10,171 research outputs found
Hashing based Answer Selection
Answer selection is an important subtask of question answering (QA), where
deep models usually achieve better performance. Most deep models adopt
question-answer interaction mechanisms, such as attention, to get vector
representations for answers. When these interaction based deep models are
deployed for online prediction, the representations of all answers need to be
recalculated for each question. This procedure is time-consuming for deep
models with complex encoders like BERT which usually have better accuracy than
simple encoders. One possible solution is to store the matrix representation
(encoder output) of each answer in memory to avoid recalculation. But this will
bring large memory cost. In this paper, we propose a novel method, called
hashing based answer selection (HAS), to tackle this problem. HAS adopts a
hashing strategy to learn a binary matrix representation for each answer, which
can dramatically reduce the memory cost for storing the matrix representations
of answers. Hence, HAS can adopt complex encoders like BERT in the model, but
the online prediction of HAS is still fast with a low memory cost. Experimental
results on three popular answer selection datasets show that HAS can outperform
existing models to achieve state-of-the-art performance
A retrieval-based dialogue system utilizing utterance and context embeddings
Finding semantically rich and computer-understandable representations for
textual dialogues, utterances and words is crucial for dialogue systems (or
conversational agents), as their performance mostly depends on understanding
the context of conversations. Recent research aims at finding distributed
vector representations (embeddings) for words, such that semantically similar
words are relatively close within the vector-space. Encoding the "meaning" of
text into vectors is a current trend, and text can range from words, phrases
and documents to actual human-to-human conversations. In recent research
approaches, responses have been generated utilizing a decoder architecture,
given the vector representation of the current conversation. In this paper, the
utilization of embeddings for answer retrieval is explored by using
Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor
(ANN) model, to find similar conversations in a corpus and rank possible
candidates. Experimental results on the well-known Ubuntu Corpus (in English)
and a customer service chat dataset (in Dutch) show that, in combination with a
candidate selection method, retrieval-based approaches outperform generative
ones and reveal promising future research directions towards the usability of
such a system.Comment: A shorter version is accepted at ICMLA2017 conference;
acknowledgement added; typos correcte
Why Do Developers Get Password Storage Wrong? A Qualitative Usability Study
Passwords are still a mainstay of various security systems, as well as the
cause of many usability issues. For end-users, many of these issues have been
studied extensively, highlighting problems and informing design decisions for
better policies and motivating research into alternatives. However, end-users
are not the only ones who have usability problems with passwords! Developers
who are tasked with writing the code by which passwords are stored must do so
securely. Yet history has shown that this complex task often fails due to human
error with catastrophic results. While an end-user who selects a bad password
can have dire consequences, the consequences of a developer who forgets to hash
and salt a password database can lead to far larger problems. In this paper we
present a first qualitative usability study with 20 computer science students
to discover how developers deal with password storage and to inform research
into aiding developers in the creation of secure password systems
The Stability and the Security of the Tangle
In this paper we study the stability and the security of the distributed data structure at the base of the IOTA protocol, called the Tangle. The contribution of this paper is twofold. First, we present a simple model to analyze the Tangle and give the first discrete time formal analyzes of the average number of unconfirmed transactions and the average confirmation time of a transaction.
Then, we define the notion of assiduous honest majority that captures the fact that the honest nodes have more hashing power than the adversarial nodes and that all this hashing power is constantly used to create transactions. This notion is important because we prove that it is a necessary assumption to protect the Tangle against double-spending attacks, and this is true for any tip selection algorithm (which is a fundamental building block of the protocol) that verifies some reasonable assumptions. In particular, the same is true with the Markov Chain Monte Carlo selection tip algorithm currently used in the IOTA protocol.
Our work shows that either all the honest nodes must constantly use all their hashing power to validate the main chain (similarly to the Bitcoin protocol) or some kind of authority must be provided to avoid this kind of attack (like in the current version of the IOTA where a coordinator is used).
The work presented here constitute a theoretical analysis and cannot be used to attack the current IOTA implementation. The goal of this paper is to present a formalization of the protocol and, as a starting point, to prove that some assumptions are necessary in order to defend the system again double-spending attacks. We hope that it will be used to improve the current protocol with a more formal approach
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
We tackle image question answering (ImageQA) problem by learning a
convolutional neural network (CNN) with a dynamic parameter layer whose weights
are determined adaptively based on questions. For the adaptive parameter
prediction, we employ a separate parameter prediction network, which consists
of gated recurrent unit (GRU) taking a question as its input and a
fully-connected layer generating a set of candidate weights as its output.
However, it is challenging to construct a parameter prediction network for a
large number of parameters in the fully-connected dynamic parameter layer of
the CNN. We reduce the complexity of this problem by incorporating a hashing
technique, where the candidate weights given by the parameter prediction
network are selected using a predefined hash function to determine individual
weights in the dynamic parameter layer. The proposed network---joint network
with the CNN for ImageQA and the parameter prediction network---is trained
end-to-end through back-propagation, where its weights are initialized using a
pre-trained CNN and GRU. The proposed algorithm illustrates the
state-of-the-art performance on all available public ImageQA benchmarks
Amorphous Placement and Retrieval of Sensory Data in Sparse Mobile Ad-Hoc Networks
Abstract—Personal communication devices are increasingly being equipped with sensors that are able to passively collect information from their surroundings – information that could be stored in fairly small local caches. We envision a system in which users of such devices use their collective sensing, storage, and communication resources to query the state of (possibly remote) neighborhoods. The goal of such a system is to achieve the highest query success ratio using the least communication overhead (power). We show that the use of Data Centric Storage (DCS), or directed placement, is a viable approach for achieving this goal, but only when the underlying network is well connected. Alternatively, we propose, amorphous placement, in which sensory samples are cached locally and informed exchanges of cached samples is used to diffuse the sensory data throughout the whole network. In handling queries, the local cache is searched first for potential answers. If unsuccessful, the query is forwarded to one or more direct neighbors for answers. This technique leverages node mobility and caching capabilities to avoid the multi-hop communication overhead of directed placement. Using a simplified mobility model, we provide analytical lower and upper bounds on the ability of amorphous placement to achieve uniform field coverage in one and two dimensions. We show that combining informed shuffling of cached samples upon an encounter between two nodes, with the querying of direct neighbors could lead to significant performance improvements. For instance, under realistic mobility models, our simulation experiments show that amorphous placement achieves 10% to 40% better query answering ratio at a 25% to 35% savings in consumed power over directed placement.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067
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