Reviewerly/exHarmony huggingface dataset:
Reader and Object Detector for Blind

This work aims to assist the visually impaired people for reading a text material and detect objects in their surroundings. The input is taken in the form of an image captured from the web camera. This image is then processed either for the purpose of text reading or for object detection based on user choice. The Raspberry Pi acts as the microcontroller for processing of the entire process. The text reading is supported by software named OCR. The read text is changed into an audio output using the TTS Synthesis. Other dependencies required for the process include Tesseract Library. The Object Detection is another aspect of the project which is implemented using a TensorFlow Object Detection API. It is able to detect various objects in its surroundings and provide an audio feedback about the same. The dataset can be trained on various different situations depending on the user needs, thus making it scalable.
Optimizing One by One Direct Convolution on ARMv8 Multi-core CPUs

Convolutional layers are ubiquitous in a variety of deep neural networks. Due to the lower computation complexity and the smaller number of parameters, convolutions with small filter sizes are often used, such as one by one convolution. Nevertheless, these small convolution operations are still time-consuming. A common approach to implementing convolutions is to transform them into matrix multiplications, known as GEMM-based convolutions. The approach maybe incurs additional memory overhead and calls matrix multiplication routines, which are not optimized for matrices generated by convolutions. In this paper, we present a new parallel one by one direct convolution implementation on ARMv8 multi-core CPUs, which doesn't incur any additional memory space requirement. Our implementation is verified on two ARMv8 CPUs, Phytium FT-1500A and FT-2000plus. In terms of performance and scalability, our implementation is better than GEMM-based implementations in all the tests on Phytium FT-1500A. On Phytium FT-2000plus, our approach gives much better performance and scalability than GEMM-based approaches in most cases.
Aspect-based Sentiment Analysis for Arabic Content in Social Media

Recently, the usage of social media platforms, especially Twitter, for sharing views and experiences toward different products and services among users has increased significantly. Accordingly, extracting and analyzing Twitter data has generated great interest from sentiment analysis researchers. Moreover, we noted the high-accuracy results achieved by the deep learning approach compared to the machine learning approach especially in analyzing a massive amounts of social networks data. In this paper, we present a survey about previous studies on sentiment analysis that used the machine learning, lexicon-based and deep learning approaches on Arabic and English tweets. Moreover, we report results based on both, machine learning and deep learning sentiment analysis approaches on Arabic tweets to extract customers' sentiments of the Saudi telecommunication companies. We also evaluate the impact of using Part of Speech (POS) model and Word Embedding on the performance of deep learning techniques. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN) have been utilized to detect the sentiment orientation for a dataset consist of 1098 tweets. Results suggest that deep learning technique with Word Embedding method was promising in terms of accuracy (F1=0.81). Moreover, the results show that applying LR, SVM, and RF using unigram language model is significantly better than using the bigram language representation.
Veterinary Image Enhancement using DWTDCT and Singular Value Decomposition

As imaging technology advances, one of the unavoidable part of image processing is the image enhancement. Image enhancement is to improve the visual quality for specific applications. The proposed algorithm enhances the visual quality of the vaginoscopic images of bitches using Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). The input image is decomposed into four frequency bands by using DWT. The low frequency band of the transformed image is again transformed by DCT into low frequency DCT coefficients and higher DCT coefficients. New low frequency band is formed by SVD. Inverse Discrete Cosine Transform (IDCT) is applied to produce the equalized image. Inverse Discrete Wavelet Transform (IDWT) of new low frequency band and the other three frequency band are combined to give the enhanced image. Different quality measures such as Root Mean Square Error (RMSE), Peak to Signal Noise Ratio (PSNR), Entropy, mean and Standard deviation are used to evaluate the performance of the model. The proposed model is implemented using MATLAB 2017a and obtained an average RMSE of 1.645. PSNR, Entropy, mean and standard deviation values for the proposed model are 44.12dB, 6.57, 103.35, 0.11 respectively. This qualitative analysis shows that proposed method seems to be useful for enhancing veterinary images for further processing.
Consistent constraint-based video-level learning for action recognition

Abstract This paper proposes a new neural network learning method to improve the performance for action recognition in video. Most human action recognition methods use a clip-level training strategy, which divides the video into multiple clips and trains the feature learning network by minimizing the loss function of clip classification. The video category is predicted by the voting of clips from the same video. In order to obtain more effective action feature, a new video-level feature learning method is proposed to train 3D CNN to boost the action recognition performance. Different with clip-level training which uses clips as input, video-level learning network uses the entire video as the input. Consistent constraint loss is defined to minimize the distance between clips of the same video in voting space. Further, a video-level loss function is defined to compute the video classification error. The experimental results show that the proposed video-level training is a more effective action feature learning approach compared with the clip-level training. And this paper has achieved the state-of-the-art performance on UCF101 and HMDB51 datasets without using pre-trained models of other large-scale datasets. Our code and final model are available at https://github.com/hqu-cst-mmc/VLL .
A Mobile Proximity Job Employment Recommender System

With a rapid growth of internet technologies, many companies have transformed from the old traditional ways of recruiting employees to e-recruiting. E-recruiting channels achieved a solid advantage for both employers and applicants by dropping advertising costs as well as hiring time. Job recommender system aim to help in people - job matching. In this research, a proposed mobile job employment recommender system is a client - server application that uses content - based filtering algorithm to enable the initial selection of a suitable leisure job seeker to a temporary job at a particular place and vice versa. A prototype of a mobile job recommendation application was developed to evaluate the algorithm. The experimental results of the proposed algorithm show the effectiveness of the system to recommend suitable candidates for jobs at a specified area.
How to Reduce the Effort: Comfortable Watching Techniques for Cinematic Virtual Reality

When watching omnidirectional movies with Head-Mounted Displays, viewers can freely choose the direction of view, and thus the visible section of the movie. However, looking around all the time can be exhausting and having content in the full 360° area can cause the fear to miss something. For making watching more comfortable, we implemented new methods and conducted three experiments: (1) exploring methods to inspect the full omnidirectional area by moving the head, but not the whole body; (2) comparing head, body and movie rotation and (3) studying how the reduction of the 360° area influences the viewing experience. For (3), we compared the user behavior watching a full 360°, a 225° and a 180° movie via HMD. The investigated techniques for inspecting the full 360° area in a fixed sitting position (experiments 1 and 2) perform well and could replace the often-used swivel chair. Reducing the 360° area (experiment 3), 225° movies resulted in a better score than 180° movies.
Single image super-resolution via non-local normalized graph Laplacian regularization: A self-similarity tribute

The process of producing a high-resolution image given a single low-resolution noisy measurement is called single-frame image super-resolution (SISR). Historically, many fractal-based schemes have been proposed in the literature to address the SISR problem. Many conventional interpolation schemes fail to preserve important edge information of natural images and cannot be used blindly for resolution enhancement. Generally, a-priori constraints are required in the process of high resolution image approximation. We model the SISR problem as an energy minimization procedure which balances data fidelity and a regularization term. The regularization term will implicitly incorporate natural image redundancy via a normalized graph Laplacian operator, as a self-similarity based prior. This operator applies a non-local kernel similarity measure due to the choice of a non-local operator for the weight assignment. The data fidelity term is modelled as a likelihood estimator that is scaled using a sharpening term composed from the normalized graph Laplacian operator. Finally, a conjugate gradient scheme is used to minimize the objective functional. Promising results on resolution enhancement for a variety of digital images will be presented.
Research on Detection Method of Tower Corrosion Based on Hough Transform

With the continuous development of smart grid construction, the scale of transmission lines is getting large and large, and abnormalities caused by various reasons pose a serious threat to the safe operation of smart grids. Tower body corrosion is a common anomaly of transmission line. It is necessary to carry out corresponding detection and identification technology research for this anomaly. This paper studies and verifies the abnormal detection method of the tower corrosion image of transmission line. Aiming at the deficiency of a large number of unnecessary noise introduced by the traditional Otus optimal global threshold processing method, this paper proposes an improved method combining Canny operator and Hough transform. Compared with the conventional method, the improved method can better extract the edge and the abnormal part of the target imager, filter out unwanted background noise point, and achieve effective detection of the tower corrosion.
A 2D face image texture synthesis and 3D model reconstruction based on the Unity platform

In this paper, a scheme of facial texture synthesis and 3D reconstruction based on 2D face image and 3D model under the Unity platform is proposed. First of all, this scheme uses the ASM algorithm to extract the key points of the human face. Next, the facial features in the face image and the standard skin color image are texture blended to get the texture of fused facial image. Finally, the texture of fused facial image is mapped to the 3D face model by texture mapping, and a better personalized 3D face reconstruction effect is obtained by this method. Compared with FaceGen, a software for modeling facial photos, the experimental results show that the proposed algorithm is superior to other existing algorithms in efficiency and has certain application value.
A Study on Network Size Reduction Using Sparse Input Representation in Time Delay Neural Networks

Neural networks are being increasingly used in many applications. While large deep neural networks are quickly advancing, for computing devices that lack powerful processors, it is desirable to use smaller neural networks. In this paper, we focus on smaller neural networks than common deep networks and examine how their size can be made even smaller without sacrificing the performance. We show that for some data types it is possible to make the networks smaller using sparse representation of the input. We have studied time delay neural networks (TDNN) for time series prediction using three different datasets. It is found that sparsifying input to the TDNN, using Discrete Cosine Transform (DCT), or Principal Component Analysis (PCA), can result in performance improvement. The improved performance can be traded off for network size reduction. Therefore, we can make the network smaller and maintain the same performance. It is found that for data that has more randomness and sudden changes in value (higher frequencies present), sparse representation methods using discrete cosine transform or through principal component analysis allow reducing network size by up to 40%.
An Improved Deep Convolutional Fuzzy System for Classification Problems

Fuzzy rule-based classifier is an effective classification algorithm. However, traditional fuzzy system is faced with the challenge of rule explosion and low training speed when dealing with big data problems. In this paper, an improved deep convolutional fuzzy system (DCFS) is proposed for big data classification problems. First, an improved Wang- Mendel (WM) Method is put forward for the training of each sub-fuzzy system. Second, a hierarchical DCFS is constructed for big data classification problems. The system performance is demonstrated via simulation experiments on a couple of classification datasets of varying sizes.
WebApplication Vulnerabilities:Exploitation and Prevention

Web application security has become a major challenge due to the common vulnerabilities found in web applications. Attackers possess a never-ending list of vulnerabilities and payloads to exploit them in order to gain access over various web applications maliciously. Each time when there are any changes made at some layer of web-application architecture, there exists a chance of creating novel vulnerabilities. In our work, the analysis is mainly focused on common and familiar vulnerabilities like Sql Injection (SQLi), Cross site Scripting (XSS) and Cross site Request Forgery (CSRF) and demonstrating the exploitation of these vulnerabilities by considering DVWA (Damn Vulnerable Web Application), a highly vulnerable web application designed for education purpose. The exploitation is carried out both manually and through automated tools. Thereby our research is concluded by inferring some preventive mechanisms to be adopted while designing the web applications to mitigate such types of attacks.
Chaotic Quantum Key Distribution

The ability to send information securely is a vital aspect of today’s society, and with the developments in quantum computing, new ways to communicate have to be researched. We explored a novel application of quantum key distribution (QKD) and synchronized chaos which was utilized to mask a transmitted message. This communication scheme is not hampered by the ability to send single photons and consequently is not vulnerable to number splitting attacks like other QKD schemes that rely on single photon emission. This was shown by an eavesdropper gaining a maximum amount of information on the key during the first setup and listening to the key reconciliation to gain more information. We proved that there is a maximum amount of information an eavesdropper can gain during the communication, and this is insufficient to decode the message.
A Quantum Walk Model of Financial Options

Financial markets are often modeled using a random walk, for example in the binomial option pricing model which is a discrete version of the Black-Scholes formula. This paper presents an alternative approach to option pricing based on a quantum walk model. The quantum walk, which incorporates superposition states and allows for effects such as interference, was originally developed in physics, but has also seen application in areas such as cognitive psychology, where it is used to model dynamic decision-making processes. It is shown here that the quantum walk model captures key aspects of investor behavior, while the collapsed state captures the observed behavior of markets. The resulting option price model agrees quite closely with the classical random walk model, but helps to explain some observed anomalies. The method also has the advantage that it can be run directly on a quantum computer. The aim of this paper is to initiate a discussion about how non-classical models that are native to quantum computers can be applied in finance.
Conditional generation of multiphoton-subtracted squeezed vacuum states: loss consideration and operator description

In terms of the characteristic functions of the quantum states, we present a complete operator description of a lossy photon-subtraction scheme. Feeding a single-mode squeezed vacuum into a variable beam splitter and counting the photons in one of the output channels, a broad class of multiphoton-subtracted squeezed vacuum states (MSSVSs) can be generated in other channel. Here, the losses are considered in the beginning and the end channels in the circuit. Indeed, this scheme has been discussed in Ref. [Phys. Rev. A 100, 022341 (2019)]. However, different from the above work, we give all the details of the optical fields in all stages. In addition, we present the analytical expressions and numerical simulations for the success probability, the quadrature and number squeezing effect, photon number distribution, and Wigner function of the MSSVSs. Some interesting results effected by the losses are obtained.
Simulation of the electromagnetic weft insertion system by adaptive neuro fuzzy interface system

There are different weft insertion methods used in the weaving machines. Electromagnetic launcher can be used as accelerator to yarn which is attached to ferromagnetic projectile. A validated finite element model can predict the system behavior accurately and faster than experiments. For optimization of the new weft insertion, the system outputs should be predicted by numerous states of inputs. Thus, prediction of the system outputs in less computation time is very important. The present study is an attempt to examine the use of an adaptive neuro fuzzy interface system (ANFIS) to simulate this new weft insertion system. The results show that simulation of this weft insertion system by ANFIS can function more efficiently and about 26 times faster than the finite element model.
Gender Obfuscation through Face Morphing

With the advancement of machine learning, facial biometric data has been widely adopted for person recognition.Soft biometrics such as gender, age and ethnicity can be extracted automatically from the facial photographs without permission.This raises privacy concerns since such auxiliary information might be utilized improperly.In this work, we apply face morphing to obfuscate gender information in face images, so that gender classifiers can no longer accurately predict gender, but the resulting images can still be used for identity verification.We further explore the reversibility of our approach and the results show that gender obfuscated through face morphing cannot be recovered or retrieved easily.Our approach is especially useful for an identity verification system which is sensitive to morphing attacks.
Multiple-Instance Learning Support Vector Machine Algorithm based Pedestrian Detection

Pedestrian detection is one of the significant task for the intelligent transportation system.so the pedestrian detection become much relevant in automotive field for improving the safety systems. Many of the existing research papers focused on efficient pedestrian detection. The challenges that are facing the existing researches are (1)Various style of clothing appearances (2)Different possible poses of pedestrian (3) Presence of hidden objects(4)Frequent occlusion. In this paper, to resolve the above challenges by formulate the pedestrian detection based on the multiple instance learning support vector machine algorithm (MILSVM).Therefore the pedestrian performing various action will be accurately detected.
Comparative study of wireless sensors for measuring the energy consumption of human running

With the rapid development of technologies such as communications, embedded computing, and sensors, wireless sensor network technology has become more mature and its applications have become more and more extensive. The development of technology has brought great convenience to people's lives and work. At the same time, people's activity and exercise are greatly reduced, causing health problems such as physical weakness. In this regard, designing a research based on wireless sensors to measure human walking and running is of great significance to promote the development of people's physical health. This paper presents a design scheme for human exercise energy consumption detection, researches and designs the coordinator, router node and host computer monitoring system of wireless sensor network. In the human motion energy consumption detection system implemented in this paper, the communication effect between the coordinator and the router node is good, and there are fewer problems such as node loss and data loss. The host computer monitoring interface is simple and easy to operate, easy to use, and can run stably. This solution does not need wiring, reduces the point of failure, reduces the cost, and has good scalability, laying a foundation for the further development of the human motion detection system, and has a wide range of application prospects and practical values. This paper is based on two constant beam width algorithms to carry out the experimental research on the energy consumption of human walking and running, and analyze the results of the energy consumption experiment. Experimental results show that the two constant beamwidth beamforming algorithms have relatively good constant beamwidth effect, which basically verifies the correctness of the theoretical analysis results and can be applied to target noise measurement.
Analysis of Student Feedback and Recommendation to Tutors

An important criterion in teaching is to analyze how well the teaching has been effective for the student’s. In order to do such analysis student’s feedback is obtained which would depict the quality and quantity of teaching. The existing evaluation technique tells about the opinion levels but does not clearly inform is it necessary to bring immediate changes in teaching or can proceed with the current teaching strategy. To address this drawback an automated ideology is proposed to initially analyze the student feedback comments and further based on the analysis introduce a recommendation system that would give a clear idea whether to bring in changes or proceed with the currently adopted teaching technique. This would provide tutors insight about opinion and allow them to make professionally sound decision so as to upgrade the performance of students.
Receptive Field Size as a Key Design Parameter for Ultrasound Image Segmentation with U-Net

Automatic and accurate segmentation of medical images is an important task due to the direct impact of this procedure on both disease diagnosis and treatment. Segmentation of ultrasound (US) imaging is particularly challenging due to the presence of speckle noise. Recent deep learning approaches have demonstrated remarkable findings in image segmentation tasks, including segmentation of US images. However, many of the newly proposed structures are either task specific and suffer from poor generalization, or are computationally expensive. In this paper, we show that the receptive field plays a more significant role in the network's performance compared to the network's depth or the number of parameters. We further show that by controlling the size of the receptive field, a deep network can instead be replaced by a shallow network.