Reviewerly/exHarmony huggingface dataset:
Saliency Aware: Weakly Supervised Object Localization

Object localization aims at localizing the object in a given image. Due to the recent success of convolutional neural networks (CNNs), existing methods have shown promising results in weakly-supervised learning fashion. By training a classifier, these methods learn to localize the objects by visualizing the class discriminative localization maps based on the classification prediction. However, correct classification results would not guarantee sufficient localization performance since the model may only focus on the most discriminative parts rather than the entire object. To address the aforementioned issue, we propose a novel and end-to-end trainable network for weakly-supervised object localization. The key insights to our algorithm are two-fold. First, to encourage our model to focus on detecting foreground objects, we develop a salient object detection module. Second, we propose a perceptual triplet loss that further enhances the foreground object detection capability. As such, our model learns to predict objectness, resulting in more accurate localization results. We conduct experiments on the challenging ILSVRC dataset. Extensive experimental results demonstrate that the proposed approach performs favorably against the state-of-the-arts.
View-invariant Action Recognition from RGB Data via 3D Pose Estimation

In this paper, we propose a novel view-invariant action recognition method using a single monocular RGB camera. View-invariance remains a very challenging topic in 2D action recognition due to the lack of 3D information in RGB images. Most successful approaches make use of the concept of knowledge transfer by projecting 3D synthetic data to multiple viewpoints. Instead of relying on knowledge transfer, we propose to augment the RGB data by a third dimension by means of 3D skeleton estimation from 2D images using a CNN-based pose estimator. In order to ensure view-invariance, a pre-processing for alignment is applied followed by data expansion as a way for denoising. Finally, a Long-Short Term Memory (LSTM) architecture is used to model the temporal dependency between skeletons. The proposed network is trained to directly recognize actions from aligned 3D skeletons. The experiments performed on the challenging Northwestern-UCLA dataset show the superiority of our approach as compared to state-of-the-art ones.
A Simple Way of Multimodal and Arbitrary Style Transfer

We re-define multimodality and introduce a simple approach to multimodal and arbitrary style transfer. Conventionally, style transfer methods are limited to synthesizing a deterministic output based on a single style, and there has been no work that can generate multiple images of various details, or multimodality, given a single style. In this work, we explore a way to achieve multimodal and arbitrary style transfer by injecting noise to a unimodal method. This novel approach does not require any trainable parameters, and can be readily applied to any unimodal style transfer methods with separate style encoding sub-network in literature. Experimental results show that while being able to transfer an image to multiple domains in various ways, the image quality is highly competitive with contemporary models in style transfer.
Prediction and Decision-Making in Intelligent Environments Supported by Knowledge Graphs, A Systematic Review

Ambient Intelligence is currently a lively application domain of Artificial Intelligence and has become the central subject of multiple initiatives worldwide. Several approaches inside this domain make use of knowledge bases or knowledge graphs, both previously existing and ad hoc. This form of representation allows heterogeneous data gathered from diverse sources to be contextualized and combined to create relevant information for intelligent systems, usually following higher level constraints defined by an ontology. In this work, we conduct a systematic review of the existing usages of knowledge bases in intelligent environments, as well as an in-depth study of the predictive and decision-making models employed. Finally, we present a use case for smart homes and illustrate the use and advantages of Knowledge Graph Embeddings in this context.
Image-text inconsistency effect on product evaluation in online retailing

This study examined the cognitive influence of inconsistency between image and text of product description. We found that the inconsistency hampers processing fluency, which in turn, negatively influences product evaluation. The effect was asymmetric, stronger when the image was negatively, rather than positively, discrepant from the text. Since people use the text as an anchor in resolving such discrepancy, the effect is strengthened by the negative affect when the value represented by the image is inferior to what is described in the text. The study provides insights for the retailing managers of e-commerce operations as well as flyer-based promotions.
Automatic Spam Detection on Gulf Dialectical Arabic Tweets

The usage of social media is increasing rapidly in the Arab region. One of the popular social networking sites for sharing news and spreading propaganda is Twitter. Spammers use these sites to disseminate adult content and false political news in Arabic tweets. Within the Arab region, distributing adult materials is illegal and some governments attempted to block malicious URLs. In this paper, we study both user and content attributes to differentiate between legitimate and illegitimate users. Then, we use those attributes with machine learning algorithms to detect spam on Twitter. We use Naive Bayes (NB) and Support Vector Machine (SVM) classification methods to find malicious contents in the tweets. Our results show that NB produces more accurate outcomes for detecting spam in Gulf Dialectical Arabic tweets.
Multimodal Neural Machine Translation With Weakly Labeled Images

Machine translation refers to a fully automated process that translates a user's input text into a target language.To improve the accuracy of machine translation, studies usually exploit not only the input text itself but also various background knowledge related to the text, such as visual information or prior knowledge.Herein, in this paper, we propose a multimodal neural machine translation system that uses both texts and their related images to translate Korean image captions into English.The data in the experiment is a set of unlabeled images only containing bilingual captions.To train the system with a supervised learning approach, we propose a weak-labeling method that selects a keyword from an image caption using feature selection methods.The keywords are used to roughly determine an image label.We also introduce an improved feature selection method using sentence clustering to select keywords that reflect the characteristics of the image captions more accurately.We found that our multimodal system achieves an improved performance compared to a text-only neural machine translation system (baseline).Furthermore, the additional images have positive impacts on addressing the issue of under-translation, where some words in a source sentence are falsely translated or not translated at all.
Transmission Line Cochlear Model Based AM-FM Features for Replay Attack Detection

This paper focuses on providing a countermeasure to replay attack which is the simplest and more accessible form of attack used to spoof automatic speaker verification systems. Specifically, it proposes the use of the transmission line cochlear model, which resembles the human cochlea more accurately than parallel filter bank models, in the front-end of replay detection systems. Here the basilar membrane is modeled as a cascade of digital filters with decreasing resonant frequencies. In this context, we propose two features - transmission line cochlea-amplitude modulation (TLC-AM) and frequency modulation (TLC-FM) - to extract the modulation features of the speech from the simulated membrane displacements. TLC-AM is analogous to the output of the inner hair cell bending movement, which accurately captures the amplitude modulation component of the speech. TLC-FM is extracted by deriving the in-phase and out of phase signals of basilar membrane displacement. Results show that individual TLC-AM and TLC-FM features perform better than the best parallel filter bank baseline system. Experiments suggest that higher frequency selectivity is beneficial for replay detection, especially for AM, and the proposed TLC model is better able to achieve this property than parallel filter bank models.
A Privacy-Preserving Authentication and Key Agreement Scheme with Deniability for IoT

User authentication for the Internet of Things (IoT) is a vital measure as it consists of numerous unattended connected devices and sensors. For security, only the user authenticated by the gateway node can access the real-time data gathered by sensor nodes. In this article, an efficient privacy-preserving authentication and key agreement scheme for IoT is developed which enables the user, the gateway node and sensor nodes to authenticate with each other. Only the trusted gateway node can determine the real identity of user; however, no other entities can get information about user’ identity by just intercepting all exchanged messages during authentication phase. The gateway cannot prove the received messages from the sender to a third party, and thus preserving the privacy of the sender. The correctness of the proposed scheme is proved to be feasible by using BAN logic, and its security is proved under the random oracle model. The execution time of the proposed scheme is evaluated and compared with existing similar schemes, and the results demonstrate that our proposed scheme is more efficient and applicable for IoT applications.
Identifying Neuroticism from User Generated Content of Social Media based on Psycholinguistic Cues

Social media has become a huge repository of textual data and images as each of the users' are creating posts, sharing views or news, capturing the moments via photos etc. Sharing or posting statuses/tweets could be considered as a common feature among the popular social networking sites like Facebook, Twitter, Google+ etc. User generated textual data such as statuses or tweets could be considered as the essential language to communicate in social media with others. This paper investigates the possibilities of identifying negative personality trait based on the psycholinguistic cues extracted from the language used in social media. Predicting personality traits based on widely accepted framework of Big Five Factor Model (BFFM) is a challenging task. According to the model, there are four positive traits namely openness to experience, conscientiousness, agreeableness and extraversion, while there is only one negative trait neuroticism. The tendency of experiencing negative emotions such as anger, sad, anxiety, depression, instability are referred as neuroticism. We have used psycholinguistic cues extracted using linguistic enquiry and word count (LIWC) for predicting neuroticism. We have applied five different classifiers to evaluate the prediction model.
Language-invariant Bottleneck Features from Adversarial End-to-end Acoustic Models for Low Resource Speech Recognition

This paper proposes to learn language-invariant bottleneck features from an adversarial end-to-end acoustic model for low resource languages. The multilingual end-to-end model is trained with a connectionist temporal classification loss function. The model has shared and private layers. The shared layers are the hidden layers utilized to learn universal features for all the languages. The private layers are the language-dependent layers used to capture language-specific features. Attention based adversarial end-to-end language identification is used to capture enough language information. Furthermore, orthogonality constraints are used to make private and shared features dissimilar. Experiments are conducted on IARPA Babel datasets. The results show that the target model trained with the proposed language-invariant bottleneck features outperforms the target model trained with the conventional multilingual bottleneck features by up to 9.7% relative word error rate reduction.
A Study about Principle Component Analysis and Eigenface for Facial Extraction

Abstract Facial recognition is one of the most successful applications of image analysis and understanding. This paper presents a Principal Component Analysis (PCA) and eigenface method for facial feature extraction. Several performance metrics, i.e. accuracy, precision, and recall are taken into account as a baseline of experiment. Furthermore, two public data sets, namely SOF (Speech on faces) and MIT CBCL Facerec are incorporated in the experiment. Based on our experimental result, it can be revealed that PCA has performed well in terms of accuracy, precision, and recall metrics by 0.598, 0.63, and 0.598, respectively.
Privacy and protection of medical images ROI using SPLSB and bit-plane based watermarking

In the current digital world, health departments and telemedicine systems are one among the sensitive data management systems of the users. Currently, at the time of managing the information by the authorities, the attackers have to tamper the medical data. It should happen while transmitting, storing and computing the data. In this paper, we propose a methodology for protecting the ROI of medical images data using Squint-Penultimate-Least Significant bits and Bit-planes of the covered image based watermarking technique. In this method, initially, the sensitive data of the medical image such as ROI segment can be detected using edge contour extraction method. Extracted ROI can act as a watermark which has required protection from the attackers. Later this watermark has protected by embedding into the chosen bit-plane of the source or cover image with SPLSB method. Thus generates a watermarked image which holds the watermark. Experiments have done on various medical images and the results were analyzed by considering performance evaluation factors. Thus, the proposed scheme is the robustness and effective method to protect the ROI of a medical image.
Tied Normal Variance–Mean Mixtures for Linear Score Calibration

A speaker verification system decides whether two voice segments belong to the same speaker based on a threshold. An optimal threshold can be set if the recognition scores are well calibrated, i.e., they represent Log-Likelihood Ratios. Logistic Regression (LogReg) is a standard approach for score calibration. While training this discriminative model requires labeled scores, Gaussian and non-Gaussian generative calibration models have been recently proposed. They not only have similar or better performance with respect to LogReg, but also allow for unsupervised or semi-supervised training of the models.The goal of this work is to extend these models. In particular, we show that normal variance-mean mixture distributions are able to model well-calibrated non-Gaussian distributed scores, provided that their parameters for the target and non-target score distributions are properly tied. As for the Gaussian case, a linear calibration model can then be estimated by computing Maximum Likelihood estimates of the distributions parameters and of the score transformation. The quality of all these approaches has been compared on a dataset of segments of variable duration obtained by cutting the NIST 2010 evaluation test data.
International Network Conference: New Technologies of Interaction for the Development of Engineering Education

Today’s global processes of post-industrial society and digital economy development are closely related engineering education. Vigorous growth of technology and communications tools implies mobility and reducing national barriers in professional interaction. Globalization processes lead to integrating and unifying national economies, policies, and cultures.
Chaos game theory and its application for offline signature identification

Chaos game mechanism is a procedure for creating fractal phenomena from another fractal using a polygon and random walk. Here, the authors propose a novel identification approach and efficient application of Chaos Game theory towards signature identification. In fact, a signature is produced with hand scripting with a special rhythm that belongs to a specific person. Although a signature is a behavioural feature among the human biometric features, this behavioural feature expresses a chaotic property and can be analysed with chaotic systems and fractal theory. With authors’ technique, by using a Chaos Game theory, a new fractal is created for each signature instance and, during the creation of the fractal, new features are extracted. These features express the fractal properties of the signature and are unique. In addition, by using fractal theory, this technique benefits from the advantages of fractal phenomena such as stability against rotation, losing some parts of the signature and scale that is desirable for biometrics applications. Authors’ approach for offline signature analysis can segregate and identify many signature instances with a desirable time complexity. The authors name the technique that the authors present here the chaos game signature identification (CGSI).
Memorization Capacity of Deep Neural Networks under Parameter Quantization

Most deep neural networks (DNNs) require complex models to achieve high performance. Parameter quantization is widely used for reducing the implementation complexities. Previous studies on quantization were mostly based on extensive simulation using training data on a specific model. We choose a different approach and attempt to measure the per-parameter capacity of DNN models and interpret the results to obtain insights on optimum quantization of parameters. This research uses artificially generated data and generic forms of fully connected DNNs, convolutional neural networks, and recurrent neural networks. We conduct memorization and classification tests to study the effects of the number and precision of the parameters on the performance. The model and the per-parameter capacities are assessed by measuring the mutual information between the input and the classified output. To get insight for parameter quantization when performing real tasks, the training and test performances are compared.
The sentiment analysis of Indonesia commuter line using machine learning based on twitter data

This paper presents The Sentiment Analysis of Indonesia Commuter Line (KRL) using Twitter data. Some of people are expressing their complaints about public transportation, especially the satisfaction of Commuter Line service on Twitter. We collected the data from Twitter to classify many public opinions into positive or negative label then Machine Learning model will be used as classification model to classify positive or negative opinion. Multinomial Naive Bayes (MNB), Random Forest (RF) and Support Vector Machine (SVM) is used as a model, then we measure all of model performances. And the result is Support Vector Machine produced has the highest accuracy of all with 85%. Net Sentiment Score (NSS) is also computed in order to determine whether KRL features meet customer's satisfaction.
A high precision signal processing method for laser Doppler velocimeter

Fast Fourier transform (FFT) is commonly adopted as the signal processing method of laser Doppler velocimeter (LDV) system to get Doppler frequency which is the key factor of the calculation of object velocity. However, precision of FFT is restricted by phenomena caused by asynchronous sampling, such as spectral interference, fence effect and spectrum leakage. Aiming at enhancing the precision of LDV signal processing, an algorithm based on the association of spectral refinement and interpolation is put forward. Zoom-FFT was utilized to refine the local range of Doppler frequency estimated by FFT. The refined spectrum was further corrected by Rife-Vincent (Ⅲ) window interpolation and got the accurate estimate of Doppler frequency. Cubic spline fitting was applied to calculate the interpolation coefficient of Rife-Vincent (Ⅲ) window without solving a quintic equation in the computing procedure of interpolation. For experimental signals, the minimum relative error of proposed method is 0.02%. And for simulated signals, the minimum relative error reaches only 0.0013%. The proposed method has capability to significantly improve the precision of signal processing and is applicable for a LDV system.
eHealth: Challenges Far Integrating BlockChain within Healthcare

This paper discusses the use of Blockchain and the Internet of Things (IoT) in the healthcare domain, identifying areas where current processes can be enhanced through the use of these new digital technologies while highlighting the issues these early developments present for an industry that is averse to risk, yet open to new technologies that improve patient safety and survival. The report discusses two uses of IoT with Blockchain as examples for the industry with many other possibilities being adoptable in a proposed single architecture that would integrate industry procedures to a common platform. While identifying technological improvements to the industry the paper also identifies the practical aspects of such a development, with varying interests across the sector, restricted funds and advancing computing power all being significant factors in whether and how to invest in such technologies. With reference to other industry attempts to make improved decisions from IoT technologies, the necessity for industry representation and leadership is seen as a significant factor in the success of any large-scale industry-wide development of a size where significant industry improvements would be achieved. While small scale isolated projects can assist individual institutions the savings industry-wide from these is likely to be much less significant than a single platform addressing total industry issues, however, the complexity in achieving this is not to be underestimated. A government body or the healthcare industry, is potentially required to coordinate the design and implementation, compiling the scope and design documentation with funding and development/implementation being provided for the industry as a whole. The paper concludes that the technologies can offer considerable technical advantages to healthcare but these are in the early stages of development. The opportunities and requirements for healthcare are significant and need early consideration and industry coordination, using existing network developments as a basis for healthcare platform designs and developed specifically to address the needs of the industry.
Space event detection method based on cluster analysis of satellite historical orbital data

A new method of detecting space events using satellite two-line element (TLE) histories is proposed. Anomalous data segments in the TLE-derived time series of a specific orbital element is detected to locate space events. After data preprocessing, a series of equal-length data segments are extracted from the time series and converted into a data form that is uniformly sampled in the time domain. Anomaly detection is achieved by clustering the data segments using a one-dimensional self-organizing map. After the clustering operation, the same type of data segments are grouped together and different types of data segments are divided into different groups. Thus, the type of space event and the associated orbital anomaly pattern can be obtained simultaneously. Space event detection results of typical active satellites show that the proposed method can accurately avoid false detections while maintaining a high detection rate. By comparing detection results of different clustering granularities, the ability to obtain detailed information of space events is also proved.