Reviewerly/exHarmony huggingface dataset:
Integrating visual stimuli for enhancing neural text style transfer with EEG sensors

Font Designers need to create each font character by hand. With the help of what we propose, designers finish the process quickly and automatically. We use a neural network to learn and generate new fonts. We provide two vectors as input (Bigrams and Style Vectors) of Urdu language, encoded manually with one-hot encoding and t-distributed neighbor embedding. The transposed convolution neural network takes care of learning from input, where it decodes the input into beautiful fonts. Thus, by changing the style vector, the required changes are reflected in the resultant font style. Additionally, with the help of simulated annealing, we generate meaningful and full-length sentences. To evaluate whether the fonts generated are aesthetically sound, we provide the generated sentences to the end-users as visual stimuli and measure their responses in terms of their attention and meditation levels with EEG sensors. Higher sensor levels suggest the font quality and visual appeal. • Urdu Font generation via neural text style transfer. • Deep learning-based transposed Convolution neural network. • Urdu character (bigrams) dataset creation. • Simulated Annealing for full-length Sentence generation evaluated via EEG sensors.
Detection of Racist Language in French Tweets

Toxic online content has become a major issue in recent years due to the exponential increase in the use of the internet. In France, there has been a significant increase in hate speech against migrant and Muslim communities following events such as Great Britain’s exit from the EU, the Charlie Hebdo attacks, and the Bataclan attacks. Therefore, the automated detection of offensive language and racism is in high demand, and it is a serious challenge. Unfortunately, there are fewer datasets annotated for racist speech than for general hate speech available, especially for French. This paper attempts to breach this gap by (1) proposing and evaluating a new dataset intended for automated racist speech detection in French; (2) performing a case study with multiple supervised models and text representations for the task of racist language detection in French; and (3) performing cross-lingual experiments.
English Speech Recognition and Pronunciation Quality Evaluation Model Based on Neural Network

An in-depth neural network-based approach is proposed to better develop an assessment model for English speech recognition and call quality assessment. By studying the structure of a deep nonlinear network, you can approximate complex functions, define distributed representations of input data, demonstrate a strong ability to learn important data set characteristics from some sample sets, and better simulate human brain analysis, and learning. The author uses in-depth learning technology to recognize English speech and has developed a speech recognition model with a deep belief network using the characteristics of the honey frequency centrum based on human hearing patterns. The test results show that examples include 210 machine and manual evaluations and 30 samples with first-grade differences. The overall compatibility level of the machine and human evaluation is 90.65%, and the adjacency consistency level is 90.65%. This is 100%, and the correlation coefficient is 0.798. We need to evaluate the quality of speech and pronunciation in English, which indicates a strong correlation between machine estimates and human estimates.
Real Time Signature Forgery Detection Using Machine Learning

Every individual has their own signature, which is primarily used for personal identification and verification of vital papers or legal transactions. Even today, in many commercial instances, such as check payment, register office the signature verification process is still relied on a single known sample being reviewed by a human. The probability of two signatures made by the same person is very less. So, forgery detection becomes a huge task. The signatures that undergoing verification is not only difficult but also time consuming, especially when they are signed offline and no information about the procedure is available. To prevent the possibility of theft or fraud, a system that can distinguish between real and faked signatures is required. Before recognizing the signature is forged or not certain stages of pre-processing must be done for the images (signature) are acquired from the dataset since it is in raw form. The proposed work is based on off-line signature verification using deep learning model incorporating Convolution Neural Network (CNN) and novel method for extracting local features. This method can be used in various organizations where the number of individuals are confined so the model can train those before detecting any forged signatures in the future.
Survey comparison of Monkey-Based Optimization techniques in LEACH protocol WSNs

Wireless sensor network (WSN) deployments have increased at an exponential rate in recent years, and this trend is expected to continue. Wireless sensor networks have been focusing on enhancing their performance in terms of energy efficiency, communication efficacy, and system throughput due to their rapid development. Numerous unique processes have been developed by adapting natural organisms’ social behaviors, such as bats, birds, ants, fish, and honeybees. These systems, known as nature-inspired systems or swarm intelligence, are capable of developing optimization techniques, managing large-scale networks, and circumventing resource limits. By structuring the social foraging behavior of spider monkeys, spider monkey optimization (SMO) is a recent addition to the family of swarm intelligence algorithms. Additionally, a bio-inspired clustering technique based on the social behavior of Rhesus Macaque (RM) monkeys has been discussed with the goal of extending network longevity. The purpose of this article is to investigate the mechanism of SMO and RM in the context of WSNs by developing a mathematical model of the behavior patterns to which the cluster-based SMO and RM approach is applied. Additionally, our suggested methods, which are based on the behavioral structure of the monkeys, intend to improve the energy efficiency and network quality of Low Energy Adaptive Clustering Hierarchy (LEACH) protocols.
Panacea: A Novel Architecture for Electronic Health Records System using Blockchain and Machine Learning

In the information innovation unrest, electronic clinical records are a standard method for putting away patients' data in emergency clinics. Albeit some emergency clinic frameworks utilize server-based patient detail the board frameworks, they need a lot of capacity to store every one of the patients' clinical reports, in this manner influencing the versatility. Simultaneously, they are confronting a few troubles, for example, interoperability concerns, security and protection issues, digital as-saults to the concentrated stockpiling, and keeping up with sticking to clinical approaches. The proposed model is a private blockchain-based patient detail the board framework as most would consider to be normal to resolve the above issues. Arrangement proposes an appropriate secure record to grants effective framework access and frameworks recovery, which is secure and unchanging and man-made conscious’s fuelled instruments for clinical choice help. A better consensus system accomplishes the consensus of the information without huge energy use and organization congestion. Also, our model accomplishes high information security standards given a combination of crossbreed access control components, public-key cryptography, and a protected live ailment checking instrument. The proposed arrangement brings about effectively conveyed shrewd contracts according to the jobs of the framework, Ethereum based accreditation age and approval, Post Recovery Co-Morbidity Prediction, Disease Prediction Based on Symptoms, and Drug Recommendation gave Side Effects. The general goal of this arrangement is to bring the whole clinical industry into a common stage by utilizing a decentralized way to deal with a store, share clinical subtleties while disposing of the need to keep up with printed clinical records, and backing specialists utilizing exceptionally precise machine learning tools
Food Review Analysis and Sentiment Prediction using Machine Learning Models

In this era of the digital world, text, messages, comments, numbers and videos have become an essential source of information. The trend of people trading through e-commerce giants like Amazon and Flipkart is proliferating. It’s necessary to have a model or tool that helps retrieve helpful information from the customers’ online reviews quickly that can also help product manufacturers have a better idea of their product. This paper targets the food industry, and a model is proposed that analyzes the customer reviews based on NLP techniques- TF-IDF Vectorizer and Count Vectorizer. Based on these analyses, customer sentiments are predicted using different machine learning classification algorithms like Logistic regression, Dummy classifier and Random forest classifier.
P‐144: The Effect of the Reference Image on the Side Window of the Car of Motion Sickness

The fusion information on the side window of the car may cause the passenger's motion sickness as moving. This motion sickness mixed the visual‐induced and caused by riding a vehicle. In this research, we propose a method of the reference image to solve reduce motion sickness and used the MSSQ‐short to judge motion sickness degree.
Some Prevalent Sets in Multifractal Analysis: How Smooth is Almost Every Function in $$T_p^\alpha (x)$$

We present prevalent results concerning generalized versions of the $$T_p^\alpha $$ spaces, initially introduced by Calderón and Zygmund. We notably show that the logarithmic correction appearing in the quasi-characterization of such spaces is mandatory for almost every function; it is in particular true for the Hölder spaces, for which the existence of the correction was showed necessary for a specific function. We also show that almost every function from $$T_p^\alpha (x_{0})$$ has $$\alpha $$ as generalized Hölder exponent at $$x_0$$ .
P‐40: Techniques of Touch Sensing and Display Driving for Avoiding Display Artifacts for Flexible OLED Applications

Currently, use of flexible OLED (Organic Light Emitting Diode) displays is increasing, especially in mobile phone applications due to the high flexibility in industrial design they provide for curved edge and foldable smartphones and tablets. For flexible OLED, TFE (Thin Film Encapsulation) technology is widely used to prevent water and oxygen permeation, and as the name suggests, it is extremely thin. Accordingly, when on‐cell touch panels transition from rigid OLED with ~200 um glass encapsulation to flexible OLED with ~8 um TFE, the distance between display electrodes and the touch sensor becomes considerably closer. The dramatically increased capacitive coupling between the display and touch systems produces a correspondingly increased risk of errors in voltages driven to the display pixels due to touch sensing modulation coupling into the display system, resulting in deteriorated display image quality. In this paper, we introduce touch sensing and display driving techniques for avoiding display artifacts for flexible OLED applications.
Gated tree-structured RecurNN for Detecting Biomedical Event Trigger

It is critical to deeply mine semantic features for information extraction. Tree-structured model is a linguistically attractive option due to its linguistic representations of sentence syntactic structure. Tree-LSTM has been introduced to represent tree-structured network topologies for the syntactic properties. To alleviate the limitation of the Tree-LSTM, we work towards addressing the issue by developing gated mechanism variants for the tree-structured network. The gated mechanism is more complex and diverse for the tree-structured model. We apply Child-Sum Tree-LSTM and Child-Sum Tree-GRU for recognizing biomedical event triggers, and develop two new gated mechanism variants incorporating peephole connection and coupled mechanism into the tree-structured model. The experimental results showed the advantage of gated units. The Child-Sum Tree-LSTM achieved the best results among the gated tree-structured models, and the performance of variants is nearly the same as Child-Sum Tree-LSTM . However, Child-Sum Tree-GRU and Child-Sum Tree-coupled reach reduction in computation time. • Integrating peephole connection into Child-Sum Tree-LSTM and removing the output activation function. • Build gated tree-structured variant with a coupled mechanism by coupling forgetting gate and new memory. • Compare the performance of different models for detecting biomedical event triggers.
Cross-Border Credit Information Sharing Mechanism and Legal Countermeasures Based on Blockchain 3.0

The development of new technologies drives the digital transformation of the credit reporting industry and also brings new opportunities for cross-border credit reporting cooperation. This study focuses on a cross-border credit information sharing platform based on blockchain 3.0 and its legal guarantee, with an aim to promote the integrated development of credit reporting across the Taiwan Straits. This study first points out the difficulties of cross-border credit information sharing; second, the technical characteristics of blockchain such as distributed storage, point-to-point transmission, security and credibility, open source, and programmability are naturally suitable for solving the information sharing problems in the credit reporting industry. So, this study proposes a framework and a specific design of a cross-strait credit information sharing platform based on blockchain 3.0 infrastructure; finally, in order to ensure the smooth operation of the platform, this study suggests signing a series of nonnormative “soft law” documents in the principles of equality, willingness, and consultation to provide legal guarantee for cross-border credit sharing.
SynthPipe : AI based Human in the Loop Video Dubbing Pipeline

Video content consumption via OTT platforms has been increasing over the years, with content creators worldwide creating exciting new content everyday. With this, regional content has skyrocketed in almost every language possible on platforms like YouTube, Twitch, etc. Such content may include daily soaps, educational or instructional videos, documentaries, movies, vlogs, etc which most of the time are in a language familiar to the creator. The same may not be the case for the viewer, who could be located anywhere and may not know the language of the creator. This creates a language barrier between the creator and viewer where every party involved suffers a loss - the creator loses on a global presence, the consumer loses on international content and both lose on a global community. To combat this, SynthPipe, a simple, modifiable, and efficient pipeline comprising of state-of-the-art techniques for video dubbing is proposed in this paper.
Design of a Robotic Arm equipped Fully Automated Burger Assembly Machine

Automation plays a vital role in food preparation since it reduces human intervention, leading to maintaining hygiene. In India, a patty on the bun (called burger) is a popular fast food. However, most of the restaurants and café are lacking automated burger assembly machines. In this paper, the design of a robotic arm-equipped fully automatic burger assembly machine has been proposed. Initially, the automated burger assembly machine is fabricated, and later the robotic arm has been introduced to make the machine fully automatic. The design and analysis are performed using SolidWorks®. The performance is evaluated through the simulation in ProModal® software. The performance of both scenarios, (i) partially automated machine and (ii) fully automated machine, has been evaluated and verified. Results reveal that the total output of burgers (i.e., 479 units) is not changed after robotic arm deployment. The proposed design can be improved by making IoT enabled ordering and servicing system to achieve the next level of automation.
Reduction of Ultrasound Images using Combined Bilateral Filter &amp; Median Modified Wiener Filter

Image de-noising is a pre-processing task as it reduces the effect of the noise signals in the desired images. Image processing is itself a huge application area which includes law enforcement, remote sensing, weather forecasting, medical imaging etc. In any of the field which uses image processing requires noise free images but noise introduces in all types of images. In this paper; speckle noise removal technique has been discussed which generally affects the ultrasound images. These images are used as medical images used to diagnose abnormalities or disease in the inner body of the human beings. Many filters have been proposed for de-speckling the ultrasound images however there is still room to improve the quality of de-noised image so that any false detection can be avoided. In this paper optimized bilateral filter has been used. Results chant the efficacy of the proposed technique over conventional bilateral filter.
Adaptive Emotional Support for Groups

When faced with a difficult situation, searching for emotional support is one of the most natural things to do. Being supported not only helps regulating the negative emotions, but it also promotes coping skills and psychological adjustment to stressful situations. However, not all emotional support attempts are effective and always available. The increasing usage of technology may offer a solution by providing an emotional support virtual agent, capable to deliver support via smartphone or computer, anytime and anywhere. Such agent may adapt to one’s characteristics and situation, providing supportive feedback tailored to the needs. In my thesis, I will focus on how to provide emotional support to groups of students. Emotional support will be adapted to the stressors that students typically encounter, and to the challenges linked to working in groups. I will study how people adapt emotional support statements to both individual and situational factors, and investigate how this can be implemented in a virtual agent.
Interactive Media Art by Applying Depth Sensing

Recent developments in depth sensing devices have enabled real-time communication between viewers and works of art in interactive media art. In other words, the use of depth sensing devices has greatly contributed to deepening the relationship between viewers and works. In this research, it was proposed to create an interactive media art work based on human body position measurement and biological signal measurement using RealSense, Intel's depth camera. In addition, the author actually created an installation art work and examined the possibility of new expression through depth sensing.
CSBRCA: Cloud Security Breaches and Its Root Cause Analysis

Over the last three decades, the computing world has shifted from centralized to distributed systems, and we are now returning to virtual centralization like Cloud Computing (CC) systems. An individual user has complete control over the data and operations in his or her machine. On the other hand, there is CC, in which the operation and data preservation are given by a vendor, leaving the client/customer oblivious of where the activities are operating or where the information is saved. As a result, the customer has no management control over it. The Internet is used as a communication medium in CC. When it comes to data security in the cloud, the vendor must provide some guarantee in service level agreements (SLA) to persuade the user on privacy concerns. As a result, the SLA must specify several degrees of security. Their complexities are based on the services in order for the customer to comprehend the security measures that are in operation. Regardless of the supplier, there must be a defined method for preparing the SLA. The results obtained here indicates management of the SLAs using the Vitrage plugin of the Open stack public cloud eco-system. The results readings were based on the CPU utility of the cloud IaaS resources. The proposed approach shows how the prediction of the resources are managed in real-time when the occurrence of the attack will be identified.
Sentiment Analysis of Twitter Data Using Clustering and Classification

Data mining helps in collecting and managing data besides performing analysis and prediction analysis. The process that is implemented to discover useful data patterns may have different names. Statisticians, database researchers, and professional organizations were among the first to use term data mining. The fundamental steps for sarcasm detection are dataset collection, feature extraction, and classification. This work puts forward a new model of sarcasm detection formed by fusing K-mean, PCA, and SVM classifiers together. With respect to common evaluation metrics like accuracy, precision, and recall, the architecture designed for this work is especially productive.
Improved linear cryptanalysis on 25‐round SMS4

SMS4 is the Chinese national standard for WLAN, which is also an ISO/IEC international standard. This paper presents a linear cryptanalysis on 25-round SMS4 for the first time. Firstly, this paper adopts a new statistic in linear cryptanalysis, which is aimed to compute the distance between one cipher system and one random permutation, which can be used to distinguish the right key from the wrong keys by the different probability distributions they follow. Secondly, the attack principal and data complexity are given when this new statistic is used in multiple linear cryptanalysis. Thirdly, this paper presents 21-round linear approximations with absolute correlation 2 − 62 ${2^{ - 62}}$ by fixing the least significant input bit of the Sbox with nonzero mask in the first round to be 1. Furthermore, this paper gives 48 21-round linear approximations with absolute correlation 2 − 62 ${2^{ - 62}}$ . Based on the restrictions between those 48 linear approximations and fast Fourier transformation technique, the whole attack needs 2 125.62 ${2^{125.62}}$ known plaintexts, 2 126.56 ${2^{126.{\rm{56}}}}$ 25-round SMS4 encryptions along with 2 113 . 75 ${{\rm{2}}^{{\rm{113}}{\rm{.75}}}}$ memory accesses, and 2 111 . 7 ${{\rm{2}}^{{\rm{111}}{\rm{.7}}}}$ bytes of memory, which is the best cryptanalysis result on SMS4 so far. Finally, this paper generalizes the linear cryptanalysis method with conditioned linear approximation, which can also be adopted in attacking other ciphers.
A Variety of Choice Methods for Image-Based Artistic Rendering

Neural style transfer (NST) is a technique based on the deep learning of a convolutional neural network (CNN) to create entertaining pictures by cleverly stylizing ordinary pictures with the predetermined visual art style. However, three issues must be carefully investigated during the generation of neural-stylized artwork: the color scheme, the strength of style of the strokes, and the adjustment of image contrast. To solve these problems and select image colorization based on personal preference, in this paper, we propose modified universal-style transfer (UST) method combined with the image fusion and color enhancement methods to design a good post-processing framework to tackle the three above-mentioned issues simultaneously. This work provides more visual effects for stylized images, and also can integrate into the UST method effectively. In addition, the proposed method is suitable for stylized images generated by any NST method, but it also works similarly to the Multi-Style Transfer (MST) method, which mixes two different stylized images. Finally, our proposed method successfully combined the modified UST method and post-processing method to meet personal preference.