Reviewerly/exHarmony huggingface dataset:
Cybercrime and Insider Threats in Healthcare Organizations

Cybercrime costs an estimated $575 billion per year and healthcare continues to be the most targeted sector in the world. News headlines are rife with data leaks and data breach incidents. These breaches lead to the theft of personal, financial, and health information from users who are often only notified of the breach well after it occurred, and the damage has already been done. Insider threats can be loosely categorized as malicious or negligent, based on their intent (or lack thereof). This case study focuses on one such malicious incident where a ring of hospital employees was indicted for allegedly selling patient credit card and medical information online. The authors provide practitioners with a deeper understanding of how PII is misused, motivations for its theft, and recommendations to avoid such incidents in their own organizations.
Cybersecurity Risk

Technology as the single solution to risk is outdated and enables cybersecurity incidents. The no-involvement integrated focus on business and IT elements continues. Cyberattacks cost hundreds of billions of dollars. Consistent hacks and ransomware attacks help with the comprehension that cybersecurity risks should be managed in part under strategic management and organizational external business aspects, but apply risk tables and strategy evaluation processes. Organizations must focus more on managing risk. Project managers must be empowered with appropriate knowledge and skills, and receive consistent education as opposed to ineffective once-a-year education. Solutions must be crafted that include evolving cyberattacks. Offered is an agenda for project risk management to include cybersecurity. Described are positives that actions can provide to cybersecurity and basic project management. Intertwined are tools to use to assure effectiveness. The mission for this text is to catalyze research at the interface of cybersecurity, business, and technology.
Suspect Recommendation For Memory Devices

During manufacturing of memory devices, there may be various defects which may be introduced at multiple stages of manufacturing such as fabrication, production and testing. These defects such as assembly line defects, external defects, equipment or process defects, may results in the failure of that memory device and may be identified using automated or manual mass production tests. For large-scale manufacturing, manual inspection of these defects is time-inefficient and error-prone. This process of identifying probable causes of failure in embedded memory devices is known as suspect recommendation. In this paper, a machine learning approach to identify defective device lots from non-defective device lots is proposed. The proposed algorithm consolidates the predictions from three different feature importance methods such that critical features affecting the manufacturing memory devices can be identified with confidence. Moreover, class-specific feature importance scores are derived from the three feature importance methods so that critical features affecting defective and non-defective device lots can be detected and a novel way of combining them is proposed. Furthermore, we design a domain specific performance criterion, namely Feature Match Index (FMI) to compare the subject matter expert’s (SME) view with the critical features identified by the proposed solution and achieve an 88.89% match. The proposed solution has been deployed successfully in the large-scale production environment of memory devices.
Memory Footprint Optimization for Neural Network Inference in Mobile SoCs

In the last decade, deep neural networks (DNNs) have gained widespread acceptance and used heavily in mobile SoCs for performing complex tasks. Memory footprint is one of the key factor that determines the overall performance of the mobile platform while running DNN inference because DNNs use significant amount of memory for storing the weights and the intermediate feature maps computed by the hidden layers. Since the available DRAM in mobile SoCs is limited, memory footprint optimization becomes a key challenge. In this paper we address the problem by proposing two strategies for optimizing the DRAM allocation. The first strategy targets to increase the utilization of the different buffers used during the inference and uses best fit iteratively to come up with a better allocation. In the second strategy we explore a variant of the best-fit to address the allocation of feature maps related to concat operation. Experimental result on a set of proprietary DNNs show that the proposed strategy could reduce memory footprint by 15% on an average. For DNNs of certain category, more than 35% reduction is observed.
A Unified Framework for Detecting Domain and Intent Misclassifications in Large-Scale Dialogue Systems

The popularity of large-scale dialogue systems such as Google Assistant, Siri, and Bixby has grown considerably in recent years. However, these systems often make errors in domain and intent classification, resulting in incorrect responses and a suboptimal user experience. In this paper, we propose a solution that utilizes unsupervised data augmentation for consistency training (UDACT) to analyze virtual assistant’s user logs and identify instances where the system has misclassified the user’s intended domain or intent. We also analyze the characteristics of various domains, considering factors such as noise, simplicity, diversity, complexity, density and sparsity, and apply augmentation techniques accordingly to improve the system’s performance. Our solution also generates a report that highlights instances of misclassification and provides insights for the types of errors that the system is making. We conduct empirical analysis by comparing the suitability & performance with various state-of-the-art methods. Currently, our solution has achieved an accuracy of 92.4% on our In-House dataset that comprises of 30 domains and more than 300 intents and an accuracy of 98.2% on standard SNIPS-MultiWOZ2.2 dataset that comprises of 14 domains and 37 intents. This paper demonstrates the effectiveness of our solution on virtual assistant’s multi-domain user logs, showing its ability to identify misclassifications in large scale dialogue systems.
An Efficient Reversible Universal Shift Register with Minimal Quantum Cost

This paper introduces an innovative and highly efficient 4-bit Reversible Universal Shift Register (RUSR) design that employs reversible logic gates. The RUSR is capable of performing shifting operations, including left shift, right shift, and parallel load, akin to conventional CMOS-designed universal shift registers. In this study, we focus on the creation of a 4-Bit RUSR using a combination of a reversible Master-Slave D-Flipflop (RMSDFF) and a reversible 4X1 Multiplexer (RMUX41). The design of the RMSDFF and RMUX41 incorporates the use of Feynman (FG), Fredkin (F), and Modified Fredkin (MF) gates. Notably, the Modified Fredkin gate plays a pivotal role in optimizing the proposed architecture. The efficacy of our proposed architecture is demonstrated through key metrics such as garbage outputs (GO) and quantum cost (QC). Our architecture excels in terms of reduced power consumption, minimal quantum cost, and a decreased number of reversible logic gates and garbage outputs. As a result, this design holds significant promise for applications in quantum computing, Low-power CMOS design, and various other domains. The implementation of the RUSR in this paper relies on the utilization of the proposed RMUX41 and RMSDFF. These components are constructed using reversible logic gates and described in the hardware description language Verilog. The validity of our design is affirmed through rigorous verification with simulated results, employing the ModelSim Altera tool.
Synthetic Data Generation Pipeline for Private ID Cards Detection

Modern deep learning methods have achieved remarkable performance on many real-world problems. However, the availability of large real data is a prerequisite for these methods to work. For applications like private ID card detection, it is difficult to gather real-world data because of privacy reasons and the sensitive nature of the data. Therefore, we propose a pipeline for synthetic card generations with random details followed by different methodologies for inserting them in random images such that they present different real-world scenarios such as cards held in the hand, placed on flat surfaces, etc. To simulate the environmental conditions affecting the card appearance in the image, we also propose to apply different image transformations to cards like rotation, blurring, etc. Finally, we also show the quality of the generated dataset by training the lightweight EfficientDet-Lite1 model with it and then testing it with images containing the real ID cards.
Assistive Device Based on Machine Learning Approach for Communication of Visually Challenged and Muted Community

It is a great challenge to find a means of communication for people who are suffering from visual or hearing impairment and also for those who are speechless. This research study aims to develop a device from Raspberry Pi which can communicate with the visually challenged by converting the messages to audio. The proposed device also helps the people with hearing loss by converting audios to text and display the same. It also helps the speech impaired by converting the sign language into text or audio by using image to text conversions.
Intelligent Video Surveillance: Artificial Intelligence and its Applications on Security Systems

Artificial Intelligence has found its way through applications within diverse fields such as education, engineering, academics, among others. And even though constant improvement is made upon this technology in areas like data processing and text generation based on machine learning, other areas like the security field might need a different approach to be improved upon by this technology. Smart home security could come into mind as a primary example of how people could monitor their day-to-day life and prevent different occurrences by having an AI based system that focuses on intelligent video surveillance, but there is a need to answer the questions regarding how well does AI adapt to this field, what are its different applications and what are the challenges presented within said applications. For this matter, a systematic Literature Review was conducted in order to analyze the current and previous knowledge regarding this subject, in order to determine the different appliances that AI can have on video surveillance and security. The results of this study show the versatility of AI assisted security, in areas such as video surveillance, monitoring systems and even facial recognition. This study also concludes that, even though there are some impressive advancements regarding these applications, further development could be recommended to explore a greater number of possibilities for AI assisted security.
Object Detection Using a YOLOv4 Model in Video Surveillance

One of the most significant issues in surveillance videos is the detection of vehicles and objects at real time. Adverse weather conditions and lighting (illumination) variations are a few of the challenges faced while detecting objects through surveillance videos. In this work, a You Only Look Once v4 model, combined with a Spatial Pyramid Pooling and a Path Aggregation Network block and a contrast enhancement framework for effective detection of objects with higher accuracy is proposed. The video is converted to frames and is completely enhanced throughout using the Exposure Fusion Network. The brightness of the frames is equalized such that every point in the frame is clearly visible to the naked eye. Finally, the objects in the frames are detected using a YOLOv4 model, which is coupled with a SPP layer and a PAN network. The model generates an accuracy of 92.6%.
An Optimal Attribute-Focused Privacy-Preserving Anonymization Scheme for Healthcare Data in Cloud Systems

Modern healthcare systems rely on the accumulation and analysis of enormous datasets about individual patients, which is where innovative computer technologies such as cloud-based systems come into play. When it comes to protecting the privacy and security of sensitive data stored in cloud services, users face a significant challenge. Protecting people's privacy and securing their personal information is essential. Utilizing the K-means clustering technique, this study presents a novel method for identifying the optimal attribute-focused anonymization algorithm. Here, the Gaussian distribution is applied to eradicate outliers, thereby dramatically improving the quality of anonymized data. Extensive experiments demonstrate that the proposed method reduces information loss and execution time by a factor of 2 and 4, respectively, when compared to current best practices. In addition, its scalability surpasses that of alternative methods for safeguarding the privacy of cloud-stored healthcare information.
Facial Movement and Voice Recognition Based Mouse Cursor Control

Some individuals with illnesses face difficulties in using laptops. However, there is a technique available that can be easily accessed by physically challenged individuals. Instead of relying on traditional hardware and software, this method utilizes the human eye and voice as alternative means of operating a laptop. To achieve this, a network protocol camera captures an image of the eye, enabling cursor movement. The proposed technique involves eye tracking using the Eye Aspect Ratio (EAR) and a simple circuitry. Additionally, speech recognition is employed to convert speech signals into commands, facilitating natural voice communication. This system proves highly advantageous in addressing the challenges faced by individuals with disabilities.
Role of Cyber Physical System in Insider Trading

Illegal insider trading detection and enforcement of insider trading laws is a challenging task that attracts the attention of regulators due to its serious harm to confidence of investors and sustainable development of financial markets. Due to the finite availability of resources, it's still very difficult for the regulators to book the offenders into the act of doing it. The paper focuses on the significance of having advanced technology in this area while it discusses the recent studies on insider trading detection. The author explicitly discusses the challenges faced in detection of financial crime by applying machine learning and data mining techniques. The paper also examines several market surveillance strategies that are used to protect financial markets. The primary goal of the study is to provide a thorough review of the potential effects of data mining and machine learning on market data and how to use them to ensure the integrity of financial markets. Furthermore, the author identifies some of the key challenges that currently exist and proposes potential solutions, such as integrating new tools and techniques with existing mechanisms to detect insider trading. Finally, the paper concludes with some recommendations that could be implemented in practice.
Beyond the Basics: A Study of Advanced Techniques for Detecting and Preventing SQL Injection Attacks

Attacks using SQL (Structured Query Language) injection pose a serious risk to the confidentiality and integrity of databases and web applications. Although tried-and-true methods like input validation and parameterized queries still work well against adversaries, they do so in more creative ways. Researchers and professionals have therefore created cutting-edge techniques to improve detection and mitigation capabilities. Advanced methodologies are examined in this study, including runtime monitoring, behavior-based detection, static and dynamic taint analysis, and anomaly detection based on machine learning. It examines the use of secure coding techniques and web application firewalls as supplementary solutions. The most recent research on SQL injection attacks is reviewed in this research study, along with examples of some of the most recent and effective models, methodologies, and strategies for mitigating, detecting, and avoiding this kind of risky attack. Organizations may strengthen their defenses against SQLi attacks, protect sensitive data, and guarantee the stability of their online applications by utilizing these cutting-edge techniques.
A Novel Mobile App Based College Event Management System

The school has many events and celebrations throughout the year. All work for events and festivals is done by hand. The “Online Event Management” project is an Android application that allows you to remotely create, cancel, edit and manage online events. It allows school administrators to add, remove or edit events and allows students to view all events available in each school. It allows students to record and view all activities in this app and you can pay through the app. This program reduces the need for information and saves time in running a smooth event. Anyone can follow any student and keep track of many things through this app. The main purpose of this app is to keep all college students aware of everything going on at the university, further reducing the work of books and digitizing everything.
Blockchain Technology for Agricultural Supply Chain Management to Enhance Farming

Blockchain (BC) technology provides secure record storage and digital transactions in terms of security, transparency and efficiency. BC verifies the authenticity of the products and maintains records from the origin to the entire supply chain. Agricultural blockchain supply chain management (BCSCM) facilitates the automated delivery of agricultural products to the farmers. The agricultural supply chain involves too many intermediaries and the lack of transactions leads to too many manual interventions of handling the commodities in the supply chain. BC technology is one such innovation to overcome this problem and it maintains a record of immutable transactions to automate the agricultural supply chain. The proposed study explores the mechanisms and methods for agricultural blockchain supply chain to develop a secure and reliable supply chain management (SCM).
A Generic Approach on Current Technologies in Metaverse

The “metaverse,” the newest buzzword, has drawn a lot of interest from both industry and academia. Through the seamless blending of the actual and virtual worlds known as the metaverse, avatars are now able to take part in a variety of activities, such as commerce, social networking, entertainment, and production. Thus, it is possible to develop an interesting digital setting and change the real world for the good by investigating the metaverse. This research study explores the metaverse by discussing about the possible ways in which blockchain technology and machine learning interact with it by considering the cutting-edge research on its various elements, digital currencies, ML applications in the cybernetic world, and blockchain-enabled devices. Both academics and business will need to work together for additional manipulation and interdisciplinary research on the convergence of ML and Blockchain on the way to metaverse.
A CNN Based Attendance Management System Using Face Recognition

In the present era of digital advancements, face recognition technology is utilized across various industries. It serves as a popular biometric solution for ensuring detection and recognition. Despite having relatively lower accuracy compared to other biometric methods such as iris or fingerprint recognition, face recognition remains widely adopted due to its non-intrusive and touchless nature. Moreover, it offers a valuable application in streamlining attendance management within educational institutions, colleges, and offices by eliminating the time-consuming manual processes that often result in proxy attendance. The solution is designed to develop a class attendance system that makes use of facial recognition technologies. The system consists of four main steps: creating a database, using face detection technology to identify individuals in the vicinity, comparing the facial features of the detected individuals with those in the database, and updating attendance records based on the recognition results. This will allow the system to keep track of which individuals were present at the given location and time. Images of the class are taken to establish the database. In this paper we have used CNN encodings to measure the face images and we used SVM classifier also. The system is designed to detect and recognize faces in real-time from classroom's live streaming video. At the end of the session, the attendance information is automatically sent via mail to the respective faculty member.
A Proportionate Study of Cybersecurity Audit Models to Provide Robust Security

Nowadays Private businesses and governmental organizations are frequently affected with highly sophisticated cyberthreats and cyberattacks. As a general caution, business organizations must create and grow their cybersecurity awareness in order to combat fraudsters. In order to handle cyber threats, and hazards, there is a need to provide audit as well technological support to content in competitive cyber security environment. But the conventional cyber security model has its own security architecture within itself in the name of information security. It was highly effective in the past; this work is attempting to converge into cybersecurity audits. In order to create the most effective and cogent synthesis, the genuine scope, its strength and weakness of cyber security techniques are to be analyzed to forecast the futuristic cyber security model. Robust Cyber Security Model (RCSM). For all corporate functional domains, RCSM model will provide assistance in assesses and validates audit, preventative, forensic, and detective controlling mechanism to improve security. Based on the above study RCSM will promote the organizations and business with highly robust with suitable audit techniques.
A Deep Learning-Based Framework for Opinion Mining of Patient Reviews on Web Forums

The number of users and daily tweets on Twitter is among the highest of all social media outlets. New forms of communication like social media greatly contribute to the rapid treatment of panic amid global health crises like pandemics. The purpose of this research is to examine and evaluate the tone of the Tweets that were posted during the MONKEYPOX disease, for that purpose Long Short-Term Memory (LSTM), CNN, and a hybrid model of CNN-LSTM is used, in this the negative and positive sentiment classes of tweets are found. The findings indicate that there is only a tiny difference between positive and negative tweets, which highlights the necessity for attention to be paid to increase awareness and, as a consequence, decrease negativity. In addition, the LSTM was able to accurately predict the sentiment classes (Negative and Positive), with an accuracy rate of 72.15%. CNN with an accuracy of 82% and hybrid model CNN-LSTM with an accuracy of 91% The great accuracy leads one to the conclusion that Hybrid model CNN-LSTM is capable of adapting to new situations and predicting the emotion of a text.
Disaster Tweet Classification Using Machine Learning Model

Social media is playing a vital role in communication, forums like Twitter and Instagram are where people socialize. Recently social media has played a crucial role during disasters. People could post messages and get the required help. If the messages posted can be classified into Normal and disaster posts it would be very helpful. In this work, the aim is to classify the Tweets posted on Twitter into Normal and disaster Tweets. The Multinomial Naive Bayesian, Passive Aggressive (PA) Classifier, and Support Vector Machine(SVM) were the models that were built and tested using Twitter data. Evaluation of these models with various measures and were able to conclude that the Passive Aggressive classifier outperforms the other two models.