Reviewerly/exHarmony huggingface dataset:
A Novel Scrambling Method Based on Coupling Mode Switching Strategy for Digital Chaos

The dynamical degradation of digital chaos brings serious defects to chaos-based digital systems and greatly restricts its applications. In this paper, we first discuss the integer logistic map (ILM) and calculate its cycle lengths under different precision. Then, a novel scrambling method based on coupling mode switching (CMS) strategy named CMSSM is proposed to enhance the chaotic features of digital chaos. Following the selection principles, we use three coupling mode candidates to construct 3CMSSM. Theoretical analysis shows that the output cycle length of 3CMSSM is three times that of scrambling methods used in single coupling mode (SCM) strategy. Numerical simulations are carried out to demonstrate the desirable statistical and dynamical properties of scrambled ILM. Finally, we design a (pseudo-random number generator) PRNG based on 3CMSSM. Both results of statistical test suites NIST SP800-22 and TestU01 show that the suggested PRNG has highly credible randomness compared to other algorithms.
Anonymization of Multi-relations Datasets Using Single Table Algorithms

Nowadays, immense educational data is available for analysis. To extract useful information from huge data, data analysis is required. For analysis, data has to be shared with a third party called data analyst. Sharing data with data analyst introduces the risk of privacy for individuals whose sensitive information is present in the shared data. Privacy is the right of an individual, so it needs to be maintained while sharing the data with a third party. There are many privacy-preserving algorithms available. k-anonymization is one of the most popular and traditional privacy-preserving methods. It modifies data to protect an individual's sensitive information by disconnecting the link between an individual and its sensitive information. k-anonymization uses generalization and suppression tools to anonymize the data. Generalization replaces specific values of attributes with general values, and suppression deletes unmatched values of attributes. There are many existing single table anonymization algorithms. The proposed work presents how a single table anonymization algorithm can be applied on multi-relation dataset. It also discusses the problems encountered when single table anonymization algorithms are used for the multi-relation dataset. Present work also discusses the limitations of k-anonymization and gives future research directions.
Reforming Title Deeds via Blockchain and Challenges—Indian Perspective

It is crucial to have accurate title records that assure the property’s ownership. In India, the challenges in uniquely identifying property, verifying the title ownership, syncing database of different departments, no implicit linkage to the property registrations lead to the creation of fake registration documents and double registration. For citizens to trust the system, it has to be transparent and with accurate title records. We leverage the Hyperledger Fabric blockchain framework to improve the existing workflow’s limitations. For this, the existing workflow followed for the registration of property in the state of Telangana is studied comprehensively. The designed system integrates fully with the existing workflow without disturbing the current system. We present example-based scenarios to illustrate the benefits of being able to track (a) fraudulent transactions via the creation of fake registration documents, (b) insider attacks, (c) double registrations, (d) typing errors and avoiding manual verification of data, and (e) reliable encumbrance search for encumbrance certificates. We present the challenges faced during the system design. A pilot deployment at the sub-registrar office (SRO) in the Shamshabad district of Telangana state shows the practical usability of the designed system. Migration of the actual data from existing registration records makes the designed system work on real-time data. The performance benchmarking data of the developed system using the Hyperledger Caliper framework shows its scalability to the entire state of Telangana.
Application of Industrial Communication Device-Based Mobile Learning in Ideological and Political Education

Mobile learning theory and the demand analysis of mobile learning systems for ideological and political education at universities are presented in this study. The study analyses the use of mobile learning based on industrial communication devices (ICDs) in ideological and political education by doing research on mobile development technology and mobile learning systems. ICD-based mobile learning platforms can be used for ideological and political education by conducting a questionnaire. The main advantage of using this ICD-based mobile learning platform is the teaching and learning process will be continued under any circumstances. Even if the teacher and the students are in distant location, secured access privileges can be provided for effective communication for the processes. For ideological and political education, a remote supervision algorithm was developed to evaluate the performance of industrial communication devices. The proposed algorithm generated 90% more accuracy than the current technique.
Variability-Weighted Interpolation Algorithm Based on Fixed-Frame Sampling of Data

A spatial data interpolation algorithm is a method to transform scattered and sparse measurement data into regular and continuous applicable data. This algorithm can make the display results smooth and continuous without changing the data characteristics, which is closer to the real acquisition object. In response to the low interpolation accuracy of the inverse distance weighting (IDW) algorithm and the poor interpolation efficiency of the ordinary kriging (OK) algorithm, this study proposes a variational-weighted (VW) interpolation algorithm based on data fixed-frame sampling. By introducing the variational function of OK into IDW and taking different weight coefficients from OK, a new computational model is constructed to improve the interpolation accuracy and adapt to different types of data characteristics. In the whole interpolation process, the data are sampled in a fixed frame and a new sampling point search method is proposed to improve the computational efficiency. The study not only compares the accuracy and efficiency of the algorithm using two types of data but also tests the stability of the new algorithm for different data volumes. It is shown that VW based on a fixed-grid sampling of the data is more accurate than the inverse distance interpolation algorithm for both data types. The VW interpolation algorithm based on data lattice sampling improves the computational efficiency for featureless data and exhibits better performance in terms of accuracy and computational efficiency for data with features, compared to the common kriging interpolation algorithm. In addition, the VW interpolation algorithm based on a fixed-grid sampling of data has a more stable performance for different amounts of data.
Photo Realistic Generation from Arabic Text Description Based on Generative Adversarial Networks

Generating accurate high-resolution images from text representations is a difficult problem in computer vision that has a wide range of functional applications. Text-to-image conversion is not dissimilar to the difficulties inherent in language processing. For example, each text meaning can be encoded in two distinct human languages, while photographs and text are two distinct encoding languages for similar data. However, these are two distinct issues, since text-to-image or image-to-text conversions are extremely multimodal in nature. The proposed model for creating 256 × 256 realistic images from Arabic text descriptions is discussed in this article. The relationship between an Arabic word in a sentence and its component in a picture as introduced in this paper using the DAMSM model. This model teaches two neural networks how to map the Arabic picture and word sub-regions of a full sentence to a shared semantic model. It performs well as an Arabic-text encoder and a picture encoder. We start with the Modified-Arabic dataset and train the model from scratch. The proposed model establishes a new standard for the conversion of Arabic text to realistic pictures. A mutation happens when Arabic is used as the primary language for converting Arabic texts to real images. The inception score of the newly introduced model reported by 3.42 ± .05 on the CUB database.
Masked Face Classification using Convolutional Neural Network

Since December 2019, the Coronavirus Disease 2019 (COVID-19) has spread globally. According to World Health Organization (WHO), using a mask can protect against and limit the diffusion of COVID-19. Consequently, many governments require most people to wear face coverings in indoor public settings. However, most previous works categorized the face mask into two classes, unmasked and masked. Nevertheless, the excellent way to handle these pandemics is to wear a mask properly. Therefore, this paper proposed the face masked classification technique using Convolution Neural Network (CNN). Our approach was classified into three classes: wearing masks, unmasked, and wrong wearing masks. We applied three datasets to evaluate our proposed work, which consists of the Correctly Masked Face Dataset (CMFD), Incorrectly Masked Face Dataset (IMFD), and Flickr-Faces-HQ Dataset (FFHQ). Our research achieved 99.84 percent accuracy, which outperformed the existing model. Moreover, our model has fewer layers than the previous research.
A Method of Dual-spectrum Feature Fusion for Face Recognition Under Non-ideal Lighting Conditions

Face recognition technology is widely used in the field of public security. To improve the recognition accuracy under non-ideal lighting conditions, a face recognition method with dual-spectrum feature fusion is proposed using the property that the infrared spectrum is insensitive to visible light. The fused face images of visible and near-infrared spectra are obtained with the Non-Subsampled Shearlet Transform (NSST) algorithm, and then been put into the FaceNet as input and trained using transfer learning to renew the FaceNet model parameters for recognizing the fused face images. Compared with existing methods, experimental results show that the accuracy of face recognition is significantly improved under non-ideal lighting conditions which better meets the practical application requirements of public security.
Perception of Temporal Quality Fluctuation for HEVC Coded Surveillance Videos

The advanced high efficiency video coding (HEVC) standard has been widely used in video surveillance system which plays a critical role in the public security. In order to meet the real-time communication requirements of video surveillance system, the low-delay configuration in HEVC is usually used to encode surveillance videos. However, the low-delay configurations adopt hierarchical prediction structure which inevitably brings in the temporal quality fluctuation (TQF). And large TQF are easily perceived by human eyes which has significant impact on the quality and effective analysis (recognition, comparison, tracking, retrieval, etc.) of reconstructed surveillance videos. In this paper, the perceptual characteristics of TQF related to multiple perception degrees are analyzed for HEVC coded surveillance videos. These findings are foundations for further proposing strategies to reduce or even eliminate the perception of TQF in HEVC coded surveillance videos which helps to significantly improve the analysis efficiency of surveillance videos.
Gesture Recognition based Fingertip Air Writing to Text Convertor using Image Processing and HCI

Gesture Recognition based fingertip air writing to text converter which permits to write and sketch text characters in fore of a web camera in the laptop computer over the air. The two main tasks of proposed methods are a blue-colored object is taken in the hand. To perceive the written characters from video frames, a blue-colored object is being tracked and optical English text reformation is applied over the plotted images. Additionally, a stylus pen, touchscreen pen, keyboard, glove or any other device is not required for giving input characters a habitual human-computer interaction is developed to address it. Application based on Open CV with the python language is designed for experimental purposes. It is relevantly a straightforward software-based approach, which is effortless and effective. Moreover, it requires a web camera and a blue-colored object other than hardware or any other sensors.
Hardware Software Co-simulation of Watermarking algorithm for Image Processing Applications

In recent days, the unauthorized replication problem causes many copyright ownership disputes regarding the originality of the digital contents. Digital watermarking is the best solution for this problem. The main challenges in designing a digital watermarking are robustness, speed and tradeoffs involved. A watermark inclusion and extraction system is designed without affecting the quality of the image considering the security. To overcome the unaccepted delays a speedy embedding technique is required. A novel water marking algorithm for images using Discrete Wavelet Transform (DWT) is proposed which minimizes the complexity, robustness, resource utilization and security for data processing. The proposed watermark embedding and extraction algorithm is implemented using Xilinx System Generator.
Deep Residual Learning based Attendance Monitoring System

In the field of personal identification Face recognition technology is more trustworthy than other technologies. In the proposed work, we elaborated on the use of face recognition in the monitoring of attendance of a class without the intervention of humans. Face recognition gives fruitful results compared to previous existing techniques like a fingerprint, iris, and RFID techniques. Using the capabilities of the dlib framework, this project provides a method for detecting and identifying the faces of students present in a classroom in real-time. Our trained network uses the CNN or HOG method to identify and locate students&#x0027; faces in the captured frames. The facial features are extracted from those images using Deep Residual Networks called ResNets. And distinguishes between feature vectors using the KNN classifier. This network has an accuracy of 99.83&#x0025; on the Labelled Faces in the Wild benchmark. And finally, we have shown some performance metrics and plots by comparing them with other existing methods in face recognition. Hence this method is cost-efficient and requires little maintenance.
Role of Artificial Intelligence in Generating Video

Artificial intelligence is a big revolution in the field of automation &#x0026; engineering. Specifically, Computer vision is one of the biggest boons for our upcoming industries. This paper is focused on one of the applications of PC Vision. We humans can read, listen, imagine, visualize, and extract an image out from the conveyed text in our brain and even we can draw it. By using artificial intelligence our evolving machines can also do the same. Videography is a very vast field and as the consumers of smart devices are increasing we know that evolution in the field of videography is also required. By the constant evolution in the field of computer vision using deep learning, our smart devices are capable of portraying images or video from static or dynamic data in the form of text. By using PC Vision we can develop the raw image by organization development of text input. Dynamic components are executed by changing over the data text into an image channel. And the basic methodology of converting is by processing all the available image data on the internet and collecting them in a dataset and then by using deep learning we will correlate it with text data. The troubles are picking key articulations to highlight in the video, removing the meaning of the text, and showing them in a video showing simply the elements and reliably a huge articulation
Automatic Detecftion of White Blood Cancer From Blood Cells Using Novel Machine Lerning Techniques

The precise recognition of cells is required for the diagnosis of numerous blood illnesses, such as Leukemia and Myeloma, using WBCs or Leukocytes. In order to assist oncologists an automated system is necessary to minimize time and improve accuracy. The segmentation and classification of immature white blood cells are the most crucial phases in such systems. In this aper we have discussed the methodology for detection of white blood cancer caused because of immature leukocytes by using preprocessing, segmentation by Otsu algorithm, Salp swarm algorithm and random forest algorithm. In this paper we have explored the performance of three techniques such as random forest algorithm (RSA) Salp swarm algorithm (SSA) and Statistically enhanced Salp swarm algorithm (SESSA). We have achieved better segmentation and detection accuracy compared to other peer soft computing algorithms. The results shall be beneficiary for hematologists and oncologist as diagnostic reference before proceeding t the treatment strategy
Distinguishing Reviews Through Sentiment Analysis Using Machine Learning Techniques

Opinion Mining is a technique of automated extraction of information from the opinion of people about any certain subject or problem. The feasibility of Opinion mining and Sentiment Analysis tool is to &#x201C;process a bunch of indexed lists for a given thing, creating a rundown of item credits (quality, highlights and so forth) and accumulating opinion&#x201D;. Yet, with the progression of time additional fascinating applications and improvements appeared around here and presently its fundamental objective is to make PC ready to perceive and produce feelings like human. This paper will attempt to zero in on the essential meanings of Opinion Mining, investigation and Sentiment Analysis is the name given to this new field of study. In recent years, scientists have come up with a number of solutions to the problem. Information Retrieval (IR) and Natural Language Processing (NLP) interact in the subject of Opinion Mining and Sentiment Analysis, which has a few distinct trains, such as message mining and Information Extraction. Sentiment Analysis may be performed using a variety of NLP tools, all of which this paper aimed to demonstrate, from basic definitions to a broad range of applications. Recently, it&#x0027;s been a really active exploring zone. In fact, it has made its way into everything from board science to software engineering of etymological assets needed for Opinion Mining, scarcely any AI methods based on their utilization and significance for the examination, assessment of Sentiment arrangements and its different applications. They are attempting to get assessment data to investigate and sum up the suppositions communicated naturally with PCs. Opinion Mining
Data and Compute Efficient Image Inpainting

With the ever-advancing field of Image Generation and Inpainting, there are more and more techniques such as the usage of AutoEncoders, Gated Convolutions, Partial convolutions and even GANs keep getting proposed. While this means better results, normally these new architecture and techniques are more resource-intensive and cannot be implemented or worked on by aspiring researchers and in more contemporary workflows. In this paper, we try to propose a better methodology for training Image Inpainting models with a limited set of training data. We use a partial convolution-based U-Net like Convolution model that G. Liu, F. A. Reda et al. [1] proposed. This is more performant than traditional autoencoders but also not as intensive as Gated/ Attention-based Convolution models. They also don&#x0027;t use multiple models as in the case of GANs. We optimized the input pipeline and add support for resizing and augmenting the input dataset too. We also integrated AugMix [2] for expanding and better representing an extended space. We show our approach and the performance of the model at the end of 25 Epochs.
Block Chain based Government Tender Allocation in Cloud

Government and public area substances all throughout the planet are effectively investigating better approaches to stay aware of innovative headways to accomplish keen administration, work effectiveness, and cost improvement. Square chain innovation is an illustration of such innovation that has been drawing in the consideration of Governments across the globe lately. Upgraded security, further developed discernibility and most reduced expense foundation enable the square chain to enter different spaces. By and large, legislatures discharge Tenders to some outsider associations for various activities. During this cycle, different competitors endeavor to tune in the fragile potential gains of others to win the sensitive. The corrupt government specialists furthermore charge significant compensation off to pass the fragile for some specific outsider. This paper implies protected and straightforward system using blockchain for government Tenders. Square chain is used as a secured and changeless information construction to store the public power records that are exceptionally helpless to altering. This work intends to make a straightforward and secure edge processing foundation for the process in tenders of government to execute plans and approaches by restricting oversight of the human to the insignificant.
Multi Kernel Scaled Deep Time Series Imputation

Multivariate time series data is widely used in medical, meteorology, finance analysis and this data consist of miss rate due to the sensor, power failure, etc. For the given multivariate correlated time series data, it is necessary to fill inappropriate missing values to predict or make decisions based on data. Conventional imputation methods do not undergo predicting nearly accurate missing values but makes difference. Advanced techniques for machine learning and deep learning provide better results than standard mathematical methods. In this paper, we propose a combined method Multi-Scaled Deep Networks using alignment of missing values by Variational Auto-Encoders (VAEs) and flattening with non-linear regression by introducing kernel matrices and activating neurons uses that Scaled Exponential Linear Unit (SELU) to bypass dead neurons in the second phase of VAEs which includes decoding. The proposed method multi-kernel scaled deep time series imputation (MSDTSI) introduces high performance compared to the conventional and earlier machine learning algorithms by imputing missing values to predict the mortality rate of the patients through this Physionet Challenge data set. Our proposed model outperforms other models in terms of performance metric Area Under Receiver Operating Characteristic (AUROC) score as 74.8 and Mean Absolute Error(MAE) as 0.44.
Sentiment Analysis and Emoji Mapping

The World around us is driven by emotions and sentiments. Each and every activity of us is based on the emotions and mood swings. Let it be a song choice or signing billion-dollar deals to determining major results, all things around us are driven by emotions. Those who understand the emotions have a greater edge than those who do not. That&#x0027;s where our sentiment analysis system comes and plays a major role. Built using the RNN and sequential model, SENTIFY-sentiment analysis and emoji mapping using deep learning helps to track the live emotions of people and recommend a choice(emoji) based on their emotion. Since it tracks the emotions of people by live video feed, this system can be used to predict things in number of areas namely the business, social media, customer support and so on where a right choice at a right time may help to make huge profits and minimize losses. This system can also be used to monitor the mood swings and emotions of psychiatric patients and help in soothing their moods. Keywords- Sentiment, analysis, emotion, emoji, detect