Utilizing Multi-modal Weak Signals to Improve User Stance Inference in Social Media

Social media has become an integral component of the daily life. There are millions of various types of content being released into social networks daily. This allows for an interesting view into a users\u27 view on everyday life. Exploring the opinions of users in social media networks has always been an interesting subject for the Natural Language Processing researchers. Knowing the social opinions of a mass will allow anyone to make informed policy or marketing related decisions. This is exactly why it is desirable to find comprehensive social opinions. The nature of social media is complex and therefore obtaining the social opinion becomes a challenging task. Because of how diverse and complex social media networks are, they typically resonate with the actual social connections but in a digital platform. Similar to how users make friends and companions in the real world, the digital platforms enable users to mimic similar social connections. This work mainly looks at how to obtain a comprehensive social opinion out of social media network. Typical social opinion quantifiers will look at text contributions made by users to find the opinions. Currently, it is challenging because the majority of users on social media will be consuming content rather than expressing their opinions out into the world. This makes natural language processing based methods impractical due to not having linguistic features. In our work we look to improve a method named stance inference which can utilize multi-domain features to extract the social opinion. We also introduce a method which can expose users opinions even though they do not have on-topical content. We also note how by introducing weak supervision to an unsupervised task of stance inference we can improve the performance. The weak supervision we bring into the pipeline is through hashtags. We show how hashtags are contextual indicators added by humans which will be much likelier to be related than a topic model. Lastly we introduce disentanglement methods for chronological social media networks which allows one to utilize the methods we introduce above to be applied in these type of platforms

Fabrication and Characterization of Novel AgNPs Functionalized with Chlorothymol (C@AgNPs)

In this study, novel silver nanoparticles (AgNPs) were successfully synthesized and functionalized with an antibacterial agent, namely chlorothymol (denoted C@AgNPs). The resulting colloid (C@AgNPs) was purified by two comparative methods: ultrafiltration and ultracentrifugation. Ultrafiltration proved to be more efficient in purifying and size selecting (10 kD filter) and concentrating the C@AgNPs than ultracentrifugation. The physicochemical properties of the filtered C@AgNPs were then characterized by UV-Vis absorption spectroscopy, inductively coupled plasma optical emission spectroscopy (ICP-OES), Raman spectroscopy, Cytoviva hyperspectral imaging, and Scanning electron microscopy. These measurements confirmed the functionalization of the core AgNPs with chlorothymol and suggest the proposed mechanism of C@AgNP formation through a coordinate covalent bond between the oxygen atom of chlorothymol and the Ag atoms at the nano surface

eYield: Testing the Adoption and Outcomes of a Novel Online Growth and Yield Model

eYield is an online growth and yield platform designed to assist landowners and land managers in making the best choices for their properties. eYield aims to strike a balance between the necessary data to run growth and yield models while remaining accessible to its landowner userbase. The results of this paper point to an encouraging amount of user interest in computer-aided forestry tools, specifically in growth and yield models like eYield. The pre- and post-surveys of eYield from respondents suggest that there is a yearning for tools like eYield and that eYield is reasonably representative of the real world. These results point to the continuing march of technology through all sectors and the need for technological integration through most facets of life. Survey participants indicate that they are open and willing to accept new technology to address questions that are environmentally complex and highly variable associated with future forest growth

WISE: Automated support for software project management and measurement

One important aspect of software development and IV&V is measurement. Unless a software development effort is measured in some way, it is difficult to judge the effectiveness of current efforts and predict future performances. Collection of metrics and adherence to a process are difficult tasks in a software project. Change activity is a powerful indicator of project status. Automated systems that can handle change requests, issues, and other process documents provide an excellent platform for tracking the status of the project. A World Wide Web based architecture is developed for (a) making metrics collection an implicit part of the software process, (b) providing metric analysis dynamically, (c) supporting automated tools that can complement current practices of in-process improvement, and (d) overcoming geographical barrier. An operational system (WISE) instantiates this architecture allowing for the improvement of software process in a realistic environment. The tool tracks issues in software development process, provides informal communication between the users with different roles, supports to-do lists (TDL), and helps in software process improvement. WISE minimizes the time devoted to metrics collection, analysis, and captures software change data. Automated tools like WISE focus on understanding and managing the software process. The goal is improvement through measurement

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

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https://egrove.olemiss.edu/aicpa_indev/2065/thumbnail.jp