Reviewerly/exHarmony huggingface dataset:
Chapter 9. Building Robust and Explainable AI with Commonsense Knowledge Graphs and Neural Models

Commonsense reasoning is an attractive test bed for neuro-symbolic techniques, because it is a difficult challenge where pure neural and symbolic methods fall short. In this chapter, we review commonsense reasoning methods that combine large-scale knowledge resources with generalizable neural models to achieve both robustness and explainability. We discuss knowledge representation and consolidation efforts that harmonize heterogeneous knowledge. We cover representative neuro-symbolic commonsense methods that leverage this commonsense knowledge to reason over questions and stories. The range of reasoning mechanisms includes procedural reasoning, reasoning by analogy, and reasoning by imagination. We discuss different strategies to design systems with native explainability, such as engineering the knowledge dimensions used for pretraining, generating scene graphs, and learning to produce knowledge paths.
BUILDING COMMUNITY LITERATURE INTEREST THROUGH THE PERPUSJAL PROGRAM IN JATIROKEH VILLAGE, SONGGOM DISTRICT, BREBES REGENCY

Literacy is a vital talent in life because it includes individual talents and competencies in reading, writing, speaking, calculating, and solving problems. Literacy is closely related to language skills. Roughly, literacy can also be interpreted as reading and writing, which is also the basis for achieving a higher level of literacy skills and abilities that are useful in everyday life. The case of the COVID pandemic, which stopped the activities of the entire community, was no exception to the teaching and learning process in schools, which resulted in an online system and activities at home. Judging from the observations of the current situation and conditions, there are many children and teenagers in the Jatirokeh area where online learning is considered less than optimal for educating character. This study aims to provide awareness and build interest in reading for residents during the COVID-19 pandemic, especially in Jatirokeh Village, Songgom District, and Brebes Regency. This service method uses several methods, including: 1) Opening communication with parties that allow for book donations 2) Collecting books from donations from various parties 3) Holding a Street Library (PERPUSJAL) 4) Holding "Ngabuburead" activities during the month of Ramadan. The subject of this research is local residents and youth in Jatirokeh Village, Songgom District, Brebes Regency. Data collection was obtained using library techniques, followed by data processing techniques using data analysis. And for the interpretation of data, interpret in the form of a description so as to get good data. Efforts are being made to build and increase awareness of people's interest in reading by providing reading facilities, visiting people's places, and organizing interesting activity programs.
CNN-Based Deep Learning Approach Over Image Data for Cyber Forensic Investigation

A deep learning approach is gaining popularity day by day in image data classification. The process of classification of graphical data considering training network is managed by conventional neural network. Such types of networks allow automatic classification by making use of CNN approach. But the issues that are faced during forensic investigation are slow in performance and lack accuracy. The major objective of work is to consider the CNN approach that is processing graphic data in order to perform cyber forensic investigation.
A Comprehensive Review on Algorithms of Image Processing for Autonomous and Electric Vehicles

The development of autonomous electric vehicles has gained significant attention due to their potential to reduce carbon emissions and improve road safety. Image processing has become an important tool in the development of these vehicles, enabling them to detect and respond to objects and obstacles in their environment. In this review paper, we explore the use of image processing in electric vehicles and driverless cars, with a focus on the various techniques proposed by authors. The comparison of the performance and effectiveness of different approaches, including deep learning, computer vision, and sensor fusion, in detecting and recognizing objects in the environment. Our review highlights the advantages and limitations of each technique and their potential for future development in the field of electric vehicles. Overall, image processing has shown to be a promising solution for the development of safe and efficient autonomous electric vehicles.
AI and Crime Prevention With Image and Video Analytics Using IoT

Artificial intelligence system has been frequently used for crime prevention. Image and video analytics are playing significant role during such operation. However it has become quite challenging to implement over IoT environment, with the development of artificial intelligence where the discipline of research and analysis has entered a new age. The use of video analytics, a branch of artificial intelligence that is undergoing fast advancement, is supporting law enforcement agencies in significantly reducing crime rates. Video Analytics examines video footage using algorithms to categorize a wide variety of object types and distinguish certain behaviors or activities in order to deliver real-time alerts and insights to customers. Traditional CCTV cameras are so yesterday. But there is need to reduce the time consumption and space consumption during image analytic and video analytics operations. Present work is focusing on performance enhancement during image and video analytics.
Crime Forecasting Using Historical Crime Location Using CNN-Based Images Classification Mechanism

The present research is focused on crime forecasting and CNN has been used for image classification in order to categorize crime events. However, there are different classification mechanisms used in conventional research work. But CNN is playing a significant role in identification and prediction of crime. The major issues during CNN based classification are time consumption and accuracy. However, proposed research has resolved issue of time consumption by reducing image size by applying the RGB2GRAY model, and images are resized before training operation. Simulation results conclude that the proposed work provides a scalable and reliable approach for crime forecasting.
A Novel Phishing Attack Prediction Model With Crowdsouring in Wireless Networks

As the web and applications for knowledge technology developed, many attacks and security problems started to emerge. The last couple of years have seen a significant development in 6G wireless networking. It is challenging to create a secure wireless network. In phishing attacks on wireless networks, attackers create phishing websites that allow users to enter personal information such as usernames, passwords, security numbers, and credit card details. Phishing emails that contain links to websites that are used to spread malware. This project suggests a real time phishing detection plug-in for the web browser which uses a random forest classifier to identify and notify users. As a result, the consumer can get an alert right away. The suggested systems specify wireless phishing attack detection in the current context and produce superior results. The authors proposed 18 traits in order to cover every aspect of phish behavior. With the help of an accepted dataset, the suggested phishing detection system was trained.
Identification and Characterization of Spammers from Lists of Recipients

For millions of users and their friends, online social networks (OSNs) are popular platforms for communication and cooperation. Regrettably, they may be efficient instruments for running spam campaigns and dispersing malware when in the wrong hands. Social spam is more effective than traditional email because users are logically more inclined to reply to messages from friends on Facebook than from complete strangers. In fact, evidence already in existence indicates that hostile actors are seeking to steal OSN account credentials in order to fund these "high-return" spam campaigns. All the more definitively, we see spam crusades that are coordinated and begun on KETTO, a sizable publicly supporting stage situated in India, and we follow the group laborers to their spamming exercises on Quora, the country's greatest local area based Q & A (QA) stage. We can recognize the total biological system that upholds publicly supported spam attacks by associating the spam crusades, laborers, spammer accounts, and spamming ways of behaving. We give a careful and scientific assessment of the biological system from a few points, including the size and degree of spam assaults, Sybil records and spammers' conniving strategies, work costs and monetary motivations, quality looks at conveyed by spam crusades, and so forth.
Securing Medical Image Transmission using Memetic Algorithm

In recent times, medical industry faces adverse issue in terms of protecting the medical records of people all over the world. In remote areas, in case of severe health issues, if they want to contact the doctors in urban areas, the new technology has been introduced is teleradiology where you can send the data through IoT based distributed systems. Securing the medical images while transferring the medical records is an important task to maintain the privacy. Then for securing these records, we have to implement two techniques that i steganography and cryptography. These technologies act as a second layer security in the IoT based distributed systems. Here we will be using "Memetic algorithm", a bio-inspired genetic algorithm. This algorithm is used effectively to encrypt and decrypt the data.This paper proposes a novel approach called "Memetic Optimization" for intensifying the security and efficiency of Medical Data(MD) transmission through cryptography. The proposed method involves using memetic algorithms to optimize the key generation process and improve the randomness of the keys used for encryption. By incorporating memetic optimization techniques into the cryptographic process, the security of MD transmission is strengthened, making it more resistant to attacks by unauthorized users. The results of our experiments show the effectual of the proposed method, and its potential for improving the security and efficiency of MD transmission.
Multi-Key Privacy-Preserving Training and Classification using Supervised Machine Learning Techniques in Cloud Computing

Cloud computing contains lots of processing power and storage. Cloud computing and machine learning (ML) techniques enable large-scale data processing. The enhanced ML-based categorization technique is established in the cloud. However, there is a risk of privacy leaking of training data in the data processing. The computational and communication costs of the information possessor(s) must be maintained to a minimum. This study suggests a multi-key enhanced support vector machine (MK-FHE) and multi-key fully homomorphic encryption (MK-FHE) supervised machine learning method for encrypted data (ESVM).The results suggest that MK-FHE protects data privacy and is more effective in processing.
Cursive Hand writing Recognition Using Neural Networks with VGG-16

Recognition of cursive handwritten text from photographs is a method for finding handwritten text. Identification is difficult because every author has a distinctive writing style. The proposed work uses cutting-edge technologies including RNN, KNN, MLP, and CRNN twith VGG16 to push the boundaries of character and handwriting recognition. Additionally, it compares neural networks to CNNwith VGG16 and finds that neural networks are more accurate. In the first stage, image acquisition, the scanned image is acquired along with its normalization, feature extraction, and segmentation. Additionally, it combines with the most effective feature extraction technique to improve the accuracy of our model. It also compares the accuracy with six uniquepretreat. approach, which are image segmentation image data generator data augmentation closed loop pre-processing, sigmoid Stretching and geometric transformation.CNN provides the highest accuracy (98.36%) when using data augmentation. CNN has the highest accuracy (95.25%) when using picture segmentation. The third experiment's results for the image data generator show that CNN produced the best results (92.47%), while the highest model, RNN, gave closed loop processing accuracy of 92.20%. a change in geometry KNN has the highest accuracy at 95.39%.
Effective Methods to Detect Liver Cancer Using CNN and Deep Learning Algorithms

Deaths from liver cancer are considerable. time- consuming manual cancer tissue detection. Hence, a computer- aided diagnostic (CAD) aids in selecting the proper course of action. Mammography, histology, MRI, ultrasound, and tomog- raphy images are the most accurate ways to find cancerous tissue. In medical imaging, CNNs, and deep learning enhance tumor identification and categorization. This study uses a CNN classifier with a cutting-edge methodology to accurately identify liver cancer. With the growth of deep learning, medical image analysis has become an important area of study. Typically, Medical Image Analysis refers to the use of several Medical professionals may employ several imaging modalities and procedures to obtain pictures of the human body, which are then used for patient diagnosis and treatment. This study gives an overview of the many ways that DL approaches have been used to improve medical picture analysis for different identification purposes.
A Comparative Analysis of Credit Card Fraud Detection with Machine Learning Algorithms and Convolutional Neural Network

Credit cards offer an effective and user-friendly function, so people can use them for online transactions. Utilization of credit cards has increased, and with that, so has the potential for credit card fraud. Financial institutions as well as cardholders suffer considerable financial losses as a result of credit card theft. The major objective is to identify fraudulent credit card transactions with the help of machine learning and Deep Learning algorithms. The recent development of Deep learning algorithms has been the key area of concentration in this regard. To get effective results, a comparative study on Deep Learning and Machine Learning algorithms was conducted.
Secured Data Access and Control Abilities Management over Cloud Environment using Novel Cryptographic Principles

In a digital world, each and every individual or organization required to store their data in the digital form over a secured environment. The main problem with that is the maintenance cost and the security, in which the public clouds allow the users to maintain the records in a safe manner with limited accessing and free of costing. But the assurance for users is important to make sure that the data is in a correct place with proper security. So that a new crypto technology is required to ensure the data is properly secured. In this paper, a new methodology is introduced called Intelligent Cipher Encryption Norms (ICEN), in which it is derived from the conventional cryptographic algorithm called Attribute Based Encryption. The most effective approaches that may be used to protect the service guarantee over the cloud environment are Intelligent Cipher Encryption Norms. Due to its "all-or-nothing" decryption function, ICEN increases the risk of a security breach known as the exploitation of accessing credentials (that is decryption privileges). In this work, we look at the two most common types of access credential abuse: those perpetrated by moderately authorities and those committed by cloud users. To prevent abuse, a new cloud-based data storage solution is suggested called Intelligent Cipher Encryption Norms, in which it combines data integrity, transparency and monitoring with a responsible authority as well as revocable cipher policies. The resulting section of this paper provides the proper proof to the outcome efficiency of the proposed methodology.
GGPA

Currently, graph data are particularly common in various fields, and graph algorithms are increasingly widely used. However, due to the features of graph datasets, such as sparsity, the acceleration of graph algorithms with traditional architectures is faced with great challenges. In terms of domain-specific architecture (DSA), accelerators are mostly designed for a specific graph algorithm because of the different features of different graph algorithms. There is still a great need for a general graph algorithmic processing architecture. In this work, we propose a parallel General Graph Processing Architecture, GGPA. As a general graph computing architecture, GGPA can support multiple graph algorithms, realize parallel processing and fully explore the parallelism through a unique and effective subgraph partitioning method. GGPA implements flexibility at the execution paradigm level. During algorithm iteration, GGPA dynamically selects the execution paradigm by analyzing vertex update conditions to achieve the best performance. We verify GGPA at the CPU level and architecture simulator level, and experimental results show that GGPA achieves 1.01x to 5.86x speedup compared to other related start-of-the-art work.
Holistic RISC-V Virtualization

This work describes our efforts to provide a holistic hardware RISC-V virtualization SoC based on the CVA6 core. At the core level, we implemented hardware support for virtualization through the ratified Hypervisor instruction set architecture (ISA) extension version 1.0. At the system level, we are working on providing reference open-source IPs for two non-ISA components needed to build a virtualization-aware platform: (i) the advanced interrupt architecture (AIA) to enable hardware support for interrupt virtualization; (ii) the input/output memory management unit (IOMMU) to protect memory accesses from direct memory access (DMA) devices. All these IPs will be open and freely available to the RISC-V community under permissive open-source licenses.
Accelerating Type Confusion Detection by Identifying Harmless Type Castings

C++ allows reinterpretation of memory objects via type casting, which facilitates easier manipulation of class fields and virtual methods inside the class hierarchy. However, misinterpretation of memory objects, which is called type confusion, can result in illegal access of class fields or methods. Type confusion accounts for many security vulnerabilities for programs written in C++. Previous type confusion detection techniques report a type confusion bug when an object of a parent class is casted to a child class. However, a downcast is safe as long as no illegal fields or methods are accessed. This paper presents Harmless Type Casting Detection (htade), which identifies safe downcast instructions and removes redundant runtime verifications before them by analyzing the type and access information of casted objects. We evaluated htade against 11 SPEC CPU 2006/2017 C++ programs. Compared with LLVM-CFI, htade can reduce the runtime performance overhead by 58.98% on average.
A Secure and Efficient Blockchain-based Multi-Cloud Medical File Sharing

The blockchain-powered scheme for sharing medical files in a practical manner decentralized quality-based encryption is a secure and efficient approach for sharing sensitive medical information among healthcare providers. The system utilizes blockchain technology to store and handle the framework for restricting entry to specific areas and permissions for medical files, providing a tamper-proof and transparent mechanism for managing access to the data. Additionally, the use of decentralized attributes- based encryption ensures that patient data remains confidential and only accessible to those with the appropriate permissions. The scheme enables data owners to define granular access control policies based on specific attributes such as the type of data, patient information, and the role of the requesting party, enabling better collaboration and decision-making while ensuring data privacy and confidentiality.
Quantum Computing Management of a Cloud/Edge Architecture

Modern Cloud/Edge architectures are composed of computing nodes belonging to multiple layers, including Cloud facilities, Edge/Fog nodes and sensors/actuators. In this paper, we present an architecture that includes also quantum computing devices, in two ways:
Memory-Aware DNN Algorithm-Hardware Mapping via Integer Linear Programming

Mapping a deep neural network (DNN) layer onto domain-specific accelerators can require an intractable number of choices regarding loop factorization, ordering, and spatial unrolling. Determining the optimal mapping that achieves the best figures in terms of latency and energy efficiency can be difficult due to the vast number of possible candidates that need to be exhaustively evaluated. Many techniques have been recently proposed for fast and efficient mapping space exploration; some of them adopt a black-box optimization approach, others make assumptions on the underlying accelerator memory hierarchy or require time-consuming model retraining. We propose an integer linear programming (ILP) approach and formulate a mathematical model, namely LEMON, that takes into account number of accesses to each buffer, energy costs and buffer bandwidths in the accelerator and is flexible enough to work with different memory hierarchies. Compared with state-of-the-art techniques, LEMON achieves up to 83% energy-delay product reduction when compared to another ILP-based approach (CoSA) and 27% when compared to a genetic algorithm approach (GAMMA).
Relational Wide Column Store

Driven by Big Data constraints, several new database paradigms have emerged, one of them is the Wide Column Store (WCS) paradigm. Current WCS solutions are relatively mature, but they have many drawbacks. This project aims to provide a WCS alternative by combining the WCS paradigm with the Relational DataBase Management System (RDBMS) architecture, which is called: the Relational Wide Column Store paradigm. Early results show a 31 % performance increase with read queries and relatively solid writing performances. This early-stage paper is encouraging and demonstrates that there is still room for improvements with RDBMS systems.
Performance Evaluation of Tsunami Evacuation Route Planning on Multiple Annealing Machines

This paper focuses on the performance evaluation of annealing machines based on quantum annealing and simulated annealing, i.e., D-wave advantage, Fixstars Amplify Annealing Engine, D-wave Neal, and Vector Annealing, by solving a combinatorial optimization problem, tsunami evacuation route planning. First, the problem is modeled into mathematical formulas and converted into the Quadratic Unconstrained Binary Optimization (QUBO) form as an input, and will be executed on the four annealing machines. Then, the relationship between constraint weight in the objective function and the constraint compliance conditions is also discussed. Finally, the evaluation and discussion are conducted from three aspects, the maximum number of evacuees, Time to Solution (TTS), and the solutions quality.
Exploring the Efficacy of Deep Learning Models for Multiclass Toxic Comment Classification in Social Media Using Natural Language Processing

Our thesis focuses on developing a deep learning-based toxic comment classifier. The classifier will be used to find and report potentially unpleasant or hazardous information on websites like social media, discussion boards, and comment sections. We want to encourage a welcoming and safe online community and stop the growth of online abuse such as cyberbullying and hate speech. Long Short-Term Memory (LSTM) and Hybrid LSTM-CNN (Convolutional Neural Network and LSTM based Approach) are two algorithms that the classifier uses to categorize the comments depending on their level of toxicity, such as threats, obscenity, insults, and identity-based hatred. The classifier’s input data came from Kaggle and underwent a number of pre-processing processes, including lemmatization and normalizing the text data.