Violeta Holmes

Bio: Violeta Holmes is an academic researcher from University of Huddersfield. The author has contributed to research in topics: Photovoltaic system & Fault detection and isolation. The author has an hindex of 18, co-authored 71 publications receiving 1186 citations. Previous affiliations of Violeta Holmes include Durham University.

IMTeract Tool for Monitoring and Profiling HPC Systems and Applications

Abstract: Energy usage of computing equipment is an important consideration and energy inefficiency of computer systems is identified as the single biggest obstacle to advances in computing. Research into low-energy computing products ranges from operating system codes, applications and energy-aware schedulers to cooling systems for data centres. To monitor energy consumption in data and HPC centres it is necessary to develop tools for measuring the energy usage of computer equipment and applications. We have developed power measuring apparatus and a tool, IMTeract, for measuring energy consumption of HPC applications. IMTeract was used for energy usage profiling of HPC clusters running FLUENT and DL-POLY software and a GPU cluster running different implementations of an FFT algorithm. Our experimental results are encouraging and suggest that the IMTeract tool can be used to measure the CPU, Memory, Disk I/O and Network I/O for an application or a process and report on the energy used.

Multi-Agent System Development in the Java Agent Development Framework (JADE)

Abstract: This poster presents a tool for agent-mediated information exchange between users/children while chatting online. The Internet plays a significant role in the lives of children today by opening up a whole new world. It provides excellent educational opportunities, access to a huge range of information and can be fun. However, it also plays a role in the abuse of children in a variety of ways. We are aware of the potential for paedophiles to misuse modern technology to abuse children’s trust by attempting to contact them through chat rooms. Hence, there is a need to automate the process of monitoring information exchange when children chat on-line.

New Avenues for Automated Railway Safety Information Processing in Enterprise Architecture: An NLP Approach

Abstract: Enterprise Architecture (EA) is crucial in any organisation as it defines the basic building blocks of a business. It is typically presented as a set of documents that help all departments understand the business model. In EA, safety documents are used to manage and understand safety risks. A novel similarity system for railway safety document processing is presented in this work. It measures the feasibility of automated updating of EA models with the Rule Book by verifying whether Rail Safety and Standards Board (RSSB’s) Rule Book clauses are present and complete in existing EA models. Additionally, a Natural Language Processing (NLP) based search feature was developed to drill through the database to find similar existing rules, principles, and clauses based on semantic similarity. The result will display the most similar clauses and rules with similarity scores and document names. In this study, different pre-trained Electra Small, DistilBERT (Distillation Bidirectional Encoder Representations from Transformers) Base and BERT (Bidirectional Encoder Representations from Transformers) Base were used to embed text. Additionally, the similarity between document rules was measured by cosine similarity metrics. With conclusive evidence, our findings show that BERT Base exceeds the other embedding methods in the semantic comparison of documents.

Deep Learning Approaches for Spectrum Sensing in Cognitive Radio Networks

Abstract: The number of network devices continues to rise as we advance towards 6G communication systems. A new range of frequencies is allocated while the earlier resources remain underutilized. Cognitive Radio (CR) enables the dynamic spectrum management of frequencies while detecting the unoccupied bands with the aid of spectrum sensing. By adopting Deep Learning (DL) for spectrum sensing, the performance of the 6G networks can be made more robust. This paper presents a survey of several DL algorithms such as, Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks and combination of CNNs and LSTMs that have been applied for performing spectrum sensing. The application of DL to spectrum sensing is then demonstrated by considering it as a binary classification problem. An MLP with its hyperparameters optimized by using Grid Search algorithm is proposed to classify a dataset consisting of RadioML 2018.01A and noise samples with high accuracy.

Flow The Psychology Of Optimal Experience

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