bluuebunny/crossref_metadata_2025 huggingface dataset:
Suitability of different primers for specific molecular detection of Monilinia spp.
Authors: Duduk Natasa , Vasic Miljan , Vuckovic Nina , Zebeljan Aleksandra , Vico Ivana
Published: July 2017

Monilinia spp. are economically important pathogens of pome and stone fruits.
Four Monilinia species are present in Serbia - Monilinia fructigena, M. laxa,
M. fructicola and Monilia polystroma. As detection and identification of
Monilinia species are complex, the aim of this research was to evaluate
species-specific primers in PCR in order to standardize fast and reliable
molecular methods for differentiation between the four Monilinia species.
Isolates of M. fructigena, M. laxa, M. fructicola and M. polystroma from
apple fruit and referent isolates from Italy and Japan were used for testing.
Specific molecular detection of M. laxa was obtained using ITS1Mlx/ITS4Mlx
and Ml-Mfg-F2/Ml-Mfc-R1 primer pairs, and M. fructicola using
ITS1Mfcl/ITS4Mfcl and Mfc-F1/Mfc-R1 primer pairs. ITS1Mfgn/ITS4Mfgn and
ITS1/Mfg-R2 primer pairs, described as M. fructigena species-specific,
amplified M. fructigena and M. polystroma, as well. Specific detection of
these two species as well as of all four tested Monilinia species was
obtained using the reverse primer MO368-5 with forward primers MO368-8R,
Laxa-R2 and MO368-10R in separate or in Multiplex PCR reactions.
Introductory Audit Homework Handouts: A Bridge from Class to Auditors' Professional Responsibilities
Authors: Hansen James
Published: July 2017

Three Homework Handouts are presented for use by instructors in undergraduate or graduate introductory audit courses. The Homework Handouts give students an opportunity to bridge the material they are learning in class to real-world situations they may be facing in the auditing profession. The Homework Handouts focus on auditor switches, independence, ethical dilemmas, and PCAOB inspection reports. Students complete the Homework Handouts throughout a semester of introductory audit. After completing each assignment, students should be ready for class discussion.
Comparison of Ipragliflozin and Pioglitazone Effects on Nonalcoholic Fatty Liver Disease in Patients With Type 2 Diabetes: A Randomized, 24-Week, Open-Label, Active-Controlled Trial
Authors: Ito Daisuke , Shimizu Satoshi , Inoue Kazuyuki , Saito Daigo , Yanagisawa Morifumi , Inukai Kouichi , Akiyama Yuji , Morimoto Yoshihiro , Noda Mitsuhiko , Shimada Akira
Published: July 2017

OBJECTIVE

To compare the efficacy of ipragliflozin versus pioglitazone in patients with type 2 diabetes complicated by nonalcoholic fatty liver disease (NAFLD).

RESEARCH DESIGN AND METHODS

In this open-label, randomized, active-controlled trial, we randomly assigned 66 patients with type 2 diabetes and NAFLD to receive ipragliflozin 50 mg (n = 32) or pioglitazone 15–30 mg (n = 34) orally once daily. The primary outcome was a change from baseline in the liver-to-spleen attenuation ratio (L/S ratio) on computed tomography at week 24.

RESULTS

At week 24, the mean ± SD L/S ratio had increased by 0.22 (from 0.80 ± 0.24 to 1.00 ± 0.18) in the ipragliflozin group and 0.21 (from 0.78 ± 0.26 to 0.98 ± 0.16) in the pioglitazone group (P = 0.90). Serum aspartate and alanine aminotransferase levels, HbA1c, and fasting plasma glucose were similarly reduced in the two treatment groups. Nevertheless, body weight and visceral fat area showed significant reductions only in the ipragliflozin group compared with the pioglitazone group (P < 0.0001 and P = 0.0013, respectively). There were no serious adverse events in either group.

CONCLUSIONS

Compared with pioglitazone, ipragliflozin exerts equally beneficial effects on NAFLD and glycemic control during the treatment of patients with type 2 diabetes complicated by NAFLD. Furthermore, ipragliflozin significantly reduced body weight and abdominal fat area.
Assessment the knowledge of mothers ‘toward home accident among children less than five years in Polytechnic University of Sulaimani
Authors: Qasem Attallah O. , Abas Layla A. , Hama Hussein Burhan A. , Qadir Muhammed S. , Salih Faisal Abdula
Published: July 2017

Children accidents are an essential source of death and wounds among children during the first of life. Annually, millions of children are constantly becoming handicap or blemished because of mishaps.Objective: To assess knowledge of children mothers toward home accidents in polytechnic University of Sulaimani. Methodology: A simple random sample of (110) respondents were selected.. The data collected and composed during the utilization of a created questionnaire, with two parts; the first part assessed the knowledge of children mothers with (20) yes or no questions and the second part includes the mothers' demographic attributes. A pilot study was carried out for the period of December 18th to 28th 2015 in order to conclude the questionnaire accuracy during the use of (Test – Retest). A panel of (8) experts was include in the determination of the questionnaire content validity Data were analyzed through the application of descriptive statistical data analysis approach (frequency and percentage), and inferential data analysis approach (Mean of Scores).).The results of the study revealed, the mean age of the mothers was (1.86) years, (58.2%) of them had completed secondary school, (88.2%) of mothers have more than five children, (89.1%) of mother’s were in moderate socio-economic status, (47.3%) of mothers have personal reading information,(58.2) of the child were male,(13.6%) of mothers have knowledge about safe housing condition held in Maternal and Child Health Centers.The conclusion of this study revealed that, inadequate children mothers’ knowledge regarding home accidents among children less than six years held in Maternal and Child Health Centers during the antenatal period. So the recommendation are suggested, reinforces the need for effective education program via mass media, for mothers.
El cancionero de la Sablonara. Ed., introd. y notas Judith Etzion. Tamesis, London, 1996; clxiii + 254 pp.
Authors: Frenk Margit
Published: July 2017

Se reseñó el libro: El cancionero de la Sablonara. Ed., introd. y notas Judith Etzion.
Luis Estepa, La colección madrileña de romances de ciego que perteneció a don Luis Usoz y Río. Pról. de jean-François Botrel; diálogo con Luis Díaz Viana. Comunidad, Madrid, 1998; 746 pp.
Authors: Pedrosa José Manuel
Published: July 2017

Se reseñó el libro: La colección madrileña de romances de ciego que perteneció a don Luis Usoz y Río.
Edna Aizenber, Borges, el tejedor del Aleph y otros ensayos: del hebraísmo al poscolonialismo. Vervuert-Iberoamericana, Frankfurt/M.-Madrid, 1997; 169 pp.
Authors: Márquez Aguayo César Alejandro
Published: July 2017

Se reseñó el libro: Borges, el tejedor del Aleph y otros ensayos: del hebraísmo al poscolonialismo.
Francisco de la Torre y su muy probable patria: Santa Fe de Bogotá
Authors: Alatorre Antonio
Published: July 2017

En este número no se incluyeron resúmenes ni palabras clave.
Historia, razón de Estado y burocracia. Antonio de Solís contra Jerónimo Mascareñas (1662-1663)
Authors: Cid Jesús Antonio
Published: July 2017

En este número no se incluyeron resúmenes ni palabras clave.
Juan Bautista Corvera, Obra literaria. Ed. y est. de Sergio López Mena. UNAM, México, 1995; 177 pp. (Letras de la Nueva España, 1). / Hombres y letras del virreinato. Homenaje a Humberto Maldonado. Ed. de José Quiñones Melgoza y Ma. Elena Victoria Jardón. UNAM, México, 1995; 390 pp. (Letras de la Nueva España, 2).
Authors: Alatorre Antonio
Published: July 2017

Se reseñaron los libros: Obra literaria de Juan Bautista Corvera y Hombres y letras del virreinato. Homenaje a Humberto Maldonado.
Hugo J. Verani, De la vanguardia a la posmodernidad: narrativa uruguaya (1920-1995). Trilce, Montevideo, 1996; 238 pp.
Authors: Corral Rose
Published: July 2017

Se reseñó el libro: De la vanguardia a la posmodernidad: narrativa uruguaya (1920-1995).
PSIKOTERAPI ISLAMI TERHADAP PSIKOPATOLOGI (Perspektif Psikologi Pendidikan Islam)
Authors: Wahidah Evita Yuliatul
Published: July 2017

In a review of psychology, psychopathology can be started from three assumptions. First, the Freudian assumed that the human soul was basically born in sickness, evil, negative / destructive. To be positive, it need companion ways that are impersonal and directive / directing. Second, as a behaviorist, Skinner assumed that the human soul was born in neutral (not sick and unhealthy), where the environment plays a role in determining the direction of its development. Third, Maslow and Rogers as a humanistic figure assumed that the human soul was born in a state of conscious, free, responsible and guided by positive forces emanating from itself to the expansion of all human potential to its fullest. Whereas in Islamic studies of psychopathology can be divided into two categories, namely the worldly, as has been formulated by contemporary psychology, and the hereafter. This paper attempts to explain kinds of psychotherapy according to the study of Islam and how Islam is doing its part in the psychotherapy world and ukhrowi psychopathology. Discussion of Islam against psychopathology of psychotherapy becomes important. In addition to see the growing of Islamization method, as well as to explore therapist aspects in Islam based on the Qur'an and Hadith. The Islamic Psychotherapy is also to provide an alternative to the criticisms made against modern psychology that break away from the values of divinity. This paper begins his discussion of the notion of Psychopathology, Islamic psychotherapy understanding and the existing concept of the theories.
On Neighborhood Singleton Consistencies
Authors: Paparrizou Anastasia , Stergiou Kostas
Published: July 2017

CP solvers predominantly use arc consistency (AC) as the default propagation method. Many stronger consistencies, such as triangle consistencies (e.g. RPC and maxRPC) exist, but their use is limited despite results showing that they outperform AC on many problems. This is due to the intricacies involved in incorporating them into solvers. On the other hand, singleton consistencies such as SAC can be easily crafted into solvers but they are too expensive. We seek a balance between the efficiency of triangle consistencies and the ease of implementation of singleton ones. Using the recently proposed variant of SAC called Neighborhood SAC as basis, we propose a family of weaker singleton consistencies. We study them theoretically, comparing their pruning power to existing consistencies. We make a detailed experimental study using a very simple algorithm for their implementation. Results demonstrate that they outperform the existing propagation techniques, often by orders of magnitude, on a wide range of problems.
Temporal Planning with Clock-Based SMT Encodings
Authors: Rintanen Jussi
Published: July 2017

We propose more scalable encodings of temporal planning in SMT. The first contribution is practical clock-based encodings of resources and effect delays. Existing encodings of effect delays (Shin and Davis, 2015) have a quadratic size, due to the necessity to determine the time differences between steps for a linear number of steps. Clocks improve this to linear. The second contribution is a new relaxed scheme for steps. Existing schemes require a step for every time point with discontinuous change. This is relaxed, improving scalability.
A Study of Unrestricted Abstract Argumentation Frameworks
Authors: Baumann Ringo , Spanring Christof
Published: July 2017

Research in abstract argumentation typically per-tains to finite argumentation frameworks (AFs). Ac-tual or potential infinite AFs frequently occur if theyare used for the purpose of nonmonotonic entail-ment, so-called instantiation-based argumentation,or if they are involved as modeling tool for dia-logues, n-person-games or action sequences. Apartfrom these practical cases a profound analysis yieldsa better understanding of how the nonmonotonic the-ory of abstract argumentation works in general. Inthis paper we study a bunch of abstract propertieslike SCC-recursiveness, expressiveness or intertrans-latability for unrestricted AFs.
Ontology-Mediated Query Answering for Key-Value Stores
Authors: Bienvenu Meghyn , Bourhis Pierre , Mugnier Marie-Laure , Tison Sophie , Ulliana Federico
Published: July 2017

We propose a novel rule-based ontology language for JSON records and investigate its computational properties. After providing a natural translation into first-order logic, we identify relationships to existing ontology languages, which yield decidability of query answering but only rough complexity bounds. By establishing an interesting and non-trivial connection to word rewriting, we are able to pinpoint the exact combined complexity of query answering in our framework and obtain tractability results for data complexity. The upper bounds are proven using a query reformulation technique, which can be implemented on top of key-value stores, thereby exploiting their querying facilities.
Learning from Ontology Streams with Semantic Concept Drift
Authors: Chen Jiaoyan , Lecue Freddy , Pan Jeff Z. , Chen Huajun
Published: July 2017

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.
Relatedness-based Multi-Entity Summarization
Authors: Gunaratna Kalpa , Yazdavar Amir Hossein , Thirunarayan Krishnaprasad , Sheth Amit , Cheng Gong
Published: July 2017

Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple's Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-of-the-art entity summarization approaches.
Combining DL-Lite_{bool}^N with Branching Time: A gentle Marriage
Authors: Gutiérrez-Basulto Víctor , Jung Jean Christoph
Published: July 2017

We study combinations of the description logic DL-Lite_{bool}^N with the branching temporal logics CTL* and CTL. We analyse two types of combinations, both with rigid roles: (i) temporal operators are applied to concepts and to ABox assertions, and (ii) temporal operators are applied to concepts and Boolean combinations of concept inclusions and ABox assertions. For the resulting logics, we present algorithms for the satisfiability problem and (mostly tight) complexity bounds ranging from ExpTime to 3ExpTime.
Parameterised Verification of Data-aware Multi-Agent Systems
Authors: Belardinelli Francesco , Kouvaros Panagiotis , Lomuscio Alessio
Published: July 2017

We introduce parameterised data-aware multi-agent systems, a formalism to reason about the temporal-epistemic properties of arbitrarily large collections of homogeneous agents, each operating on an infinite data domain. We show that their parameterised verification problem is semi-decidable for classes of interest. This is demonstrated by separately addressing the unboundedness of the number of agents and the the data domain. In doing so we reduce the parameterised model checking problem for these systems to that of parameterised verification for interleaved interpreted systems. We illustrate the expressivity of the formal model by modelling English auctions with an unbounded number of bidders on unbouded data and show how the technique here introduced can be used to give formal guarantees on the resulting system behaviour.
Characterising the Manipulability of Boolean Games
Authors: Harrenstein Paul , Turrini Paolo , Wooldridge Michael
Published: July 2017

The existence of (Nash) equilibria with undesirable properties is a well-known problem in game theory, which has motivated much research directed at the possibility of mechanisms for modifying games in order to eliminate undesirable equilibria, or induce desirable ones. Taxation schemes are a well-known mechanism for modifying games in this way. In the multi-agent systems community, taxation mechanisms for incentive engineering have been studied in the context of Boolean games with costs. These are games in which each player assigns truth-values to a set of propositional variables she uniquely controls in pursuit of satisfying an individual propositional goal formula; different choices for the player are also associated with different costs. In such a game, each player prefers primarily to see the satisfaction of their goal, and secondarily, to minimise the cost of their choice, thereby giving rise to lexicographic preferences over goal-satisfaction and costs. Within this setting, where taxes operate on costs only, however, it may well happen that the elimination or introduction of equilibria can only be achieved at the cost of simultaneously introducing less desirable equilibria or eliminating more attractive ones. Although this framework has been studied extensively, the problem of precisely characterising the equilibria that may be induced or eliminated has remained open. In this paper we close this problem, giving a complete characterisation of those mechanisms that can induce a set of outcomes of the game to be exactly the set of Nash Equilibrium outcomes.
Induction of Interpretable Possibilistic Logic Theories from Relational Data
Authors: Kuzelka Ondrej , Davis Jesse , Schockaert Steven
Published: July 2017

The field of statistical relational learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kind of weighted logical formulas, which makes them considerably more interpretable than those obtained by e.g. neural networks. In practice, however, these models are often still difficult to interpret correctly, as they can contain many formulas that interact in non-trivial ways and weights do not always have an intuitive meaning. To address this, we propose a new SRL method which uses possibilistic logic to encode relational models. Learned models are then essentially stratified classical theories, which explicitly encode what can be derived with a given level of certainty. Compared to Markov Logic Networks (MLNs), our method is faster and produces considerably more interpretable models.
Logic on MARS: Ontologies for Generalised Property Graphs
Authors: Marx Maximilian , Krötzsch Markus , Thost Veronika
Published: July 2017

Graph-structured data is used to represent large information collections, called knowledge graphs, in many applications. Their exact format may vary, but they often share the concept that edges can be annotated with additional information, such as validity time or provenance information. Property Graph is a popular graph database format that also provides this feature. We give a formalisation of a generalised notion of Property Graphs, called multi-attributed relational structures (MARS), and introduce a matching knowledge representation formalism, multi-attributed predicate logic (MAPL). We analyse the expressive power of MAPL and suggest a simpler, rule-based fragment of MAPL that can be used for ontological reasoning on Property Graphs. To the best of our knowledge, this is the first approach to making Property Graphs and related data structures accessible to symbolic AI.
Context-aware Path Ranking for Knowledge Base Completion
Authors: Mazumder Sahisnu , Liu Bing
Published: July 2017

Knowledge base (KB) completion aims to infer missing facts from existing ones in a KB. Among various approaches, path ranking (PR) algorithms have received increasing attention in recent years. PR algorithms enumerate paths between entity-pairs in a KB and use those paths as features to train a model for missing fact prediction. Due to their good performances and high model interpretability, several methods have been proposed. However, most existing methods suffer from scalability (high RAM consumption) and feature explosion (trains on an exponentially large number of features) problems. This paper proposes a Context-aware Path Ranking (C-PR) algorithm to solve these problems by introducing a selective path exploration strategy. C-PR learns global semantics of entities in the KB using word embedding and leverages the knowledge of entity semantics to enumerate contextually relevant paths using bidirectional random walk. Experimental results on three large KBs show that the path features (fewer in number) discovered by C-PR not only improve predictive performance but also are more interpretable than existing baselines.
Generalized Target Assignment and Path Finding Using Answer Set Programming
Authors: Nguyen Van , Obermeier Philipp , Son Tran Cao , Schaub Torsten , Yeoh William
Published: July 2017

In Multi-Agent Path Finding (MAPF), a team of agents needs to find collision-free paths from their starting locations to their respective targets. Combined Target Assignment and Path Finding (TAPF) extends MAPF by including the problem of assigning targets to agents as a precursor to the MAPF problem. A limitation of both models is their assumption that the number of agents and targets are equal, which is invalid in some applications such as autonomous warehouse systems. We address this limitation by generalizing TAPF to allow for (1)~unequal number of agents and tasks; (2)~tasks to have deadlines by which they must be completed; (3)~ordering of groups of tasks to be completed; and (4)~tasks that are composed of a sequence of checkpoints that must be visited in a specific order. Further, we model the problem using answer set programming (ASP) to show that customizing the desired variant of the problem is simple one only needs to choose the appropriate combination of ASP rules to enforce it. We also demonstrate experimentally that if problem specific information can be incorporated into the ASP encoding then ASP based method can be efficient and can scale up to solve practical applications.
Efficient and Complete FD-solving for extended array constraints
Authors: Plazar Quentin , Acher Mathieu , Bardin Sébastien , Gotlieb Arnaud
Published: July 2017

Array constraints are essential for handling data structures in automated reasoning and software verification. Unfortunately, the use of a typical finite domain (FD) solver based on local consistency-based filtering has strong limitations when constraints on indexes are combined with constraints on array elements and size. This paper proposes an efficient and complete FD-solving technique for extended constraints over (possibly unbounded) arrays. We describe a simple but particularly powerful transformation for building an equisatisfiable formula that can be efficiently solved using standard FD reasoning over arrays, even in the unbounded case. Experiments show that the proposed solver significantly outperforms FD solvers, and successfully competes with the best SMT-solvers.
Computing Bayes-Nash Equilibria in Combinatorial Auctions with Continuous Value and Action Spaces
Authors: Bosshard Vitor , Bünz Benedikt , Lubin Benjamin , Seuken Sven
Published: July 2017

Combinatorial auctions (CAs) are widely used in practice, which is why understanding their incentive properties is an important problem. However, finding Bayes-Nash equilibria (BNEs) of CAs analytically is tedious, and prior algorithmic work has only considered limited solution concepts (e.g. restricted action spaces). In this paper, we present a fast, general algorithm for computing symmetric pure ε-BNEs in CAs with continuous values and actions. In contrast to prior work, we separate the search phase (for finding the BNE) from the verification step (for estimating the ε), and always consider the full (continuous) action space in the best response computation. We evaluate our method in the well-studied LLG domain, against a benchmark of 16 CAs for which analytical BNEs are known. In all cases, our algorithm converges quickly, matching the known results with high precision. Furthermore, for CAs with quasi-linear utility functions and independently distributed valuations, we derive a theoretical bound on ε. Finally, we introduce the new Multi-Minded LLLLGG domain with eight goods and six bidders, and apply our algorithm to finding an equilibrium in this domain. Our algorithm is the first to find an accurate BNE in a CA of this size.
A Characterization Theorem for a Modal Description Logic
Authors: Wild Paul , Schröder Lutz
Published: July 2017

Modal description logics feature modalities that capture dependence of knowledge on parameters such as time, place, or the information state of agents. E.g., the logic S5-ALC combines the standard description logic ALC with an S5-modality that can be understood as an epistemic operator or as representing (undirected) change. This logic embeds into a corresponding modal first-order logic S5-FOL. We prove a modal characterization theorem for this embedding, in analogy to results by van Benthem and Rosen relating ALC to standard first-order logic: We show that S5-ALC with only local roles is, both over finite and over unrestricted models, precisely the bisimulation-invariant fragment of S5-FOL, thus giving an exact description of the expressive power of S5-ALC with only local roles.
Transfer Learning in Multi-Armed Bandits: A Causal Approach
Authors: Zhang Junzhe , Bareinboim Elias
Published: July 2017

Reinforcement learning (RL) agents have been deployed in complex environments where interactions are costly, and learning is usually slow. One prominent task in these settings is to reuse interactions performed by other agents to accelerate the learning process. Causal inference provides a family of methods to infer the effects of actions from a combination of data and qualitative assumptions about the underlying environment. Despite its success of transferring invariant knowledge across domains in the empirical sciences, causal inference has not been fully realized in the context of transfer learning in interactive domains. In this paper, we use causal inference as a basis to support a principled and more robust transfer of knowledge in RL settings. In particular, we tackle the problem of transferring knowledge across bandit agents in settings where causal effects cannot be identified by do-calculus [Pearl, 2000] and standard learning techniques. Our new identification strategy combines two steps -- first, deriving bounds over the arm’s distribution based on structural knowledge; second, incorporating these bounds in a dynamic allocation procedure so as to guide the search towards more promising actions. We formally prove that our strategy dominates previously known algorithms and achieves orders of magnitude faster convergence rates than these algorithms. Finally, we perform simulations and empirically demonstrate that our strategy is consistently more efficient than the current (non-causal) state-of-the-art methods
A Unifying Framework for Probabilistic Belief Revision
Authors: Zhuang Zhiqiang , Delgrande James , Nayak Abhaya , Sattar Abdul
Published: July 2017

In this paper we provide a general, unifying framework for probabilistic belief revision. We first introduce a probabilistic logic called p-logic that is capable of representing and reasoning with basic probabilistic information. With p-logic as the background logic, we define a revision function called p-revision that resembles partial meet revision in the AGM framework. We provide a representation theorem for p-revision which shows that it can be characterised by the set of basic AGM revision postulates. P-revision represents an "all purpose" method for revising probabilistic information that can be used for, but not limited to, the revision problems behind Bayesian conditionalisation, Jeffrey conditionalisation, and Lewis's imaging. Importantly, p-revision subsumes all three approaches indicating that Bayesian conditionalisation, Jeffrey conditionalisation, and Lewis' imaging all obey the basic principles of AGM revision. As well our investigation sheds light on the corresponding operation of AGM expansion in the probabilistic setting.
Grounding of Human Environments and Activities for Autonomous Robots
Authors: Alomari Muhannad , Duckworth Paul , Bore Nils , Hawasly Majd , Hogg David C. , Cohn Anthony G.
Published: July 2017

With the recent proliferation of human-oriented robotic applications in domestic and industrial scenarios, it is vital for robots to continually learn about their environments and about the humans they share their environments with. In this paper, we present a novel, online, incremental framework for unsupervised symbol grounding in real-world, human environments for autonomous robots. We demonstrate the flexibility of the framework by learning about colours, people names, usable objects and simple human activities, integrating state-of-the-art object segmentation, pose estimation, activity analysis along with a number of sensory input encodings into a continual learning framework. Natural language is grounded to the learned concepts, enabling the robot to communicate in a human-understandable way. We show, using a challenging real-world dataset of human activities as perceived by a mobile robot, that our framework is able to extract useful concepts, ground natural language descriptions to them, and, as a proof-of-concept, generate simple sentences from templates to describe people and the activities they are engaged in.
Bayesian Aggregation of Categorical Distributions with Applications in Crowdsourcing
Authors: Augustin Alexandry , Venanzi Matteo , Rogers Alex , Jennings Nicholas R.
Published: July 2017

A key problem in crowdsourcing is the aggregation of judgments of proportions. For example, workers might be presented with a news article or an image, and be asked to identify the proportion of each topic, sentiment, object, or colour present in it. These varying judgments then need to be aggregated to form a consensus view of the document’s or image’s contents. Often, however, these judgments are skewed by workers who provide judgments randomly. Such spammers make the cost of acquiring judgments more expensive and degrade the accuracy of the aggregation. For such cases, we provide a new Bayesian framework for aggregating these responses (expressed in the form of categorical distributions) that for the first time accounts for spammers. We elicit 796 judgments about proportions of objects and coloursin images. Experimental results show comparable aggregation accuracy when 60% of the workers are spammers, as other state of the art approaches do when there are no spammers.
Deep Learning at Alibaba
Authors: Jin Rong
Published: July 2017

In this talk, I will focus on the applications and the latest development of deep learning technologies at Alibaba. More specifically, I will discuss (a) how to handle high dimensional data in DNN and its application to recommender system, (b) the development of deep learning models for transfer learning and its application to multimedia data analysis, (c) the development of combinatorial optimization techniques for DNN model compression and its application to large-scale image classification, and (d) the exploration of deep learning technique for combinatorial optimization and its application to the packing problem in shipping industry. I will conclude my talk with a discussion of new directions for deep learning that are under development at Alibaba.
Bounding the Inefficiency of Compromise
Authors: Caragiannis Ioannis , Kanellopoulos Panagiotis , Voudouris Alexandros A.
Published: July 2017

Social networks on the Internet have seen an enormous growth recently and play a crucial role in different aspects of today's life. They have facilitated information dissemination in ways that have been beneficial for their users but it is also a common belief that they are often used strategically in order to spread information that only serves the objectives of particular users. These properties have inspired a revision of classical opinion formation models from sociology using game-theoretic notions and tools. We follow the same modeling approach, focusing on scenarios where the opinion expressed by each user is a compromise between her internal belief and the opinions of a small number of neighbors among her social acquaintances. We formulate simple games that capture this behavior and quantify the inefficiency of equilibria using the well-known notion of the price of anarchy. Our results indicate that compromise comes at a cost that strongly depends on the neighborhood size.
End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks
Authors: Choi Min-je , Jeong Sehun , Oh Hakjoo , Choo Jaegul
Published: July 2017

Detecting buffer overruns from a source code is one of the most common and yet challenging tasks in program analysis. Current approaches based on rigid rules and handcrafted features are limited in terms of flexible applicability and robustness due to diverse bug patterns and characteristics existing in sophisticated real-world software programs. In this paper, we propose a novel, data-driven approach that is completely end-to-end without requiring any hand-crafted features, thus free from any program language-specific structural limitations. In particular, our approach leverages a recently proposed neural network model called memory networks that have shown the state-of-the-art performances mainly in question-answering tasks. Our experimental results using source code samples demonstrate that our proposed model is capable of accurately detecting different types of buffer overruns. We also present in-depth analyses on how a memory network can learn to understand the semantics in programming languages solely from raw source codes, such as tracing variables of interest, identifying numerical values, and performing their quantitative comparisons.
Top-k Supervise Feature Selection via ADMM for Integer Programming
Authors: Fan Mingyu , Chang Xiaojun , Zhang Xiaoqin , Wang Di , Du Liang
Published: July 2017

Recently, structured sparsity inducing based feature selection has become a hot topic in machine learning and pattern recognition. Most of the sparsity inducing feature selection methods are designed to rank all features by certain criterion and then select the k top ranked features, where k is an integer. However, the k top features are usually not the top k features and therefore maybe a suboptimal result. In this paper, we propose a novel supervised feature selection method to directly identify the top k features. The new method is formulated as a classic regularized least squares regression model with two groups of variables. The problem with respect to one group of the variables turn out to be a 0-1 integer programming, which had been considered very hard to solve. To address this, we utilize an efficient optimization method to solve the integer programming, which first replaces the discrete 0-1 constraints with two continuous constraints and then utilizes the alternating direction method of multipliers to optimize the equivalent problem. The obtained result is the top subset with k features under the proposed criterion rather than the subset of k top features. Experiments have been conducted on benchmark data sets to show the effectiveness of proposed method.
Improved Bounded Matrix Completion for Large-Scale Recommender Systems
Authors: Fang Huang , Zhen Zhang , Shao Yiqun , Hsieh Cho-Jui
Published: July 2017

Matrix completion is a widely used technique for personalized recommender system. In this paper, we focus on the idea of Bounded Matrix Completion (BMC) which imposes bounded constraint into the original matrix completion problem. It has been shown that BMC works well for several real world datasets, and an efficient coordinate descent solver called BMA has been proposed in~\cite{bma}. However, we observe that the BMA algorithm sometimes fails to converge to a stationary point, resulting in a relatively poor accuracy in those cases. To overcome this issue, we propose our new approach for solving BMC under the ADMM framework. The proposed algorithm is gauranteed to converge to stationary points. Experimental results on real world datasets show that our algorithm can reach a lower objective value, obtain a higher predict accuracy rate and have better scalability compared with BMA. We also present that our method outperforms the state-of-art standard matrix factorization in most cases.
Synthesizing Samples for Zero-shot Learning
Authors: Guo Yuchen , Ding Guiguang , Han Jungong , Gao Yue
Published: July 2017

Zero-shot learning (ZSL) is to construct recognition models for unseen target classes that have no labeled samples for training. It utilizes the class attributes or semantic vectors as side information and transfers supervision information from related source classes with abundant labeled samples. Existing ZSL approaches adopt an intermediary embedding space to measure the similarity between a sample and the attributes of a target class to perform zero-shot classification. However, this way may suffer from the information loss caused by the embedding process and the similarity measure cannot fully make use of the data distribution. In this paper, we propose a novel approach which turns the ZSL problem into a conventional supervised learning problem by synthesizing samples for the unseen classes. Firstly, the probability distribution of an unseen class is estimated by using the knowledge from seen classes and the class attributes. Secondly, the samples are synthesized based on the distribution for the unseen class. Finally, we can train any supervised classifiers based on the synthesized samples. Extensive experiments on benchmarks demonstrate the superiority of the proposed approach to the state-of-the-art ZSL approaches.
Orthogonal and Nonnegative Graph Reconstruction for Large Scale Clustering
Authors: Han Junwei , Xiong Kai , Nie Feiping
Published: July 2017

Spectral clustering has been widely used due to its simplicity for solving graph clustering problem in recent years. However, it suffers from the high computational cost as data grow in scale, and is limited by the performance of post-processing. To address these two problems simultaneously, in this paper, we propose a novel approach denoted by orthogonal and nonnegative graph reconstruction (ONGR) that scales linearly with the data size. For the relaxation of Normalized Cut, we add nonnegative constraint to the objective. Due to the nonnegativity, ONGR offers interpretability that the final cluster labels can be directly obtained without post-processing. Extensive experiments on clustering tasks demonstrate the effectiveness of the proposed method.
Online Multitask Relative Similarity Learning
Authors: Hao Shuji , Zhao Peilin , Liu Yong , Hoi Steven C. H. , Miao Chunyan
Published: July 2017

Relative similarity learning~(RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real-world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose an active learning algorithm to save the labeling cost. The proposed algorithms not only enjoy theoretical guarantee, but also show high efficacy and efficiency in extensive experiments on real-world datasets.
No Pizza for You: Value-based Plan Selection in BDI Agents
Authors: Cranefield Stephen , Winikoff Michael , Dignum Virginia , Dignum Frank
Published: July 2017

Autonomous agents are increasingly required to be able to make moral decisions. In these situations, the agent should be able to reason about the ethical bases of the decision and explain its decision in terms of the moral values involved. This is of special importance when the agent is interacting with a user and should understand the value priorities of the user in order to provide adequate support. This paper presents a model of agent behavior that takes into account user preferences and moral values.
Cost-Effective Active Learning from Diverse Labelers
Authors: Huang Sheng-Jun , Chen Jia-Lve , Mu Xin , Zhou Zhi-Hua
Published: July 2017

In traditional active learning, there is only one labeler that always returns the ground truth of queried labels. However, in many applications, multiple labelers are available to offer diverse qualities of labeling with different costs. In this paper, we perform active selection on both instances and labelers, aiming to improve the classification model most with the lowest cost. While the cost of a labeler is proportional to its overall labeling quality, we also observe that different labelers usually have diverse expertise, and thus it is likely that labelers with a low overall quality can provide accurate labels on some specific instances. Based on this fact, we propose a novel active selection criterion to evaluate the cost-effectiveness of instance-labeler pairs, which ensures that the selected instance is helpful for improving the classification model, and meanwhile the selected labeler can provide an accurate label for the instance with a relative low cost. Experiments on both UCI and real crowdsourcing data sets demonstrate the superiority of our proposed approach on selecting cost-effective queries.
Cross-modal Common Representation Learning by Hybrid Transfer Network
Authors: Huang Xin , Peng Yuxin , Yuan Mingkuan
Published: July 2017

DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities as image and text, but existing methods often face the challenge of insufficient cross-modal training data. In single-modal scenario, similar problem is usually relieved by transferring knowledge from large-scale auxiliary datasets (as ImageNet). Knowledge from such single-modal datasets is also very useful for cross-modal retrieval, which can provide rich general semantic information that can be shared across different modalities. However, it is challenging to transfer useful knowledge from single-modal (as image) source domain to cross-modal (as image/text) target domain. Knowledge in source domain cannot be directly transferred to both two different modalities in target domain, and the inherent cross-modal correlation contained in target domain provides key hints for cross-modal retrieval which should be preserved during transfer process. This paper proposes Cross-modal Hybrid Transfer Network (CHTN) with two subnetworks: Modal-sharing transfer subnetwork utilizes the modality in both source and target domains as a bridge, for transferring knowledge to both two modalities simultaneously; Layer-sharing correlation subnetwork preserves the inherent cross-modal semantic correlation to further adapt to cross-modal retrieval task. Cross-modal data can be converted to common representation by CHTN for retrieval, and comprehensive experiment on 3 datasets shows its effectiveness.
Interaction-based ontology alignment repair with expansion and relaxation
Authors: Euzenat Jérôme
Published: July 2017

Agents may use ontology alignments to communicate when they represent knowledge with different ontologies: alignments help reclassifying objects from one ontology to the other. These alignments may not be perfectly correct, yet agents have to proceed. They can take advantage of their experience in order to evolve alignments: upon communication failure, they will adapt the alignments to avoid reproducing the same mistake. Such repair experiments had been performed in the framework of networks of ontologies related by alignments. They revealed that, by playing simple interaction games, agents can effectively repair random networks of ontologies. Here we repeat these experiments and, using new measures, show that previous results were underestimated. We introduce new adaptation operators that improve those previously considered. We also allow agents to go beyond the initial operators in two ways: they can generate new correspondences when they discard incorrect ones, and they can provide less precise answers. The combination of these modalities satisfy the following properties: (1) Agents still converge to a state in which no mistake occurs. (2) They achieve results far closer to the correct alignments than previously found. (3) They reach again 100\% precision and coherent alignments.
Exploration of Tree-based Hierarchical Softmax for Recurrent Language Models
Authors: Jiang Nan , Rong Wenge , Gao Min , Shen Yikang , Xiong Zhang
Published: July 2017

Recently, variants of neural networks for computational linguistics have been proposed and successfully applied to neural language modeling and neural machine translation. These neural models can leverage knowledge from massive corpora but they are extremely slow as they predict candidate words from a large vocabulary during training and inference. As an alternative to gradient approximation and softmax with class decomposition, we explore the tree-based hierarchical softmax method and reform its architecture, making it compatible with modern GPUs and introducing a compact tree-based loss function. When combined with several word hierarchical clustering algorithms, improved performance is achieved in language modelling task with intrinsic evaluation criterions on PTB, WikiText-2 and WikiText-103 datasets.
Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning
Authors: Jiang Wenhao , Deng Cheng , Liu Wei , Nie Feiping , Chung Fu-lai , Huang Heng
Published: July 2017

Domain adaptation problems arise in a variety of applications, where a training dataset from the source domain and a test dataset from the target domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation. Our analysis shows that in order to achieve good adaptation performance, the second moments of the source domain distribution and target domain distribution should be similar. Based on our new analysis, a novel extremely easy feature learning algorithm for domain adaptation is proposed. Furthermore, our algorithm is extended by leveraging multiple layers, leading to another feature learning algorithm. We evaluate the effectiveness of the proposed algorithms in terms of domain adaptation tasks on Amazon review and spam datasets from the ECML/PKDD 2006 discovery challenge.