Reviewerly/exHarmony huggingface dataset:
Segment-level Training of ANNs Based on Acoustic Confidence Measures for Hybrid HMM/ANN Speech Recognition

We show that confidence measures estimated from local posterior probabilities can serve as objective functions for training ANNs in hybrid HMM based speech recognition systems. This leads to a segment-level training paradigm that overcomes the limitation of frame-level updates ignoring the sequence structure in speech. We propose measures that train at the state and phone segment levels, while still decoding in the conventional framework. Experimental results on multiple corpora show that such trainings not only yield better systems in terms of performance, but also give additional improvements with sequence discriminative training. These techniques generalise across front-ends and model architectures, and efficiently handle the effect of segment duration variations on the ANN training.
System and VLSI Implementation of Phase-based View Synthesis

View synthesis is one of the important techniques utilized in 3D TV devices. Traditional methods such as depth image-based rendering usually rely on accurate depth maps which require computation-intensive stereo matching. In this work, we present a hardware system of phase-based view synthesis that is able to convert stereoscopic videos to multi-view content with low-resolution depth maps. When compared to the view synthesis reference software, the phase-based method does not suffer from severe arifacts on object boundaries and provides higher quality views in our experimental result. There are two major contributions in our implementation. First, we propose a cross-band disparity correction scheme that not only enables the usage of low-resolution disparity maps but also improves the quality of novel views. Second, we propose a hardware-friendly wavelet re-projection engine to reduce the hardware complexity. We implemented a VLSI circuit for 8-view 4K Ultra-HD (UHD) 3DTV in TSMC 40nm technology. It delivers 30 frames per second (fps) for UHD display when operating at 200MHz. It uses 228-KB SRAM and 2M-gate logic . We also implemented the system on FPGA, and it can provide 4K UHD multi-view content at 12 fps.
Looking Behind the Mask: A framework for Detecting Character Assassination via Troll Comments on Social media using Psycholinguistic Tools

With the facilities of social media platforms like Facebook, Twitter, Google+, YouTube etc. people are capable of expressing their views & news, sharing moments via photos, liking, commenting and sharing others posts. The online social networks (OSNs) are not only giving positive supports to its users, but also creating opportunities to assassin personals by the trolls. Trolls are usually the OSN users who try to hide themselves while doing bad comments, false accusations, starting controversies, spreading fake news or rumors which could be considered as character assassination of individuals. The online behavior of an OSN user could be tracked via his/her digital footprints. Though tracking huge number of users who are generating billions of textual and image data every day, could be considered as a challenging task. In this paper, we have proposed a novel detection system for identifying character assassination from social media platforms. The proposed method first predicts the personality traits using users' textual data. Therefore, LIWC, SlangNet, SentiWordNet, SentiStrength, Colloquial WordNet has been utilized as psycholinguistic tool. LIWC-based feature engineering has been performed on the comments of the trolls as well as the victim user. SlangNet and Colloquial WordNet is used for detecting English slang words in the comments as it is evident that slangs are the basic communicative way to defame someone.
A Brief Review on: Implementation of Digital Watermarking for Color Image using DWT Method

This paper describes a watermarking scheme for digital color images consisting of invisible watermarks. This scheme involves the embedding of the dual watermarks i.e, robust & fragile. By using these two watermarks for same image makes the scheme more secure. The first watermark is obtained by using DWT in YCbCr space, the second one i.e fragile watermark is used for improving least significant bits (LSB). Thus without differentiating with input image, it could be verified blindly for authentication. Therefore by using robust and fragile watermarking techniques, the original images could be protected.
A Tale of Two Perspectives

We present a conceptual framework grounded in both users' reviews and HCI theories, residing between practices and theories as a form of intermediate-level knowledge in interaction design. Previous research has examined different forms of intermediary knowledge such as conceptual structures, strong concepts, and bridging concepts. Within HCI, these forms are generic and rise either from theories or particular instances. In this work, we created and evaluated a conceptual framework for a specific domain (instructional fitness apps). We first extracted the particular instances using users' online reviews and conceptualised them as an expectations and experiences framework. Second, within the framework, we evaluated the artefact related constructs using Norman's design principles. Third, we evaluated beyond the artefact related constructs using distributed cognition theory. We present an analysis of such intermediate-level knowledge with the aim of informing future designs.
QuteVis: Visually Studying Transportation Patterns Using Multisketch Query of Joint Traffic Situations

QuteVis uses multisketch query and visualization to discover specific times and days in history with specified joint traffic patterns at different city locations. Users can use touch input devices to define, edit, and modify multiple sketches on a city map. A set of visualizations and interactions is provided to help users browse and compare retrieved traffic situations and discover potential influential factors. QuteVis is built upon a transport database that integrates heterogeneous data sources with an optimized spatial indexing and weighted similarity computation. An evaluation with real-world data and domain experts demonstrates that QuteVis is useful in urban transportation applications in modern cities.
Crack Detection by Optical Voice Recorder Based on Digital Holography

The detection of crack on materials is an important issue in industry. On the contrary to conventional methods, such as manual inspection, sensor detection, and image processing techniques, a new simple method to detect the crack is proposed with optical voice recorder based on digital holography in this paper. Holograms obtained with sound waves passing through the materials are recorded by using the digital holography technique. Temporal behavior of the sound wave passing through the material, which is obtained from these holograms, gives image of crack. In this article, cracks in various materials are determined by the proposed new method, and crack images obtained with this new system are presented.
Incorporating Task-Oriented Representation in Text Classification

Text classification (TC) is an important task in natural language processing. Recently neural network has been applied to text classification and achieves significant improvement in performance. Since some documents are short and ambiguous, recent research enriches document representation with concepts of words extracted from an external knowledge base. However, this approach might incorporate task-irrelevant concepts or coarse granularity concepts that could not discriminate classes in a TC task. This might add noise to document representation and degrade TC performance. To tackle this problem, we propose a task-oriented representation that captures word-class relevance as task-relevant information. We integrate task-oriented representation in a CNN classification model to perform TC. Experimental results on widely used datasets show our approach outperforms comparison models.
Virtual and Augmented Reality for Configuring, Promoting and Servicing Household Appliances

In this paper, we present several virtual and augmented reality systems for interactive visualization of household appliances. The presented systems can support the wholesale of series of products configured and then ordered in a manufacturers’ showroom as well as individual sales by enabling customers to visualize different versions of the products in their home setting before making a purchase decision. The system provides also training and support for servicing of household appliances. The visualization software, created with the use of the Unity3D engine, takes advantage of a wide range of visualization hardware, such as a smartphone, an HMD, and a Powerwall, to enable its use in different settings. With the use of the developed solutions, users can configure and manipulate components of virtual home appliances directly in a 3D space. The visualization software, which has been developed in cooperation with Amica S.A. – a major household appliance manufacturer in Poland, can be an important marketing element and an innovation that has the potential to attract new customers.
Automatic de-noising and recognition algorithm for drilling fluid pulse signal

Wavelet forced de-noising algorithm is suitable for denoising of unsteady drilling fluid pulse signal, including baseline drift rectification and two-stage de-noising processing of frame synchronization signal and instruction signal. Two-stage de-noising processing can reduce the impact of baseline drift and determine automatic peak detection threshold range for signal recognition by distinguishing different features of frame synchronization pulse and instruction pulse. Rising and falling edge relative protruding threshold is defined for peak detection in signal recognition, which can make full use of the degree of the signal peak change and detect peaks flexibly with rising and falling edge relative protruding threshold combination. A synchronous decoding method was designed to reduce position uncertainty of the frame synchronization pulse and eliminate the accumulative error of time base drift, which determines the first instruction pulse position according to position of the frame synchronization pulse and decodes subsequent instruction pulse by taking current instruction pulse as new bit synchronization pulse. Special tool software was developed to tune algorithm parameters, which has a decoding success rate of about 95% for the universal coded signals. For the special coded signals with check byte, decoding success rate using the automatic threshold adjustment algorithm is as high as 99%.
Mismatch Removal for Remote Sensing Images Based on Non-Rigid Transformation and Local Geometrical Constraint

Feature matching, which refers to the establishment of accurate correspondence between two sets of feature points, plays an important role in the field of computer vision and remote sensing. Focusing on the characteristics of remote sensing images, this paper proposes a new algorithm for robust feature matching based on non-rigid transformation, which formulates the feature matching problem as a probabilistic model. Specifically, we first utilize the scale-invariant feature transform to establish the initial feature correspondences between an image pair, and the thin-plate spline is adopted for non-rigid transformation modeling, where a local geometric constraint is introduced to maintain local structures of neighboring feature points after the transformation. Under the Bayesian framework, we seek a maximum a posteriori solution of our model by using the expectation-maximization algorithm. In addition, without sacrificing the matching accuracy, we provide a fast implementation to reduce the computational complexity of the algorithm based on sparse approximation. We verify the performance of our method on a large number of remote sensing images, and the qualitative and quantitative results reveal its superiority over the state-of-the-art.
What Makes for Effective Visualisation in Cyber Situational Awareness for Non-Expert Users?

As cyber threats continue to become more prevalent, there is a need to consider how best we can understand the cyber landscape when acting online, especially so for non-expert users. Satellite navigation systems provide the de facto standard for many modern day navigation tasks in the physical domain, so we consider the question of how one could navigate the online domain using similar concepts. In this paper, we study the design of a cyber sat nav for improving situational awareness of nonexpert users. We focus on three core tasks: understanding where we are in cyber space, understanding how we got there, and understanding future states that we may traverse to. To support understanding, we explore the use of visualisation techniques to portray complex online activities in clear and engaging formats for non-expert users.
DotCHA: A 3D Text-Based Scatter-Type CAPTCHA

We introduce a new type of 3D text-based CAPTCHA, called DotCHA, which relies on human interaction. DotCHA asks users to rotate a 3D text model to identify the correct letters. The 3D text model is a twisted form of sequential 3D letters around a center pivot axis, and it shows different letters depending on the rotation angle. The model is not composed of a solid letter model, but a number of spheres to resist character segmentation attacks, and this is why DotCHA is classified as a scatter-type CAPTCHA. DotCHA is tolerant to machine learning attacks because each letter is only identified in each particular direction. We demonstrate that DotCHA, while maintaining usability, is resistant to existing types of attacks.
Monte Carlo mean for non-Gaussian autonomous object tracking

Object tracking is highly applicable in emerging technologies and is normally done using measurements from sensors. Unfortunately, due to the presence of deleterious noise, measurements are inaccurate and different estimation methods have been developed. Most of them are mainly for Gaussian noise, leaving non-Gaussian noise scenarios unresolved. Also, while particle filters were introduced to address a more general noise scenario, they are mathematically complex especially when used in high dimensional systems. To circumvent these problems, we propose the Separate Monte Carlo Mean (SMC-MEAN) which is formulated on the Bayesian particle filtering framework. The proposed method is applied to an autonomous object tracking problem in both Gaussian and non-Gaussian scenarios. Results are compared to the Kalman filter and Maximum A Posteriori (MAP) in Exponential and Logistic distributed noise. The proposed method outperforms the other methods by an average of 17% yet maintaining low mathematical complexity.
An Exploration of Cross-Modal Retrieval for Unseen Concepts

Cross-modal hashing has drawn increasing research interests in cross-modal retrieval due to the explosive growth of multimedia big data. However, most of the existing models are trained and tested in a close-set circumstance, which may easily fail on the newly emerged concepts that are never present in the training stage. In this paper, we propose a novel cross-modal hashing model, named Cross-Modal Attribute Hashing (CMAH), which can handle cross-modal retrieval of unseen categories. Inspired by zero-shot learning, attribute space is employed to transfer knowledge from seen categories to unseen categories. Specifically, the cross-modal hashing functions learning and knowledge transfer are conducted by modeling the relationships among features, attributes, and classes as a dual multi-layer network. In addition, graph regularization and binary constraints are imposed to preserve the local structure information in each modality and to reduce quantization loss, respectively. Extensive experiments are carried out on three datasets, and the results demonstrate the effectiveness of CMAH in handling cross-modal retrieval for both seen and unseen concepts.
Toward Fusing Domain Knowledge with Generative Adversarial Networks to Improve Supervised Learning for Medical Diagnoses

This paper addresses the challenges of small training data in deep learning. We share our experiences in the medical domain and present promises and limitations. In particular, we show through experimental results that GANs are ineffective in generating quality training data to improve supervised learning. We suggest plausible research directions to remedy the problems.
A series of microscope objective lenses combined with an interferometer for individual nanoparticles detection

In this paper, we uniquely introduce a series of microscope objectives (MOs) combined with a polar interferometer for imaging individual nanoparticles in real-time. The MOs with cascade magnifications are used to magnify the individual nanoparticle interferogram in the entire field of the CCD camera. Single shot, interferogram is captured and then reconstructed by the angular spectrum method. Size of the individual nanoparticle is then calculated by a simple geometric optical equation. Using this technique, important applications in drug delivery that require precision in size and shape of nanocarriers can be demonstrated. Moreover, using this technology, biological samples with birefringence can be investigated precisely.
Multi-source multiple change on belief bases

We will propose an approach to multi-source multiple belief revision where the trust or credibility assigned to informant agents is considered in the revision process; the credibility associated to each informant is maintained as a strict partial order among informant agents. In the context of a collaborative Multi-Agent System, our goal is to formalize a change operator for an agent which, upon the arrival of a set of new information provided by its peers, will be capable of revising its belief base considering the information sources to decide which information prevails. For the operator, we give a constructive definition and an axiomatic characterization, connecting them through the corresponding representation theorem. Pragmatically, the operator introduced here can be conceptualized as a reasoning skill that helps improve the collective performance of agents working in a multi-agent system. We will describe a real-world example that will motivate the practical importance of having this ability when implementing applications.
Visualization of Photo Album: Selecting a Representative Photo of a Specific Event

In order to effectively manage photos in personal photo album and improve the efficiency of re-finding photos, the visualization of photo album has received attention. The most popular and reasonable visualization method is to display a representative photo of each photo cluster. We studied the characteristics of representative photos and then proposed a method of selecting the representative photos from a set of photos related to a specific event. The method mainly considered two aspects of photos: aesthetic quality and memorable factor. Aesthetic quality contains the area and location of the salient region and the sharpness of photo; memorable factors contain the salient people and text information. The experimental data sets are real-world personal photo collections, including more than 7,000 photos and more than 2000 specific events. The experimental results show the efficiency and reliability of selecting representative photos to visualization of photo album.
Web archaeology in The Netherlands: the selection and harvest of the Dutch web incunables of provider Euronet (1994–2000)

This article describes the method and first results of the ongoing pilot research project on internet archaeology and the quest for web incunables of Koninklijke Bibliotheek - National Library of the Netherlands (KB). It is about web archiving a selection of web incunables published on the Dutch web before 2000 and has a special focus on home pages or user websites from internet provider Euronet* Internet from the period between 1994 and 2000. It explains how internet archaeology can be an innovative way to investigate the new digital cultures through insights from the past. Although many Dutch websites dating from before 2000 are still present on the web and some fine examples of web incunables could be saved, due to technical, legal and other issues it is still hard to archive them. The web collection which is a result of our research cannot therefore be considered as a representative selection of born digital heritage from service provider Euronet, but as the outcome of a learning process.
Designing educational game android to improve mathematical understanding ability on fraction

In the modern era, a lot of smartphones that have been created to make it easier for the people in their daily activities. The more people using the smartphone, provide the opportunity to this technology can be used to support action in the education sector one of these games education. Game education specially designed to give the learning to the users and give them the motivation to play the game to increase the ability of students understanding. The purpose of this research is to produce game education in junior high school students are valid and practical well as to expand the knowledge of students to see after using these of media. Research methods that used in this research are research of development by model ADDIE was (analysis, design, development, implementation, and evaluation). The instrument used is sheets interview to know student learning difficulties, validation sheets, sheets practically, question test (pretest and posttest). According to the discussions of students said that it takes the media in learning that students not bored in learning. Results of this educational game data analysis are valid, practical and can improve the ability of student's mathematical understanding related to fraction material with sufficient improvement interpretation.
Weakly Supervised Deep Metric Learning for Template Matching

Template matching by normalized cross correlation (NCC) is widely used for finding image correspondences. NCCNet improves the robustness of this algorithm by transforming image features with siamese convolutional nets trained to maximize the contrast between NCC values of true and false matches. The main technical contribution is a weakly supervised learning algorithm for the training. Unlike fully supervised approaches to metric learning, the method can improve upon vanilla NCC without receiving locations of true matches during training. The improvement is quantified through patches of brain images from serial section electron microscopy. Relative to a parameter-tuned bandpass filter, siamese convolutional nets significantly reduce false matches. The improved accuracy of the method could be essential for connectomics, because emerging petascale datasets may require billions of template matches during assembly. Our method is also expected to generalize to other computer vision applications that use template matching to find image correspondences.
Disagreement-Based Active Learning in Online Settings

We study online active learning for classifying streaming instances within the framework of statistical learning theory. At each time, the learner either queries the label of the current instance or predicts the label based on past seen examples. The objective is to minimize the number of queries while constraining the number of prediction errors over a horizon of length <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula>. We develop a disagreement-based online learning algorithm for a general hypothesis space and under the Tsybakov noise and establish its label complexity under a constraint of bounded regret in terms of classification errors. We further establish a matching (up to a poly-logarithmic factor) lower bound, demonstrating the order optimality of the proposed algorithm. We address the tradeoff between label complexity and regret and show that the algorithm can be modified to operate at a different point on the tradeoff curve.