83 research outputs found
Corporate Social Responsibility Employment Narratives : a Linguistic Analysis
Peer reviewedPostprin
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing
For time-critical IoT applications using deep learning, inference
acceleration through distributed computing is a promising approach to meet a
stringent deadline. In this paper, we implement a working prototype of a new
distributed inference acceleration method HALP using three raspberry Pi 4. HALP
accelerates inference by designing a seamless collaboration among edge devices
(EDs) in Edge Computing. We maximize the parallelization between communication
and computation among the collaborative EDs by optimizing the task partitioning
ratio based on the segment-based partitioning. Experimental results show that
the distributed inference HALP achieves 1.7x inference acceleration for VGG-16.
Then, we combine distributed inference with conventional neural network model
compression by setting up different shrinking hyperparameters for MobileNet-V1.
In this way, we can further accelerate inference but at the cost of inference
accuracy loss. To strike a balance between latency and accuracy, we propose
dynamic model selection to select a model which provides the highest accuracy
within the latency constraint. It is shown that the model selection with
distributed inference HALP can significantly improve service reliability
compared to the conventional stand-alone computation.Comment: Accepted by European Wireless 202
An empirical study of touch-based authentication methods on smartwatches
The emergence of smartwatches poses new challenges to information security.
Although there are mature touch-based authentication methods for smartphones,
the effectiveness of using these methods on smartwatches is still unclear. We
conducted a user study (n=16) to evaluate how authentication methods (PIN and
Pattern), UIs (Square and Circular), and display sizes (38mm and 42mm) affect
authentication accuracy, speed, and security. Circular UIs are tailored to
smartwatches with fewer UI elements. Results show that 1) PIN is more accurate
and secure than Pattern; 2) Pattern is much faster than PIN; 3) Square UIs are
more secure but less accurate than Circular UIs; 4) display size does not
affect accuracy or speed, but security; 5) Square PIN is the most secure method
of all. The study also reveals a security concern that participants' favorite
method is not the best in any of the measures. We finally discuss implications
for future touch-based smartwatch authentication design.Comment: ISWC '17, Proceedings of the 2017 ACM International Symposium on
Wearable Computers, 122-125, ACM New York, NY, US
Community Detection in the Multi-View Stochastic Block Model
This paper considers the problem of community detection on multiple
potentially correlated graphs from an information-theoretical perspective. We
first put forth a random graph model, called the multi-view stochastic block
model (MVSBM), designed to generate correlated graphs on the same set of nodes
(with cardinality ). The nodes are partitioned into two disjoint
communities of equal size. The presence or absence of edges in the graphs for
each pair of nodes depends on whether the two nodes belong to the same
community or not. The objective for the learner is to recover the hidden
communities with observed graphs. Our technical contributions are two-fold: (i)
We establish an information-theoretic upper bound (Theorem~1) showing that
exact recovery of community is achievable when the model parameters of MVSBM
exceed a certain threshold. (ii) Conversely, we derive an information-theoretic
lower bound (Theorem~2) showing that when the model parameters of MVSBM fall
below the aforementioned threshold, then for any estimator, the expected number
of misclassified nodes will always be greater than one. Our results for the
MVSBM recover several prior results for community detection in the standard SBM
as well as in multiple independent SBMs as special cases.Comment: Submitted to IEEE for possible publicatio
UniRQR: A Unified Model for Retrieval Decision, Query, and Response Generation in Internet-Based Knowledge Dialogue Systems
Knowledge-based dialogue systems with internet retrieval have recently
attracted considerable attention from researchers. The dialogue systems
overcome a major limitation of traditional knowledge dialogue systems, where
the timeliness of knowledge cannot be assured, hence providing greater
practical application value. Knowledge-based dialogue systems with internet
retrieval can be typically segmented into three tasks: Retrieval Decision,
Query Generation, and Response Generation. However, many of studies assumed
that all conversations require external knowledge to continue, neglecting the
critical step of determining when retrieval is necessary. This assumption often
leads to an over-dependence on external knowledge, even when it may not be
required. Our work addresses this oversight by employing a single unified model
facilitated by prompt and multi-task learning approaches. This model not only
decides whether retrieval is necessary but also generates retrieval queries and
responses. By integrating these functions, our system leverages the full
potential of pre-trained models and reduces the complexity and costs associated
with deploying multiple models. We conducted extensive experiments to
investigate the mutual enhancement among the three tasks in our system. What is
more, the experiment results on the Wizint and Dusinc datasets not only
demonstrate that our unified model surpasses the baseline performance for
individual tasks, but also reveal that it achieves comparable results when
contrasted with SOTA systems that deploy separate, specialized models for each
task
Inkjet Patterned Anodic Aluminum Oxide for Rear Metal Contacts of Silicon Solar Cells
AbstractLocal rear metal contacting through passivating dielectric layers has the ability to increase silicon solar cell efficiencies to over 20%. To-date most contact schemes have involved the formation of localised aluminium-alloyed regions through patterned AlOx or SiNx passivating layers. Recently electrochemically-formed anodic aluminium oxide (AAO) layers have been shown to enhance minority carrier lifetimes of phosphorus–diffused p-type CZ wafers when formed over an intervening layer of SiO2 or SiNx, suggesting that these layers may find applications as passivation layers for cells. We report here on the inkjet patterning of AAO layers formed over a thermally-grown thin oxide layer on p-type silicon surfaces. The process, which involves the inkjet printing of 50% (w/w) phosphoric acid, was used to form well-resolved arrays of holes with a diameter as small as 20-40μm in the dielectric stack. Alloying of aluminium, which was evaporated over the patterned dielectric stack, resulted in the formation of localised back surface field (BSF) regions having a thickness up to 8μm. Future work will focus on adapting this process for use in local rear metal contacting of silicon solar cells
Unified Loss of Pair Similarity Optimization for Vision-Language Retrieval
There are two popular loss functions used for vision-language retrieval,
i.e., triplet loss and contrastive learning loss, both of them essentially
minimize the difference between the similarities of negative pairs and positive
pairs. More specifically, Triplet loss with Hard Negative mining (Triplet-HN),
which is widely used in existing retrieval models to improve the discriminative
ability, is easy to fall into local minima in training. On the other hand,
Vision-Language Contrastive learning loss (VLC), which is widely used in the
vision-language pre-training, has been shown to achieve significant performance
gains on vision-language retrieval, but the performance of fine-tuning with VLC
on small datasets is not satisfactory. This paper proposes a unified loss of
pair similarity optimization for vision-language retrieval, providing a
powerful tool for understanding existing loss functions. Our unified loss
includes the hard sample mining strategy of VLC and introduces the margin used
by the triplet loss for better similarity separation. It is shown that both
Triplet-HN and VLC are special forms of our unified loss. Compared with the
Triplet-HN, our unified loss has a fast convergence speed. Compared with the
VLC, our unified loss is more discriminative and can provide better
generalization in downstream fine-tuning tasks. Experiments on image-text and
video-text retrieval benchmarks show that our unified loss can significantly
improve the performance of the state-of-the-art retrieval models.Comment: 16 pages, 5 figure
Corporate social responsibility : a review of empirical research using Thomson Reuters Asset4 data
Open access publishing facilitated by The University of Auckland, as part of the Wiley - The
University of Auckland agreement via the Council of Australian University Librarians.Thomson Reuters Asset4 (Asset4) is a leading corporate
social responsibility (CSR) database often used by practitioners
and researchers. This review offers a precise
understanding of prior studies using Asset4 and their
justification for selecting Asset4, and identifies research
opportunities. We review 285 studies using Asset4 data
published in quality academic journals, analysing: (1)
the usage of Asset4 pillars, categories, data points, and
indicators; (2) the justification for using Asset4; and (3) research
themes. Our findings provide valuable information
for practitioners and researchers who (plan to) use CSR
databases, including our guidance on promising avenues
for future studies.http://www.wileyonlinelibrary.com/journal/acfiam2023Accountin
Visual-Guided Mesh Repair
Mesh repair is a long-standing challenge in computer graphics and related
fields. Converting defective meshes into watertight manifold meshes can greatly
benefit downstream applications such as geometric processing, simulation,
fabrication, learning, and synthesis. In this work, we first introduce three
visual measures for visibility, orientation, and openness, based on
ray-tracing. We then present a novel mesh repair framework that incorporates
visual measures with several critical steps, i.e., open surface closing, face
reorientation, and global optimization, to effectively repair defective meshes,
including gaps, holes, self-intersections, degenerate elements, and
inconsistent orientations. Our method reduces unnecessary mesh complexity
without compromising geometric accuracy or visual quality while preserving
input attributes such as UV coordinates for rendering. We evaluate our approach
on hundreds of models randomly selected from ShapeNet and Thingi10K,
demonstrating its effectiveness and robustness compared to existing approaches
DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning
We propose Multiple Experts Fine-tuning Framework to build a financial large
language model (LLM), DISC-FinLLM. Our methodology improves general LLMs by
endowing them with multi-turn question answering abilities, domain text
processing capabilities, mathematical computation skills, and
retrieval-enhanced generation capabilities. We build a financial
instruction-tuning dataset named DISC-FIN-SFT, including instruction samples of
four categories (consulting, NLP tasks, computing and retrieval-augmented
generation). Evaluations conducted on multiple benchmarks demonstrate that our
model performs better than baseline models in various financial scenarios.
Further resources can be found at https://github.com/FudanDISC/DISC-FinLLM.Comment: 18 pages, 13 figures, 7 table
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