158 research outputs found
Parsec: a state channel for the Internet of Value
We propose Parsec, a web-scale State channel for the Internet of Value to exterminate the consensus bottleneck in Blockchain by leveraging a network of state channels which enable to robustly transfer value off-chain. It acts as an infrastructure layer developed on top of Ethereum Blockchain, as a network protocol which allows coherent routing and interlocking channel transfers for trade-off between parties. A web-scale solution for state channels is implemented to enable a layer of value transfer to the internet. Existing network protocol on State Channels include Raiden for Ethereum and Lightning Network for Bitcoin. However, we intend to leverage existing web-scale technologies used by large Internet companies such as Uber, LinkedIn or Netflix. We use Apache Kafka to scale the global payment operation to trillions of operations per day enabling near-instant, low-fee, scalable, and privacy-sustainable payments. Our architecture follows Event Sourcing pattern which solves current issues of payment solutions such as scaling, transfer, interoperability, low-fees, micropayments and to name a few. To the best of knowledge, our proposed model achieve better performance than state-of-the-art lightning network on the Ethereum based (fork) cryptocoins
Lightweight Adaptation of Neural Language Models via Subspace Embedding
Traditional neural word embeddings are usually dependent on a richer
diversity of vocabulary. However, the language models recline to cover major
vocabularies via the word embedding parameters, in particular, for multilingual
language models that generally cover a significant part of their overall
learning parameters. In this work, we present a new compact embedding structure
to reduce the memory footprint of the pre-trained language models with a
sacrifice of up to 4% absolute accuracy. The embeddings vectors reconstruction
follows a set of subspace embeddings and an assignment procedure via the
contextual relationship among tokens from pre-trained language models. The
subspace embedding structure calibrates to masked language models, to evaluate
our compact embedding structure on similarity and textual entailment tasks,
sentence and paraphrase tasks. Our experimental evaluation shows that the
subspace embeddings achieve compression rates beyond 99.8% in comparison with
the original embeddings for the language models on XNLI and GLUE benchmark
suites.Comment: 5 pages, Accepted as a Main Conference Short Paper at CIKM 202
A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings
Item representation holds significant importance in recommendation systems,
which encompasses domains such as news, retail, and videos. Retrieval and
ranking models utilise item representation to capture the user-item
relationship based on user behaviours. While existing representation learning
methods primarily focus on optimising item-based mechanisms, such as attention
and sequential modelling. However, these methods lack a modelling mechanism to
directly reflect user interests within the learned item representations.
Consequently, these methods may be less effective in capturing user interests
indirectly. To address this challenge, we propose a novel Interest-aware
Capsule network (IaCN) recommendation model, a model-agnostic framework that
directly learns interest-oriented item representations. IaCN serves as an
auxiliary task, enabling the joint learning of both item-based and
interest-based representations. This framework adopts existing recommendation
models without requiring substantial redesign. We evaluate the proposed
approach on benchmark datasets, exploring various scenarios involving different
deep neural networks, behaviour sequence lengths, and joint learning ratios of
interest-oriented item representations. Experimental results demonstrate
significant performance enhancements across diverse recommendation models,
validating the effectiveness of our approach.Comment: Accepted Paper under LBR track in the Seventeenth ACM Conference on
Recommender Systems (RecSys) 202
Faster Algorithms for the Constrained k-Means Problem
The classical center based clustering problems such as k-means/median/center assume that the optimal clusters satisfy the locality property that the points in the same cluster are close to each other. A number of clustering problems arise in machine learning where the optimal clusters do not follow such a locality property. For instance, consider the r-gather clustering problem where there is an additional constraint that each of the clusters should have at least r points or the capacitated clustering problem where there is an upper bound on the cluster sizes. Consider a variant of the k-means problem that may be regarded as a general version of such problems. Here, the optimal clusters O_1, ..., O_k are an arbitrary partition of the dataset and the goal is to output k-centers c_1, ..., c_k such that the objective function sum_{i=1}^{k} sum_{x in O_{i}} ||x - c_{i}||^2 is minimized. It is not difficult to argue that any algorithm (without knowing the optimal clusters) that outputs a single set of k centers, will not behave well as far as optimizing the above objective function is concerned. However, this does not rule out the existence of algorithms that output a list of such k centers such that at least one of these k centers behaves well. Given an error parameter epsilon > 0, let l denote the size of the smallest list of k-centers such that at least one of the k-centers gives a (1+epsilon) approximation w.r.t. the objective function above. In this paper, we show an upper bound on l by giving a randomized algorithm that outputs a list of 2^{~O(k/epsilon)} k-centers. We also give a closely matching lower bound of 2^{~Omega(k/sqrt{epsilon})}. Moreover, our algorithm runs in time O(n * d * 2^{~O(k/epsilon)}). This is a significant improvement over the previous result of Ding and Xu who gave an algorithm with running time O(n * d * (log{n})^{k} * 2^{poly(k/epsilon)}) and output a list of size O((log{n})^k * 2^{poly(k/epsilon)}). Our techniques generalize for the k-median problem and for many other settings where non-Euclidean distance measures are involved
A simple D^2-sampling based PTAS for k-means and other Clustering Problems
Given a set of points , the -means clustering
problem is to find a set of {\em centers} such that the objective function ,
where denotes the distance between and the closest center in ,
is minimized. This is one of the most prominent objective functions that have
been studied with respect to clustering.
-sampling \cite{ArthurV07} is a simple non-uniform sampling technique
for choosing points from a set of points. It works as follows: given a set of
points , the first point is chosen uniformly at
random from . Subsequently, a point from is chosen as the next sample
with probability proportional to the square of the distance of this point to
the nearest previously sampled points.
-sampling has been shown to have nice properties with respect to the
-means clustering problem. Arthur and Vassilvitskii \cite{ArthurV07} show
that points chosen as centers from using -sampling gives an
approximation in expectation. Ailon et. al. \cite{AJMonteleoni09}
and Aggarwal et. al. \cite{AggarwalDK09} extended results of \cite{ArthurV07}
to show that points chosen as centers using -sampling give
approximation to the -means objective function with high probability. In
this paper, we further demonstrate the power of -sampling by giving a
simple randomized -approximation algorithm that uses the
-sampling in its core
FPT Approximation for Constrained Metric k-Median/Means
The Metric -median problem over a metric space is
defined as follows: given a set of facility locations
and a set of clients, open a set of
facilities such that the total service cost, defined as , is minimised. The metric -means
problem is defined similarly using squared distances. In many applications
there are additional constraints that any solution needs to satisfy. This gives
rise to different constrained versions of the problem such as -gather,
fault-tolerant, outlier -means/-median problem. Surprisingly, for many of
these constrained problems, no constant-approximation algorithm is known. We
give FPT algorithms with constant approximation guarantee for a range of
constrained -median/means problems. For some of the constrained problems,
ours is the first constant factor approximation algorithm whereas for others,
we improve or match the approximation guarantee of previous works. We work
within the unified framework of Ding and Xu that allows us to simultaneously
obtain algorithms for a range of constrained problems. In particular, we obtain
a -approximation and -approximation for the
constrained versions of the -median and -means problem respectively in
FPT time. In many practical settings of the -median/means problem, one is
allowed to open a facility at any client location, i.e., . For
this special case, our algorithm gives a -approximation and
-approximation for the constrained versions of -median and
-means problem respectively in FPT time. Since our algorithm is based on
simple sampling technique, it can also be converted to a constant-pass
log-space streaming algorithm
Development of stabilized Fly Ash composite materials for Haul Road Application
Generation of fly ash from the thermal power stations is and will remain a major challenge for the near future. At present out of 140 MT fly ash about 50% are being gainfully used. Rest remain potential environment hazard. Filling of low lying area, underground voids are some of the potential areas of bulk uses. Sub-base of haul road is one such area. An essential attributes of such usage is the strength of fly ash at different period of time. Fly ash does not have any strength. It gains strength in presence of free lime. This investigation is an attempt in that direction. The sub-base of opencast haul road typically suffers from low bearing capacity material as the local material is used. It is envisioned that stabilised fly ash has strong potential to replace the sub-base material and provide adequate resistance to t road degradation. Lime and cement were used as additives to provide reactive lime at different proportions. Laboratory experiments were carried out to evaluate the strength gain in the fly ash. Standard proctor hammer test, unconfined compressive test, Brazilian tensile test and tri-axial test were carried out to determine respective properties. Lime and cement show to be enhancing the strength profiles of the fly ash. Curing periods also has strong influence on the fly ash strength properties. 90 % fly ash and 10% lime shows the maximum strength values at 100 days curing
Towards Subject Agnostic Affective Emotion Recognition
This paper focuses on affective emotion recognition, aiming to perform in the
subject-agnostic paradigm based on EEG signals. However, EEG signals manifest
subject instability in subject-agnostic affective Brain-computer interfaces
(aBCIs), which led to the problem of distributional shift. Furthermore, this
problem is alleviated by approaches such as domain generalisation and domain
adaptation. Typically, methods based on domain adaptation confer comparatively
better results than the domain generalisation methods but demand more
computational resources given new subjects. We propose a novel framework,
meta-learning based augmented domain adaptation for subject-agnostic aBCIs. Our
domain adaptation approach is augmented through meta-learning, which consists
of a recurrent neural network, a classifier, and a distributional shift
controller based on a sum-decomposable function. Also, we present that a neural
network explicating a sum-decomposable function can effectively estimate the
divergence between varied domains. The network setting for augmented domain
adaptation follows meta-learning and adversarial learning, where the controller
promptly adapts to new domains employing the target data via a few
self-adaptation steps in the test phase. Our proposed approach is shown to be
effective in experiments on a public aBICs dataset and achieves similar
performance to state-of-the-art domain adaptation methods while avoiding the
use of additional computational resources.Comment: To Appear in MUWS workshop at the 32nd ACM International Conference
on Information and Knowledge Management (CIKM) 202
Effects of foraging in personalized content-based image recommendation
A major challenge of recommender systems is to help users locating interesting items. Personalized recommender systems have become very popular as they attempt to predetermine the needs of users and provide them with recommendations to personalize their navigation. However, few studies have addressed the question of what drives the users' attention to specific content within the collection and what influences the selection of interesting items. To this end, we employ the lens of Information Foraging Theory (IFT) to image recommendation to demonstrate how the user could utilize visual bookmarks to locate interesting images. We investigate a personalized content-based image recommendation system to understand what affects user attention by reinforcing visual attention cues based on IFT. We further find that visual bookmarks (cues) lead to a stronger scent of the recommended image collection. Our evaluation is based on the Pinterest image collection
- …