9,830 research outputs found
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
Federated Recommender Systems (FedRecs) are considered privacy-preserving
techniques to collaboratively learn a recommendation model without sharing user
data. Since all participants can directly influence the systems by uploading
gradients, FedRecs are vulnerable to poisoning attacks of malicious clients.
However, most existing poisoning attacks on FedRecs are either based on some
prior knowledge or with less effectiveness. To reveal the real vulnerability of
FedRecs, in this paper, we present a new poisoning attack method to manipulate
target items' ranks and exposure rates effectively in the top-
recommendation without relying on any prior knowledge. Specifically, our attack
manipulates target items' exposure rate by a group of synthetic malicious users
who upload poisoned gradients considering target items' alternative products.
We conduct extensive experiments with two widely used FedRecs (Fed-NCF and
Fed-LightGCN) on two real-world recommendation datasets. The experimental
results show that our attack can significantly improve the exposure rate of
unpopular target items with extremely fewer malicious users and fewer global
epochs than state-of-the-art attacks. In addition to disclosing the security
hole, we design a novel countermeasure for poisoning attacks on FedRecs.
Specifically, we propose a hierarchical gradient clipping with sparsified
updating to defend against existing poisoning attacks. The empirical results
demonstrate that the proposed defending mechanism improves the robustness of
FedRecs.Comment: This paper has been accepted by SIGIR202
Ausubel's meaningful learning re-visited
This review provides a critique of David Ausubel’s theory of meaningful learning and the use of advance organizers in teaching. It takes into account the developments in cognition and neuroscience which have taken place in the 50 or so years since he advanced his ideas, developments which challenge our understanding of cognitive structure and the recall of prior learning. These include (i) how effective questioning to ascertain previous knowledge necessitates in-depth Socratic dialogue; (ii) how many findings in cognition and neuroscience indicate that memory may be non-representational, thereby affecting our interpretation of student recollections; (iii) the now recognised dynamism of memory; (iv) usefully regarding concepts as abilities or simulators and skills; (v) acknowledging conscious and unconscious memory and imagery; (vi) how conceptual change involves conceptual coexistence and revision; (vii) noting linguistic and neural pathways as a result of experience and neural selection; and (viii) recommending that wider concepts of scaffolding should be adopted, particularly given the increasing focus on collaborative learning in a technological world
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Redefining quality interpersonal communication and communication activities in marriage from divorcees’ perspectives
Quality interpersonal communication is essential in the development and maintenance of any relationship, including marriage. As society adapts to new avenues of communication, married couples often underestimate the relevance of interpersonal communication in their relationship due to their lack of understanding of quality interpersonal communication. Therefore, this study investigated the conceptualisation of quality interpersonal communication through the lens of Relational Dialectic Theory and communication activities in marriage from the perspectives of divorcees. This study also explored the antecedents of poor-quality interpersonal communication and its repercussions on married couples. The present study also extended Knapp’s Relational Development Model by incorporating communication technology as a medium of communication. In-depth interviews were conducted on 20 divorcees from different states in Malaysia, chosen through a purposive sampling technique. The gathered data was then evaluated and combined in a thematic data analysis using the NVivo 12 software. This study discovers that the definitions of quality interpersonal communication are divided into seven (7) categories, with communication skills, intimacy, and characters identified as the top three significant traits. Results of this study also indicate that spouses use various medium of communication based on their circumstances but prefer face-to-face communication. However, communication occurrences between spouses are low and mostly negative, with the majority of them mainly involving households and children. The other antecedents of poor-quality interpersonal communication are communication skills, attitudes, third-party involvement, and emotional condition. The current study concludes that emotional condition is one of the protuberant effects of poor-quality interpersonal communication. All in all, the current study provides a new paradigm in Knapp’s Relational Development Model through the incorporation of the effects of poor-quality interpersonal communication into the deterioration stages of the model
Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?
The use of pretrained embeddings has become widespread in modern e-commerce
machine learning (ML) systems. In practice, however, we have encountered
several key issues when using pretrained embedding in a real-world production
system, many of which cannot be fully explained by current knowledge.
Unfortunately, we find that there is a lack of a thorough understanding of how
pre-trained embeddings work, especially their intrinsic properties and
interactions with downstream tasks. Consequently, it becomes challenging to
make interactive and scalable decisions regarding the use of pre-trained
embeddings in practice.
Our investigation leads to two significant discoveries about using pretrained
embeddings in e-commerce applications. Firstly, we find that the design of the
pretraining and downstream models, particularly how they encode and decode
information via embedding vectors, can have a profound impact. Secondly, we
establish a principled perspective of pre-trained embeddings via the lens of
kernel analysis, which can be used to evaluate their predictability,
interactively and scalably. These findings help to address the practical
challenges we faced and offer valuable guidance for successful adoption of
pretrained embeddings in real-world production. Our conclusions are backed by
solid theoretical reasoning, benchmark experiments, as well as online testings
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
We propose Compressed Vertical Federated Learning (C-VFL) for
communication-efficient training on vertically partitioned data. In C-VFL, a
server and multiple parties collaboratively train a model on their respective
features utilizing several local iterations and sharing compressed intermediate
results periodically. Our work provides the first theoretical analysis of the
effect message compression has on distributed training over vertically
partitioned data. We prove convergence of non-convex objectives at a rate of
when the compression error is bounded over the course
of training. We provide specific requirements for convergence with common
compression techniques, such as quantization and top- sparsification.
Finally, we experimentally show compression can reduce communication by over
without a significant decrease in accuracy over VFL without compression
Interactive System-wise Anomaly Detection
Anomaly detection, where data instances are discovered containing feature
patterns different from the majority, plays a fundamental role in various
applications. However, it is challenging for existing methods to handle the
scenarios where the instances are systems whose characteristics are not readily
observed as data. Appropriate interactions are needed to interact with the
systems and identify those with abnormal responses. Detecting system-wise
anomalies is a challenging task due to several reasons including: how to
formally define the system-wise anomaly detection problem; how to find the
effective activation signal for interacting with systems to progressively
collect the data and learn the detector; how to guarantee stable training in
such a non-stationary scenario with real-time interactions? To address the
challenges, we propose InterSAD (Interactive System-wise Anomaly Detection).
Specifically, first, we adopt Markov decision process to model the interactive
systems, and define anomalous systems as anomalous transition and anomalous
reward systems. Then, we develop an end-to-end approach which includes an
encoder-decoder module that learns system embeddings, and a policy network to
generate effective activation for separating embeddings of normal and anomaly
systems. Finally, we design a training method to stabilize the learning
process, which includes a replay buffer to store historical interaction data
and allow them to be re-sampled. Experiments on two benchmark environments,
including identifying the anomalous robotic systems and detecting user data
poisoning in recommendation models, demonstrate the superiority of InterSAD
compared with state-of-the-art baselines methods
Discreetly Exploiting Inter-session Information for Session-based Recommendation
Limited intra-session information is the performance bottleneck of the early
GNN based SBR models. Therefore, some GNN based SBR models have evolved to
introduce additional inter-session information to facilitate the next-item
prediction. However, we found that the introduction of inter-session
information may bring interference to these models. The possible reasons are
twofold. First, inter-session dependencies are not differentiated at the
factor-level. Second, measuring inter-session weight by similarity is not
enough. In this paper, we propose DEISI to solve the problems. For the first
problem, DEISI differentiates the types of inter-session dependencies at the
factor-level with the help of DRL technology. For the second problem, DEISI
introduces stability as a new metric for weighting inter-session dependencies
together with the similarity. Moreover, CL is used to improve the robustness of
the model. Extensive experiments on three datasets show the superior
performance of the DEISI model compared with the state-of-the-art models
Network Transmission Flags Data Affinity-based Classification by K-Nearest Neighbor
Abstract—This research is concerned with the data generated during a network transmission session to understand how to extract value from the data generated and be able to conduct tasks. Instead of comparing all of the transmission flags for a transmission session at the same time to conduct any analysis, this paper conceptualized the influence of each transmission flag on network-aware applications by comparing the flags one by one on their impact to the application during the transmission session, rather than comparing all of the transmission flags at the same time. The K-nearest neighbor (KNN) type classification was used becauseit is a simple distance-based learning algorithm that remembers earlier training samples and is suitable for taking various flags withtheir effect on application protocols by comparing each new sample with the K-nearest points to make a decision. We used transmission session datasets received from Kaggle for IP flow with 87 features and 3.577.296 instances. We picked 13 features from the datasets and ran them through KNN. RapidMiner was used for the study, and the results of the experiments revealed that the KNN-based model was not only significantly more accurate in categorizing data, but it was also significantly more efficient due to the decreased processing costs
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