Reviewerly/exHarmony huggingface dataset:
Deep Cross-Modal Face Naming for People News Retrieval

How to integrate multimodal information sources for face naming in multimodal news is a hot and yet challenging problem. A novel deep cross-modal face naming scheme is developed in this paper to facilitate more effective people news retrieval for large-scale multimodal news. This scheme integrates deep multimodal analysis, cross-modal correlation learning, and multimodal information mining, in which the efficient naming mechanism aims to cluster the deep features of different modalities into a common space to explore their inter-related correlations, and a special Web mining pattern is designed to optimize the name-face matching for rare non-celebrity. Such a cross-modal face naming model can be treated as a problem of bi-media semantic mapping and modeled as an inter-related correlation distribution over deep representations of multimodal news, in which the most important is to create more effective cross-modal name-face correlation and measure to what degree they are correlated. The experiments on a large number of public data from Yahoo! News have obtained very positive results and demonstrated the effectiveness of the proposed model.
Automatic Disease Detection and Report Generation for Gastrointestinal Tract Examination

In this paper, we present a method to automatically identify diseases from videos of gastrointestinal (GI) tract examinations using a Deep Convolutional Neural Network (DCNN) that processes images from digital endoscopes. Our goal is to aid domain experts by automatically detecting abnormalities and generating a report that summarizes the main findings. We have implemented a model that uses two different DCNN architectures to generate our predictions, which are also capable of running on a mobile device. Using this architecture, we are able to predict findings on individual images. Combined with class activations maps (CAM), we can also automatically generate a textual report describing a video in detail while giving hints about the spatial location of findings and anatomical landmarks. Our work shows one way to use a multi-disease detection pipeline to also generate video reports that summarize key findings.
k-Nearest Neighbor (k-NN) Classification for Recognition of the Batik Lampung Motifs

Abstract Batik is a famous name of a traditional fabric from Java. It has been admitted as one if the traditional cultural heritage of Indonesia by UNESCO since October 2 nd , 2009. Over the time, Batik is copied and modified by many regions in Indonesia resulting some new unique motifs. Batik Lampung is an sample of them. This paper deals with the k-Nearest Neighbor classification of the motifs (pattern) of the Batik Lampung. The known motifs of Batik Lampung consist of Jung Agung, Siger Kembang Cengkih, Siger Ratu Agung, and Sembagi . The original image samples are stored in RGB. They are firstly resized into 50 x 50 pixels and then converted to grayscale image. To recognize them, the Gray Level Co-Occurence Matrix (GLCM) feature is extracted and k-Nearest Neighbor (k-NN) with values of k = 3, 5, 7, 9, 11 and orientation angle of 0 0 45 0 , 90 0 , 135 0 is applied to classify the motifs. The best accuracy is achieved at the rate 97,96% for k = 7 and angle135 0 .
Deep learning-based super-resolution of 3D magnetic resonance images by regularly spaced shifting

The image acquisition process in the field of magnetic resonance imaging (MRI) does not always provide high resolution results that may be useful for a clinical analysis. Super-resolution (SR) techniques manage to increase the image resolution, being especially effective those based on examples that determine a correspondence between patterns of low resolution and high resolution. Deep learning neural networks have been applied in recent years to estimate this association with very competitive results. In this work, the starting point is a convolutional neuronal network to which a regularly spaced shifting mechanism over the input image is applied, with the aim of substantially improving the quality of the resulting image. This hybrid proposal has been compared with several SR techniques using the peak signal-to-noise ratio, structural similarity index and Bhattacharyya coefficient metrics. The results obtained on different MR images show a considerable improvement both in the restored image and in the residual image without an excessive increase in computing time.
Simulation-Based Performance Validation of Homomorphic Encryption Algorithms in the Internet of Things

IoT systems consist of Hardware/Software systems (e.g., sensors) that are embedded in a physical world, networked and that interact with complex software platforms. The validation of such systems is a challenge and currently mostly done by prototypes. This paper presents the virtual environment for simulation, emulation and validation of an IoT platform and its semantic model in real life scenarios. It is based on a decentralized, bottom up approach that offers interoperability of IoT devices and the value-added services they want to use across different domains. The framework is demonstrated by a comprehensive case study. The example consists of the complete IoT “Smart Energy” use case with focus on data privacy by homomorphic encryption. The performance of the network is compared while using partially homomorphic encryption, fully homomorphic encryption and no encryption at all.As a major result, we found that our framework is capable of simulating big IoT networks and the overhead introduced by homomorphic encryption is feasible for VICINITY.
Learning elastic memory online for fast time series forecasting

It is well known that any kind of time series algorithm requires past information to model the inherent temporal relationship between past and future. This temporal dependency (i.e. number of past samples required for a good prediction) is generally addressed by feeding a number of past instances to the model in an empirical manner. Conventional approaches mostly rely on offline model, making them impractical to be adopted in the online or streaming context. Hence, a novel method of online temporality analysis is proposed in this paper. The estimated temporality is then employed to form an Adaptive Temporal Neural Network (ATNN) with an elastic memory capable of automatically selecting number of past samples to be used. Temporality change or drift can be a common occurrence in data streams. Hence a drift detection mechanism is also proposed. Once such drift is detected, a drift handling mechanism kicks in which utilizes the rate of drift, making our solution truly autonomous. The entire mechanism is termed as LEMON: Learning Elastic Memory Online. LEMON although not a time series model in itself, can work with any predictive models to improve their performance. Synthetic datasets are used here as proof of correct temporality estimation and drift detection whereas real world datasets are employed to demonstrate how LEMON improves the predictive performance and speed of an existing model with the knowledge of temporality and drift.
Dialectical Understanding of Information in the Context of the Artificial Intelligence Problems

Abstract It is shown that the implementation of breakthrough progress in artificial intelligence cannot be accomplished without a radical modernization of the methodological framework for research in this area. This upgrade can be performed only with the use of conceptual and methodological apparatus of dialectics. Its consistent application to objects, in either case connected with ideas about the intellect, results in the formulation of the dialectic symmetry principle. The first practical steps for the implementation of the proposed concept in practice are associated with the creation of a new education paradigm, which operates, including ideas that “education” being understood as a public institution, is not reducible to individual learning. Education in modern conditions can and should be viewed primarily as a tool to influence public consciousness, or more precisely, its subsystems.
A Quantified-Self Framework for Exploring and Enhancing Personal Productivity

A variety of self-tracking applications and devices have been developed in recent years to support users in tracking their weight, calories eaten, physical activities, sleep and productivity. The availability of all this data from multiple streams provides a rich environment for experimentation that allows users to improve certain aspects of their lives such as losing weight, getting better sleep or being more productive. In this paper we propose a framework that guides users to define, track, analyse, improve and control goals for better personal productivity. We present the outcome of a single-subject case study that was implemented over one year based on the proposed framework for academic productivity. This pilot study demonstrates how longitudinal multistream self-tracking data can be leveraged to gain actionable insights into personal productivity.
Learning Facial Recognition Biases through VAE Latent Representations

The use of facial recognition technology in fields such as law enforcement is creating increased concern about algorithm-embedded biases. MIT research recently demonstrated that commercial facial recognition technology performs poorly on people of color, specifically women. The objective of this work is to implement a variational autoencoder (VAE) that generates low-dimensional latent representations of faces and then to analyze these low-dimensional latent representations to interpret potentially learned biases. While the field of interpreting facial recognition biases is still emerging, previous work has also relied on VAEs to better understand the relationship between images and their latent representations. We implement a 10-layer VAE and analyze its image encoding to a single latent feature and to ten latent features. For the single latent feature encoding, through observing the images corresponding to the highest and lowest activation values, we hypothesized that the model focuses on the overall brightness and darkness of an image. For example, faces of darker tones are assigned the lowest values and faces with lighter tones are assigned the highest values. To test this hypothesis, we manually input values for the single latent feature into the decoder at equally spaced increments and observed the reconstructed images. The reconstructed images support the hypothesis---as the latent feature value increases, the image appears to become brighter with whiter face attributes. This shows that protected features such as race (e.g. skin color) play a role in latent representations. This lays the groundwork for interpreting the encodings of individual latent features to address algorithmic biases. Future work could entail finding a set of latent features that more accurately represents said protected characteristics.
Sparse isocon analysis: A data-driven approach for material transfer estimation

Isocon analysis has been widely applied to various geoscientific problems as a simple standard tool for quantitative estimation of material transfer. Despite its usefulness, similar to all material transfer calculations, this method generally requires the presumptive specification of immobile elements or the assumption of conservation of mass or volume. However, the validity of such assumptions is particularly controversial. Here we propose a novel data-driven method that automatically estimates the mass gain or loss of elements based on compositional data of multiple samples that have been altered from the original rock without assuming immobile elements. The proposed method uses a mathematical framework, called sparse modeling, that can extract essential information from high-dimensional datasets based on the sparsity of the system. In this case, it is assumed that some elements show higher immobility than others (i.e., the material transfer of such elements is near zero). By optimizing the evaluation function, the immobile elements are automatically selected. By inputting only the bulk compositional datasets, the user can obtain the material gain or loss with total mass change ratio for each sample relative to the reference (original) rock. The effectiveness of the method is validated and discussed using synthetic and natural sample data. Software packages are available from the authors in MATLAB function and Excel workbook forms.
Polarisation and the Sustenance of Biafra Secessionist Discourses Online

This study examines polarisation in citizens’ online discourses about Biafra agitations in Nigeria. Using critical discourse analysis and the appraisal framework, it analyses 350 Biafra-related posts sampled from Nigerian digital communities. Analysis reveals that polarisation is discursively strengthened through labelling, ethnocentrism, generalisations, and historical allusions. This study concludes that the creation, consumption and unfettered distribution of such polarised discourses reflect Nigeria’s unitarist-federalism. It adds that the instigation of this socio-political fact about Nigeria can foster the cultivation/reinforcement of cognitive biases, harmful ideologies, and consequently, radicalisation/violence. It recommends that the minders of the Nigerian state should amicably address the Biafran agitations.
Latency Aware Adaptive Video Streaming using Ensemble Deep Reinforcement Learning

The development of live broadcasting represents many new technical challenges on adaptive bitrate(ABR) algorithms, which not only requires stable and high-quality transmission but also low end-to-end latency. Reinforcement learning(RL) achieves promising results and can learn ABR algorithms automatically without using any pre-programmed control rules. However, existing methods only consider bitrate control and ignore latency control. Therefore, in order to effectively reduce the end-to-end latency, we propose an independent latency limit model to control the frame skipping. Moreover, a model ensemble algorithm is implemented to reduce performance variance and improve the user quality of experience (QoE). Experimental results show that our model outperforms base- line methods and demonstrate the effectiveness of our model.
Comprehensive SVM based Indian Sign Language Recognition

The Sign Language is a communication method for the deaf and dumb people. Problem arises when a dumb person is trying to communicate with a blind person. This paper proposes a method that provides a basis for the development of Sign Language Recognition system, using Support Vector Machine, where the signs presented by the dumb person will be converted into an observable format for a blind person.
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters

Due to the sparsity of features, noise has proven to be a great inhibitor in the classification of handwritten characters. To combat this, most techniques perform denoising of the data before classification. In this paper, we consolidate the approach by training an all-in-one model that is able to classify even noisy characters. For classification, we progressively train a classifier generative adversarial network on the characters from low to high resolution. We show that by learning the features at each resolution independently a trained model is able to accurately classify characters even in the presence of noise. We experimentally demonstrate the effectiveness of our approach by classifying noisy versions of MNIST [13], handwritten Bangla Numeral, and Basic Character datasets [5, 6].
Automated Traffic Volume Analytics at Road Intersections Using Computer Vision Techniques

As the hardware cost decreases, more and more cameras have been deployed and used to monitor traffics. Widely deployed cameras enable a wide range of computer vision-based applications for traffic analytics. In this work, we propose a computer vision-based intelligent system which can analysis traffic at road interactions. Our system leverages existing traffic monitoring cameras and applies computer vision techniques to provide detailed traffic analysis results. To achieve these goals, we develop a deep learning object detection model based on Single Shot MultiBox Detector (SSD). Our approach is able to detect and track vehicles, pedestrians, traffic signs and other related-objects on the road. We build a robust model to analysis the detected objects, our system could estimate traffic volume and further infer traffic congestion, traffic rule violation and so on. To evaluate the proposed model, we use video data collected from multiple cameras at several intersections in real-world environments. The evaluation results show that our system is able to provide reliable and accurate traffic analysis results in real time. In addition, we also tested our system under different light conditions, and results show that our system could achieve similar accuracies under different light conditions.
Assessment of Autoencoder Architectures for Data Representation

Efficient representation learning of data distribution is part and parcel of successful execution of any machine learning based model. Autoencoders are good at learning the representation of data with lower dimensions. Traditionally, autoencoders have been widely used for data compression in order to represent the structural data. Data compression is one of the most important tasks in applications based on Computer Vision, Information Retrieval, Natural Language Processing, etc. The aim of data compression is to convert the input data into smaller representation retaining the quality of input data. Many lossy and lossless data compression techniques like Flate/deflate compression, Lempel–Ziv–Welch compression, Huffman compression, Run-length encoding compression, JPEG compression are available. Similarly, autoencoders are unsupervised neural networks used for representing the structural data by data compression. Due to wide availability of high-end processing chips and large datasets, deep learning has gained a lot attention from academia, industries and research centers to solve multitude of problems. Considering the state-of-the-art literature, autoencoders are widely used architectures in many deep learning applications for representation and manifold learning and serve as popular option for dimensionality reduction. Therefore, this chapter aims to shed light upon applicability of variants of autoencoders to multiple application domains. In this chapter, basic architecture and variants of autoencoder viz. Convolutional autoencoder, Variational autoencoder, Sparse autoencoder, stacked autoencoder, Deep autoencoder, to name a few, have been thoroughly studied. How the layer size and depth of deep autoencoder model affect the overall performance of the system has also been discussed. We also outlined the suitability of various autoencoder architectures to different application areas. This would help the research community to choose the suitable autoencoder architecture for the problem to be solved.
A Blockchain Based Framework for Fraud Detection

Corruption or frauds has become common terms which are associated with government bodies working across the globe. It often leads to several social and economic problems, if remain unchecked. Increase in the rate of corruption adversely affects the development of any country. The government funds or money which is intended for the welfare of the public goes in the pocket of greedy officers. This research work is aimed to reduce corruption or frauds using blockchain technology. To establish our framework, we have worked on a generic scenario in which a government has various schemes running for the welfare of common people and the funds are disbursed through a layered architecture of government passing through various organisations. Non-transparency, poor management of government records, delay in verification process can lead to corruption in various schemes at various levels. Blockchain being a transparent, immutable and decentralized mechanism is found to be a mightier technology which can help fighting corruption in the experimental generic scenario.
Automated Fall Detection From a Camera Using Support Vector Machine

Falls and fall-related fractures of a person is a major health problem and this issue is increasing day-by-day, especially for elderly who live alone. In this paper, we propose a camera based, novel, real-time automated fall detection framework for indoor environments. We process the frames on-the-fly for real time processing. We first apply background subtraction for detecting moving personin the indoor environment. We then extract relevant geometric features to classify fall from other daily activities of a person. Support vector machine (SVM) is applied to distinguish fall and other activities of a person. We have done experiments on publicly available dataset which is UR Fall Dataset. Our experiments demonstrate that our proposed framework has achieved detection rates compared to the state-of-art methods.
Adaptive shock-diffusion model for restoration of degraded document images

This paper presents an adaptive enhancement model with selective smoothing for restoration of degraded document images with blur, noise and bleed-through, which involves adaptive shock filtering and selective diffusion processes. A novel hybrid scheme is developed to solve the proposed model numerically, which combines explicit finite difference and exponential smoothing. Numerical experiments show that the proposed model is very effective for restoration of degraded document images with blur, noise and bleed-through, and has averagely the best performance on the DIBCO (Document Image Binarization Competition) series datasets, compared to five PDE (partial differential equation)-based models for restoration of degraded document images.
Wigner function of noisy accelerated two-qubit system

In this manuscript, the behavior of the Wigner function of accelerated and non-accelerated two-qubit system passing through different noisy channels is discussed. The decoherence of the initial quantum correlation due to the noisy channels and the acceleration process is investigated by means of Wigner function. The negative (positive) behavior of the Wigner function predicts the gain of the quantum (classical) correlations. Based on the upper and lower bounds of the Wigner function, the entangled initial state loses its quantum correlation due to the acceleration process and the strengths of the noisy channels. However, by controlling the distribution angles, the decoherence of these quantum correlations may be suppressed. For the accelerated state, the robustness of the quantum correlations that contained in the initial state appears in different ranges of the distribution angles depending on the noisy type. For the bit-phase flip and the phase flip channels, the robustness of the quantum correlations are shown at any acceleration and large range of distribution angles. However, the fragility of the quantum correlation is depicted for large values for strength of the bit flip channel. Different profiles of the Wigner function are exhibited for the quantum and classical correlations, cup, lune and hemisphere. The amount of quantum correlation is quantified by using the quantum discord, where its maximum/minimum bounds are consistence with that depicted by the Wigner function. It is shown that the degree of withstanding against the decoherence due to the acceleration is depicted for the amplitude damping channel.