Energy consumption in data centers remains a critical concern for sustainable IT operations. This study analyzes energy usage patterns in the Data Center at Montclair State University, a Higher Education Institution in United States. It uses real-time environmental and power consumption data collected by a sensor network created as part of the Green IT Project, a previously funded campus project. The dataset includes temperature, humidity, energy supply and demand, and system performance metrics. Data were ingested, stored, and processed using a combination of database technologies and data visualization tools. Graphs and queries were generated to identify trends, correlations, and potential inefficiencies in the system’s energy behavior. The approach enables continuous updates and dynamic interaction with the underlying datasets. Preliminary results indicate strong correlations between environmental conditions and energy demand, with visualizations revealing specific patterns of inefficiency. These findings demonstrate the potential of data-driven monitoring to inform energy optimization strategies in institutional data centers.
Recent advances in natural language processing (NLP), particularly large language models (LLMs), have motivated the automatic translation of natural language statements into formal logic without human intervention. This enables automated reasoning and facilitates debugging, finding loop invariants, and adhering to specifications in software systems. However, hallucinations-incorrect outputs generated by LLMs are challenging, particularly for logical translation tasks requiring precision. This work introduces a novel framework that inputs English sentences, converts them into logical expressions, and then translates them into Conjunctive Normal Form (CNF) for satisfiability solving. It employs classical NLP techniques with self-defined grammar, symbolic computation libraries, and a fine-tuned language model to reduce hallucinations. In the early experiments, we observed that the fine-tuned model, trained on different grammar settings, could intentionally correct the same types of hallucinations made by the original model. Thus, it provides reliable CNF generation.
Preserving privacy is an undeniable benefit to users online. However, this benefit (unfortunately) also extends to those who conduct cyber attacks and other types of malfeasance. In this work, we consider the scenario in which Privacy Preserving Technologies (PPTs) have been used to obfuscate users who are communicating online with ill intentions. We present a novel methodology that is effective at deobfuscating such sources by synthesizing measurements from key locations along protocol transaction paths. Our approach links online personas with their origin IP addresses based on a Pattern of Life (PoL) analysis, and is successful even when different PPTs are used. We show that, when monitoring in the correct places on the Internet, DNS over HTTPS (DoH) and DNS over TLS (DoT) can be deobfuscated with up to 100% accuracy, when they are the only privacy-preserving technologies used. Our evaluation used multiple simulated monitoring points and communications are sampled from an actual multiyear-long social network message board to replay actual user behavior. Our evaluation compared plain old DNS, DoH, DoT, and VPN in order to quantify their relative privacy-preserving abilities and provide recommendations for where ideal monitoring vantage points would be in the Internet to achieve the best performance. To illustrate the utility of our methodology, we created a proof-of-concept cybersecurity analyst dashboard (with backend processing infrastructure) that uses a search engine interface to allow analysts to deobfuscate sources based on observed screen names and by providing packet captures from subsets of vantage points.
In medical chatbots and other healthcare domains, government agencies and other essential industries use sentiment analysis for public safety, improving the service quality of products, and other necessary purposes. Sometimes, the data is noisy, ambiguous, and contains mixed sentiments, where different parts of the text may convey different emotions, creating a problem in adequately detecting the sentiment. We proposed a novel recurrent neural network called the Bayesian-Lipschitz RNN with an attention mechanism to address these challenges. The Bayesian approach manages noisy and ambiguous data by representing uncertainty and avoiding overfitting. At the same time, the attention mechanism focuses on the most relevant parts of the text, effectively capturing context and mixed sentiments. Then, the Lipschitz Recurrent Neural Networks (LRNN), based on hidden-to-hidden matrices, are employed and constructed using a symmetric-skew decomposition scheme. The hidden-to-hidden matrices are used to update the hidden state and this hidden state is used to predict the sentiment, mitigate the vanishing gradient and stability issues, and got accuracy with 92.45 %.
Sentiment analysis has emerged as an indispensable resource for gleaning invaluable insights from the copious amounts of textual data produced by customer reviews, social media, and feedback forms. This research proposes a novel methodology for sentiment analysis that integrates sophisticated natural language processing (NLP) strategies with cutting-edge machine learning models to enhance the precision of sentiment categorization. The proposed methodology addresses the limitations of traditional approaches by leveraging logistic regression and Term Frequency-Inverse Document Frequency (TF -IDF) feature extraction to categorize textual sentiment into positive, negative, or neutral categories. The model is trained and evaluated using the 2023 Amazon Product Dataset, specifically focusing on software reviews. The study compares the performance of the proposed model with established sentiment analysis tools like VADER and TextBlob. Additionally, the research highlights the ethical considerations and potential biases associated with sentiment analysis in product reviews and customer feedback. The findings emphasize the importance of developing context-aware and domain-specific sentiment analysis models while addressing privacy concerns and establishing ethical guidelines for responsible implementation. This study contributes to the growing field of sentiment analysis by presenting a robust and accurate methodology that can be applied across various industries to gain a deeper understanding of customer opinions and inform decision-making processes.
