Reviewerly/exHarmony huggingface dataset:
A Joint Demodulation and Estimation Algorithm for Plasma Sheath Channel: Extract Principal Curves With Deep Learning

For reentry communication, owing to the influence of the time-varying plasma sheath, the received IQ baseband signals are severely rotated on constellation. Researches have shown that the frequency of electron density varies from 20kHz to 100 kHz which is on the same order as the symbol rate of most TT&C communication systems and traditional estimation algorithms cannot track the variation of channel. In this letter, motivated by principal curve analysis, we propose a deep learning (DL) algorithm which is called symmetric manifold network (SMN) to extract the curves on the constellation and classify the signals based on the curves. The key advantage is that SMN can achieve joint optimization of demodulation and estimation. From our simulation results, the new algorithm significantly reduces the symbol error rate (SER) compared to existing algorithms and enables accurate estimation of fading with extremely high bandwidth utilization rate.
University Classroom Attendance System Using FaceNet and Support Vector Machine

Nowadays, face recognition system becomes popular in research area. Face recognition is also used in many application areas such as attendance management system, people tracking system, and access control system. For multi-face recognition, it has still many challenges for detection and recognition because it is not easy to detect multiple faces from one frame and it is also difficult to recognize the faces with poor resolution. Therefore, the main objective of this paper is to get a better accuracy for multi-face recognition by using the combination of FaceNet and Support Vector Machine (SVM). In this proposed system, FaceNet is used for feature extraction by embedding 128 dimensions per face and SVM is used to classify the given training data with the extracted feature of FaceNet. University Classroom Attendance System is applied by the proposed multi-face recognition. The Experimental result show that the proposed approach is good enough for multi-face recognition with an accuracy of 99.6%. It is better than VGG16 model on the same data-set.
Recognizing hand gestures for controlling home appliances with mobile sensors

Mobile and ambient sensors provide a scalable platform for the integration of computing devices and smart appliances for smart home. In which mobile devices, such as smart watches and smart-phone commonly embedded with actuators and sensors i.e., accelerometers and gyroscopes, have opened up chances for the user to easily control home appliances. This paper proposes an integrated method and system that utilize several deep models and mobile sensors for hand gestures applicable for smart homes. The system consists of three components of actual smart home configurations: (i) smart-watch worn on the user's wrist for capturing gesture patterns (ii) a recognition application that runs on the smart mobile phone and sends correspond commands to the home automation platform; and (iii) home automation platform with connected smart devices instrumented with ambient sensors. In addition, we define a simple yet easy-to-learn hand-gesture vocabulary composing of 18 gestures to the user. With the F-score of over 75%, our experiment on our self-collected data-set consisting of 18 gestures from 20 subjects, demonstrates that the feasibility of the gesture recognition for controlling home appliances.
Self-supervised monocular image depth learning and confidence estimation

We present a novel self-supervised framework for monocular image depth learning and confidence estimation. Our framework reduces the amount of ground truth annotation data required for training Convolutional Neural Networks (CNNs), which is often a challenging problem for the fast deployment of CNNs in many computer vision tasks. Our DepthNet adopts a novel fully differential patch-based cost function through the Zero-Mean Normalized Cross Correlation (ZNCC) to take multi-scale patches as matching and learning strategies. This approach greatly increases the accuracy and robustness of the depth learning. Whilst the proposed patch-based cost function naturally provides a 0-to-1 confidence, it is then used to self-supervise the training of a parallel network for confidence map learning and estimation by exploiting the fact that ZNCC is a normalized measure of similarity which can be approximated as the confidence of the depth estimation. Therefore, the proposed corresponding confidence map learning and estimation operate in a self-supervised manner and is a parallel network to the DepthNet. Evaluation on the KITTI depth prediction evaluation dataset and Make3D dataset show that our method outperforms the state-of-the-art results.
Application of Best-Worst method and Additive Ratio Assessment in mineral prospectivity mapping: A case study of vein-type copper mineralization in the Kuhsiah-e-Urmak Area, Iran

This study proposes the application of a hybrid methodology combining the Best-Worst Method (BWM) and Additive Ratio Assessment (ARAS) approaches to mineral prospectivity mapping. The ARAS approach is used to prioritize and rank individual cells. The BWM, on the other hand, is useful for assigning weights to the spatial proxies of the mineralized system, representing mappable expressions of the ore-forming processes. A dataset of vein-type copper mineralization, including geochemical, geophysical, geological, and remotely sensed data from the Kuhsiah-e-Urmak area, Iran, was translated into a set of mappable spatial proxies representing the genetic processes involved in the formation of the vein-type copper mineralization. A hybrid BWM-ARAS method was applied to the spatial proxies used to generate the mineral potential map. The results of the proposed methodology were statistically compared to those obtained from the Index Overlay and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods using the rate of correct classification in confusion matrices and demonstrating the superiority of the proposed methodology. A field trip was conducted to assess the most prospective target zones and to collect samples for analysis. The majority of these samples returned significant copper contents with copper ores observed in most of the polished sections prepared from these samples. Considering these results, the newly proposed BWM-ARAS approach appears to offer a viable alternative to the conventional methods applied to knowledge-driven mineral prospectivity mapping.
Proposal to Reuse Rubber Waste from End-Of-Life Tires Using Thermosetting Resin

Due to the increasing production of motor vehicles, a large amount of waste with different characteristics and compositions is generated, notably end-of-life tires, which are harmful to the environment when not properly disposed. Their composition contains contaminating chemical elements, resulting in negative impacts on the environment. This research aims to present a process that favors the recycling of rubber waste from end-of-life tires. For the construction of the state of the art and state of the technique, a review of the literature on end-of-life tire rubber, and a search on Google Patents and Espacenet was done using Methodi Ordinatio. For the experimental work, samples were made using concentrations of 20%, 40%, and 60% of end-of-life tire rubber particles, with the addition of thermoset polymeric matrix of isophthalic polyester resin, catalyst, and dyes. In order to evaluate the quality of the mixture, some tests with the material resulting from the mixture were performed: Izod impact strength, Shore D hardness, immersion density determination, flexural strength, and scanning electron microscopy analysis. The results from the tests indicate that the composition with 60% of rubber particles had better mechanical results than samples containing 20% and 40%. The tests also show that end-of-life tire particles promote chemical adsorption (interaction) with the thermoset polymer matrix, favoring the mechanical properties. The final results of this research are: the literature review and the search on granted patents showed that this study is original; the experimental work suggests that practical applications are possible, generating a new product, harder with a proportion of 60% of rubber particles, as indicated by the tests, with a smooth surface that does not require polishing. Thus, this research is characterized as innovative as well as having sustainable characteristics.
Automated ore microscopy based on multispectral measurements of specular reflectance. I – A comparative study of some supervised classification techniques

Abstract Automated mineralogy, including quantitative compositional and textural information, is a requirement for an efficient ore processing, and is comprised as an important input for geometallurgical planning. Classical ore microscopy is seen by many potential users as an outdated, time consuming, tool. Thus, SEM-based systems are often the choice for those who can afford them, in spite of their evident limitations for some minerals (e.g. for iron oxide ores). However, automated and quantitative mineral characterisation of metallic ores is also possible with optical (reflected light) microscopes, attaining similar performance at a much lower price than SEM systems, as shown by the AMCO (Automated Microscopic Characterisation of Ores) prototype. This system relies on the measurement of multispectral specular reflectance, R, on polished ore sections, to achieve automated identification of the ore species (reflectance is computed from grey levels of digital images acquired by automated scanning of the sample). The performance of this approach, supported by a multispectral reflectance database covering the VNIR range (370–1000 nm) built with the AMCO System, is analysed in this paper, comparing the reliability of different classification methods to achieve ore identification. The work outlined in this article focuses on checking the actual behaviour of four classification techniques, based respectively on spectral angle mapper, euclidean distance, Mahalanobis distance and linear discriminant analysis. The tests carried out reveal that the last two techniques are powerful tools to determine to which mineral corresponds a pixel based on its reflectance spectrum. The obtained results prove that automated multispectral optical microscopy is a reliable tool for mineral characterisation of common/industrial ores, with few exceptions (distinction of cassiterite and chromite, or sphalerite and wolframite). For optimisation of its performance, the multispectral information may be complemented with some additional criteria, such as paragenesis or type of deposit (e.g. to differentiate cassiterite from chromite). The reflectance database can also be enriched with measures of local ores, to account for existing reflectance variations due to local conditions or to learn new ores. The performance is particularly good for iron oxide ores (hematite, magnetite, goethite), a very important commodity. In the tests carried out, they are successfully identified in more than 99 % of the cases.
Coding of Light Fields Using Disparity-Based Sparse Prediction

In this paper we introduce a new light field codec, dubbed WaSPR (warping and sparse prediction on regions), which has additional features and improved performance when compared to our recently introduced WaSP (warping and sparse prediction) codec.We present in detail the overall scheme, including the initial WaSP structure, and the additional new features of WaSPR, including a region based sparse prediction stage, and the addition of a more efficient way to encode the prediction residuals by including separate inter-view coding of residuals obtained at each hierarchical level.The new scheme is demonstrated to improve over both modes of the JPEG Pleno Verification Model software version 2.1, and is shown to provide competitive results when compared against several recently published light field codecs.
Utilization of Oversampling for multiclass sentiment analysis on Amazon Review Dataset

Sentiment Analysis is a major element in Artificial Intelligence. Its applications include machine translation, text analysis, computational linguistics, etc. In most cases, classification of sentiment is done into two or three classes. But in some situations, for example rating a product from Amazon, there are multiple classes. One major challenge in such tasks is the class imbalance which reduces the accuracy by making the model biased. To deal with this problem, we use oversampling to reduce the class imbalance of the dataset before training the model. In this research work, first we use variations of recurrent neural networks, such as simple RNN, GRU, LSTM and Bidirectional LSTM, to find out which model performs the best in multi-class classification of sentiment. Then, we use that model to understand the effect of oversampling a dataset before using it to train a model.
An Analysis on Use of Deep Learning and Lexical-Semantic Based Sentiment Analysis Method on Twitter Data to Understand the Demographic Trend of Telemedicine

Technology has turned into a fundamental piece of everybody's life. Social media technology is already used widely by the public to speak out once mind openly. This data can be leveraged to have a better understanding of the current state of decision making. However, Twitter data is highly unstructured. Sentiment analysis can be applied to such health-related data to extract useful information regarding public opinion. The aim of the research is to understand: (i) the correlation between Deep Learning versus lexical and semantic-based sentiment prediction methods, (ii) the sentiment prediction accuracy of these methods on manually annotated sentiment dataset (iii) domain-specific knowledge on accuracy of the sentiment prediction methods, and (iv) to utilize Twitterbased sentiment to understand the influence of telemedicine in regards to heart attack and epilepsy. Four sentiment prediction methods are utilized for the research; Lexical and Semantic-based (Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob) and Deep Learning based (Long Short Term Memory (LSTM) and sentiment model from Stanford CoreNLP). The dataset that we retrieved consists of 1.84 million old health-related tweets. Our finding suggests that lexical and semantic-based methods for sentiment prediction offer better accuracy than Deep Learning methods; when a large enough and evenly distributed training dataset is not available. We observed that domain-specific knowledge affects the prediction accuracy of sentiment, mainly when the target text contains more domain-specific words. Sentiment prediction on Twitter data can be utilized to understand the demographic distribution of sentiment. In our case, we observed that telemedicine has a high number of positive sentiment. It is still in its infancy and has not spread to a broader demographic.
Methods to Account for Uncertainty in Latent Class Assignments When Using Latent Classes as Predictors in Regression Models, with Application to Acculturation Strategy Measures

Latent class models have become a popular means of summarizing survey questionnaires and other large sets of categorical variables. Often these classes are of primary interest to better understand complex patterns in data. Increasingly, these latent classes are reified into predictors of other outcomes of interests, treating the most likely class as the true class to which an individual belongs even though there is uncertainty in class membership. This uncertainty can be viewed as a form of measurement error in predictors, leading to bias in the estimates of the regression parameters associated with the latent classes. Despite this fact, there is very limited literature treating latent class predictors as measurement error models. Most applications ignore this issue and fit a two-stage model that treats the modal class prediction as truth. Here, we develop two approaches-one likelihood-based, the other Bayesian-to implement a joint model for latent class analysis and outcome prediction. We apply these methods to an analysis of how acculturation behaviors predict depression in South Asian immigrants to the United States. A simulation study gives guidance for when a two-stage model can be safely implemented and when the joint model may be required.
Automated Grading for Handwritten Answer Sheets using Convolutional Neural Networks

Optical Character Recognition (OCR) is an extensive research field in image processing and pattern recognition. Traditional character recognition methods cannot distinguish a character or a word from a scanned image. This paper proposes a system, which is to develop a method that uses a personal computer, a portable scanner and an application program that would automatically correct the handwritten answer sheets. For handwritten character recognition, the scanned images are fed through a machine learning classifier known as the Convolutional Neural Network (CNN). Two CNN models were proposed and trained on 250 images that were collected from students at Prince Mohammad Bin Fahd University. The proposed system will finally output the final score of the student by comparing each classified answer with the correct answer. The experimental results exhibited that the proposed system performed a high testing accuracy of 92.86%. The system can be used by the instructors in several educational institutions to automatically grade the handwritten answer sheets of students effectively.
Extreme Low Resolution Action Recognition with Spatial-Temporal Multi-Head Self-Attention and Knowledge Distillation

This paper proposes a two-stream network with a novel spatial-temporal multi-head self-attention mechanism for action recognition in extreme low resolution (LR) videos. The new approach first utilizes a super resolution (SR) mechanism to provide better visual information to facilitate the network training. To provide more discriminative spatio-temporal features, a knowledge distillation scheme that consists of teacher and student models is employed to enhance the network model using the knowledge from a high resolution (HR) model. Moreover, the two-stream network is combined with a new spatial-temporal multi-head self-attention network to efficaciously learn the long-term temporal dependency. Simulations demonstrate that the proposed method surpasses the state-of-the-art works for extreme LR action recognition on two widespread HMDB-51 and IXMAS datasets.
FPGA implementation of XOR-MUX full adder based DWT for signal processing applications

In the recent past there is a rapid development in the field of digital technology especially in signal processing and image processing based applications Excellent performance high speed, compactable in size low power and less delay are the essential needs of the devices used for applications such as signal processing, audio processing and software define radio and so on. Particularly, digital gadgets are prone to have more critical logic size and power consumption and take large area in VLSI Implementation due to arithmetic operations of adders and multiplier designs. Thus priority architecture of Digital Wavelet Transform (DWT) is affected as it comprises a number of Filter banks in level basics, thus all Filter banks have number of adders and multipliers due to coefficient decompositions of low and high pass filters. On this n-size repeated filter logic takes more logic size and power consumption. Here, the proposed work presents a novel approach of DWT by replacing conventional adders and multipliers with XOR-MUX adders and Truncations multipliers thereby reducing the 2n logic size to n-size logic. Finally, the proposed DWT architecture designed in VHDL and also implemented in FPGA XC6SLX9-2TQG144 proved the performance in terms of delay, area and power.
American Sign Language Character Recognition with Capsule Networks

Sign language is an argument that has vital importance in the life of hard of hearing people for communication with others. With sign language, a person with disabilities can convey his/her opinions and thoughts by using gestures and mimics to another person. Today, understanding and using sign language of people without disabilities along with the people with disabilities is really important to socialize them. With the development of technology, software tools become usable for identification of sign language and various software products are produced. In this study, recognition of sign language characters via a system, which was trained using images of letters in American sign language, is aimed. Capsule networks, which is proposed in the recent period, are used for training and test processes and compared with LeNet which is one of the first successful and currently using a model of deep learning. As a result of the study, it has been seen that capsule networks are useful for sign language character recognition and produced more successful results than LeNet.
Character-Oriented Video Summarization With Visual and Textual Cues

With the booming of content “re-creation” in social media platforms, character-orientedvideo summary has become a crucial form of user-generated video content. However, artificial extraction could be time-consuming with high missing rate, while traditional techniques on person search may incur heavy burden of computing resources. At the same time, in social media platforms, videos are usually accompanied with rich textual information, e.g., subtitles or bullet-screen comments which provide the multi-view description of videos. Thus, there exists a potential to leverage textual information to enhance the character-oriented video summarization. To that end, in this paper, we propose a novel framework for jointly modeling visual and textual information. Specifically, we first locate characters indiscriminately through detection methods, and then identify these characters via re-identification to extract potential key-frames, in which appropriate source of textual information will be automatically selected and integrated based on the features of specific frame. Finally, key-frames will be aggregated as the character-oriented summarization. Experiments on real-world data sets validate that our solution outperforms several state-of-the-art baselines on both person search and summarization tasks, which prove the effectiveness of our solution on the character-oriented video summarization problem.
Experimental Parity-Time Symmetric Quantum Walks for Centrality Ranking on Directed Graphs

Quantum walks (QW) are of crucial importance in the development of quantum information processing algorithms. Recently, several quantum algorithms have been proposed to implement network analysis, in particular to rank the centrality of nodes in networks represented by graphs. Employing QW in centrality ranking is advantageous comparing to certain widely used classical algorithms (e.g. PageRank) because QW approach can lift the vertex rank degeneracy in certain graphs. However, it is challenging to implement a directed graph via QW, since it corresponds to a non-Hermitian Hamiltonian and thus cannot be accomplished by conventional QW. Here we report the realizations of centrality rankings of both a three-vertex and four-vertex directed graphs with parity-time (PT) symmetric quantum walks. To achieve this, we use high-dimensional photonic quantum states, optical circuitries consisting of multiple concatenated interferometers and dimension dependent loss. Importantly, we demonstrate the advantage of QW approach experimentally by breaking the vertex rank degeneracy in a four-vertex graph. Our work shows that PT-symmetric quantum walks may be useful for realizing advanced algorithm in a quantum network.