Reviewerly/exHarmony huggingface dataset:
On quantifying the quality of acoustic models in hybrid DNN-HMM ASR

We propose an information theoretic framework for quantitative assessment of acoustic models used in hidden Markov model (HMM) based automatic speech recognition (ASR). The HMM backend expects that (i) the acoustic model yields accurate state conditional emission probabilities for the observations at each time step, and (ii) the conditional probability distribution of the data given the underlying hidden state is independent of any other state in the sequence. The latter property is also known as the Markovian conditional independence assumption of HMM based modeling. In this work, we cast HMM based ASR as a communication channel in which the acoustic model computes the state emission probabilities as the input of the channel and the channel outputs the most probable hidden state sequence. The quality of the acoustic model is thus quantified in terms of the amount of information transmitted through this channel as well as how robust this channel is against the mismatch between the data and HMM’s conditional independence assumption. To formulate the required information theoretic terms, we utilize the gamma posterior (or state occupancy) probabilities of HMM hidden states to derive a simple and straightforward analysis framework which assesses the benefits and shortcomings of various acoustic models in HMM based ASR. Our approach enables us to analyse acoustic modeling with Gaussian mixture models (GMM) as well as deep neural networks (DNN) (with different number of hidden layers) without actually evaluating their ASR performance explicitly. As use cases, we apply our analysis on sequence discriminatively trained DNN acoustic models as well as state-of-the-art recurrent and time-delay neural networks to compare their efficacy as acoustic models in HMM based ASR. In addition, we also use our analysis to study the contribution of sparse and low-dimensional models in enhancing acoustic modeling for better compliance with the HMM requirements.
Light pattern generation with hybrid refractive microoptics under Gaussian beam illumination

The generation of wide-angle diffraction patterns can be done in different ways using either thin diffractive optical elements with small features sizes or arrays of microoptics with large optical paths that are thick diffractive optical elements. Our aim is to create as many high contrast diffraction-limited dots in the far-field as possible with a uniform intensity distribution. As a model system, we use a sinusoidal phase grating and as a peculiarity, we introduce non-uniform illumination using a Gaussian beam illumination. By making use of the self-imaging phenomenon, a large number of peaks with uniform distribution are generated for a defined range of the phase grating thicknesses due to the sinusoidal curvature. For very high structures, the pattern distribution is not uniform and it demonstrates that very thick sinusoidal phase gratings are not suitable pattern generators. For simulation, we compare thin element approximation, fast Fourier transform beam propagation method, and the rigorous finite difference time domain method. The large-angle diffraction is considered using a high numerical aperture propagator for far-field simulation. We demonstrate that the beam propagation and the Fraunhofer approximation are not accurate enough. Also, our rigorous near-field calculation versus phase grating thickness confirms the significant influence of reflection of thick structures on the far-field distribution, especially on pattern uniformity. Finally, experiments were carried out to confirm our findings and a good agreement between the simulation and experimental far-field distributions confirms our approach.
LCA approach for Image Encryption Based on Chaos to Secure Multimedia Data in IoT

Recently, improvement of the internet usages, huge amount of multimedia data is transmitted through the network. The encryption method is introduced to secure the multimedia data for improve individual privacy, copyrights and other security reasons. In this paper proposed the LCA algorithm for chaotic logistic map based image encryption. Trendy this map of logistic parameter tuning is attained with the help of the LCA algorithm. The LCA algorithm is working based on the competition of sports teams in a league match. Various people as game groups contend in a league match for a few weeks; founded on the LCA process, logistic maps are tuned. The proposed algorithm for encryption is process constructed scheduled the two tasks such as confusion in addition diffusion. In confusion, produce the cipher image from the plain image by conversion. Second, the diffusion procedure is developed to diffuse the sequences equally. Here, the logistic maps parameters are taken as the encryption key. To prove the secure and reliable of the technique of proposed which is executed in the MATLAB and simulation results and performance analysis are described popular in paper. The presentation of proposed technique be there analysis through the way, statistical test and security test which is competitive with other freshly developed image encryption algorithm. To prove the efficiency of the proposed method which paralleled with the existing methods such as Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) respectively.
Robust visual tracking by embedding combination and weighted-gradient optimization

Existing tracking-by-detection approaches build trackers on binary classifiers. Despite achieving state-of-the-art performance on tracking benchmarks, these trackers pay limited attention to data imbalance issue, e.g, positive and negative, easy and hard. In this paper, we demonstrate that separately learning feature embeddings corresponding to negative samples with different semantic characteristics is effective in reducing the background diversity to handle the imbalance between positive and negative samples, which facilitates background awareness of classifiers. Specifically, we propose a negative sample embedding combination network, which helps to learn several sub-embeddings and combine them to build a robust classifier. In addition, we propose a weighted-gradient loss to handle the imbalance between easy and hard samples. The gradient contribution of each sample to model training is dynamically weighted according to the gradient distribution, which prevents easy samples from overwhelming model training. Extensive experiments on benchmarks demonstrate that our tracker performs favorably against state-of-the-art algorithms.
Short Paper: Neuromorphic Chip Embedded Electronic Systems to Expand Artificial Intelligence

Neuromorphic chips are electronic hardware mimicking neurons in human brain in an electronic structure. These ASICs (Application Specific Integrated Circuits) provide artificial neural networks with computational power comparatively higher than most neural networks generated by software algorithms. 'CM1K' is an electronic chip in this family of products. It has a parallel neural network of 1024 neurons. These neurons provide K-Nearest Neighbor (KNN) data classification. The chip requires to be embedded in an electronic system to access all its capabilities. This paper deliver a novel hardware system embedding CM1K neuromorphic chip. The system was implemented in image and video frame analysis for evaluation. The results prove that the system could benefit various applications including security, asset management, home appliances, mail sorting and manufacturing. Since the embedded system provide opportunity to integrate AI in to simple electronics, it helps on extending AI applications.
Experimental investigation on the efficacy of Affine-DTW in the quality of voice conversion

In this paper, the performance of Affine-DTW, which performs appropriate time alignment between source and target features in voice conversion (VC), is experimentally and thoroughly investigated. In traditional VC, parallel data are often required to train a mapping model between source and target features. While VC with non-parallel data is also studied to avoid collecting parallel data, the quality of its converted speech is still inferior to the traditional one with parallel data. One approach to further progress in VC is exploiting both parallel and non-parallel data, the former of which is pre-stored and the latter of which is assumed to be easily collected. In this case, it is still worthwhile to study time-alignment techniques to obtain appropriate alignment of parallel data. Affine-DTW is a technique in which dynamic time warping (DTW) and coarse conversion based on affine transformation are iteratively performed. In Affine-DTW, time alignment and parameters of affine transformation can be analytically calculated so that it can be easily adopted as pre-processing in VC. However, the influence on the performance of trained models based on the obtained alignments has not been well investigated experimentally. Hence, this paper investigates the performance of Affine-DTW in terms of quality improvement of converted speech in traditional VC methods based on Gaussian mixture models, non-negative matrix factorization and neural networks. Experimental results show that Affine-DTW obtains appropriate alignments and the naturalness improvement of converted speech in subjective assessments is observed in trained models based on the alignments.
Photographic High-Dynamic-Range Scalar Visualization

We propose a photographic method to show scalar values of high dynamic range (HDR) by color mapping for 2D visualization. We combine (1) tone-mapping operators that transform the data to the display range of the monitor while preserving perceptually important features, based on a systematic evaluation, and (2) simulated glares that highlight high-value regions. Simulated glares are effective for highlighting small areas (of a few pixels) that may not be visible with conventional visualizations; through a controlled perception study, we confirm that glare is preattentive. The usefulness of our overall photographic HDR visualization is validated through the feedback of expert users.
SOC for image processing using SIFT accelerator

In this paper, we have implemented System on Chip (SoC) for SIFT algorithm by integrating designed and library IPs. In this algorithm, we have verified for feature transforms like rotation, intensity increase and decrease. The key points are detected for the given image and compared with that of transformed images. The key point matching achieved is up to 99.8%. It is also verified that Scale Invariant Feature Transform (SIFT) algorithm works for any image format. The algorithm is implemented in Xilinx model composer and then it is converted to Xilinx system generator block for Field Programable Gate Arrays (FPGA) implementation. FPGA used is Zynq-7000 xc7z020clg484-1.
Opening up the Baseboard Management Controller

In 2011 Facebook announced the Open Compute Project to form a community around open-source designs and specifications for data center hardware. Since then, the project has expanded to all aspects of the open data center. This column focuses on the BMC and is an introduction to a complicated topic. The intention is to provide a full picture of the world of the open-source BMC ecosystem, starting with a brief overview of the BMC’s role in a system, touching on security concerns around the BMC, and then diving into some of the projects that have developed in the open-source ecosystem.
Predicting All-condition, In-hospital Mortality of Elective Patients at Time of Scheduling

In general, in-hospital mortality of elective patients is low, as such admissions do not correspond to an emergency or urgent admission, but rather are doctor scheduled admissions and so the death of a patient would not in general be expected. Nevertheless, there are still some cases of in-hospital death for elective admissions. In this work we have developed all-health condition, machine learning-based models to predict death for the case of elective admissions, applicable at the time of scheduling the admission, drawing upon a large cross-provider, state-wide dataset of discharge records. The best performing of the developed models demonstrated an AUC of 0.924, showing high discriminative performance. Based on a review of the literature there appears to be no equivalent previously published all-condition predictive model to predict in-hospital mortality of elective admission patients. This makes the work an important novel contribution to the field, and such models can potentially contribute to life-saving decisions for elective patients.
Taxi Drivers' Smoking Behavior Detection in Traffic Monitoring Video

Taxi is an indispensable part of the urban public transportation industry. However, the existence of illegal operating behaviors brings security risks to passengers and affects the city's civilization. The paper studies the detection methods of taxi drivers' smoking behavior during operation in the surveillance video environment. Firstly, the Vibe algorithm is used to detect the motion foreground of the video. Then the Haar-Adaboost algorithm is used to identify the taxi and extract the target detection area. The HSV color model is used to extract the color characteristics of the smoking smoke, and separate the target area from the moving objects which is similar to smoking smoke. Finally, combined changes in shape and movement characteristics of smoking smoke to further eliminate the interference objects, and the final detection results are obtained. The experimental results show that this algorithm has high realtime and accuracy, which is beneficial to off-site law enforcement in intelligent traffic management.
Generating Spoofing Tweets considering Points of Interest of Target User

Personal information of legitimate users shared on social networking services (SNS) can be used for identity spoofing. The simplest approach is to clone the profile information of the target user. Recent deep learning techniques have enabled us to even automatically generate spoofing messages by imitating the past messages of the target user; however, such message generators can only be trained for target users who have posted sufficient number of messages to train the generator. Further, since the legitimate users actually exist in the real world, their messages are often related to the situations in the real world. Such relations to the real world have not been considered in generating the spoofing messages, which can be the cues for detecting the identity spoofing. This paper further examines the possibility of identity spoofing even for target users who have posted only a limited number of messages based on the assumptions that messages about semantically related points of interest (PoIs) in the real world can be similar regardless of users. Our proposed method firstly collects messages about various PoIs posted by arbitrary users and estimates the semantic topic of each PoI based on the content of its messages. A topic-based message generator trained on the collected messages can be commonly used to generate spoofing messages about PoIs in the real world according to the interest of each target user.
Pan

We present an early prototype conversational agent (CA), called Pan, for retrieving information to support criminal investigations. Our approach tackles the issue of algorithmic transparency, which is critical in unpredictable, high risk, and high consequence domains. We present a novel method to flexibly model CA intentions and provide transparency of attributes that is underpinned with human recognition. We propose that Pan can be used for experimentation to probe analyst requirements and to evaluate the effectiveness of our explanation structure.
Feature-Set for Sentiment analysis

For sentiment analysis, we address the problem of supervised-learning being domain-dependent. Additionally, we try to solve the limitations faced during unsupervised-learning where the bag-of-words (lexicon) are not reliable enough, as they might not cover the broad spectrum of words that represent sentiments. We try to overcome these limitations using a novel approach where we combine multiple lexicons and filter out the corpus using the lexicons, before forwarding it to the classifiers. As discussed in the results section, this approach led to the overall improvement in the accuracy of the classifiers.
A Semantic-Based Dummy Generation Strategy for Location Privacy

With the widespread adoption of mobile devices and technical improvement of localization technologies, users can benefit from Location-Based Services (LBSs), which need users' private information. However, users' privacy are vulnerable. Even though the k-anonymity technique has been widely used, adversaries who have external spatiotemporal information still reveal users' privacy. Therefore, in this paper, we propose a novel dummy-location generation mechanism. Firstly, through computing the Super Concept-based Distance (SCD) between candidate dummies and the real location, we select 4k dummies which are semantically similar with the real one. Secondly, by checking the Euclidean distances with the real location, we choose 2k satisfied dummies. Finally, by comparing the multiplication of semantic similarities and Euclidean distance with the real location, we get k-1 dummy locations. Our scheme can generate semantically similar dummy locations, which provides better location protection. Evaluation results show that our method can significantly improve the privacy level in terms of entropy.
Design and Implementation of Configurable Convolutional Neural Network on FPGA

Convolutional neural network (CNN) is successful in the Computer Vision applications such as object detection, image segmentation, image classification... Nowadays, most of the CNN applications are implemented on the server due to the large data inputting, which takes time for transferring data from clients to server and vice versa. In recent years, there has been an increasing interest in implementing CNN on Field-Programmable Gate Arrays (FPGA) due to its' high performance, low power usage, and reconfigurability. In this paper, we design a synthesizable HDL-based CNN Core Generator; and it can configure these parameters (input size, number of layers, filter size, number of classes). For the accuracy and minimizing the loss of calculating, our design bases on Single Precision Floating-Point Arithmetic. For verification, we create the HDL-based CNN module of 2 layers, and it is applied on MNIST dataset. On the other hand, we build the exact model on Keras framework, extract weights and load the weights to the CNN module; and then two results will be compared. The maximum error of the two results is 2.056360244750977x10-6. This design is synthesized on the Virtex-7 VC707 Evaluation Board (XC7VX485T-2).
PhishFry - A Proactive Approach to Classify Phishing Sites Using SCIKIT Learn

Phishing is a type of malicious attack that involves the fooling of unsuspecting victims into providing or sharing personal information such as names, addresses, and banking information, which may lead to damages to the individual such as identity theft and financial losses. To combat phishing attacks, there have been many strides toward the use of newer technologies instead of conventional approaches such as personnel training and physical security. These technologies involve a proactive approach towards identifying Phishing websites that utilize machine learning and have become more and more efficient. In this paper, a more proactive and online machine learning approach is proposed that utilize features that have been well-accepted among industries and academia. Within the algorithm, prioritizing of features will be broken up into layers, and the output of the tool will be a digital tag that could be included in web browsers for quick identification and classification. If a site is tagged, the website owner will have the opportunity to legitimize the website through a detailed informational session and will allow them to fix any features that may be classified as malevolent in nature.
Extractive multi-document summarization using relative redundancy and coherence scores

Most extractive multi-document summarization (MDS) methods relies on extraction of content relevant sentences ignoring sentence relationships. In this work, we propose a unified framework for extractive MDS that also considers sentence relationships. We argue that adding a sentence to the summary increases summary score by relevance score of the new sentence plus some additional score which depends on the relationships of new sentence with other summary sentences. The quantification of additional score depends on how coherent the new sentence is with respect to the existing sentences in the summary. Simultaneously, some score is decreased from the summary score due to the redundancy which depends on overlap between new and existing summary sentences. To find the exact solution, sentence extraction problem is modeled as integer linear problem. The sentence relevance score is found using content and surface features of the sentence using topic model and regression framework. To find the relative coherence score, transition probabilities in the entity grid model are used. Redundancy between sentences is found using support vector regression that uses sentence overlapping features. The proposed method is evaluated on DUC datasets over query based multi-document summarization task. DUC 2006 dataset is used as training and development set for tuning parameters. Experimental results produce ROUGE score comparable to the state-of-the-art methods demonstrating the effectiveness of the proposed method.
Application of the SSD Algorithm in a People Flow Monitoring System

For places where people flow concentrates, such as scenic spots, bus platforms, and shopping mall entrances, it is important to regulate and disperse the people flow where. This system uses an object detection algorithm named Single Shot Multibox Detector (abbreviated as SSD) to monitor the people flow in various public places. The system includes the following two parts: the embedded application layer, and the web application layer. The lightweight embedded device based on the SSD object detection algorithm can realize realtime monitoring statistics and detect abnormal crowd condition monitoring. It is convenient for managers of public places to know the flow of people in an area in realtime to deal with emergencies promptly and dispatch staff. Meanwhile, the system can improve people's experience and reduce the potential harm caused by overcrowding.
Impacts of Spherical Harmonics Shape Descriptors on the Inter-Slice Interpolation of MR Images

One of the prevalent challenges in heart imaging via some 3D medical image modalities like cine-MRI is the large voxel anisotropy in acquired short-axis slices. In fact, due to some of the inherent limitation in the capacity of these imaging systems, segmented images suffer from significant staircase artifact in the direction of the heart long-axis. Therefore reconstructed 3D surfaces from these labeled images are not ideal for other accurate 3D analyses and inter-slice interpolation is needed to generate new slices between existing ones. The purpose of this paper is to generate 3D Spherical Harmonic (SH) surface of the heart left ventricle (LV) using anisotropic resolution images and assessment this claim that these SH surfaces are included of interpolated slices with good approximations. So the main contribution of this paper is to demonstrate the SPHARM robustness and ability to generate a smooth surface of the left ventricle without abrupt changes in edges nevertheless keeps the anatomical details and without production of over smooth interpolated shape.