How Do You Picture It? Suggested Guidelines for Alt-text in Art Books

This document is the result of the 2024 Elspeth McConnell Fine Arts Internship Award undertaken by Mehrnoosh Alborzi, with Concordia University Press. Alborzi spent several months exploring, researching, and engaging in writing alt-text specifically for academic and art-historical books. The internship concluded with a presentation where Alborzi shared these reflections in conversation with CUP's Ryan Van Huijstee in October 2024 at Concordia University’s 4th Space during Open Access Week. A recording of this presentation is available on the 4th Space YouTube channel. The following set of suggested guidelines and practices is a result and summary of the internship and presentation

Strategic Resource Planning in Gold Mining: Optimizing Supply Chain Management with Neural Networks-Based Gold Price Forecasting

Strategic resource planning is crucial for optimizing supply chain management and ensuring efficient operations. This study aims to enhance strategic planning in gold mines by leveraging advanced gold price forecasting models. By predicting future gold prices accurately, mining companies can better plan their extraction, processing, and distribution activities, thereby improving overall supply chain efficiency. We employed various advanced forecasting models, including Unidirectional and Bidirectional Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN), to predict gold prices and analysed how these predictions can inform strategic decisions in the gold mining supply chain. Our approach includes evaluating the performance of these models using metrics such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute deviation (MAD). Results show that Artificial Neural Network (ANN) performed best, with the lowest (0.3514), RMSE (0.5928), and MAPE (0.34%), while Bidirectional Gated Recurrent Unit (GRU) was the poorest performer with an of 88.5474 and MAPE of 6.94%. The feature selection process, facilitated by Recursive Feature Elimination (RFE), identified critical predictors such as 'High,' 'Low,' 'Volume,' and various external market factors. Optimizing model parameters through techniques like grid search and cross-validation further improved model accuracy. Additionally, advanced forecasting models, particularly Artificial Neural Network (ANN) and Convolutional Neural Network (CNN), proved highly effective in refining gold mining companies' resource planning and supply chain management strategies, providing critical managerial implications for navigating the dynamic and volatile gold market

A middle to late Holocene paleo-environmental study of L’Anse aux Meadows National Historic Site, Newfoundland, Canada

Paleoecological reconstructions offer insight into environmental and climatic conditions of the past, allowing us to understand how changing climate conditions have shaped Canada’s landscapes over millennia. While instrumental data only reaches back a few centuries, paleo-reconstructions allow us to understand past environmental variability over much longer periods of time as well as predict future changes. They also contextualize archaeological sites within the broader context of ecological conditions of the period. Using high-resolution, multi-proxy analysis, this study reconstructs the past 6,000 years of vegetation and fire history around the UNESCO World Heritage archaeological site of L’Anse aux Meadows and provides new understanding into long-term ecological changes and their relationship with regional climate variations and human activity. While many previous analyses have studied samples from directly within the archaeological remains, my research offers the first regional combined charcoal and pollen record, with a well-dated chronology based on a larger suite of AMS radiocarbon dates than previously used, for the area directly downwind of the site. First, I situate this site within the human history of the North Atlantic as well as review previous paleo-ecological work done within the region, including the Northern Peninsula of Newfoundland and that of southern Labrador. Then, I present a reconstruction of pollen, macrocharcoal, and loss-on-ignition analysis based on a 2 m peat core located 300 m east of the archaeological site. The core sequence was dated using 14C dating methods with the bottommost core-section dating to ~4055 BCE (6000 cal BP). The analysis shows transitions from fen- to bog-like environments, punctuated by fire events and shifts in vegetation composition. Early fen conditions (4055–1740 BCE) transitioned to a more bog-like environment, following a significant fire disturbance. The early fen conditions were followed by a prolonged period of low peat accumulation (1095 BCE–50 CE), potentially due drier conditions. Fire frequency increased during the first millennium CE, peaking during the Medieval Climate Anomaly (920–1280 CE), suggesting warmer conditions preceding the onset of the Little Ice Age. The long-term decline in pollen influx aligns with regional cooling trends documented in the other paleo-ecological studies in the North Atlantic, driven by decreasing solar radiation and sea-surface temperature changes. This research contributes to our understanding of Holocene environmental dynamics in northern Newfoundland, situating L’Anse aux Meadows within a broader climatic and ecological context, and explores potential anthropogenic impacts on fire regimes and landscape changes

Digital Interactivity in Space AI-Augmented Eco-Didactic Experience in Public Realm

The ecological crisis is advancing rapidly, and it is crucial to spread awareness, create dialogues, and educate about environment and sustainability to encourage behavioral change and eco-action. The public realm, characterized by its high human circulation, serves as an ideal space to initiate, and foster these environmental conversations. Public artworks stand out as one of the most prevalent and impactful approaches for engaging with the society. In the environmental context, eco-art facilitates sharing of eco-messages, fosters community dialogues around critical issues and solutions, and motivates individuals to take meaningful action. Although public eco-art installations significantly engage audiences, the potential to amplify this impact through Artificial Intelligence (AI) augmented interactive gamification within a didactic framework remains largely unexplored. AI technologies are rapidly infiltrating both professional and personal spheres, significantly influencing consumer behavior through the advertising industry shaping visions for future urban landscapes, as evidenced in conceptual designs for smart cities. However, the high energy consumption associated with AI raises environmental concerns, even as its adoption in daily life becomes inevitable. This research explores how AI technologies can enhance environmental learning by developing an AI-augmented, eco-didactic interactive game. The goal is to support the dissemination of the United Nations’ (UN) Sustainable Development Goals (SDGs) related to the built environment. By integrating AI, interactivity, and gamification with an artistic approach, the study seeks to transform urban spaces into creative and interactive hubs. These engaging experiences aim to spark curiosity, inspire enthusiasm, and encourage proactive eco-friendly actions within the public realm. This research advances studies in creative AI, autonomous AI, and social AI focusing on their integration within physical environments, eco-didactic spaces and public domains in design, fine arts, architecture, urban studies, and environmental fields. It contributes directly to development of engaging environmental and educational public space experiences both in Canada and globally. The findings promise to be broadly applicable, offering a pioneering framework for interactive eco-didactic design practices and providing unique insights for future advancements. By illustrating the benefits of AI in fostering ecological awareness and sustainable engagement, this research supports the dissemination of SDGs, particularly in educating for sustainability within urban environments

Design of Multi-Sine Watermark using Power Spectral Analysis for Replay Attack Detection

Design of multi-sine watermark using power spectral analysis for replay attack detection Sunitha George Replay attacks are a critical security concern in cyber-physical systems (CPS), where adversaries record legitimate data transmissions and maliciously retransmit them later to disrupt normal system operations. These attacks are particularly dangerous because they often replay legitimate data, making them difficult to detect using traditional intrusion detection systems. As CPS continue to integrate deeper into critical infrastructure such as power systems, industrial automation, and transportation networks, the need for better safety measures becomes increasingly urgent. One promising line of defense involves watermarking techniques, in particular, using multi-sine watermarks with switching frequencies. This thesis studies the problem of choosing the parameters of multi-sine watermarks to achieve replay attack detection with desired level of confidence. The proposed method is derived from a power spectral analysis of the output of the plant in both normal (no attack) and during attack operation. A flow control process involving a tank is utilized as an illustrative example. Through this example, the effectiveness of the proposed method is validated, showing its capability to design a watermark that can successfully detect replay attacks and thus enhance the security of the control system

Online Steiner Cover Problems in Hypergraphs

The online Steiner cover problem in hypergraphs (\treePN) is a generalization of the online Steiner tree problem in graphs. An edge-weighted hypergraph $H = (V, E, w)$ is given offline and a set of terminal vertices $R\subseteq V$ is requested sequentially online. Upon receiving each request $r_i\in R$ an algorithm for the problem must buy some edges $P_i\subseteq E$ which connect $r_i$ to the previous solution. The solution after satisfying the $i^{\text{th}}$ request is then the union over all edges bought up to that point, $F_i = \bigcup_{j = 1}^i P_j$. The goal is to minimize the total cost of the solution to connect the requests, i.e., for $|R| = n$ let $F = F_n$, then we want to minimize $\sum_{e\in F}w(e)$. The generalized \treePN (\forestPN) is a generalization of both the \treePN and the Steiner forest problem in graphs. Again, we are given an edge-weighted hypergraph $H = (V, E, w)$ offline, but instead of a set of terminals as the online portion of the input we are given a set of terminal pairs $R\subseteq V\times V$. Upon receiving the $i^{\text{th}}$ request pair $p_i\in R$ an algorithm for this second problem must buy some set of edges $P_i\subset E$ which connect the terminals from $p_i$. We define the instantaneous solution after connecting request $p_i$ as before, so $F_i = \bigcup_{j = 1}^i P_i$ and the final solution is again denoted $F = F_n$ for a request sequence of size $|R|=n$. The worst-case performance of an online algorithm is measured by the competitive ratio, which is the ratio between the cost of a solution obtained by the online algorithm to that of an optimal offline solution. Besides some simpler preliminary results, we obtain a lower bound on the competitive ratio for \treePN (which also applies to \forestPN) of $\Omega(k\log{n})$, where $k$ is the rank of the hypergraph, and a matching upper bound for the simple $Greedy$ algorithm. For \forestPN we show that the simple $Greedy$ algorithm is $O(k\log^2{n})$-competitive and provide another algorithm, called $GGSC$, which achieves a competitive ratio of $O(k\log{n})$

Art Therapy for Preverbal Trauma: Integrating the ETC for Sensory-Based Treatment

This theoretical intervention research explores the design of a sensory-based art therapy program for children aged four to five who have experienced preverbal trauma. Grounded in the sensory level of the Expressive Therapies Continuum (ETC), the proposed model addresses the unique therapeutic needs of young children whose traumatic experiences occurred before the development of language. Drawing on trauma theory, neurodevelopmental research, and sensory integration practices, the program offers a developmentally appropriate, trauma-informed framework that supports emotional regulation, nervous system healing, and relational repair through art-making. The methodology employs a theoretical intervention research design, with a focus on formative program development rather than implementation or outcome testing. This research contributes to the growing body of literature on early childhood trauma and highlights the potential of sensory-based art therapy as a vital avenue for healing preverbal trauma

Towards Efficient Device-State Integrity Verification in Smart Homes using Device-App Causality Relationships

The device-state in smart homes depends on both its physical channel (sensing and actuating in the environment) and its cyber-physical channel (interactions with apps and other devices). Ensuring device-state integrity is crucial for proper operation but can be compromised by security threats from devices, apps, and their interactions due to vulnerabilities and misconfigurations, posing risks to users. Existing works focus on either devices or apps, but none comprehensively address device-state integrity across device-app interactions. Furthermore, there exist several challenges in offering device-state integrity verification for smart homes. First, efficiently and comprehensively collecting data (that is an essential verification step) becomes more difficult as code instrumentation (used in several existing works) becomes impossible by changes in platform design and existing logging mechanisms (if any) generate humongous amount of data (including security unaware data). Second, efficiently and accurately verifying the device state integrity needs device-specific analysis to account for all interacting apps and devices. In this thesis, we tackle these challenges by presenting an efficient device-state integrity verification approach for smart homes. Specifically, our key ideas are to: model the interactions of all components in a smart home using causality relationships that affect a specific device, and verify device state based on this model. We implement our approach on SmartThings, build a new smart home dataset, and evaluate its effectiveness (e.g., 81.34% reduction in verification time and 56.49% reduction in response time)

Robust Design of a Manufacturing Network for Mass Personalization

The Fourth Industrial Revolution (Industry 4.0 or I4.0) is transforming manufacturing through the integration of cyber-physical systems, artificial intelligence, and the Internet of Things. At its core is mass personalization (MP), enabling the production of customized products, particularly in high-tech sectors such as aerospace, medical devices, and precision optics. These industries require resilient supply networks to handle low-volume, high-complexity production and uncertainties in customer demands and supplier performance. Traditional supply chain models fall short in addressing these challenges, calling for advanced optimization frameworks. This thesis explores the design of resilient and reconfigurable supply networks tailored to MP under I4.0. It makes three primary contributions. First, a strategic mixed-integer programming (MIP) model is proposed for optimizing supplier selection and order allocation, balancing design complexity with economies of scale. Second, a two-stage stochastic programming (2SP) model is developed for platform-based manufacturing networks, integrating crowdsourcing to enhance resilience by assigning primary and backup suppliers under uncertain capabilities. Third, an adjustable robust optimization (ARO) model is introduced for multi-echelon networks, addressing variability in supplier capacity and bill-of-material complexity, supported by an efficient math-heuristic algorithm. Extensive numerical experiments and sensitivity analyses validate the models’ effectiveness in mitigating risk and improving resilience. This research offers actionable insights for high-tech manufacturers aiming to build agile, cost-efficient supply networks that meet the evolving demands of mass personalization in the era of Industry 4.0

Deep Learning Approximation of Matrix Functions: From Feedforward Neural Networks to Transformers

Deep Neural Networks (DNNs) have been at the forefront of Artificial Intelligence (AI) over the last decade. Transformers, a type of DNN, have revolutionized Natural Language Processing (NLP) through models like ChatGPT, Llama and more recently, Deepseek. While transformers are used mostly in NLP tasks, their potential for advanced numerical computations remains largely unexplored. This presents opportunities in areas like surrogate modeling and raises fundamental questions about AI's mathematical capabilities. We investigate the use of transformers for approximating matrix functions, which are mappings that extend scalar functions to matrices. These functions are ubiquitous in scientific applications, from continuous-time Markov chains (matrix exponential) to stability analysis of dynamical systems (matrix sign function). Our work makes two main contributions. First, we prove theoretical bounds on the depth and width requirements for ReLU DNNs to approximate the matrix exponential. Second, we use transformers with encoded matrix data to approximate general matrix functions and compare their performance to feedforward DNNs. Through extensive numerical experiments, we demonstrate that the choice of matrix encoding scheme significantly impacts transformer performance. Our results show strong accuracy in approximating the matrix sign function, suggesting transformers' potential for advanced mathematical computations