Databases and Information Systems Commons^™

Privobfnet: A Weakly Supervised Semantic Segmentation Model For Data Protection, Chiat Pin Tay, Vigneshwaran Subbaraju, Thivya Kandappu

Research Collection School Of Computing and Information Systems

The use of social media has made it easy to communicate and share information over the internet. However, it also brings issues such as data privacy leakage, which can be exploited by recipients with malicious intentions to harm the sender. In this paper, we propose a deep neural network that analyzes user’s image for privacy sensitive content and automatically locates sensitive regions for obfuscation. Our approach relies solely on image level annotations and learns to (a) predict an overall privacy score, (b) detect sensitive attributes and (c) demarcate the sensitive regions for obfuscation, in a given input image. We validated …

Causal Disentangled Recommendation Against User Preference Shifts, Wenjie Wang, Xinyu Lin, Liuhui Wang, Fuli Feng, Yunshan Ma, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Recommender systems easily face the issue of user preference shifts. User representations will become outof-date and lead to inappropriate recommendations if user preference has shifted over time. To solve theissue, existing work focuses on learning robust representations or predicting the shifting pattern. Therelacks a comprehensive view to discover the underlying reasons for user preference shifts. To understand thepreference shift, we abstract a causal graph to describe the generation procedure of user interaction sequences.Assuming user preference is stable within a short period, we abstract the interaction sequence as a set ofchronological environments. From the causal graph, we find that the changes …

Wakening Past Concepts Without Past Data: Class-Incremental Learning From Online Placebos, Yaoyao Liu, Yingying Li, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

Not forgetting old class knowledge is a key challenge for class-incremental learning (CIL) when the model continuously adapts to new classes. A common technique to address this is knowledge distillation (KD), which penalizes prediction inconsistencies between old and new models. Such prediction is made with almost new class data, as old class data is extremely scarce due to the strict memory limitation in CIL. In this paper, we take a deep dive into KD losses and find that "using new class data for KD"not only hinders the model adaption (for learning new classes) but also results in low efficiency for …

Dynamic Memory Management For Key-Value Store, Yuchen Wang

Dissertations, Master's Theses and Master's Reports

To minimize the latency of accessing back-end servers, modern web services often use in-memory key-value (k-v) stores at the front end to cache frequently accessed objects. Due to the limited memory capacity, these stores must be configured with a fixed amount of memory. Consequently, cache replacement is required when the footprint of the accessed objects exceeds the cache size.

This thesis presents a comprehensive exploration of advanced dynamic memory management techniques for k-v stores. The first study conducts a detailed analysis of K-LRU, a random sampling-based replacement policy, proposing a dynamic K configuration scheme to exploit the potential miss ratio …

Optimizing Sports Outcome Prediction Through Feature Engineering And Machine Learning, Vitor S. Freitas

Graduate Theses/Dissertations

The challenge of predicting the outcome of a team game lies in the high complexity and dynamics of the sports data. This thesis focuses on the aspect of using feature engineering and the genetic algorithm to predict the winner and the score of various sports events. Generally, it deals with how machine learning algorithms are combined with state-of-the-art feature engineering techniques in sports datasets derived from various sports disciplines. In this thesis, five different machine learning models have been applied, classification and regression trees (CART), random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and extreme learning machine …

The Determination Of A Place Of Popular Tourism On The Island Of Madura Using Weighted Product (Wp), Sigit Susanto Putro, Eka Malasari Rochman, Aery Rachmad

Elinvo (Electronics, Informatics, and Vocational Education)

This research explored the diverse aspects of Madura Island, including its cultural, societal, and touristic facets. The primary focus was on developing a recommendation system to identify Madura's most popular tourist destinations. Utilizing the Weighted Product (WP) method, a decision support system model, this study assessed the popularity of various tourist attractions in Madura, aiding tourists in selecting destinations through a multi-criteria weighting process. Key parameters included the number of both foreign and local visitors, proximity to the city center, and visitor ratings. The study encompassed 62 tourist sites across four districts in Madura, evaluating the most popular attractions in …

Visitor Decision System In Selection Of Tourist Sites Based On Hybrid Of Chi-Square And K-Nn Methods, Devie Rosa Anamisa, Fifin Ayu Mufarroha, Achmad Jauhari

Elinvo (Electronics, Informatics, and Vocational Education)

Madura Island is one of the islands with a lot of tourism spread over four districts, such as natural, religious, and cultural tourism. And every year, various visitors visit various tourist sites in Madura, so an increase in the number of visitors has been found in multiple places. This is influenced in addition to the type of tourist attraction but also changes in tourist behavior in making decisions to visit tourist objects. Most of the researchers have applied the right decision-making with intelligence-based measurement. However, the accuracy obtained has not yet reached the optimal solution. Therefore, this study uses the …

Decision Support System For Major Selection In Higher Education For Multimedia Graduate Students Using Fuzzy Mamdani Logic, Khasna Nur Fauziah, Fatchul Arifin

Elinvo (Electronics, Informatics, and Vocational Education)

Students at the vocational high school level are indeed prepared to be able to work directly, but it does not rule out the possibility that vocational high school students can continue higher education such as universities. But the problem that will be faced again if students who graduate from vocational high schools choose to continue their education in college is what major they will take. One of the vocational high school majors, namely Multimedia, has a wide scope, so grade 3 vocational high school students who want to go to college have a dilemma in deciding on a major. This …

Design And Development Of Industrial Practice Monitoring And Assessment Systems Using Tsukamoto Fuzzy Logic, Tri Yuli Pahtoni, Fatchul Arifin

Elinvo (Electronics, Informatics, and Vocational Education)

Vocational high schools are given flexibility for their students to carry out direct learning in the industry as part of the practical education activities of implementing student skills. The implementation of industrial practice requires a special way to find out and monitor each student's activities so that the achievements of the implementation of industrial practice can be carried out properly. The implementation of industrial work practice assessment has several assessment criteria. These criteria include attendance, neatness, attitude, skills, and knowledge. The problems found in the assessment system are still done manually so that the effectiveness is minimal. This study aims …

Implementation Of Fdss (Fuzzy Decision Support System) Sugeno Model In Optimizing Bandwidth Requirement Management Of Web-Based Networks, Indah Wardati Lahiya, Fatchul Arifin

Elinvo (Electronics, Informatics, and Vocational Education)

To increase the efficacy of bandwidth allocation at PT. Digdaya Monokrom Group, this study describes the development of a Fuzzy Decision Support System (FDSS) utilizing the Sugeno methodology. The Waterfall development process is employed for the purposes of system planning, construction, and maintenance. The study consists of three primary stages: the creation of fuzzy sets, the development of fuzzy rules, and the process of defuzzification. The study findings demonstrate that the utilization of FDSS has effectively improved the distribution of bandwidth. The distribution has shifted from a uniform one to a more optimized allocation, focusing on the Execution, Content Creator, …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Movie Recommendation System Using Content Based Filtering, Sribhashyam Rakesh

Al-Bahir

The movie recommendation system plays a crucial role in assisting movie enthusiasts in finding movies that match their interests, saving them from the overwhelming task of sifting through countless options. In this paper, we present a content-grounded movie recommendation system that leverages an attribute-based approach to offer personalized movie suggestions to users. The proposed method focuses on attributes such as cast, keywords, crew, and genres of movies to predict users' preferences accurately. Through extensive evaluation, our content-grounded recommendation system demonstrated significant improvements in performance compared to conventional methods. The precision and recall scores increased by an average of 20% and …

A Conceptual Decentralized Identity Solution For State Government, Martin Duclos

Theses and Dissertations

In recent years, state governments, exemplified by Mississippi, have significantly expanded their online service offerings to reduce costs and improve efficiency. However, this shift has led to challenges in managing digital identities effectively, with multiple fragmented solutions in use. This paper proposes a Self-Sovereign Identity (SSI) framework based on distributed ledger technology. SSI grants individuals control over their digital identities, enhancing privacy and security without relying on a centralized authority. The contributions of this research include increased efficiency, improved privacy and security, enhanced user satisfaction, and reduced costs in state government digital identity management. The paper provides background on digital …

Unmasking Shadows: Unraveling Crime Patterns In Nyc's Boroughs, Jack Hachicho, Muhammad Hassan Butt

Publications and Research

New York City's crime dynamics have been on the rise for decades. Brooklyn and The Bronx have been disproportionately affected. This research aims to understand the crime landscape in these boroughs to formulate effective policies. Using crime data from official sources, statistical analyses, and data visualizations, the study identifies patterns and trends. The data encompasses over 400,000 reported incidents collected over the past 10 years, meticulously categorized by borough, crime type, and demographic information. Brooklyn has the highest overall crime rate, followed by The Bronx. Most shooting victims are Black. This highlights the need for holistic community programs to address …

Les Expositions Turnus, Une Page D’Histoire Transnationale Des Beaux-Arts En Suisse À La Fin Du Xixe Siècle. Et Comment Découvrir Les Humanités Numériques, Béatrice Joyeux-Prunel

Artl@s Bulletin

Cet article présente le travail de la classe d’introduction aux humanités numériques de l’Université de Genève sur les expositions Turnus en Suisse à partir des années 1840. Près de 50 catalogues ont été retranscrits, décrits et structurés à l’aide de scripts Python, puis géolocalisés. Les données ont été ajoutées à BasArt, le répertoire mondial de catalogues d’expositions d’Artl@s (https://artlas.huma-num.fr/map). Elles permettent de mieux comprendre les premières années de ces expositions et leurs dynamiques locales, fédérales et internationales. Le Turnus fut une plaque tournante pour les artistes suisses, voire un tremplin vers le marché européen de l’art.

Index Bucketing: A Novel Approach To Manipulating Data Structures, Jeffrey Myers

Masters Theses & Specialist Projects

Handling nested data collections in large-scale distributed systems poses considerable challenges in query processing, often resulting in substantial costs and error susceptibility. While substantial efforts have been directed toward overcoming computation hurdles in querying vast data collections within relational databases, scant attention has been devoted to the manipulation and flattening procedures necessary for unnesting these data collections. Flattening operations, integral to unnesting, frequently yield copious duplicate data and entail a loss of information, devoid of mechanisms for reconstructing the original structure. These challenges exacerbate in scenarios involving skewed, nested data with irregular inner data collections. Processing such data demands an …

Monocular Depth Estimation For Glass Walls With Context: A New Dataset And Method, Yuan Liang, Bailin Deng, Wenxi Liu, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Traditional monocular depth estimation assumes that all objects are reliably visible in the RGB color domain. However, this is not always the case as more and more buildings are decorated with transparent glass walls. This problem has not been explored due to the difficulties in annotating the depth levels of glass walls, as commercial depth sensors cannot provide correct feedbacks on transparent objects. Furthermore, estimating depths from transparent glass walls requires the aids of surrounding context, which has not been considered in prior works. To cope with this problem, we introduce the first Glass Walls Depth Dataset (GW-Depth dataset). We …

Benchmarking Foundation Models With Language-Model-As-An-Examiner, Yushi Bai, Jiahao Ying, Yixin Cao, Xin Lv, Yuze He, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Yijia Xiao, Haozhe Lyu, Jiayin Zhang, Juanzi Li, Lei Hou

Research Collection School Of Computing and Information Systems

Numerous benchmarks have been established to assess the performance of foundation models on open-ended question answering, which serves as a comprehensive test of a model’s ability to understand and generate language in a manner similar to humans. Most of these works focus on proposing new datasets, however, we see two main issues within previous benchmarking pipelines, namely testing leakage and evaluation automation. In this paper, we propose a novel benchmarking framework, Language-Model-as-an-Examiner, where the LM serves as a knowledgeable examiner that formulates questions based on its knowledge and evaluates responses in a reference-free manner. Our framework allows for effortless extensibility …

Efficient Meta Neural Heuristic For Multi-Objective Combinatorial Optimization, Jinbiao Chen, Zizhen Zhang, Te Ye, Zhiguang Cao, Siyuan Chen, Jiahai Wang

Research Collection School Of Computing and Information Systems

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta model, so as to speed up the training; meanwhile, a scaled symmetric sampling method with respect to the …

Extending The Horizon By Empowering Government Customer Service Officers With Acqar For Enhanced Citizen Service Delivery, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

A previous study on the use of the Empath library in the prediction of Service Level Agreements (SLA) reveals the quality levels required for meaningful interaction between government customer service officers and citizens. On the other hand, past implementation of the Citizen Question-Answer system (CQAS), a type of Question-Answer model, suggests that such models if put in place can empower government customer service officers to reply faster and better with recommended answers. This study builds upon the research outcomes from both arenas of studies and introduces an innovative system design that allows the officers to incorporate the outputs from Empath …