Reviewerly/exHarmony huggingface dataset:
ConfSys - An Intelligent Conference Management System

This paper offers a brief history of, ConfSys, a conference management system, that has been used for over 15 years to support a number of international academic conferences. It is a complete system that has all functions automated with the possibility of the program chair overriding any of its decision. We have found that in most instances, the decisions made by the system need very minor changes. This paper describes another step in its automation process involving the submission made by authors and its processing by a proposed intelligent module. The new module will extract the salient metadata which we believe are more relevant than the ones entered by authors. This would ensure reliable paper-related details like title, author, coauthor, organization, abstract, keyword, etc. are being captured instead of users adding these details first-hand. The system requires users to verify the extracted information and correct them if required, further improving the paper allocation process to reviewers based on matching the reviewer’s interests with extracted keywords and topics, thus improving the quality of relevance of the reviews and comments to the authors. This in turn would improve the quality of the publications.
Malicious ADS-B data Generation Based on Improved GAN

Automatic dependent surveillance broadcast (ADS-B) system sends messages over unencrypted wireless channels without any information integrity protection measures, and its messages are at risk of interception and tampering, which can easily lead to impersonation and forgery attacks. At present, although the ADS-B data anomaly detection model based on machine learning has excellent performance in predicting normal samples, the machine learning model may face different degrees of risk in each stage of its life cycle due to the existence of a large number of attackers in real scenes. To build secure and reliable machine learning systems, exploit potential vulnerabilities. Aiming at the ADS-B abnormal data detection model based on machine learning, this paper studies a construction method of poisoning data with strong applicability and establishes attack model. By injecting malicious data generated by the Generative adversarial network into the machine learning model, the performance of the trained model deteriorates and data misclassification occurs. Experimental results show that the malicious ADS-B data generation method proposed in this paper achieves good results, which lays a foundation for optimizing system defense technology and guaranteeing ADS-B security.
An Efficient Transformer with Distance-aware Attention

In recent years, the transformer model has become one of the main highlights of advances in natural language processing (NLP). The attention mechanism of the transformer model makes it possible to track the relations between words across very long text sequences in both forward and reverse directions. However, the complexity of the attention mechanism is quadratic and introduces a performance bottleneck in the transformer. We propose a distance-aware attention mechanism which integrates the locality information by assigning different weights to query-key pairs according to the distance between the query and the key. By doing so, we in fact shrink the dimension of the matrix in the vector matrix multiplication and reduce the complexity of the attention to O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /m). Experiments show that the distance-aware attention is superior to or close to the original model and other variants in various NLP tasks.
Research and Application of Blind Watermark Based on DCT

As the content of electronic documents has become increasingly important and prominent in the present day, most companies or enterprises pay more and more attention to the security of internal electronic documents. To solve the problem of information leakage in electronic documents, this paper proposes a method about digital watermark technology based on DCT transform and develops a system to assist companies or enterprises in adding and extracting blind watermarks to image materials. The system uses Hough transform as well as Arnold scrambling algorithm and provides pre-processing methods for images in different situations. It provides a blind watermark application scheme which does not seriously disturb the user's vision or spoil the content of the images, but also can resist a series of attacks such as extraction, rotation, and cropping. This scheme not only allows companies or enterprises to trace images back to the source of the leak to make sure that the significant internal electronic documents are not leaked by such a deterrent approach, but also considers the security of personal information of the operators to prevent further vulnerabilities. The system developed in this paper meets the needs of the companies or enterprises in specific application scenarios and is easy to embed in the real development environment.
An Efficient Privacy-preserving Join-Aggregation Scheme in Smart Grids

Data aggregation is a common technique used in the Internet of Things (IoT) to combine fine-grained data from different sources. With increasing demands for data privacy, it has become a hot research topic. How to perform data aggregation without privacy leakage is a challenge. In smart grids, power data often has the characteristics of large volume and high sensitivity, while the limited computing resources of power bureaus can lead to corresponding delays and decreased service quality. This paper proposes an efficient and lightweight privacy-preserving scheme for data aggregation with the background of smart grid data aggregation. The proposed scheme ensures the privacy and security of sensitive data when processing join-aggregation task. The security of the scheme in the semi-honest model is proved using the Real-Ideal paradigm. Finally, the computational and communication costs of the proposed scheme are analyzed and compared, which shows that the total number of homomorphic operations is fewer than that of other related literature. The communication cost of the proposed scheme is four rounds, which is communication friendly.
Comprehending the Consequences of Emerging Technologies on Commercial Enterprises

This scholarly investigation delves into the influence of contemporary computer science trends on commercial enterprises. By conducting an examination of the amalgamation of advanced technology in the business world, this research paper will furnish a thorough comprehension of the paramount computer science trends and their consequences on the digital transformation of businesses. The evaluation will be based on the way the companies operate, from automating processes to improving decision-making and enhancing customer experiences with an emphasis on the fields of quantum computing, bioinformatics, data mining, cloud computing, and parallel computation. The conclusions drawn from this study will furnish valuable information for businesses, decision-makers, and individuals seeking to remain ahead of the rapidly advancing technological landscape.
SFLaaS: Software Fault Localization as a Service

Many tools for enabling developers to locate bugs in their programs have been proposed in the literature. The majority of programs they target are based on C/C++ and Java. In this paper, we offer a tool named "SFLaaS" for locating faults in programs written in Python and is provided as a service rather than as a plugin or a command-line tool to be installed. Thus, our tool can be accessed anytime and from anywhere. The tool employs Spectrum-based fault localization (SBFL) to help Python developers automatically analyze their programs and generate useful data at run-time to be used to produce a ranked list of potentially faulty program elements (i.e., statements). Our tool supports different important features in fault localization such as supporting about 80 SBFL formulas, different tie-breaking methods, showing code elements with different colors, ranging from most suspicious (red) not suspicious (green) based on their suspicious scores, allowing the user to define his/her own formula, etc. Using our tool could help developers to efficiently find the locations of different types of faults in their programs.
Cluster Based MANET Security with N-TH Degree Truncated Polynomial Ring (NTRU) Public Key Cryptosystem

Mobile Ad Hoc Networks (MANETs) applications hold important and confidential data transfer, so it is vital that communication medium should be secured and data must be transferred without any alteration. Security solutions based on traditional cryptosystems are susceptible to quantum attacks. The result of continuous research to find enhanced security solutions, capable of resisting Quantum attacks, is development of cryptosystems based on lattice structures. Many cryptosystems have been designed (NTRU, GGH, LWE), which are based on certain hard problems over lattices such as finding shortest vector problem and approximate vector problem. N-th Degree Truncated Polynomial Ring (NTRU), implemented on the same chip with Elliptic Curve Cryptography (ECC), provides faster speed than ECC. In this paper, we have developed security architecture for MANET using N-th Degree Truncated Polynomial Ring (NTRU) cryptosystem. NTRU based Key agreement scheme is used to derive pair wise keys and group session keys for the fully distributed MANET environment. (t,n) threshold based multiple secret sharing scheme, derived by using Shamir secret sharing scheme and (t,n) threshold based sharing scheme based on NTRU is used for calculating secret shares of clusterhead nodes. This security design is compared with the previous designs based on RSA and ECC based cryptosystems.
Distance Validation and Role of High Fidelity VR Environments on Embodiment and Enjoyment

Gamification, especially in Virtual Reality (VR) have been gaining importance lately for which there arises a need in understanding the interaction mechanism as to which would lead to engagement and motivation. To assess this, our work uses a virtual hand representation to embody the user in three different VR environment of varying interaction fidelity. Through both qualitative and quantitative data gathered, we assess the users’ perceived sense of embodiment, engagement, and motivation. Results show that hand tracking interactions increase the sense of body ownership but lacks tracking reliability and haptic feedback. The virtual controller representation showcased better sense of agency and faster learning towards interactions. The sense of embodiment, engagement and motivation was found to be the highest in the high-fidelity VR environment.As a sub part of this work, we also showcase how distances can be measured in virtual environments especially in the stereoscopic Head Mounted Displays (HMD), VR. The virtual environment measured distances is then compared with the premeasured real-world distance for which the high correlation is observed.
Sarcasm Detection: A Systematic Review of Methods and Approaches

Social media is a common source of communication for various formal and informal contextual use cases. The conversation in both structured and unstructured forms can be broadly classified as positive/negative. In addition to “sarcasm,” the research about unstructured language has become very interesting due to the fact that very few researchers have offered solutions to problems associated with it. By using deep learning models, some hybrid approaches are used to identify sarcasm sentences. The identification is further refined to mark the content as sarcasm, irony, humour and offensive. This article analyzes and summarizes various works on irony/sarcasm detection in terms of features, approach, architecture and performance. This study analyzed that, the hybrid models superseded the performance of the traditional machine learning approaches for classifying the sarcasm/irony content. Finally, this study has briefed the identified challenges and research directions for building better models for classifying sarcasm/irony content.
Deep Learning with Histogram of Oriented Gradients- based Computer-Aided Diagnosis for Breast Cancer Detection and Classification

In the modern era, cancer is a major public health concern. Breast cancer is one of the leading causes of death among women. Breast cancer is becoming the top cause of death in women worldwide. Early identification of breast cancer allows patients to receive proper treatment, improving their chances of survival. The proposed Generative Adversarial Networks (GAN) approach is designed to aid in the detection and diagnosis of breast cancer. GANs are deep learning algorithms that generate new data instances that mimic the training data. GAN is made up of two parts: a generator that learns to generate false data and a discriminator that learns from this false data. Furthermore, the histogram of oriented gradients (HOG) is utilized as a feature descriptor in image processing and other computer vision techniques. Gradient orientation in the detection window and region of interest is determined by the histogram of oriented gradients descriptor approach. Using an image dataset and deep learning techniques, the proposed research (GAN-HOG) aims to improve the efficiency and performance of breast cancer diagnosis. The deep learning method is used here to analyze image data by segmenting and classifying the input photographs from the dataset. Unlike many existing nonlinear classification models, the proposed method employs a conditional distribution for the outputs. The proposed model GAN-HOG had an accuracy of 98.435%, a ResNet50 accuracy of 87.826%, a DCNN accuracy of 92.547%, a VGG16 accuracy of 89.453%, and an SVM accuracy of 95.546%.
Age and Gender Recognition using Deep Learning Technique

Gender classification is popular because it includes information about male and female social activities. Faces make it difficult to derive gender-discriminating visuals. Gender classification is based on looks. Automatic gender classification is popular because genders include rich social information. Classification has grown increasingly important in many industries. In a conservative society, gender classification can be usedin certain contexts. Identifying gender type is crucial to keeping extremists out of safe locations, especially in sensitive areas. A similar technique is utilized in female-only railway carriages, gender-specific marketing, and temples. Biometrics debates gender classification from facial pictures. Traditional ways categorize hand-crafted features globally and locally. These gender-identification systems need subject knowledge and are ineffective. Human gender identification is easy, but machines struggle. We listed numerous gender classification pre-processing approaches, such as contrast and brightness normalization. To create a gender and age classification framework Deep Belief Networks employs Shifted Filter Responses to identify features. The suggested model achieves 98% and 99% accuracy on the benchmark dataset.
Approaches of Security in Cloud Computing

Information integrity and secure accessibility with the various mathematical methods are essential in the cloud environment for the creation of a protected system, which compels to do a comprehensive study and presents the techniques of safeguarding systems. Many applications need a secure zone of execution for providing essential services by avoiding security flaws. This paper highlights protection techniques such as stream cipher, block cipher with the hashing function, and another strategy employed globally to ensure maximum privacy by lowering risks and threats.
Construction of Scientific Decision-Making System for Power Service Integrating Smart Cloud Platform

Construction of scientific decision-making system for the power service integrating smart cloud platform is the main topic of this study. In recent years, the development of the power system has entered a new era, and profound changes have taken place in business requirements, technical characteristics and functional forms. Hence, the combination with the cloud system is essential. This paper considers two core functionalities: (1) the unit combination problem solution; (2) online security and stability decision of the power system guarantee as the referring targets. Through the testing under cloud scenario, the performance is then validated.
A Machine Learning based Insect Bite Classification

Identifying insects by their bite marks can assist doctors in diagnosing victims and providing appropriate treatment. In recent years, researches using Machine Learning have been actively conducted and have produced excellent results in fields such as object detection, behaviour recognition, voice recognition, and cancer detection in medical field. This study has developed a classification application that can be used on mobile phones to solve the insect classification problems. Experiments were carried out on five insect species chosen for being the most common biting insects. Detailed study was conducted on different images with the help of Random Forest and Support Vector Machine models. These models need different insect bite marks images to classify them. Random forests achieve a better performance and are usually much faster than Support Vector Machines.
An Overview of Character Recognition from Palm Leaf Manuscripts

Palm leaf manuscripts are the first form of writing materials and are the special repositories of information from several ages that have enlightened humanity on a variety of themes, including medicine, astronomy, mathematics, and astrology. Due to the lack of proper preservation techniques, these manuscripts are disappearing at an alarm rate. This necessitates the digitization of these manuscripts. In this paper, initially the major steps in digitization process are presented. Then, an intensive review is conducted on the available work in every step of digitizing Palm leaf manuscript. This study also analyzes the accuracy, efficiency, and superiority of each method and identifies the best method currently available for each step in the automatic palm leaf manuscript digitization process.
Image Classification from Unsupervised Learning of 3D Objects

This article intends to propose a technique for categorizing 3D deformable objects from unprocessed single-view images. The proposed technique is built on an autoencoder, which considers the depth, albedo, and viewpoint of each input image. By considering the symmetry that many object categories exhibit, at least in theory to independently untangle these parts from one another. This manuscript shows the demonstration how, even when shading causes the appearance of an object to be nonsymmetric, Still, the underlying object symmetry by using illumination-related reasoning. Additionally, by forecasting a symmetry probability map that is learned end to end with the other model elements, and represents things that are not symmetric. Experimental results demonstrate that, without assistance or the use of a pre-existing form model, this method is capable of recovering the 3D shape of humanoid faces, cat images, and automobile images with remarkable accuracy from single-view photos. As compared to the level of 2D picture correspondences, show superior accuracy on benchmarks in comparison to another system that makes use of supervision.
Distance Estimation in Video using Machine Learning

In a video, distance estimate refers to calculating the distance between an object and the camera. The camera records the live video of a person walking in front of it. Live video is collected when a human stands in front of the camera and begins to walk in front of it. A sequence of video frames is derived from the collected live footage. These frames are handled separately. Each frame is subjected to a face detection method. The detected face is surrounded by a rectangle. The rectangle created around the detected face is used to calculate height and breadth. This is known as the perspective width. The focal length is calculated using the perspective width. The proposed system is using the focal length to determine distance once it has been calculated. The user can now walk in front of the system, which is now ready for distance estimation. The basic goal is to recognize a moving face and calculate its distance from the camera. In the realm of research, distance estimate is useful. For execution, the initiative makes use of cutting-edge technologies such as machine learning.
HarGharSolar : Recognition of Potential Rooftop PhotoVoltaic Arrays Using Geospatial Imagery for Diverse Climate Zones.

As solar energy has been recognized as an inexhaustible source of energy, the solar photovoltaic installation business has taken the lead in today's market. Nowadays, people are investing more in green energy due to its harmless and everlasting supply of energy and also its boundless applications. With the adaptation of solar panels on the building rooftops, people often fail to think of the total energy that will be generated from the solar panel and if the generated power is sufficient enough to fulfill the power requirements of the whole building. Different climate zones receive different amounts of sunlight and thus, solar energy generation varies in all regions. Artificial Intelligence has evolved to bring significant development in this field as it helps in detecting rooftops that have a potential for solar photovoltaic systems and also helps to efficiently detect how much energy can be generated using the solar panels. Latest Deep Learning models like YOLO, EfficientNet, VGG ResNet etc are able to detect rooftops using geospatial images of zones and models like U-Net, SegNet etc are used to configure the solar photovoltaic system for the consumer. An additional model for the calculation of power generated considering different parameters like climate, topography will be built using advanced AI techniques. The best performing models will be finetuned and integrated with the front end to act as a one stop destination for the end user.
Integration of Android Application and Smart Conventional Chess Board

This paper describes the design and integration of android application and smart conventional chess board using Arduino. This board was designed for the chess players to provide an actual physical experience of the game. Even though, there are plenty of online and offline chess applications available in mobile phones and desktops, the actual experience cannot be gained. Digital displays release blue light, which can damage light-sensitive cells in the retina of the eye and may also lead to several other health issues. This board provides a solution for the players from different locations, to be played in the physical board instead of looking into displays. The primary functions of the chess board are automatic detection of position and movement of each coin. A framework is built to determine the position of each coin in the two-dimensional structure using reed switches. Arduino is used as the microcontroller in this board. An electro magnet is used for the movement of chess coin from one position to another position within the chess board. With the use of rack and pinion gears, stepper motors are used to move the electro magnet to the desired location on the board. The changes in the position of the coins are frequently monitored and sent to cloud over the internet. An android application is developed using a programming language Java for monitoring and controlling the movement of chess coins over the internet.