed approach is superior to the state-of-the-art filters designed for impulsive noise removal in digital color images.
Evaluating QoE in VoIP networks with QoS mapping and machine learning algorithms

The quality of experience (QoE) of the end-users is a critical criterion of measurement in VoIP (Voice over Internet Protocol) systems for technical and commercial purposes. We investigate how quality of service (QoS) influences QoE and assesses the QoE in VoIP communication. Our contributions are three-fold. First, the impacts of QoS on QoE are comprehensively analyzed by experimental means and an association test method, instead of independently studying each parameter. Second, an algorithm is proposed to integrate the effects of QoS parameters with spatial or temporal characteristics on QoE. Third, we apply machine learning regression algorithms with QoS impairments, noise and echo impairments to nonintrusive voice quality prediction in different network environments. The results from numerous experiments show that fairly accurate prediction can be obtained from these models. Our work will achieve a more accurate evaluation of the QoE in VoIP by using QoS parameters, clarify the influence of IP network environments, noise and echo impairments on the quality and reliability of VoIP traffic, and provide QoS parameter requirements for the VoIP application that runs at the desired QoE level.
Different level of virtualization of sight and touch produces the uncanny valley of avatar’s hand embodiment

Humans increasingly often act through virtual and robotic avatars, which can feed back to their user only virtual sensory information. Since avatar is user's embodiment and body image is mostly based on senses, how virtualization of sensory inputs affects avatar self-attribution is a key question for understanding nowadays human behavior. By manipulating visual and tactile inputs in a series of experiments fashioned after the rubber hand illusion, we assessed the relative weight of the virtualization of sight (Real, Robotic, Virtual) and of touch (Real, Virtual) on artificial hand embodiment. Virtualization decreased embodiment, but unexpectedly lowest embodiment was found when only one sense was virtual. Discordant levels of virtualization of sight and touch elicited revulsion, extending the concept of the uncanny valley to avatar embodiment. Besides timing, spatial constraints and realism of feedback, a matched degree of virtualization of seen and felt stimuli is a further constraint in building the representation of the body.
Sign Languages to Speech Conversion Prototype using the SVM Classifier

Around 70 million people in this world are mute people. There are children who suffer from Nonverbal Autism. Communication between the people with speech impairment and normal people is very difficult. Normally people with speech impairment use Sign Language to communicate with others. Not each and every person can understand Sign Language. In this paper, a prototype is proposed to give speech output for the Sign Language gestures to bridge the communication gap between the people with speech impairment and normal people. This prototype consists of a glove which has flex sensors, gyroscopes and accelerometers embedded on it. These sensors capture the real time gestures made by the user. Arduino Nano micro controller is used to collect data from these sensors and sends it to the PC via Bluetooth. The PC processes the data sent by the Arduino and runs a Machine Learning Algorithm to classify the Sign Language gestures and predicts the word associated with each gesture. Support Vector Machine (SVM) is used for classification. This prototype is very compact and can recognize both American Sign Language (ASL) and Indian Sign Language (ISL). This prototype not only gives speech to the mute people but also makes them multi linguists.
Human-computer interaction based on face feature localization

Human-computer interaction is the way in which humans and machines communicate information. With the rapid development of deep learning technology, the technology of human-computer interaction has also made a corresponding breakthrough. In the past, the way human-computer interaction was mostly relied on hardware devices. Through the coordinated work of multiple sensors, people and machines can realize information interaction. However, as theoretical technology continues to mature, algorithms for human-computer interaction are also being enriched. The popularity of convolutional neural networks has made image processing problems easier to solve. Therefore, real-time human-computer interaction can be performed by using image processing, and intelligent of human-computer interaction can be realized. The main idea of this paper is to use the real-time capture of face images and video information to image the face image information. We perform feature point positioning based on the feature points of the face image. We perform expression recognition based on the feature points that are located. At the same time, we perform ray tracing for the identified human eye area. The feature points of the face and the corresponding expressions and implementation movements represent the user's use appeal. Therefore, we can analyze the user's use appeal by locating the face feature area. We define the corresponding action information for specific user face features. We extract the user's corresponding information according to the user's face features, and perform human-computer interaction according to the user's information.
OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images

Deep learning models, such as the fully convolutional network (FCN), have been widely used in 3D biomedical segmentation and achieved state-of-the-art performance. Multiple modalities are often used for disease diagnosis and quantification. Two approaches are widely used in the literature to fuse multiple modalities in the segmentation networks: early-fusion (which stacks multiple modalities as different input channels) and late-fusion (which fuses the segmentation results from different modalities at the very end). These fusion methods easily suffer from the cross-modal interference caused by the input modalities which have wide variations. To address the problem, we propose a novel deep learning architecture, namely OctopusNet, to better leverage and fuse the information contained in multi-modalities. The proposed framework employs a separate encoder for each modality for feature extraction and exploits a hyper-fusion decoder to fuse the extracted features while avoiding feature explosion. We evaluate the proposed OctopusNet on two publicly available datasets, i.e. ISLES-2018 and MRBrainS-2013. The experimental results show that our framework outperforms the commonly-used feature fusion approaches and yields the state-of-the-art segmentation accuracy.
Maintaining filter structure: A Gabor-based convolutional neural network for image analysis

In image segmentation and classification tasks, utilizing filters based on the target object improves performance and requires less training data. We use the Gabor filter as initialization to gain more discriminative power. Considering the mechanism of the error backpropagation procedure to learn the data, after a few updates, filters will lose their initial structure. In this paper, we modify the updating rule in Gradient Descent to maintain the properties of Gabor filters. We use the Left Ventricle (LV) segmentation task and handwritten digit classification task to evaluate our proposed method. We compare Gabor initialization with random initialization and transfer learning initialization using convolutional autoencoders and convolutional networks. We experimented with noisy data and we reduced the amount of training data to compare how different methods of initialization can deal with these matters. The results show that the pixel predictions for the segmentation task are highly correlated with the ground truth. In the classification task, in addition to Gabor and random initialization, we initialized the network using pre-trained weights obtained from a convolutional Autoencoder using two different data sets and pre-trained weights obtained from a convolutional neural network. The experiments confirm the out-performance of Gabor filters comparing to the other initialization method even when using noisy inputs and a lesser amount of training data.
A Measurement Study of Bitcoin Lightning Network

As a promising method to enable fast and scalable Bitcoin transactions, Bitcoin Lightning Network (LN) has experienced rapid development since the end of 2017. LN utilizes the so-called "payment channels" to provide fast off-chain transactions, thereby offloading on-chain burden and enabling instant payments. With many new protocols proposed to improve the performance of LN, little is known about the current state of the network such as its topology, channel characteristics and application performance. In this paper, we conduct a systematic measurement on LN based on the data collected over a period of fifteen months. This measurement allows us to draw a network graph to study the payment routing success rate and the level of decentralization. We also analyze payment channels regarding their functions. Our work provides an in-depth understanding of network mechanisms and helps to explore future implications of LN.