Reviewerly/exHarmony huggingface dataset:
On Popularity Bias of Multimodal-aware Recommender Systems: A Modalities-driven Analysis

Multimodal-aware recommender systems (MRSs) exploit multimodal content (e.g., product images or descriptions) as items' side information to improve recommendation accuracy. While most of such methods rely on factorization models (e.g., MFBPR) as base architecture, it has been shown that MFBPR may be affected by popularity bias, meaning that it inherently tends to boost the recommendation of popular (i.e., short-head) items at the detriment of niche (i.e., long-tail) items from the catalog. Motivated by this assumption, in this work, we provide one of the first analyses on how multimodality in recommendation could further amplify popularity bias. Concretely, we evaluate the performance of four state-of-the-art MRSs algorithms (i.e., VBPR, MMGCN, GRCN, LATTICE) on three datasets from Amazon by assessing, along with recommendation accuracy metrics, performance measures accounting for the diversity of recommended items and the portion of retrieved niche items. To better investigate this aspect, we decide to study the separate influence of each modality (i.e., visual and textual) on popularity bias in different evaluation dimensions. Results, which demonstrate how the single modality may augment the negative effect of popularity bias, shed light on the importance to provide a more rigorous analysis of the performance of such models.
A Cross-View Matching Method Based on Dense Partition Strategy for UAV Geolocalization

\beginabstract This paper reports our solution for ACM Multimedia 2023 cross-view geo-localization challenge~\citezheng2023UVA, which aims to solve real-world geo-localization task with extremely large satellite-view gallery distractors. Our solution is built on the basis of SwinV2~\citeliu2022swin and LPN~\citewang2021each. Concretely, we adopt the SwinV2-B~\citeliu2022swin, the current mainstream transformer-based feature extractor, as the backbone of our model. Inspired by the feature partition strategy of LPN~\citewang2021each, we design a more efficient partition strategy named dense partition strategy. It segments and combines features to alleviate the problem of feature discontinuity at the boundary of different partitions. We get eight place in the geographic localization track on the official leaderboard. \endabstract
Prescription Recommendation based on Intention Retrieval Network and Multimodal Medical Indicator

Knowledge based Clinical Decision Support Systems can provide precise and interpretable results for prescription recommendation. Many existing knowledge based prescription recommendation systems take into account multi-modal historical medical events to learn from past experiences. However, such approaches treat those events as independent, static information and neglect the fact that patient history is a set of chronical events. Hence, they lack the ability to extract the dynamics of prescription intentions and cannot provide precise and interpretable results for chronical disease patients with long-term or repeating visits. To address these limitations, we propose a novel Intention Aware Conditional Generation Net (IACoGNet), which introduces an optimized copy-or-predict mechanism to learn precription intentions from multi-modal health datasets and generate drug recommendations. IACoGNet first designs a knowledge representation model that captures multi-modal patient features. Then, it proposes a novel prescription intention representation model in the multi-visit scenario and predicts the diagnostic intention. Finally, it constructs a prescription recommendation framework utilizing the above two knowledge representations. We validate IACoGNet on the public MIMIC data set, and the experimental results show that IACoGNet can achieve optimum in F1 score and average precision.
Metaverse Retrieval: Finding the Best Metaverse Environment via Language

In recent years, the metaverse has sparked an increasing interest across the globe and is projected to reach a market size of more than \1000B by 2030. This is due to its many potential applications in highly heterogeneous fields, such as entertainment and multimedia consumption, training, and industry. This new technology raises many research challenges since, as opposed to the more traditional scene understanding, metaverse scenarios contain additional multimedia content, such as movies in virtual cinemas and operas in digital theaters, which greatly influence the relevance of the metaverse to a user query. For instance, if a user is looking for Impressionist exhibitions in a virtual museum, only the museums that showcase exhibitions featuring various Impressionist painters should be considered relevant. In this paper, we introduce the novel problem of text-to-metaverse retrieval, which proposes the challenging objective of ranking a list of metaverse scenarios based on a given textual query. To the best of our knowledge, this represents the first step towards understanding and automating cross-modal tasks dealing with metaverses. Since no public datasets contain these important multimedia contents inside the scenes, we also collect and annotate a dataset which serves as a proof-of-concept for the problem. To establish the foundation for it, we implement and analyze several solutions based on deep learning, whereas to promote transparency and reproducibility, we will publicly release their source code and the collected data.
Dual Path Network for Cross-view Geo-Localization

Cross-view geo-localization is to find images of the same geographical target from different views. We reevaluate the square-ring partition strategy proposed in LPN and identify a limitation in its fixed splitting strategy of features. Specifically, we observe that this method struggles to accommodate the variations in target sizes in images. Consequently, the performance of LPN experiences a substantial decline when the dataset is expanded. To optimize this problem, we propose a multi-scale ring partition strategy that effectively partitions the features at different scales to accommodate the change in the target scale. Building upon this, we construct Dual Path Network(DPN). And, we investigate the distribution of training images in University-1652 and propose a new method of sampling training data, which improves Recall@1 by 4.5%. Experimental results on the University-1652 and University-160k datasets demonstrate the effectiveness of our method.
Numerical simulation of atmospheric transmittance between heliostats and heat receiver in Tower-Type solar thermal power station

Based on the Simple Model for Atmospheric Transmission of Sunshine and meteorological observations, a simulation method for atmospheric transmittance between tower solar thermal power heliostats and heat receivers is developed. This method is verified by the vertical radiation observations in Tianjin, China. The results indicate remarkable differences between the simulated global horizontal irradiation (GHI) and the observations under different weather types, and the magnitude of the difference is mainly influenced by near-surface visibility and cloud cover. However, the ratios of the simulated GHI at the altitude of 40 m to that of 200 m are basically consistent with the observations, with deviations of less than 0.02 for more than 97 % of the moments, indicating this method is applicable to simulating the ratio of GHI at different heights. Results show a 20-m increase in altitude leads to an increase of 0.009 % in atmospheric transmittance within 200 m above the ground. Due to the influence of meteorology, the atmospheric transmittance shows obvious diurnal and seasonal variations. Assuming the vertical altitude of a heat receiver is 200 m and its horizontal distance from a heat receiver is 50 m, the average atmospheric transmittance between them is 94.72 %, and the transmittance drops to 76.49 % at the horizontal distance of 1000 m. This method estimates atmospheric transmittance between heliostats and heat receiver, and provides reference for the design of tower type solar thermal power station and the evaluation of the operation status.
Bot to School: The Impact of Generative Artificial Intelligence in Online Learning Platforms

The rapid growth of generative artificial intelligence (AI) technologies, such as ChatGPT has led to both excitement and skepticism among academics and industry, including online content platforms. However, there is little empirical evidence on how these technologies impact content creation and engagement, and the nature of content in online platforms. In this research, we conduct field experiments in an online learning platform that randomly encourage learners to use AI (i.e., AI condition) to craft their responses to weekly discussion forum prompts and not others (i.e., Non-AI condition). The results from an analysis of nearly 13,000 posts and comments by 774 learners show that while those assigned to the AI condition post more and longer content, they garner less engagement in the form of comments received from others relative to those assigned to the Non-AI condition. We replicate our results across two field experiments. Our analyses of the text of the posts help explain our findings; even though AI posts are more verbose, they are less readable, diverse, and personal relative to the Non-AI posts. Importantly, we demonstrate that a simple and low-cost strategy of simply asking learners to modify the AI-generated content in the modified AI (i.e., Mod-AI) condition mitigates the negative effects on engagement. Our results have important managerial implications for AI-related content policies overall and for the design of online learning platforms specifically.
Non-Crossing Anonymous MAPF for Tethered Robots

This paper deals with the anonymous multi-agent path finding (MAPF) problem for a team of tethered robots. The goal is to find a set of non-crossing paths such that the makespan is minimal. A difficulty comes from the fact that a safety distance must be maintained between two robots when they pass through the same subpath, to avoid collisions and cable entanglements. Hence, robots must be synchronized and waiting times must be added when computing the makespan. We show that bounds can be efficiently computed by solving linear assignment problems. We introduce a variable neighborhood search method to improve upper bounds, and a Constraint Programming model to compute optimal solutions. We experimentally evaluate our approach on three different kinds of instances.
APPLICATION OF THE CERTAINTY FACTOR METHOD IN DIAGNOSIS DAMAGE TO WEB-BASED DSLR CAMERA

system to pass the light beam to both places, namely the Focal Plane and Viewfinder, thus allowing the photographer to be able to see objects through the camera exactly the same as the photo. used sometimes experience several problems such as minor damage or even heavy damage. There are two types of camera damage, namely damage to the hardware device (both lens and camera body) and software. Therefore, the author attempts to conduct research on an expert system for diagnosing damage to web-based DSLR cameras using the certainty factor method to measure the level of certainty regarding a damage. The results of the expert system for diagnosing damage to DSLR cameras are to design a system that can provide a consultation facility for DSLR camera photographers in diagnosing damage to DSLR cameras. The system can recognize damage correctly and provide results in percentage form if there is more than one damage result and the system able to provide information on whether a damaged camera device can be repaired or replaced with a new one.
Object Tracking Based on Camera Using Anfis and Fuzzy Classifier for RGB Color

Image processing technology has a wide range of applications, such as in the medical, military, surveillance, and robotics industries. Analyzing objects in images is crucial when it comes to image processing. This study focuses on image processing to track objects of red, green, and blue (RGB) colors through the utilization of a camera. There are two research schemes: image processing and data classification. The data classification method used is the fuzzy and adaptive neuro-fuzzy inference system (ANFIS). The methods of image subtracting and region properties are commonly utilized for image processing. Based on the classification data results, the fuzzy logic classification demonstrated a higher accuracy rate of 86% when compared to Anfis' 65%. This was observed when both classification models were tested using a random sample. The value of Anfis is small due to the limited size of the training data used. As a result, it is recommended to use a fuzzy classifier for object color tracking for good performance.
Joint Representation of Entities and Relations via Graph Attention Networks for Explainable Recommendations

The latest advances in Graph Neural Networks (GNNs), have provided important new ideas for solving the Knowledge Graph (KG) representation problem for recommendation purposes. Although GNNs have an effective graph representation capability, the nonlinear transformations over the layers cause a loss of semantic information and make the generated embeddings hard to explain. In this paper, we investigate the potential of large KGs to perform interpretable recommendation using Graph Attention Networks (GATs). Our goal is to fully exploit the semantic information and preserve inherent knowledge ported in relations by jointly learning low-dimensional embeddings for nodes (i.e., entities) and edges (i.e., properties). Specifically, we feed the original data with additional knowledge from the Linked Open Data (LOD) cloud, and apply GATs to generate a vector representation for each node on the graph. Experiments conducted on three real-world datasets for the top-K recommendation task demonstrate the state-of-the-art performance of the system proposed. In addition to improving predictive performance in terms of precision, recall, and diversity, our approach fully exploits the rich structured information provided by KGs to offer explanation for recommendations.
Quality Assessment of Ikpeshi White Calcite Deposit

The research is to assess the quality of calcite deposit from GOPEX quarries. Samples were collected from five different pits and homogenized; 5 kilograms were gotten and 1 kilogram for laboratory test work and analysis. The sample was crushed to a size of 1400µm and was further reduced using ball mill to 1100µm. 100gram was riffled out for sieve analysis using set of sieves and mechanical sieve shaker and sieve size of 125µm was sampled out for chemical analysis using X-ray Fluorescence Spectrometer, from which measure elements were analyzed. The set of sieves used are 1000µm, 710µm, 500µm, 355µm, 250µm, 180µm, 125µm, 90µm, 63 µm, and (-63µm), using Gates Gaudin Schumann Method of particles size distribution and Rosin Rammler Method of particles size distribution. The result from sieve analysis shows that the economic liberation size of GOPEX calcite is at sieve number of 180µm to 63µm, showing the following mid points 6.52% to 7.82%. The equilibrium points from the graph show 50%, which signifies the existence of some if not all of these elements from the chemical analysis in the sample of GOPEX, quarries: Mg, Al, Si, P, S, K, Ca, Ti, V, Cr, Mn, Co, Fe, Ni, Cu, Zn, As, Pb, W, Au, Ag, and Rb with the following mean concentrations to be 0.5217, 1.2314, 2.7804, 0.2573, 0.5684, 0.0000, 61.2238, 0.0000, 0.0000, 0.0000, 0.0015, 0.000, 0.9938, 0.0089, 0.0117, 0.0220, 0.0000, 0.0000, 0.0000, 0.0272, 0.0025 and 0.0015 respectively, with calcium which is the major element in the sample having the following intensities 0.6241 and content of 61.2238. The limestone deposits in these location is rich in calcite which can be useful in production of cement, in agriculture and in chemical/industrial uses.
Future of Privacy-preserving Computing

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Privacy-preserving Computing Platforms

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Introduction to Privacy-preserving Computing

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Report from CBMI 2022

The 19th International Conference on Content-based Multimedia Indexing (CBMI) took place as a hybrid conference in Graz, Austria, from September 14--16, 2022, organized by JOANNEUM RESEARCH and supported by SIGMM. After the 2020 edition was postponed and held as a fully online conference in 2021, this was an important step back to a physical conference. Probably still as an effect of the COVID pandemic, the event was a bit smaller than in previous years, with around 50 participants from 18 countries (13 European countries, the rest from Asia and North America). About 60% were attending on-site, the other via web conference.
Methodological Solutions for the Challenges of Studying Racist Communication on Social Media

In considering how communication technologies are designed and used, it is essential to understand the roles of design, platform governance, and usage norms in amplifying harmful content. We sought to collect high-engagement racist posts from Reddit and Twitter, starting with search terms drawn from scholarly and activist sources, but discovered that hate groups’ tactics were evolving. When platforms suppress particular terms or hashtags, users must adjust their strategy with workarounds, which requires researchers to also adapt. We identify key failure points, false negatives and false positives, that plague researchers, offering suggestions for how to mitigate them. Then, we discuss the flexible methods we developed to address these challenges: recursive searching (mining results for more keywords), actor-based sampling (traversing profiles that posted racist material), and using algorithmically-recommended content. Our nimble research methods demonstrate how researchers can better gain insight into how the design of communication on platforms contributes to harmful uses.
Playing with AI Chat: Positioning “Dangerous” Language Model Futures through Interactive Fiction

Large language models (LLMs) use statistical models to predict the next sequence of tokens and have capabilities previously considered unattainable outside of human intelligence. Communication design can benefit from a close examination of the ongoing conversations around the adoption and use of LLMs, both the public discourse and the specific language and rhetoric used in the initial set of application interfaces and prompts. Through a survey of existing practices and a case study of how AI is used within the interactive fiction community, where procedural content generation has played with expectations and personas, this paper offers a foundation for future critique of these models as they are embedded in the digital tools we rely upon for daily communication and work.
Power Analysis Side-Channel Attacks on Same and Cross-Device Settings: A Survey of Machine Learning Techniques

Systems that use secret keys or personal details are seriously at risk from side-channel attacks, especially if they rely on power analysis. Attackers can use unintentional sources like power consumption and electromagnetic waves to extract sensitive information. Recently, machine learning has become a promising approach for executing power side-channel attacks that are efficient and effective for single and cross-device environments. This paper reviews various machine learning-based power side-channel attacks, including feature extraction techniques, classification methods, and countermeasures. This survey investigates same-device and cross-device attacks that use multiple devices for training an artificial intelligence model for this purpose. It examines the strengths and limitations of various machine learning algorithms and suggests areas for future research to address challenges.
Application of Blockchain Based e-Procurement Solution for Mitigating Corruption in Smart Cities Using Digital Identities

Procurement is an important governance tool that is used by the government and development agencies globally to manage and fulfil their complex development plans. However, corruption is a persistent and pervasive issue that hinders ethical progress and it can have a detrimental effect on the outcome of the proposed project that involves more than a few stakeholders. The implementation of Blockchain based e-procurement has been suggested as the potential solution due to its underlying characteristics of immutability and disintermediation. Blockchain technology utilizes distributed consensus, offering evident advantages to procurement by using the digital identities of the stakeholders in the bidding process and maintaining the privacy and security of the contract. This paper systematically maps and implements the existing literature to comprehend the utilization of Blockchain technology in the procurement domain and its potential to mitigate corruption in tender-based environments in smart cities by using digital identities.
Methodologies for When the Platform is on Fire

This extended abstract focuses on the methodologies used to research, examine, and understand content moderation policies on social media platforms during times of crisis.
Conducting Rolling Content Audits with Customer Journeys in Agile and Open Source Work Environments: A User-centered Approach to Planning and Improving Content

This industry insight report answers a call from professional and technical communication (PTC) academics for PTC practitioners to share “how-to literature” on the strategic and practical elements of content strategy, including content audits. In this report I share best practices for orchestrating successful content audits with cross-functional teams in an agile, open source environment and share insights into an open source, collaborative documentation environment. By completing content journey mapping exercises, we directly map specific content to each persona's task in the context of the user's emotions, identified pain points, and opportunities. Collectively, content audits and content journey mapping exercises help keep content user-focused and presented with optimal impact.
Print-and-play: Data Physicalization Methods for Research Analysis in Technical Communication

Data physicalization is an emerging interdisciplinary field which studies the use of physical materials to analyze and display interactive data. As technical communication scholars have already studied data visualization from a variety of rhetorical and design-centered perspectives, they are well-suited to contribute to this emerging field. In this experience report, the author presents an activity for teaching data physicalization to students using the university makerspace. Using crafting materials alongside 3D printing and laser cutting devices, students learn both good design and maker skills that enable them to create engaging physical data displays which they then iterate upon to further create data visualizations using design software. By including similar physicalization activities into their pedagogy, technical communication teachers can contribute to students' understanding of data analysis, making, and good visual design.