ASI: Accuracy-Stability Index for Evaluating Deep Learning Models

In the context of deep learning research, where model introductions continually occur, the need for effective and efficient evaluation remains paramount. Existing methods often emphasize accuracy metrics, overlooking stability. To address this, the paper introduces the Accuracy-Stability Index (ASI), a quantitative measure incorporating both accuracy and stability for assessing deep learning models. Experimental results demonstrate the application of ASI, and a 3D surface model is presented for visualizing ASI, mean accuracy, and coefficient of variation. This paper addresses the important issue of quantitative benchmarking metrics for deep learning models, providing a new approach for accurately evaluating accuracy and stability of deep learning models. The paper concludes with discussions on potential weaknesses and outlines future research directions.Comment: 6 pages, 3 figure

Automating Systematic Literature Reviews with Natural Language Processing and Text Mining: a Systematic Literature Review

Objectives: An SLR is presented focusing on text mining based automation of SLR creation. The present review identifies the objectives of the automation studies and the aspects of those steps that were automated. In so doing, the various ML techniques used, challenges, limitations and scope of further research are explained. Methods: Accessible published literature studies that primarily focus on automation of study selection, study quality assessment, data extraction and data synthesis portions of SLR. Twenty-nine studies were analyzed. Results: This review identifies the objectives of the automation studies, steps within the study selection, study quality assessment, data extraction and data synthesis portions that were automated, the various ML techniques used, challenges, limitations and scope of further research. Discussion: We describe uses of NLP/TM techniques to support increased automation of systematic literature reviews. This area has attracted increase attention in the last decade due to significant gaps in the applicability of TM to automate steps in the SLR process. There are significant gaps in the application of TM and related automation techniques in the areas of data extraction, monitoring, quality assessment and data synthesis. There is thus a need for continued progress in this area, and this is expected to ultimately significantly facilitate the construction of systematic literature reviews

Recent, rapid advancement in visual question answering architecture: a review

Understanding visual question answering is going to be crucial for numerous human activities. However, it presents major challenges at the heart of the artificial intelligence endeavor. This paper presents an update on the rapid advancements in visual question answering using images that have occurred in the last couple of years. Tremendous growth in research on improving visual question answering system architecture has been published recently, showing the importance of multimodal architectures. Several points on the benefits of visual question answering are mentioned in the review paper by Manmadhan et al. (2020), on which the present article builds, including subsequent updates in the field.Comment: 11 page

Moore’s Law and Space Exploration: New Insights and Next Steps

Understanding how technology changes over time is important for industry, science, and government policy. Empirical examination of the capability of technologies across various domains reveals that they often progress at an exponential rate. In addition, mathematical models of technological development have proven successful in deepening our understanding. One area that has not been shown to demonstrate exponential trends, until recently, has been space travel. This paper will present plots illustrating trends in the mean lifespan of satellites whose lifespans ended in a given year. Our study identifies both Wright’s law and Moore’s law regressions. For the Moore’s law regression, we found a doubling time of approximately 15 years. For Wright’s law we can see an approximate doubling of lifespan with every doubling of accumulated launches. We conclude by presenting a conundrum generated by the use of Moore’s law that is the subject of ongoing research

Travel to extraterrestrial bodies over time: some exploratory analyses of mission data

This paper discusses data pertaining to space missions to astronomical bodies beyond earth. The analyses provide summarizing facts and graphs obtained by mining data about (1) missions launched by all countries that go to the moon and planets, and (2) Earth satellites obtained from a Union of Concerned Scientists (UCS) dataset and lists of publically available satellite data

Computer Model for Predicting AIDS Among Intravenous Drug Users

Intravenous drug abuse (IVDA) is an important cause of HIV transmission. Computer simulation is one way to understand and predict the spread of HIV infection among IVDAs. We design and simulate HIV infection among IVDAs and the impact of AIDS on this community, and thereby predict future IVDA population, HIV levels, AIDS levels, and AIDS deaths in this group. The HIV to AIDS, and AIDS to Death latencies are described by probability density functions (PDFs) in this model. Factors such as the recruit, quit, and normal death rate of IVDAs, are considered, as well as the infection and removal rates for HIV and AIDS. All these PDFs and rates can be accessed by the user interactively. The impacts of these factors on the IVDA, HIV, and AIDS populations are demonstrated and compared. Discussion of the factors impacting the infection rate provides medical policy makers with useful information

Automatic extraction of biomolecular interactions: an empirical approach

Background We describe a method for extracting data about how biomolecule pairs interact from texts. This method relies on empirically determined characteristics of sentences. The characteristics are efficient to compute, making this approach to extraction of biomolecular interactions scalable. The results of such interaction mining can support interaction network annotation, question answering, database construction, and other applications. Results We constructed a software system to search MEDLINE for sentences likely to describe interactions between given biomolecules. The system extracts a list of the interaction-indicating terms appearing in those sentences, then ranks those terms based on their likelihood of correctly characterizing how the biomolecules interact. The ranking process uses a tf-idf (term frequency-inverse document frequency) based technique using empirically derived knowledge about sentences, and was applied to the MEDLINE literature collection. Software was developed as part of the MetNet toolkit (http://www.metnetdb.org). Conclusions Specific, efficiently computable characteristics of sentences about biomolecular interactions were analyzed to better understand how to use these characteristics to extract how biomolecules interact. The text empirics method that was investigated, though arising from a classical tradition, has yet to be fully explored for the task of extracting biomolecular interactions from the literature. The conclusions we reach about the sentence characteristics investigated in this work, as well as the technique itself, could be used by other systems to provide evidence about putative interactions, thus supporting efforts to maximize the ability of hybrid systems to support such tasks as annotating and constructing interaction networks