20 research outputs found
Online Community Transition Detection
Singapore National Research Foundation under International Research Centre @ Singapore Funding Initiativ
Online Community Transition Detection
Singapore National Research Foundation under International Research Centre @ Singapore Funding Initiativ
AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes
Smart Manufacturing refers to optimization techniques that are implemented in
production operations by utilizing advanced analytics approaches. With the
widespread increase in deploying Industrial Internet of Things (IIoT) sensors
in manufacturing processes, there is a progressive need for optimal and
effective approaches to data management. Embracing Machine Learning and
Artificial Intelligence to take advantage of manufacturing data can lead to
efficient and intelligent automation. In this paper, we conduct a comprehensive
analysis based on Evolutionary Computing and Deep Learning algorithms toward
making semiconductor manufacturing smart. We propose a dynamic algorithm for
gaining useful insights about semiconductor manufacturing processes and to
address various challenges. We elaborate on the utilization of a Genetic
Algorithm and Neural Network to propose an intelligent feature selection
algorithm. Our objective is to provide an advanced solution for controlling
manufacturing processes and to gain perspective on various dimensions that
enable manufacturers to access effective predictive technologies.Comment: 13 pages, 7 figures. To appear in IEEE/CAA JA
A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions
In recent decades, social network anonymization has become a crucial research
field due to its pivotal role in preserving users' privacy. However, the high
diversity of approaches introduced in relevant studies poses a challenge to
gaining a profound understanding of the field. In response to this, the current
study presents an exhaustive and well-structured bibliometric analysis of the
social network anonymization field. To begin our research, related studies from
the period of 2007-2022 were collected from the Scopus Database then
pre-processed. Following this, the VOSviewer was used to visualize the network
of authors' keywords. Subsequently, extensive statistical and network analyses
were performed to identify the most prominent keywords and trending topics.
Additionally, the application of co-word analysis through SciMAT and the
Alluvial diagram allowed us to explore the themes of social network
anonymization and scrutinize their evolution over time. These analyses
culminated in an innovative taxonomy of the existing approaches and
anticipation of potential trends in this domain. To the best of our knowledge,
this is the first bibliometric analysis in the social network anonymization
field, which offers a deeper understanding of the current state and an
insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure
Undermining User Privacy on Mobile Devices Using AI
Over the past years, literature has shown that attacks exploiting the
microarchitecture of modern processors pose a serious threat to the privacy of
mobile phone users. This is because applications leave distinct footprints in
the processor, which can be used by malware to infer user activities. In this
work, we show that these inference attacks are considerably more practical when
combined with advanced AI techniques. In particular, we focus on profiling the
activity in the last-level cache (LLC) of ARM processors. We employ a simple
Prime+Probe based monitoring technique to obtain cache traces, which we
classify with Deep Learning methods including Convolutional Neural Networks. We
demonstrate our approach on an off-the-shelf Android phone by launching a
successful attack from an unprivileged, zeropermission App in well under a
minute. The App thereby detects running applications with an accuracy of 98%
and reveals opened websites and streaming videos by monitoring the LLC for at
most 6 seconds. This is possible, since Deep Learning compensates measurement
disturbances stemming from the inherently noisy LLC monitoring and unfavorable
cache characteristics such as random line replacement policies. In summary, our
results show that thanks to advanced AI techniques, inference attacks are
becoming alarmingly easy to implement and execute in practice. This once more
calls for countermeasures that confine microarchitectural leakage and protect
mobile phone applications, especially those valuing the privacy of their users
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
In this paper, we propose a novel contrastive learning based deep learning
framework for patient similarity search using physiological signals. We use a
contrastive learning based approach to learn similar embeddings of patients
with similar physiological signal data. We also introduce a number of neighbor
selection algorithms to determine the patients with the highest similarity on
the generated embeddings. To validate the effectiveness of our framework for
measuring patient similarity, we select the detection of Atrial Fibrillation
(AF) through photoplethysmography (PPG) signals obtained from smartwatch
devices as our case study. We present extensive experimentation of our
framework on a dataset of over 170 individuals and compare the performance of
our framework with other baseline methods on this dataset.Comment: 10 pages, 4 figures, Preprint submitted to Journal of Computers in
Biology and Medicin
Adaptive Merging on Phase Change Memory
Indexing is a well-known database technique used to facilitate data access
and speed up query processing. Nevertheless, the construction and modification
of indexes are very expensive. In traditional approaches, all records in the
database table are equally covered by the index. It is not effective, since
some records may be queried very often and some never. To avoid this problem,
adaptive merging has been introduced. The key idea is to create index
adaptively and incrementally as a side-product of query processing. As a
result, the database table is indexed partially depending on the query
workload. This paper faces a problem of adaptive merging for phase change
memory (PCM). The most important features of this memory type are: limited
write endurance and high write latency. As a consequence, adaptive merging
should be investigated from the scratch. We solve this problem in two steps.
First, we apply several PCM optimization techniques to the traditional adaptive
merging approach. We prove that the proposed method (eAM) outperforms a
traditional approach by 60%. After that, we invent the framework for adaptive
merging (PAM) and a new PCM-optimized index. It further improves the system
performance by 20% for databases where search queries interleave with data
modifications
Schema-aware keyword search on linked data
Keyword search is a popular technique for querying the ever growing repositories of RDF graph data on the Web. This is due to the fact that the users do not need to master complex query languages (e.g., SQL, SPARQL) and they do not need to know the underlying structure of the data on the Web to compose their queries. Keyword search is simple and flexible. However, it is at the same time ambiguous since a keyword query can be interpreted in different ways. This feature of keyword search poses at least two challenges: (a) identifying relevant results among a multitude of candidate results, and (b) dealing with the performance scalability issue of the query evaluation algorithms.
In the literature, multiple schema-unaware approaches are proposed to cope with the above challenges. Some of them identify as relevant results only those candidate results which maintain the keyword instances in close proximity. Other approaches filter out irrelevant results using their structural characteristics or rank and top-k process the retrieved results based on statistical information about the data. In any case, these approaches cannot disambiguate the query to identify the intent of the user and they cannot scale satisfactorily when the size of the data and the number of the query keywords grow. In recent years, different approaches tried to exploit the schema (structural summary) of the RDF (Resource Description Framework) data graph to address the problems above. In this context, an original hierarchical clustering technique is introduced in this dissertation. This approach clusters the results based on a semantic interpretation of the keyword instances and takes advantage of relevance feedback from the user. The clustering hierarchy uses pattern graphs which are structured queries and clustering together result graphs with the same structure. Pattern graphs represent possible interpretations for the keyword query. By navigating though the hierarchy the user can select the pattern graph which is relevant to her intent.
Nevertheless, structural summaries are approximate representations of the data and, therefore, might return empty answers or miss results which are relevant to the user intent. To address this issue, a novel approach is presented which combines the use of the structural summary and the user feedback with a relaxation technique for pattern graphs to extract additional results potentially of interest to the user. Query caching and multi-query optimization techniques are leveraged for the efficient evaluation of relaxed pattern graphs. Although the approaches which consider the structural summary of the data graph are promising, they require interaction with the user.
It is claimed in this dissertation that without additional information from the user, it is not possible to produce results of high quality from keyword search on RDF data with the existing techniques. In this regard, an original keyword query language on RDF data is introduced which allows the user to convey his intention flexibly and effortlessly by specifying cohesive keyword groups. A cohesive group of keywords in a query indicates that its keywords should form a cohesive unit in the query results. It is experimentally demonstrated that cohesive keyword queries improve the result quality effectively and prune the search space of the pattern graphs efficiently compared to traditional keyword queries. Most importantly, these benefits are achieved while retaining the simplicity and the convenience of traditional keyword search.
The last issue addressed in this dissertation is the diversification problem for keyword search on RDF data. The goal of diversification is to trade off relevance and diversity in the results set of a keyword query in order to minimize the dissatisfaction of the average user. Novel metrics are developed for assessing relevance and diversity along with techniques for the generation of a relevant and diversified set of query interpretations for a keyword query on an RDF data graph. Experimental results show the effectiveness of the metrics and the efficiency of the approach