Reviewerly/exHarmony huggingface dataset:
Removing of Salt-and-pepper Noise in Images Based on Adaptive Median Filtering and Improved Threshold Function

AMF-WT algorithm, based on adaptive median filtering and an improved threshold function, is proposed in this paper. After salt-and-pepper noise is removed from the image by adaptive median filtering, two-level wavelet decomposition is done on the image. The high frequency coefficients are denoised with the improved threshold function designed in this paper, then reconstructed with low frequency coefficients. MATLAB simulations show that AMF-WT algorithm is superior to adaptive median filter and median filter in eliminating salt-and-pepper noise. The improved threshold function designed has better denoising effect than hard threshold function and soft threshold function. AMF-WT algorithm can be applied to practical image denoising.
Frequent pattern mining assisted energy consumption evolutionary optimization approach based on surrogate model at GCC compile time

The evolutionary algorithms have been used to improve the energy consumption of embedded software by searching the optimal compilation options of GCC compiler. However, these algorithms do not consider the complex multivariate interactions between compilation options, which has negative effect on solution quality and convergence rate. Furthermore, it is also not investigated how to reduce the computational cost incurred by fitness evaluation using adaptive surrogate models. To address these problems, a novel approach to energy consumption optimization at GCC compile time, named FACET, is proposed in this paper. Firstly, the high-frequency multivariate interactions with positive effect on energy consumption are captured during evolution by our proposed frequent patterns mining algorithm. Secondly, the captured multivariate positive interactions are regarded as heuristic knowledge to guide the design of two mutation operators of ADD and DELETE. Thirdly, the adaptive surrogate models are introduced to assist fitness evaluation in order to reduce the high time-consumption. Finally, we evaluate our approach on 8 typical problem instances, drawn from 5 categories using 6 measurement metrics. Our results show that FACET was significantly better (p<0.05) than Tree-EDA in terms of solution quality and convergence rate in all compared experiments (with high Vargha-Delaney Aˆ12 effect size). Specifically, FACET can reduce energy consumption by 2.5% on average and 16.4% at best; accelerate convergence by 36.3% on average and 80.6% at best. Moreover, FACET was also significantly better (p<0.05) than Tree-EDA and SM-GA in terms of multivariate positive correlation in the optimal solutions (with high Vargha-Delaney Aˆ12 effect size). FACET can enhance the proportion of positive correlation by 23.3% against Tree-EDA and by 36.4% against SM-GA on average. At the same time, the adaptive surrogate models significantly save time by 73.96% on average while their accuracy reaches as high as above 99%. In addition, the adaptive surrogate models have no significant negative impact on solution quality of FACET.
Experimental demonstration of suppressing residual geometric dephasing

The geometric phase is regarded as a promising strategy in fault tolerance quantum information processing (QIP) domain due to its phase only depending on the geometry of the path executed. However, decoherence caused by environmental noise will destroy the geometric phase. Traditional dynamic decoupling sequences can eliminate dynamic dephasing but can not reduce residual geometric dephasing, which is still vital for high-precision quantum manipulation. In this work, we experimentally demonstrate effective suppression of residual geometric dephasing with modified dynamic decoupling schemes, using a single trapped 171Yb+ ion. The experimental results show that the modified schemes can reduce dephasing rate up to more than one order of magnitude compared with traditional dynamic decoupling schemes, where residual geometric dephasing dominates. Besides, we also investigate the impact of intensity and correlation time of the low-frequency noise on coherence of the quantum system. And we confirm these methods can be used in many cases.
Home Surveillance System using Computer Vision and Convolutional Neural Network

In this paper, we introduce a home surveillance system that utilizes computer vision techniques to recognize intrusions and detailed threat information including classifying types of trespassers and specific weapons used. Process of identifying intrusions is achieved by our Smart Intruder Detection and Surveillance System (SIDSS) which involves 3 stages of computer vision algorithms. 3-stage SIDSS includes an optimized convolutional neural network (CNN) for threat and intrusion detections, cascading classifiers for locating any potential intruders with correcting mechanism to overcome undetected threats from the previous stage, and principal component analysis (PCA) to efficiently train the facial recognizer to accurately differentiate passersby from potential intruders. Our system is scalable for various surveillance events and can be expanded with additional pre-processed datasets added to the SIDSS model to manage greater surveillance areas. Through this enhanced configuration, the system can achieve enhanced accuracy of recognizing broad range of weapons used and intruders.
Exploiting contextual information for recommender systems oriented to tourism

The use of contextual information like geographic, temporal (including sequential), and item features in Recommender Systems has favored their development in several different domains such as music, news, or tourism, together with new ways of evaluating the generated suggestions. This paper presents the underlying research in a PhD thesis introducing some of the fundamental considerations of the current tourism-based models, emphasizing the Point-Of-Interest (POI) problem, while proposing solutions using some of these additional contexts to analyze how the recommendations are made and how to enrich them. At the same time, we also intend to redefine some of the traditional evaluation metrics using contextual information to take into consideration other complementary aspects beyond item relevance. Our preliminary results show that there is a noticeable popularity bias in the POI recommendation domain that has not been studied in detail so far; moreover, the use of contextual information (such as temporal or geographical) help us both to improve the performance of recommenders and to get better insights of the quality of provided suggestions.
Audio Steganography Using Lifting Wavelet Transform and Dynamic Key

Steganography is an art and techniques to hide data within data can be applied to images, video files or audio files. Along with the Internet growth, threats to confidential information security needed is greater. Various threats on the Internet such as hackers and crackers make people worry about the security of information that will be sent. In this study aims to develop the steganography inside audio files as a cover using the Lifting Wavelet Transform (LWT) and Dynamic Key (DK) techniques coupled with AES encryption. This technique marks a frame will be the Dynamic Key and then the confidential data is AES encrypted using the already Dynamic Key, then the encrypted data is embedded in the audio cover frame using the LWT. The confidential data can be decrypted vice versa by using the marked frame without having to enter the key manually. The technique used is still under development stage in the Android SDK and still runs simulated inside the Android Emulator. With the development of this steganography application, this application is expected to run well on Android-based devices and can provide convenience, confidentiality, and security in the delivery and reception of information anytime anywhere.
Medical Image Segmentation with Stochastic Aggregated Loss in a Unified U-Net

Automatic segmentation of medical images, such as computed tomography (CT) or magnetic resonance imaging (MRI), plays an essential role in efficient clinical diagnosis. While deep learning have gained popularity in academia and industry, more works have to be done to improve the performance for clinical practice. U-Net architecture, along with Dice coefficient optimization, has shown its effectiveness in medical image segmentation. Although it is an efficient measurement of the difference between the ground truth and the network's output, the Dice loss struggles to train with samples that do not contain targeted objects. While the situation is unusual in standard datasets, it is commonly seen in clinical data, where many training data available without the anomalies shown in the images, such as lesions and anatomic structures in some CTs/regions. In this paper, we propose a novel loss function - Stochastic Aggregated Dice Coefficient (SA Dice) and a modification of the network structure to improve its performance. Experimentally, in our own heart aorta CT dataset, our models beats the baseline by 4% in cross-validation Dice scores. In BRATS 2017 brain tumor segmentation challenge, the models also perform better than the state-of-the-art by approximately 2%.
Recommendations in a marketplace

In recent years, two sided marketplaces have emerged as viable business models in many real world applications (e.g. Uber, AirBnb), wherein the platforms have customers not only on the demand side (e.g. users), but also on the supply side (e.g. drivers, hosts). Such multi-sided marketplace involves interaction between multiple stakeholders among which there are different individuals with assorted needs. While traditional recommender systems focused specifically towards increasing consumer satisfaction by providing relevant content to consumers, two-sided marketplaces face an interesting problem of optimizing their models for supplier preferences, and visibility. In this tutorial, we consider a number of research problems which need to be address when developing a recommendation framework powering a multi-stakeholder marketplace, and provides audience with a profound introduction to this upcoming area and presents directions of further research. Tutorial material available at: https://rishabhmehrotra.github.io/recs-in-marketplace/
A co-design toolkit for wearable e-textiles

Individuals with mobility disabilities face physical and social barriers due to a lack of accessible clothing. E-textiles garments and smart clothing could help make garments more accessible by incorporating assistive technologies directly into clothing, but there are limited methods for co-designing prototypes so that users can be involved in the design process. This is due to the specialized knowledge needed for designing smart clothing (garment construction, e-textiles, computing). To help with these issues I am developing a co-design toolkit for wearable e-textiles called Wearable Bits. Wearable Bits expands upon the swatchbook tradition in e-textiles by making swatches that can connect to form any wearable garment. This document covers the proposed studies I will be doing to evaluate the co-design toolkit and the expected contributions of this thesis project.
A family of level-dependent biorthogonal wavelet filters for image compression

In this work we explore the construction and the applications of a special family of level-dependent biorthogonal filters, i.e. filters whose taps depend on the scaling level. Such a family is generated from a class of functions all related through level-dependent (or nonstationary) refinement equations, which contains cardinal polynomial B-splines as a particular case. The greater flexibility offered by the nonstationarity of these filters allows to achieve better results in some image processing problems, such as image compression, when compared to classical biorthogonal B-spline filters.
Simplified Representation Learning Model Based on Parameter-Sharing for Knowledge Graph Completion

Knowledge graphs (KG) contain knowledge about the world and provide a structured representation of this knowledge. Current knowledge graphs contain only a small subset of what is true in the world. Knowledge Graph Completion (KGC) task aims to findin missing or errant relationships with the goal of improving the general quality of KGs. Recent years have witnessed great advance of represent learning (RL) based KGC models, which represent entities and relations as elements of a continuous vector space. However, with the deepening of the research, the scale of parameters and the complexity of KGC models become larger and larger, resulting in a serious imbalance between accuracy and computational complexity. Finally, not only the efficiency is not satisfactory, but also the training cost becomes high, which seriously restricts the flexibility and scalability of the KGC model. Therefore, this paper investigates how to enhance the simplicity of KGC model and achieve a reasonable balance between accuracy and complexity. Extensive experiments show that the proposed framework improves the performance of the current represent learning models for KGC task.
Image-based Process Monitoring via Adversarial Autoencoder with Applications to Rolling Defect Detection

Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high-dimensionality and complex spatial structures. Recent advancements in unsupervised deep models such as generative adversarial networks (GAN) and adversarial autoencoders (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational autoencoder, which serves as a nonlinear dimension reduction technique. Based on this, we propose a monitoring statistic efficiently capturing the change of the data. The performance of the proposed AAE-based anomaly detection algorithm is validated through a simulation study and real case study for rolling defect detection.
Performance evaluation of elephant flow predictors in data center networking

The dynamic characteristics and unpredictability of the traffic generated by applications and services have given rise to some problems in data center networks (DCN). In particular, the concentration of elephant flows in DCNs can lead to a load imbalance because the equal-cost multi-path (ECMP) routing maps them to the same path. This can result in packet discard, which generates packet re-transmission, causes additional latency, and further degrades link performance. In this paper, we evaluate the performance of different elephant flow predictors with the use of real DCN traces. A traffic time series from a Facebook data center is used to model the elephant flow predictors on a short-term basis and to provide the descriptive statistics and inferences the flows. The paper proposes a hybrid prediction model by combining aspects of the FARIMA and the Recurrent Neural Network (FARIMA-RNN) models. A methodology based on the rank of the prediction accuracy metrics is applied to compare the performance of the hybrid model with the ARIMA, GARCH, RBF, MLP, and LSTM models. The comparison allows the selection of an appropriate model for the implementation of elephant flow scheduling techniques in DCNs. Results show that the FARIMA-RNN model presents lower error rates than the other predictors.
Neural-Based Machine Translation System Outperforming Statistical Phrase-Based Machine Translation for Low-Resource Languages

Natural Language Processing(NLP) involves the development of computational models that aid in the development of automated tools for processing and generating natural language. Human developing these computational models require deep insight of linguistic knowledge and are a time consuming process. Hence, to automate this process and accelerate the computational science we use a data-driven approach i.e Statistical learning and Deep Learning. For devolving and sharing of information in natural language and making it accessible in other natural languages, Machine Translation(MT) is entailed. It is an application of NLP. Sanskrit being `father of informatics' [1] was considered as “lingua franca” of world intellectuals [2]. It is also an important language in the Indo-European family and considered as truly “donor” language of India. It has vast knowledge reserves in different discipline of studies such as Ayurveda, astronomy, literature etc. MT makes this rich language available to others with help of the computer. We have proposed and presented the prominent Deep Neural-based MT system for translation of Sanskrit to Hindi. We also present a comparison of Neural MT outperforming Statistical baseline system for this language pair.
Super-resolution for Selfie Biometrics: Introduction and Application to Face and Iris

Biometrics research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severely affecting the accuracy of recognition systems if not tackled appropriately. In this chapter, we give an overview of recent research in super-resolution reconstruction applied to biometrics, with a focus on face and iris images in the visible spectrum, two prevalent modalities in selfie biometrics. After an introduction to the generic topic of super-resolution, we investigate methods adapted to cater for the particularities of these two modalities. By experiments, we show the benefits of incorporating super-resolution to improve the quality of biometric images prior to recognition.
SentiFars

There is no doubt about the usefulness of public opinion toward different issues in social media and the World Wide Web. Extracting the feelings of people about an issue from text is not straightforward. Polarity lexicons that assign polarity tags or scores to words and phrases play an important role in sentiment analysis systems. As English is the richest language in this area, getting benefits from existing English resources in order to build new ones has attracted the interest of many researchers in recent years. In this article, we propose a new translation-based approach for building polarity resources in resource-lean languages such as Persian. The results of empirical evaluation of the proposed approach prove its effectiveness. The generated resource is the largest publicly available polarity lexicon for Persian.
Multilingual Short Text Classification Based on LDA and BiLSTM-CNN Neural Network

The rapid development of the Internet makes information resources have the characteristics of language diversity, and the differences between different languages can cause difficulties in information exchange. Therefore, the integration of multilingual data resources has important application value and practical significance. In this paper, we propose a novel model based on LDA and BiLSTM-CNN to solve the multilingual short text classification problem. In our method, we make full use of topic vectors and word vectors to extract text information from two aspects, in each language, to solve the sparse problem of short text features. Then, we use Long Short Term Memory (LSTM) neural network to capture the semantic features of documents and use Convolutional Neural Network (CNN) to extract local features. Finally, we cascade the features of each language and use SoftMax function to predict document categories. Experiments on parallel datasets of Chinese, English and Korean scientific and technological literature show that our proposed the short text classification model based on LDA and BiLSTM-CNN can effectively extract text information and improve the accuracy of multilingual text classification.
Feature Compensated Borehole Image Compression for Real-Time Logging While Drilling

Abstract This paper presents a novel borehole image compression algorithm for real-time (RT) logging while drilling (LWD). The compression scheme is designed to optimize the critical information required for RT decision making at low telemetry bandwidths. In the proposed algorithm we estimate the structure of the image (i.e. the amplitude and phase shift of the dip) and modify the encoding dictionary based on the features. The resulting dictionary resembles sinusoidal features, thus optimizing the reconstruction of bedding or other planar features in deviated wells. The dictionary is designed using a modified version of the 2D discrete wavelet transform (DWT). This approach has a low encoding complexity and supports the integration of directional information into the transform. Since feature estimation is a challenging step, we use a classifier to identify when directional information should be added to the transform or whether a conventional implementation is used. The algorithm has been implemented in both oil- and water-based mud LWD imager tools, where the low encoding complexity has facilitated the implementation in legacy tools with limited computation resources. We present field test results comparing the borehole images from RT and recorded mode (RM) data from one of the industry’s first RT LWD resistivity images obtained from a well drilled using oil-based mud.
Local Feature Extraction based KELM for Face Recognition

In neural networks, the success of a learning algorithm used for solving a regression or classification problem is limited by the feature extraction method used. The performance of a classifier depends on the representation of input data. This paper presents entropy based kernel-extreme learning machine (EK-ELM) algorithm for classification problem related to face images. It is a non-iterative, deterministic, single-layer feed-forward neural network learning algorithm, based on the concept of Shannon's entropy and extreme learning machine. In proposed algorithm, entropy is used for the obtaining the local features of an image that are integrated for classification. EK-ELM compares favorably with ELM together with Kernel ELM, on two popular benchmark face datasets. The comparative study formed on the experimental results shows the effectiveness of proposed methodology.
Multi-Mosquito Object Detection and 2D Pose Estimation for Automation of PfSPZ Malaria Vaccine Production

Multi-mosquito object detection and 2D pose estimation are essential steps towards fully automated extracting PfSPZ-infected mosquito salivary glands for manufacture of PfSPZ Vaccine, which has been shown to protect against malaria in multiple clinical trials in the US, Europe, and Africa. This paper presents a deep learning approach to perform cluster condition classification and bounding box detection of multiple mosquitoes in an image. It also estimates the 2D pose of each non-clustered mosquito by body part detection. This approach is based on two popular convolutional neural network (CNN) architectures, Mask R-CNN and DeeperCut. In addition, we propose a cascaded image processing approach to achieve the multi-mosquito detection, cluster condition classification, and body parts detection in a multi-step manner. We compare the two approaches in terms of their functionality, robustness, accuracy, and speed. We hope our effective approaches would push forward the automation of PfSPZ Vaccine production to facilitate the prevention and elimination of this disease worldwide.