Reviewerly/exHarmony huggingface dataset:
Smart Student Grievance Redressal System with Foul Language Detection

Grievances are quite common in educational institutes and organizations and the majority of the institutes find it difficult in solving the students&#x0027; problems. It might be that the students are diffident in approaching the management or the management is inefficient and most of the times the complaints remain unattended. The work in this paper aims to solve all these problems and presents a web portal created using Django, HTML, CSS, SQL, etc. where students can put complaints on the portal from the categories mentioned and the complaint goes to the concerned department. The student can track the complaint completely and know whether the complaint has been viewed, in progress, transferred, rejected, and solved. There is complete transparency in the proposed system and if the complaints remain unaddressed for a few days, then automatically the system redirects the complaint to the person above in the hierarchy. Special authorities have been provided to the principal/head of the organization which would help in expediting the solution providing process to the students. The quality of language is an area of concern in any redressal system since it can be used for fake propagandas by commenting on topics like religion, gender, race, sex, individual, etc. This is taken care of in this work by implementing Foul/Hate Detection using technologies like Machine Learning and Deep Learning. SVM, LSTM and Bi-LSTM models are used after training over 11,325 tweets and upon evaluating the results using evaluation metrics like recall, precision and F1 score, &#x2018;LSTM model&#x2019; outperformed the other models by achieving &#x2018;0.884&#x2019;, &#x2018;0.84&#x2019;, &#x2018;0.86&#x2019; values for the metrics respectively. The system also includes a page where all the complaints of the organization are listed, and a support feature is included which enables students to support other complaints which would in turn help the authorities in analyzing the major issues in the organization.
PAP Smear Image Enhancement Using Diffusion Stop Function Based Clahe Algorithm

Image pre-processing is a very crucial step in cervical cancer(CC) detection from Papanicolaou test images or Pap-smear(PS) image analysis. The slides prepared from pap-test will be given as input to an automated or semi automated system. The system will then analyze the images and will classify as cancerous or non-cancerous. Various researches have been done in the automatic pap-smear analysis and classification. A new approach is presented in this study for PS image enhancement(IE). The images collected from the Mendeley database are enhanced using the DSF-CLAHE(diffusion stop function-contrast limited adaptive histogram equalization) method. DSF is a process in which the edges of images are preserved while smoothening the inside pixels. When compared to some of the existing approaches such as histogram equalization and median filtering, or CLAHE combined with other filters such as adaptive median filter and guided filter, CLAHE when combined with DSF gives better results. The pre-processed results are evaluated with performance matrices MSE(mean squared error), PSNR(peak signal-to-noise ratio), SSIM(structural similarity index measure), and AMBE(absolute mean brightness error) which shows that the proposed method surpass some of the currently available methods.
Deep Convolutional Neural Network-Based Recognition of Profile Rotated Face Images

Over the past few decades, various face recognition algorithms have gained widespread deployment in different biometric access mechanisms. While earlier face recognition techniques utilized hand-picked features for computing the distance metric between face images, the advent of deep learning based techniques have automated this process and has revolutionized face recognition technology. However, most of the existing face recognition techniques are trained on frontal face posture and have very poor accuracy when there is a change in facial posture. Moreover, they have poor recognition rate when training dataset is small. In this article, we propose a dataset augmentation based approach for face recognition with convolutional neural networks (CNN). Proposed approach is especially suited for the datasets which consist of limited number of profile rotated facial images with varying brightness. Through extensive simulations, superiority of proposed approach is established by comparing the recognition rate with some of the recently proposed techniques in literature.
A Novel Authentication Mechanism for SecureData Access based on Encryption Key Sharing for Cloud Web Application

It is becoming increasingly common to outsource data to the Cloud based Web Applications (CWAPP). We recommend that the data owner encrypt his or her data ahead of outsourcing it to the CWAPP to ensure the confidentiality of the data. However, effective access for encrypted information is an essential and complex issue for CWAPP data sharing. In the past many studies are suggesting encryption data access methods as one of the most promising solution for the secure data retrieval through preserving data privacy and access control. However, these mechanisms are exposed to internal attacks and the organization in using CWAPP raises major burden for managing password-authenticated identities for access control of the data shared due to the dynamic and complex nature of operations. This raises major security concerns, especially data confidentiality and security of CWAPP. In this paper, we present a novel authentication mechanism for secure data access based on Encryption Key Sharing for Authorized Access (EKS-AA) for securely data encryption and validate the client authentication before retrieval with a shared data. The EKS is used by the sender to encrypt the keyword and by the receiver to open a data access door. The primary aim of this proposal is to make CWAPP secure from password phishing and intrusive keyword guessing attacks. The experimental evaluation in terms of time complexity measure suggests the effectiveness of the proposal for CWAPP.
A Comprehensive Study on Human Pose Estimation

The Human pose estimation is the process of digging out predefined body parts called joints from the image/frame inputs. The human body comprises of various bones, muscles and joints with their own movement angles. These are represented with different models. The keypoint representation is one among them. The pose estimation involves locating, grouping and tracing keypoints. Each of the keypoint represents various postures. The neural network the base model for pose estimation tracks these postures The human manners can be better focussed with a proper real-time fine -grained human pose tracker. The single or multiple people in the scene from the monocular or stero cameras are detected based on poses. The pose estimation is an arising research topic due to the wide range of functions in human-robot interaction, gaming, sports performance analysis. The paper discusses about fundamentals of 2D,3D poses estimations, its evaluation metrics, dataset. The paper is over and done with challenges and research directions
Hybrid LWT and DCT based High-Quality Image Compression

Research in the field of image compression has grown significantly with the increasing need to publish images in computer and mobile environments. Image abstraction plays an important role in digital image processing, which is crucial for efficient image transmission and storage. Without degrading the image quality, the image size in byte is minimized by using image compression technique. Many different ways are used in this process to compress the input image. Therefore, this research study proposes hybrid compression technique includes Lifting Wavelet Transform (LWT) with discrete cosine transform (DCT) to provide the best properties for an image that is performed in JPEG compression method. To get the high quality compression ratio, lossy and lossless methods are used by this technique and also used to maintain the reconstructed image quality. A high compression ratio is produced by lossy compression, where reconstructed images high quality is brought by lossless compression and later it will be decompressed with same results. MATLAB software tool is used to implement the proposed compression process and to verify its performance in terms of various parameters such as PSNR, MSE and SNR.
Dilated Convolution and MelSpectrum for Speaker Identification using Simple Deep Network

A challenge in the Speaker Recognition systems is variations in the speech utterances of a speaker in different instances. We propose a deep learning approach to develop the simple Convolution Neural Network(CNN) with limited layers and reduced data to train the network. The commonly used Log-MelSpectrum is employed to represent the speech signal. This network uses dilated convolution layer instead of traditional layer with deep stride and filters with increasing order. Dilated Convolution exhibits more receptive area which helps to hold the low level features in speech signal. The performance of the proposed network with clean Libri speech data and 200 samples of each speaker is compared against existing scheme. The accuracy reaches 76.1&#x0025; using existing method, while it is increased to 80.6&#x0025; when using the proposed method and without introducing greater time complexity.
Hybrid Image Processing Device as Wearable Aide for Visually Impaired

With the advent of technology and algorithms in image processing, many wearable aides are available in the market, and researchers across the globe are developing new solutions. The existing solutions and products fail to provide an economically viable and single solution to the problem of visually impaired people. This study introduces AIde, a novel system amalgamating optimized computer vision algorithms and wearable IoT devices to convert the information extracted from the camera mounted on the user to audio-based signals for assisted mobility. The proposed system has three user modes: indoor, outdoor, and hybrid, which can be modified using the buttons provided on the wearable device. The indoor mode can be used for object detection and classification to increase the accessibility of the objects inside a confined space, and the outdoor mode will provide the user with enhanced mobility and directional sense based on the highlighted path captured by the mounted camera. The hybrid model combines both the features of indoor and outdoor modes, which will detect objects and improve mobility simultaneously. The proposed model is optimized to work in a computationally efficient environment with an at par efficiency and accuracy as the state-of-art and costly market alternatives.
IoT Based Smart Mitten for Plegia Patient's Health Monitoring System

In today&#x0027;s world we come across many disabled people who aren&#x0027;t able to communicate with others. There are some dumb languages but it is hard to learn and understand. Paralyzed people seek assistance for their requirements. Paralysis is caused by injury or any disease affecting the brain and spinal cords. This means that the nerve signals sent to the muscle are interrupted. These kinds of people should depend on others even for their basic needs. Some kinds of devices are being developed but they are very large and very expensive. Our motive is to develop a device that can retrain a patient&#x0027;s motion and to use a device by themselves. We had proposed a device called IOT BASED MITTEN FOR PLEDGIA PATIENTS. The gloves which we designed are very simple. By using accelerometer sensors, we find the motion of the hand. Each motion conveys the patient&#x0027;s emotion. We implemented Arduino UNO, where the data communication between transmitter and receiver is performed by wireless serial port. During an emergency situation, an alert message will be passed through the GSM module.
iCAST: Impact of Climate on Assistive Scene Text detection for autonomous vehicles

Scene-text detection uses machine learning and computer vision methods in order to automate the process of text detection in natural environmental conditions. The study of scene text detection becomes more critical in the context of self-driving cars, where text present on traffic panels, hoardings, and other boards and signs needs to be detected and recognized from the camera installed in the car. The variations in the car&#x0027;s surrounding environment, where text is in different orientations, languages, angles, and lighting and weather conditions, makes this task more complex. This paper investigates the impact of different weather conditions by applying haze removal techniques on RoadText-1k dataset in preprocessing. These techniques have improved qualitative and quantitative results in terms of recall metric.
Effective Image Deblurring Based on Improved Image Edge Information and Blur Kernel Estimation

Image deblurring is one of the prevalent difficulties in image processing. Image blur is an unintentional loss of bandwidth that is difficult to avoid and affects image quality. Image deblurring can be done in a variety of ways, although most of them are restricted to motion blur and lack image edge information. An effective approach of image deblurring is presented to enhance the efficiency of image edge information and blur kernel estimation. In the luminance channel, the suggested technique employs high frequency layer information resulting from a input image using the 2D-haar wavelet technique. Following that, adaptive canny edge detection is performed, after that sliding window method is used in order to split the image into nine regions. The rich edge region index of each region is then used to extract the region with the most information about the edge. At the end, Convex kernel normalization and blind singular value decomposition are used to figure out the texture feature. The method is shown in MATLAB R2013a. Various quality factors has been measured to figure out how accurate it is. Efficacy tests reveal that the proposed strategy outperforms others.
Hybrid approach to minimize 51% attack in Cryptocurrencies

The implementation of Cryptocurrency came into play after the resolution of double spending problem. After a decade of its conceptual inspection there are lots of cryptocurrencies being developed and used in today&#x0027;s market. Even though the cryptocurrencies win the confidence of stakeholders using their decentralized and cryptographic algorithms they always had a possible downfall which is quoted as practically impossible and stated theoretically. One of the major problems is 51 &#x0025; attack on the blockchain of the cryptocurrency. The solution from centralized banks printing fiat currencies and causing inflation was decentralized miners mining and getting rewarded for their work However democratic the networks reward system seem they will always be dependent on any one factor which may allow a majority resource holder in PoW based system and majority stake holder in a PoS system to manipulate the blockchains consensus once they own 51 &#x0025; of the mentioned. This problem will remain practically impossible for most of the cryptocurrencies that exists because of their vast network and resources. This is practically possible problem for cryptocurrencies that are new to the market and they face a vulnerable position since their network and resource is not yet distributed enough. To avoid such an attack the currencies should start with a rewarding system that is validated based on PoW and PoS but the miner from the list is declared on a random basis. Even if a malicious attacker holds 51 &#x0025; of the resource and 51&#x0025; of asset and cleared PoW and PoS which practically impossible still the rewarding systems random miner mechanism should restrict the same user from manipulating the whole chain and take control over it.
GA-Based Load Balancing in Cloud with OS-level Virtualization

The main idea of cloud computing is to provide different computing services to the end-user online over the internet. Cloud computing uses virtualization technology to offer various services to the user. There are two different ways of virtualization; hardware virtualization and software virtualization. The hardware virtualization uses a hypervisor to create several virtual machines from one single physical machine, and software virtualization uses a container engine to create several containers. The traditional virtual machine-based virtualization techniques use more computing resources than OS-level virtualization. Efficient resource management plays an essential role in the overall performance of the cloud system. Evenly distributing the workload among all the available machines is one way of efficient resource management. This paper proposes a metaheuristic approach for load balancing in a cloud system that uses OS-level virtualization. Load balancing is the process of distributing workload or users&#x0027; tasks equally among all available systems. We implemented several algorithms such as first fit, next fit, best fit, first fit decreasing, best fit decreasing and compared their result. The experimental result shows that the best fit decreasing outperforms all other approaches. Then, we compared the result with our proposed GA-Based approach and found that our algorithm performs better than best fit decreasing.
Object based Video Retrieval with multiple features matching approach

In rummage-sale, an image retrieval is a method for browsing, searching and retrieving images from large databases of digital photos. Object Based Video Retrieval (OBVR) is the concept to be used when there is a need to search the video which contains the required object in some of its frames. The OBVR system is an energetic field of research. Numerous researchers have suggested a change from coloring histograms to blocking grounded strategies in recent years. In this proposed approach multiple feature extraction is used, based on the color, edge and shape the videos are retrieved from the database. First, objects are identified and separated from the key frames using segmentation. Then, features are extracted from the segmented object, also from the query object features are extracted and finally both features are compared with the feature database. If the feature of the query object is matched with any of the groups in the shot, all the frames in the shot are retrieved in the shot. In this proposed method, experimentally proved that the results are more efficient in standings of the precision and recall calculation.
Early Fault Detection in Solar Panels Using Machine Learning

Monitoring and detecting the set of solar panels using a Wireless Sensor Network (WSN) is our main aim of this project. Here we have illustrated an effective implementation of an intelligent remote monitoring system for wind turbine and solar PhotoVoltaic (PV) Power Conditioning Unit (PCU) which is used in a greenhouse environment. Nowadays, with the development of sensor technology, it is a very viable option to connect the wind turbine system and the solar energy to the cloud with the help of the Internet of Things. Once these systems are connected to the cloud, the productivity, efficiency and performance of the system can be calculated easily. The result of our manifestation shows that the system can manipulate and monitor data from solar PV PCU and wind turbines. The automatic remote monitoring systems are recognized in real-time. Also, we integrate the data analysis, production and pricing using R-programming. Finally, the predicted output is passed using shinyapps.io (web services). Keywords:
Recompense the Color Loss for Underwater Image using Generalized Color Compensation (GCC) Technique

Abstract The light absorption of the water medium often caused the underwater image enhancement to become challenging due to the loss in color channels where the red color vanished faster than others. The color loss often affected the underwater image quality especially having the color cast like greenish, bluish, and yellowish. Therefore, this paper introduced a Generalized Color Compensation (GCC) technique which will recompense the color loss based on maximum channel and then remove the color cast using the mean adjustment. For the experiments conducted, the GCC technique improved the image contrast successfully. The experiments showed that the proposed technique was able to remove the color cast effectively regardless of greenish, bluish, and yellowish and improve the underwater image quality.
Two-sided Sequent Calculi for FDE-like Four-valued Logics

Abstract We present a method that generates two-sided sequent calculi for four-valued logics like first degree entailment ( FDE ). (We say that a logic is FDE-like if it has finitely many operators of finite arity, including negation, and if all of its operators are truth-functional over the four truth-values ‘none’, ‘false’, ‘true’, and ‘both’, where ‘true’ and ‘both’ are designated.) First, we show that for every n -ary operator ⋆ every truth table entry f ⋆ ( x 1 ,…, x n ) = y can be characterized in terms of a pair of sequent rules. Secondly, we use these sequent rules to build sequent calculi and prove their completeness. With the help of two simplification procedures we then show that the 2 ⋅ 4 n sequent rules that characterize an n -ary operator can be systematically reduced to at most four sequent rules. Thirdly, we use our method to investigate the proof-theoretical consequences of including intuitive truth-functional implications in FDE -like logics.
Context Prior Guided Semantic Modeling for Biomedical Image Segmentation

Most state-of-the-art deep networks proposed for biomedical image segmentation are developed based on U-Net. While remarkable success has been achieved, its inherent limitations hinder it from yielding more precise segmentation. First, its receptive field is limited due to the fixed kernel size, which prevents the network from modeling global context information. Second, when spatial information captured by shallower layer is directly transmitted to higher layers by skip connections, the process inevitably introduces noise and irrelevant information to feature maps and blurs their semantic meanings. In this article, we propose a novel segmentation network equipped with a new context prior guidance (CPG) module to overcome these limitations for biomedical image segmentation, namely context prior guidance network (CPG-Net). Specifically, we first extract a set of context priors under the supervision of a coarse segmentation and then employ these context priors to model the global context information and bridge the spatial-semantic gap between high-level features and low-level features. The CPG module contains two major components: context prior representation (CPR) and semantic complement flow (SCF). CPR is used to extract pixels belonging to the same objects and hence produce more discriminative features to distinguish different objects. We further introduce deep semantic information for each CPR by the SCF mechanism to compensate the semantic information diluted during the decoding. We extensively evaluate the proposed CPG-Net on three famous biomedical image segmentation tasks with diverse imaging modalities and semantic environments. Experimental results demonstrate the effectiveness of our network, consistently outperforming state-of-the-art segmentation networks in all the three tasks. Codes are available at https://github.com/zzw-szu/CPGNet .