Reviewerly/exHarmony huggingface dataset:
Accessible Authentication Using Keyword Search through Homomorphic Encryption

Data storage presents substantial issues due to concerns about data security and privacy. In this study, we provide a search-based, interoperable, verified public key cryptography system. The searchability is significantly improved by our recommended method, which allows the cloud servers to create invert crypto indexes while needing a query trap and leveraging asymmetric cryptography developed by van Vries, Miller, Showing different, and Vaikuntanathan. Contrarily, the recommended method offers a brand-new permitted dataset built on the inversion decryption scheme and shows how it may be used to evaluate the thoroughness and correctness of serps. Additionally, using the provided method, many users may do encrypted searches on cryptography. Finally, it is shown that the offered technique is secure utilizing the approximate-GCD problem. The experiment's results showed that the proposed technique had less computational complexity than the methods currently in use.
Web Application Based Authentication System

Authentication is one of the primary problems with system security. The key component of the access control process to prevent unauthorised users from accessing data and resources is authentication, which may be described as the act of verifying a user's identity. The validity of the user cannot be guaranteed by a static technique of authentication. This led to the development of more cutting-edge authentication techniques. To increase the system's security, two factor authentication was initially deployed, followed by multi factor authentication. Later, adaptive authentication was added and it also had some problems. When authenticating a user in this study, an unique collection of user features was taken into account. A performance optimization technique was included since this research takes many user factors into account, and it improved performance by 25%.
Bank Transaction using Face Recognition

In the recent digital world, We all depend on online transactions for our day-to-day activities. They could be changes that an unauthorized user trying to collect your login details and make a forge transaction activity. To avoid this we need a strong tool to make the payment gateway more secure. Before completing each transaction, the programme confirms a person's identification. Our objective is to create a seamless, personalised experience with a strong emphasis on security. One of the securing techniques is to develop a robust automated web application for transacting money for higher-level security purposes with high facial recognition. First, we have to register our details with our faces and pass the liveness detection test. For rapid and easy face identification from the input image, a Haar-cascade-based technique was used. The picture of the face is then transformed to grayscale. While login our face will be recognized if we are authorized, then it will move to the authorized page else our system won't allow us to log in.
Implementation and Evaluation of a Modified Mel-Frequency Cepstral Coefficients based Text Independent Automatic Speaker Recognition System

Speech feature extraction, being the most important step, plays the utmost role in any automatic speech recognition system. In any speech feature extraction technique, the emphasis is on getting more and more accurate and robust features. After decades of research in automatic speaker recognition, there have been several new algorithms and advances, however, there still remains a number of challenges largely due to inconsistencies in speaker's vocal tract over a period of time and health, changing ambience and variations in the performance of speech recording systems etc. Mel frequency cepstral coefficients (MFCC) is an extensively used method in automatic speaker recognition systems. The triangular mel weighing function is a key component of the MFCC feature extraction technique. In this paper various existing modifications in the mel weighing function have been presented and sinc function based mel weighing function has been proposed. The traditional triangular weighing function based as well as Gaussian weighing function based MFCC technique have been evaluated on the TSP speech database. This database contains 11 male and 12 female speakers having utterance of varying lengths. A distortion measure between extracted features established on the least Euclidean distance was utilized for speaker recognition. The success rate of this speaker recognition investigation was estimated to be 92 % in case of traditional triangular mel weighing function and was found to be 96 % in case of Gaussian mel weighing function for the speech segments of 2 seconds.
Rule-based role estimation in werewolf games using probabilistic logic programming

The werewolf game is one of incomplete information games by multiplayer, and it is recognized widely as a new promising standard problem in Artificial Intelligence recently. In this paper, we try to evaluate the validity and effectiveness of common knowledge, i.e. common tendencies felt by a large number of players, in werewolf games. Specifically, we extract such tendencies as rules from the werewolf BBS data manually, and verify their effects on the role and team estimation tasks by using a probabilistic logic programming model having game rules and extracted tendencies. As a result, we succeeded in extracting some common tendencies for team estimation, and it was confirmed that the extracted knowledges have positive effects in some purposes such as the prediction of werewolves.
Play school based reality application examination

Learners nowadays are differently from their predecessors since they were raised in a technologically advanced age. Yet these youngsters continue to receive the same kind of instruction. In the 21st century, where knowledge is readily freely accessible and everybody has a device with connection to the internet, it is just unacceptable to demand pupils to learn from textbooks while studying raster graphics and their explanation. Rong-Chi Chang and Zeng-Shiang You, researchers from Asia University in Taichung, Taiwan, showed that online teaching environments improved student comprehension and memory. As a result, we may enhance the present educational system employing technologies. Our study focuses on using google glass to support kindergarten students' learning because this is effectively where their schooling begins and making learning enjoyable and engaging at this point is a nice beginning. The strategy is to create a book-accompanied smartphone 3d Virtual app. Children who own this book may use the app to supplement the picture into the real universe and engage with it in many ways. For instance, if a child augments a car, the child can control the vehicle using it on restrictions it around amplified interior. In addition to this interactive feature, the features will be fully energized and will show additional data on the object. They will also include a text-to-speech characteristic that will perused out the exposed to public. The software will include the fundamentals of what is covered in kinder, including the letters, as well as some extras, like the observable universe, to support the development process of interests.
Study on Facial Recognition to Detect Mood and Suggest Songs

A lot of people are interested in face recognition technology because of how many applications it has and how lucrative it may be. Security systems, digital video processing, and other industries utilise it widely. One school of thought holds that music is a form of art that has a closer affinity with the soul. emotion. Music lovers struggle with creating and organising their play-lists. They are unable to manually process hundreds of songs. Users have to manually change or update each song on their play-list each time. Songs that are a user's favourite music may not always be given precedence or may even be omitted from a playlist's order chosen from a list Ultimately, the system should be able to detect facial movements and provide actionable recommendations based on those interpretations. Each music is chosen based on the user's current emotional state. Track selection and organisation are still necessary for many traditional music players today. For each mood, users must create and edit playlists, which takes time. Some music players have sophisticated features like playlists and lyrics. Based on the performer or genre, similar songs are suggested. There is still room for improvement in the area of automation with regard to music players. Users will enjoy themselves more if tunes are automatically chosen and categorised based on the user's mood. The system can accomplish this by responding to the user's emotions.
Online Virtual Classroom Application In Android Studio

Online education has become the new standard in our lives, taking into consideration the major international issues that we are all facing. The major goal of creating an application for assignment submission and communication between students and professors is to find a way to make online education comfortable and convenient. The first stage of creating an application for online assignment submission by students and for uploading all of the teaching and learning materials by teachers is covered in the paper. Android Studio was used to create the front end of this android application, while Firebase was used to create the back end. For the purpose of adding functionality to the programme, Java and XML have been used. The application's design, development, and analysis are covered in the article.
Multi-Input Adaptive Activation Function for Binary Neural Networks

Binary Neural Networks (BNN) have binarized neuron and connection values so that their accelerators can be realized by extremely efficient hardware. However, there is a significant accuracy gap between BNNs and networks with wider bit-width. Conventional BNNs binarize feature maps by static globally-unified thresholds, which makes the produced bipolar image lose local details. This paper proposes a multi-input activation function to enable adaptive thresholding for binarizing feature maps: (a) At the algorithm level, instead of operating each input pixel independently, adaptive thresholding dynamically changes the threshold according to surrounding pixels of the target pixel. When optimizing weights, adaptive thresholding is equivalent to an accompanied depth-wise convolution between normal convolution and binarization. Accompanied weights in the depth-wise filters are also binarized and optimized end-to-end. (b) At the hardware level, adaptive thresholding is realized through a multi-input activation function, which is compatible with common accelerator architectures. Compact activation hardware with only one extra accumulator is devised. By equipping the proposed method on FPGA, 4.1% accuracy improvement is achieved on the original BNN with only 1.1% extra LUT resource. Compared with State-of-the-art methods, the proposed idea further increases network accuracy by 0.8% on the Cifar-10 dataset and 0.4% on the ImageNet dataset.
Table Information Extraction Using Data Augmentation on Deep Learning and Image Processing

Generally, the extraction of information in the table is done quickly if the table is within a document with a tabular structure. However, in the case of tables presented in the image document, steps are needed first to detect the table. Seeing a table in image documents becomes more complicated if the table to be seen does not have clear boundaries. This research focuses on extracting information from borderless tables in image documents. The study applies the Mask RCNN-FPN deep learning model to detect borderless tables using augmentation data. The use of data augmentation is expected to increase the accuracy of deep learning models even though there is only a small amount of training data available. The data augmentation technique proposed in this study is a fine-tuning method with CutMask augmentation data. For model formation and testing, this study uses the UNLV data set. This data set consists of scanned images of documents from various sources, including financial reports, journals, and different tabular research papers. The total amount of data used is 427 samples. After data augmentation, the amount of data used is 854 samples. The table detection model is based on the Mask RCNN created using Python. The testing parameters used for table detection quality are precise detection, partial detection, false detection, Precision, Recall, and F-Measure. The table's structure recognition rate is measured from the detection intersection value, rows, columns, and cells compared to ground truth. The test results show that using data augmentation with the CutMask technique can improve the performance of deep learning models to detect borderless tables. The use of image processing is shown to enhance table segmentation. However, the table structure recognition result does not offer a good result compared to the effects of other research.
On Multi-user Security of Schnorr Signature in Algebraic Group Model

The security of Schnorr signature Sch has been widely discussed so far. Recently, Fuchsbauer, Plouviez, and Seurin gave a tight reduction that proves EUF-CmA of Sch in the random oracle (ROM) with the algebraic group model (AGM) from the discrete logarithm (DL) assumption at EUROCRYPT 2020. Kiltz, Masny, and Pan also considered multi-user security of Sch at CRYPTO 2016, whereas Fuchsbauer et al. only considered single-user security. More precisely, Kiltz et al. constructed a tight reduction from EUF-CmA to mU-EUF-CMA. Combining these two results will likely enable us to construct a tight reduction that proves MU-EUF-CMA security of Sch in AGM+ROM from DL assumption. Against such an intuition, we show an impossibility on proving MU-EUF-CMA of Sch in AGM+ROM only by combining them in this paper. To estimate our impossibility result, we also discuss why the result by Fuchsbauer et al. cannot be applied to MU-EUF-CMA setting. Our result therefore suggests that we are required to develop a new proof technique beyond the algebraic reduction or to find a new form of public keys other than that considered in our impossibility, in order to show MU-EUF-CMA of Sch in AGM+ROM.
FPGA Implementation of Stream Cipher SOSEMANUK

SOSEMANUK is a synchronous stream cipher and is a finalist for Profile 1 of the eSTREAM conducted by ECRYPT. Its key length is variable between 128-bit and 256-bit, and it uses a 128-bit initial vector. SOSEMANUK consists of a block cipher Serpent and a stream cipher SNOW2.0. In Profile 1 of the eSTREAM competition, where SOSEMANUK was selected, stream ciphers were required for software applications with high throughput. Therefore, Profile 1 compared performance by CPUs. However, heterogeneous computing is now being considered, and computational resources other than CPU are increasingly used for acceleration. Furthermore, SOSEMANUK is stated to be suitable not only for software but also for hardware in several papers. Therefore, this paper presents FPGA implementation of SOSEMANUK to achieve higher throughput for the use of acceleration of stream ciphers. In this paper, SOSEMANUK is implemented on an FPGA with the aim of improving throughput performance. In the implementation, the critical path is analyzed and several parts of the circuit are consisted of duplicated fashion to cut the keystream generation cycle to be half. The algorithm is designed in Verilog-HDL using Xilinx Vivado v2020.1 and implemented on a Xilinx Artix-7 XC7A200T-2FBG676C FPGA. As a result, 4.9 Gbps throughput has been achieved at a clock frequency of 99.2 MHz, which has been an improvement in throughput over previous studies.
Recognition-aware Bitrate Allocation for AI-Enabled Remote Video Surveillance

With a growing number of deployed video surveillance cameras, deep learning based video recognition becomes increasingly important for analysing the large amounts of generated video content. For use-cases with computationally intensive recognition applications and with moving cameras or frequently changing camera layouts, video content is often streamed in real-time over wireless networks to cloud environments where video recognition technologies operate. However, it is in general difficult to transmit a large number of video streams and also achieve a high video recognition accuracy with only the limited radio frequency resources of wireless networks, because of video compression artefacts appearing at low video bitrates. To allow transmission of more streams and to increase recognition accuracy, we propose a dynamic prediction-based bitrate allocation method that balances encoder bitrates among cameras in a way that approximately minimizes the total recognition error. For predicting the number of recognition errors at given bitrate and video content, our method uses a neural network operating in a distributed fashion at both edge and cloud locations. In the experiments, we show that our predictions are highly correlated with the actual numbers of recognition errors (Pearson correlation of 0.872). Furthermore, an evaluation of our allocation approach in a construction site surveillance scenario with ten video streams shows that at low average video stream bitrates under 1 Mbps, our method can reduce the false negative rate of an action recognition engine by 23% over the baseline approach (evenly distributed allocation) on average.
Designing Accessible Urban AR Experiences for Digital Humanities

This paper presents the results of a design activity targeted to create urban experiences based on the use of augmented reality and focused on the domain of digital humanities. While augmented reality provides the opportunity to link physical context to digital content and services, with benefits in terms of engagement and lower cognitive loads, it reduces easy access to content. The exploration of the alternatives to permitting users to access content, while maintaining a strong link with the context, led to a possible solution which may represent the core of a multifaceted approach useful for a large number of user experiences, increasing the number of situations where content can be communicated and services accessed. The final part of the paper, starting from the current design results, discusses some directions for offering comprehensive solutions targeted to improve the accessibility of urban user experiences for different categories of users.
Application of ECEA Path Planning Algorithm in Smart City Landscape Architecture Design System

With the continuous development of smart cities, people are increasingly concerned about intelligent landscape garden design. How to improve the practical application of intelligent landscape planning systems in modern enterprises has become an important issue. ECEA is based on an integrated platform of Internet technology and information technology, and it can provide users with interactive services and management functional modules. This approach helps managers to better understand, analyze, process and optimize complex relationships, which helps to further enhance the role of forecasting and decision support for future development, thus making it more sustainable and intelligent landscape design system, this lays a good foundation for the future development of smart cities. This paper focuses on the application of ECEA system in the design of landscape gardening in smart cities, by analyzing the differences between this system and traditional planning and management methods, so as to propose the realization of its intelligent scenic construction and management based on ECEA development ideas.
Handling Data and Model Drift for World Application using Big Data

It's still unclear how to effectively extract the information concealed inside vast and massive amounts of data. The problem of “idea drift” in stream data flows is one of the difficulties. Random walk is a common issue in data analytics where the statistical features of the characteristics and the categories they are intended for change with time, decreasing the accuracy of the trained model. There are numerous approaches that have been put out for bulk data mining. A new generation of data mining methods called stream mining updates the model in a single pass whenever fresh data is received. Because of its inherent adaptability, this one-pass mechanism may be properly capable than its predecessors of coping with idea drift in data streams. In this study, we assess a group of algorithms for mining the data streams using decision trees. The collection of rules which can be derived from the induced model is the benefit of decision tree learning. The predicate logics that represent the extracted rules can then be applied in a variety of decision-supporting applications. Even in the face of concept drift, the induced decision tree has to be precise and condensed. When dealing with concept-drift data, we evaluate how well three common incremental decision tree methods (random forest, isolation forest, and forest tree) perform. In the experiment, drift data from both synthetic and actual environments are employed. It is discovered that optimization method with big data technique i.e., MapReduce yields better outcomes.
Particle Swarm Optimization for Predicting Breast Cancer

the second leading cause of mortality is cancer. The primary cause of the rise in women's death is breast cancer. One in eight females globally impacted by it. Breast cancer diagnosed by detecting the malignous cells of breast tissue. Modern medical image processing techniques works on histopathology images, which are captured by a microscope, and analyse them by using different algorithms and methods. Machine learning algorithms are widely used for processing medical images and pathological tools. In deep learning, this often carried by extracting features through the Convolutional Neural Network (CNN) and then classified using a fully connected neural network. In this paper, the algorithms used are CNN, Particle Swarm Optimization (PSO), and Support Vector Machine (SVM).
Side Channel Attack using Machine Learning

The overwhelming majority of significant security threats are hardware-based, where the attackers attempt to steal information straight from the hardware that our secure and encrypted software operates on. Unquestionably, side-channel attacks are one of the most severe risks to hardware security. Rather than depending on bugs in the program itself, a side-channel attack exploits information leaked from the program implementation in order to exfiltrate sensitive secret information such as cryptographic keys. A side channel assault could manifest in different ways including electromagnetic radiation, power consumption, timing data, or even acoustic emanation. Ever since the side-channel attacks were introduced in the 1990s, a number of significant attacks on cryptographic implementations utilizing side-channel analysis have emerged, such as template attacks, and attacks based on power analysis and electromagnetic analysis. However, Artificial Intelligence has become more prevalent. Attackers are now more interested in machine learning and deep learning technologies that enable them to interpret the extracted raw data. The aim of this paper is to highlight the main methods of machine learning and deep learning that are used in the most recent studies that deal with different types of side-channel attacks.