OpenRaiser/Intern-Atlas huggingface dataset:
Discrete Latent Factor Model for Cross-Modal Hashing
Venue: arXiv cs.IR 2017
Year: 2017

Due to its storage and retrieval efficiency, cross-modal hashing~(CMH) has been widely used for cross-modal similarity search in multimedia applications. According to the training strategy, existing CMH methods can be mainly divided into two categories: relaxation-based continuous methods and discrete methods. In general, the training of relaxation-based continuous methods is faster than discrete methods, but the accuracy of relaxation-based continuous methods is not satisfactory. On the contrary, the accuracy of discrete methods is typically better than relaxation-based continuous methods, but the training of discrete methods is time-consuming. In this paper, we propose a novel CMH method, called discrete latent factor model based cross-modal hashing~(DLFH), for cross modal similarity search. DLFH is a discrete method which can directly learn the binary hash codes for CMH. At the same time, the training of DLFH is efficient. Experiments on real datasets show that DLFH can achieve significantly better accuracy than existing methods, and the training time of DLFH is comparable to that of relaxation-based continuous methods which are much faster than existing discrete methods.
Asymmetric Deep Supervised Hashing
Venue: arXiv cs.LG 2017
Year: 2017

Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a symmetric strategy to learn one deep hash function for both query points and database (retrieval) points. The training of these symmetric deep supervised hashing methods is typically time-consuming, which makes them hard to effectively utilize the supervised information for cases with large-scale database. In this paper, we propose a novel deep supervised hashing method, called asymmetric deep supervised hashing (ADSH), for large-scale nearest neighbor search. ADSH treats the query points and database points in an asymmetric way. More specifically, ADSH learns a deep hash function only for query points, while the hash codes for database points are directly learned. The training of ADSH is much more efficient than that of traditional symmetric deep supervised hashing methods. Experiments show that ADSH can achieve state-of-the-art performance in real applications.
Composition problems for braids: Membership, Identity and Freeness
Venue: arXiv cs.CC 2017
Year: 2017

In this paper we investigate the decidability and complexity of problems related to braid composition. While all known problems for a class of braids with three strands, $B_3$, have polynomial time solutions we prove that a very natural question for braid composition, the membership problem, is NP-complete for braids with only three strands. The membership problem is decidable in NP for $B_3$, but it becomes harder for a class of braids with more strands. In particular we show that fundamental problems about braid compositions are undecidable for braids with at least five strands, but decidability of these problems for $B_4$ remains open. Finally we show that the freeness problem for semigroups of braids from $B_3$ is also decidable in NP. The paper introduces a few challenging algorithmic problems about topological braids opening new connections between braid groups, combinatorics on words, complexity theory and provides solutions for some of these problems by application of several techniques from automata theory, matrix semigroups and algorithms.
Making the best of data derived from a daily practice in clinical legal medicine for research and practice - the example of Spe3dLab
Venue: arXiv cs.CY 2017
Year: 2017

Forensic science suffers from a lack of studies with high-quality design, such as randomized controlled trials (RCT). Evidence in forensic science may be of insufficient quality, which is a major concern. Results from RCT are criticized for providing artificial results that are not useful in real life and unfit for individualized prescription. Various sources of collected data (e.g. data collected in routine practice) could be exploited for distinct goals. Obstacles remain before such data can be practically accessed and used, including technical issues. We present an easy-to-use software dedicated to innovative data analyses for practitioners and researchers. We provide 2 examples in forensics. Spe3dLab has been developed by 3 French teams: a bioinformatics laboratory (LaTIM), a private partner (Tekliko) and a department of forensic medicine (Jean Verdier Hospital). It was designed to be open source, relying on documented and maintained libraries, query-oriented and capable of handling the entire data process from capture to export of best predictive models for their integration in information systems. Spe3dLab was used for 2 specific forensics applications: i) the search for multiple causal factors and ii) the best predictive model of the functional impairment (total incapacity to work, TIW) of assault survivors. 2,892 patients were included over a 6-month period. Time to evaluation was the only direct cause identified for TIW, and victim category was an indirect cause. The specificity and sensitivity of the predictive model were 99.9% and 90%, respectively. Spe3dLab is a quick and efficient tool for accessing observational, routinely collected data and performing innovative analyses. Analyses can be exported for validation and routine use by practitioners, e.g., for computer-aided evaluation of complex problems. It can provide a fully integrated solution for individualized medicine.
A Decidable Very Expressive Description Logic for Databases (Extended Version)
Venue: arXiv cs.AI 2017
Year: 2017

We introduce $\mathcal{DLR}^+$, an extension of the n-ary propositionally closed description logic $\mathcal{DLR}$ to deal with attribute-labelled tuples (generalising the positional notation), projections of relations, and global and local objectification of relations, able to express inclusion, functional, key, and external uniqueness dependencies. The logic is equipped with both TBox and ABox axioms. We show how a simple syntactic restriction on the appearance of projections sharing common attributes in a $\mathcal{DLR}^+$ knowledge base makes reasoning in the language decidable with the same computational complexity as $\mathcal{DLR}$. The obtained $\mathcal{DLR}^\pm$ n-ary description logic is able to encode more thoroughly conceptual data models such as EER, UML, and ORM.
Guidelines for Artificial Intelligence Containment
Venue: arXiv cs.AI 2017
Year: 2017

With almost daily improvements in capabilities of artificial intelligence it is more important than ever to develop safety software for use by the AI research community. Building on our previous work on AI Containment Problem we propose a number of guidelines which should help AI safety researchers to develop reliable sandboxing software for intelligent programs of all levels. Such safety container software will make it possible to study and analyze intelligent artificial agent while maintaining certain level of safety against information leakage, social engineering attacks and cyberattacks from within the container.
Resilient Energy Allocation Model for Supply Shortage Outages
Venue: arXiv cs.SY 2017
Year: 2017

Supply Shortage Outages are a major concern during peak demand for developing countries. In the Philippines, commercial loads have unused backup generation of up to 3000 MW, at the same time there are shortages of as much as 700 MW during peak demand. This gives utilities the incentive to implement Demand Response programs to minimize this shortage. But when considering Demand Response from a modeling perspective, social welfare through profit is always the major objective for program implementation. That isn't always the case during an emergency situation as there can be a trade-off between grid resilience and cost of electricity. The question is how the Distribution Utility (DU) shall optimally allocate the unused generation to meet the shortage when this trade-off exists. We formulate a combined multi-objective optimal dispatch model where we can make a direct comparison between the least-cost and resilience objectives. We find that this trade-off is due to the monotonically increasing nature of energy cost functions. If the supply is larger than the demand, the DU can perform a least-cost approach in the optimal dispatch since maximizing the energy generated in this case can lead to multiple solutions. We also find in our simulation that in cases where the supply of energy from the customers is less than shortage quantity, the DU must prioritize maximizing the generated energy rather than minimizing cost.
A compositional modeling framework for the optimal energy management of a district network
Venue: arXiv cs.SY 2017
Year: 2017

This paper proposes a compositional modeling framework for the optimal energy management of a district network. The focus is on cooling of buildings, which can possibly share resources to the purpose of reducing maintenance costs and using devices at their maximal efficiency. Components of the network are described in terms of energy fluxes and combined via energy balance equations. Disturbances are accounted for as well through their contribution in terms of energy. Different district configurations can be built, and the dimension and complexity of the resulting model will depend on the number and type of components and on the adopted disturbance description. Control inputs are available to efficiently operate and coordinate the district components, thus enabling energy management strategies to minimize the electrical energy costs or track some consumption profile agreed with the main grid operator.
The Shape Metric for Clustering Algorithms
Venue: arXiv cs.CG 2017
Year: 2017

We construct a method by which we can calculate the precision with which an algorithm identifies the shape of a cluster. We present our results for several well known clustering algorithms and suggest ways to improve performance for newer algorithms.
Guiding Reinforcement Learning Exploration Using Natural Language
Venue: arXiv cs.AI 2017
Year: 2017

In this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments. This technique uses neural machine translation, specifically the use of encoder-decoder networks, to learn associations between natural language behavior descriptions and state-action information. We then use this learned model to guide agent exploration using a modified version of policy shaping to make it more effective at learning in unseen environments. We evaluate this technique using the popular arcade game, Frogger, under ideal and non-ideal conditions. This evaluation shows that our modified policy shaping algorithm improves over a Q-learning agent as well as a baseline version of policy shaping.
n-Dimensional Fuzzy Negations
Venue: arXiv cs.LO 2017
Year: 2017

n-Dimensional fuzzy sets is a fuzzy set extension where the membership values are n-tuples of real numbers in the unit interval [0,1] orderly increased, called n-dimensional intervals. The set of n-dimensional intervals is denoted by Ln([0,1]). This paper aims to investigate a special extension from [0,1] - n-representable fuzzy negations on Ln([0,1]), summarizing the class of such functions which are continuous and monotone by part. The main properties of (strong) fuzzy negations on [0,1] are preserved by representable (strong) fuzzy negation on Ln([0,1]), mainly related to the analysis of degenerate elements and equilibrium points. The conjugate obtained by action of an n-dimensional automorphism on an $n$-dimensional fuzzy negation provides a method to obtain other n-dimensional fuzzy negation, in which properties such as representability, continuity and monotonicity on Ln([0,1]) are preserved.
Digital Forensic Approaches for Amazon Alexa Ecosystem
Venue: arXiv cs.CR 2017
Year: 2017

Internet of Things devices such as the Amazon Echo are undoubtedly great sources of potential digital evidence due to their ubiquitous use and their always on mode of operation, constituting a human life black box. The Amazon Echo in particular plays a centric role for the cloud based intelligent virtual assistant Alexa developed by Amazon Lab126. The Alexa enabled wireless smart speaker is the gateway for all voice commands submitted to Alexa. Moreover, the IVA interacts with a plethora of compatible IoT devices and third party applications that leverage cloud resources. Understanding the complex cloud ecosystem that allows ubiquitous use of Alexa is paramount on supporting digital investigations when need raises. This paper discusses methods for digital forensics pertaining to the IVA Alexa ecosystem. The primary contribution of this paper consists of a new efficient approach of combining cloud native forensics with client side forensics, to support practical digital investigations. Based on a deep understanding of the targeted ecosystem, we propose a proof of concept tool, CIFT, that supports identification, acquisition and analysis of both native artifacts from the cloud and client centric artifacts from local devices.
Safety Requirement Specifications for Connected Vehicles
Venue: arXiv cs.CY 2017
Year: 2017

In the coming years, transportation system will be revamped in a manner that there will be more intelligent and autonomous vehicle phenomenon around us such as smart cars, auto driving system, etc. Some of automotive industries are already producing smart cars. However, the main concern of this paper is on the infrastructure for connected vehicles, which can support such intelligent transportation. Current transportation system lacks proper infrastructure to support connected vehicles. Hence, in this article, we have surveyed and analyzed the current transportation system in developed and developing countries. In contrast, we are going to introduce secure intelligent transportation (roadside) infrastructure that is user centric (Driver, Autonomous driver etc.) for connected vehicles. In this paper we present the basic requirements of safety engineering infrastructure of roadside infrastructure in ITS for connected vehicles. Connected vehicles has network infrastructure to communicate with vehicle-to-vehicle (V-to-V), vehicle-to-infrastructure (V-to-I), lane correction system, and traffic information system etc. The connected vehicle is a good model for learning demands of infrastructure for ITS process because the system having a lot of use-cases and we must understand relationship between public institutions, people, companies in order to proceed ITS System.
Common Knowledge in a Logic of Gossips
Venue: arXiv cs.LO 2017
Year: 2017

Gossip protocols aim at arriving, by means of point-to-point or group communications, at a situation in which all the agents know each other secrets. Recently a number of authors studied distributed epistemic gossip protocols. These protocols use as guards formulas from a simple epistemic logic, which makes their analysis and verification substantially easier. We study here common knowledge in the context of such a logic. First, we analyze when it can be reduced to iterated knowledge. Then we show that the semantics and truth for formulas without nested common knowledge operator are decidable. This implies that implementability, partial correctness and termination of distributed epistemic gossip protocols that use non-nested common knowledge operator is decidable, as well. Given that common knowledge is equivalent to an infinite conjunction of nested knowledge, these results are non-trivial generalizations of the corresponding decidability results for the original epistemic logic, established in (Apt & Wojtczak, 2016). K. R. Apt & D. Wojtczak (2016): On Decidability of a Logic of Gossips. In Proc. of JELIA 2016, pp. 18-33, doi:10.1007/ 978-3-319-48758-8_2.
From Type Spaces to Probability Frames and Back, via Language
Venue: arXiv cs.LO 2017
Year: 2017

We investigate the connection between the two major mathematical frameworks for modeling interactive beliefs: Harsanyi type spaces and possible-worlds style probability frames. While translating the former into the latter is straightforward, we demonstrate that the reverse translation relies implicitly on a background logical language. Once this "language parameter" is made explicit, it reveals a close relationship between universal type spaces and canonical models: namely, that they are essentially the same construct. As the nature of a canonical model depends heavily on the background logic used to generate it, this work suggests a new view into a corresponding landscape of universal type spaces.
Preservation of Semantic Properties during the Aggregation of Abstract Argumentation Frameworks
Venue: arXiv cs.AI 2017
Year: 2017

An abstract argumentation framework can be used to model the argumentative stance of an agent at a high level of abstraction, by indicating for every pair of arguments that is being considered in a debate whether the first attacks the second. When modelling a group of agents engaged in a debate, we may wish to aggregate their individual argumentation frameworks to obtain a single such framework that reflects the consensus of the group. Even when agents disagree on many details, there may well be high-level agreement on important semantic properties, such as the acceptability of a given argument. Using techniques from social choice theory, we analyse under what circumstances such semantic properties agreed upon by the individual agents can be preserved under aggregation.
Coalition and Group Announcement Logic
Venue: arXiv cs.LO 2017
Year: 2017

Dynamic epistemic logics which model abilities of agents to make various announcements and influence each other's knowledge have been studied extensively in recent years. Two notable examples of such logics are Group Announcement Logic and Coalition Announcement Logic. They allow us to reason about what groups of agents can achieve through joint announcements in non-competitive and competitive environments. In this paper, we consider a combination of these logics -- Coalition and Group Announcement Logic and provide its complete axiomatisation. Moreover, we partially answer the question of how group and coalition announcement operators interact, and settle some other open problems.
A Knowledge-Based Analysis of the Blockchain Protocol
Venue: arXiv cs.CR 2017
Year: 2017

At the heart of the Bitcoin is a blockchain protocol, a protocol for achieving consensus on a public ledger that records bitcoin transactions. To the extent that a blockchain protocol is used for applications such as contract signing and making certain transactions (such as house sales) public, we need to understand what guarantees the protocol gives us in terms of agents' knowledge. Here, we provide a complete characterization of agent's knowledge when running a blockchain protocol using a variant of common knowledge that takes into account the fact that agents can enter and leave the system, it is not known which agents are in fact following the protocol (some agents may want to deviate if they can gain by doing so), and the fact that the guarantees provided by blockchain protocols are probabilistic. We then consider some scenarios involving contracts and show that this level of knowledge suffices for some scenarios, but not others.
Condorcet's Principle and the Preference Reversal Paradox
Venue: arXiv cs.GT 2017
Year: 2017

We prove that every Condorcet-consistent voting rule can be manipulated by a voter who completely reverses their preference ranking, assuming that there are at least 4 alternatives. This corrects an error and improves a result of [Sanver, M. R. and Zwicker, W. S. (2009). One-way monotonicity as a form of strategy-proofness. Int J Game Theory 38(4), 553-574.] For the case of precisely 4 alternatives, we exactly characterise the number of voters for which this impossibility result can be proven. We also show analogues of our result for irresolute voting rules. We then leverage our result to state a strong form of the Gibbard-Satterthwaite Theorem.
An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions
Venue: arXiv cs.NE 2017
Year: 2017

This paper proposes an improved epsilon constraint-handling mechanism, and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions (RFS) in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then the fourteen benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and C-MOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem.
Analysis of Italian Word Embeddings
Venue: arXiv cs.CL 2017
Year: 2017

In this work we analyze the performances of two of the most used word embeddings algorithms, skip-gram and continuous bag of words on Italian language. These algorithms have many hyper-parameter that have to be carefully tuned in order to obtain accurate word representation in vectorial space. We provide an accurate analysis and an evaluation, showing what are the best configuration of parameters for specific tasks.
Providing Self-Aware Systems with Reflexivity
Venue: arXiv cs.AI 2017
Year: 2017

We propose a new type of self-aware systems inspired by ideas from higher-order theories of consciousness. First, we discussed the crucial distinction between introspection and reflexion. Then, we focus on computational reflexion as a mechanism by which a computer program can inspect its own code at every stage of the computation. Finally, we provide a formal definition and a proof-of-concept implementation of computational reflexion, viewed as an enriched form of program interpretation and a way to dynamically "augment" a computational process.
Multi-Stakeholder Recommendation: Applications and Challenges
Venue: arXiv cs.IR 2017
Year: 2017

Recommender systems have been successfully applied to assist decision making by producing a list of item recommendations tailored to user preferences. Traditional recommender systems only focus on optimizing the utility of the end users who are the receiver of the recommendations. By contrast, multi-stakeholder recommendation attempts to generate recommendations that satisfy the needs of both the end users and other parties or stakeholders. This paper provides an overview and discussion about the multi-stakeholder recommendations from the perspective of practical applications, available data sets, corresponding research challenges and potential solutions.