bluuebunny/crossref_metadata_2025 huggingface dataset:
Diverticular Disease: a Gut Microbiota Perspective
Authors: Ticinesi Andrea , Nouvenne Antonio , Corrente Vincenzo , Tana Claudio , Di Mario Francesco , Meschi Tiziana
Published: September 2019

Gut microbiota composition and functionality are involved in the pathophysiology of several intestinal andextraintestinal diseases, and are increasingly considered a modulator of local and systemic inflammation.However, the involvement of gut microbiota in diverticulosis and in diverticular disease is still poorlyinvestigated. In this review, we critically analyze the existing evidence on the fecal and mucosa-associatedmicrobiota composition and functionality across different stages of diverticular disease. We also explorethe influence of risk factors for diverticulosis on gut microbiota composition, and speculate on the possiblerelevance of these associations for the pathogenesis of diverticula. We overview the current treatments ofdiverticular disease targeting the intestinal microbiome, highlighting the current areas of uncertainty andthe need for future studies. Although no conclusive remarks on the relationship between microbiota anddiverticular disease can be made, preliminary data suggest that abdominal symptoms are associated withreduced representation of taxa with a possible anti-inflammatory effect, such as Clostridium cluster IV, andovergrowth of Enterobacteriaceae, Bifidobacteria and Akkermansia. The role of the microbiota in the earlystages of the disease is still very uncertain. Future studies should help to disentangle the role of the microbiomein the pathogenesis of diverticular disease and its progression towards more severe forms.
Cecal Malakoplakia - an Unusual Cause of Anemia in Liver Cirrhosis
Authors: Dina Ion , Iacobescu Claudia , Iordache Nicolae , Gheorghița Sergiu , Gurghean Adriana
Published: September 2019

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Co-housing és városi fenntarthatóság
Authors: Szabó Julianna , Kissfazekas Kornélia , Babos Annamária
Published: September 2019

Nyugat-Európa városi lakásfejlesztéseiben az 1990-es évek óta megfigyelhető a környezeti fenntarthatósági szempontok, illetve a közösségi lakhatás szempontjainak terjedése. Egymáshoz közelítő trendekről van szó, hiszen számos co-housing2közösség alapértékként fogalmazza meg a fenntartható életmód kialakítását, ugyanakkor az integrált lakóközösség kialakítása és az azzal járó társadalmi előnyök a fenntarthatóság harmadik, társadalmi pillérének tekinthetők, ami egyre több zöld ingatlanminősítési rendszerben is megjelenik. Ugyanakkor a fejlesztés egyes döntéseiben a két trend által meghatározott szempontok nem mindig esnek egybe. Cikkünkben arra a kérdésre keressük a választ, hogy a városias többlakásos lakóingatlanok helykiválasztását milyen módon befolyásolhatja a környezettudatos és a co-housing szempontrendszer, felismerhetők-e karakteres különbségek, illetve miképpen és mely feltételekkel erősítheti egymást egy beruházásban a két nézőpont.

Elemzésünket Yosef Jabareen városi fenntarthatósági kritériumaira alapozzuk. A településföldrajzi lokációs elméletek analógiájára bevezetjük a telek helyi és helyzeti energiájának fogalmát. Jabareen kritériumait az ingatlanfejlesztés lokációs szempontjainak feleltetjük meg, szembeállítva egymással a tipikus piaci ingatlanfejlesztés szempontjait, a közösségi lakásépítés szempontrendszerével. Vizsgálatainkat részben a co-housing szakirodalom megállapításaira és esettanulmányaira, részben — széles körű és releváns magyar tapasztalatok hiányában — berlini megépült közösségi lakóházak adatbázisára építjük. Végezetül felvetjük a városi önkormányzati felelősség kérdését a közösségi lakásfejlesztések orientációjában, a fenntartható lokációs döntések támogatásában.
Theistic Psychology as a Methodological Principle of the Study of Religious Experience
Authors: Tokareva Svetlana , Patrin Vyacheslav
Published: September 2019

The article investigates the genesis of psychologism as a methodological basis for the study of religious experience. The authors show that the first experience of consistent applying psychologism for the purpose of radical criticism of religion was carried out by D. Hume, who reduced the religious experience to the emotional and mental nature of man. As a result, in the philosophy of Hume, the sphere of religious experiences was completely desacralized and the concepts making up the core of spiritual and religious-ethical life of man, such as "personality", "spiritual substance", "mystical experience" were declared meaningless. In the research of F. Schleiermacher the principle of psychology was used to identify the grounds of religiosity. According to Schleiermacher, the sacred is rooted in the very nature of man in the form of original, undifferentiated "religious feeling." The type of psychologism developed by Schleiermacher is theistic because it comes from the recognition of the ontological nature of the divine spark, which is initially presented in religious experience and is not brought into it by faith, a specific creed or metaphysics. According to Schleiermacher, it is not the higher mental functions and rational consciousness that are responsible for the feeling of belonging to God, but the prethought experience together with the psychosomatic structures that provide it. Thus, theistic psychology finds the primary elements of religiosity, components creating precognitive content experienced by the human sense of the sacred beyond the usual manifestations of religion, namely faith, thinking and behavior. Later, psychologism was criticized by representatives of phenomenology, who argued that mental phenomena can be recorded as a direct evidence beyond self-consciousness. This led to the formation within the phenomenology of religion of a new kind of psychologism – transcendental psychologism, representatives of which consider religious feeling as a unity of transcendental (categorical scheme of "the sacred") and psychological (religious experience as a mental phenomenon). The significance of theistic psychologism as a methodology for the analysis of religious experience lies in the fact that it, firstly, served as the basis for the formation of the phenomenology of religion as the leading direction of the study of religious experience and, secondly, opened the possibility of studying the universal psychosomatic basis of religious feeling. This basis can be distinguished by comparison and comparative analysis of descriptions obtained as a result of conscious self-observation of personal spiritual experience of representatives of different religions and spiritual practices.
Current Image of Volgograd as Seen by the Student’s Youth
Authors: Shipitsyn Anton , Marchenko Anastasiya
Published: September 2019

The article touches upon specifics and features of students' perception of image of modern Volgograd. In the framework of the study the author interprets the image of the city as a complex structured phenomenon including people's subjective and stereotyped representations of socio-cultural, historical, socio-economic, political and other features of the territory. These representations can be formed both on the basis of specific emotions, personal impressions, and indirectly, from the materials of mass media, literary and cinematic sources, based on eyewitness stories, rumors and conjectures. At the same time, as the world experience shows, the economic development of this territory, the efficiency and legitimacy of political institutions, the state of the tourism industry, cultural life and demographic situation directly depend on how attractive the image of the city is for its residents and other members of society. Being a sophisticated and multi-component organization that can have a strong impact not only on the individual, but also on the mass perception and experience of the place and its identification, the image of the provincial city greatly affects the life strategies and migration attitudes of its residents. Especially it is true for young people and working population – graduates of schools and universities, young professionals. The authors describe the essence of the image positioning of the territory and the most important reasons for the wide spread of such practices, determines the value attitude of young people to Volgograd, proposes measures to improve the current image of the city. The researchers apply the results of quantitative and qualitative sociological research such as a questionnaire survey of students enrolled in 1–4 courses of various education fields of Volgograd State Socio-pedagogical University, interviews with experts.
Interaction of Community and Family Patterns of Life in Southern Villages (Based on Research Materials of 2018)
Authors: Namrueva Ludmila
Published: September 2019

There is a combination of two and more household practices – collective and family ones in a modern village. The author of the article describes the changes of collective farms in the South of Russia in the early 21st century. During the transformations of the 1990s, the collective way of life supported the village, rural society in the struggle for survival, thereby preventing social cataclysms. Former Soviet state farms and collective farms turned into agricultural organizations (agricultural associations) or gradually disappeared. The author analyzes the fate of collective farms in such South Russian regions as the Republic of Kalmykia, Astrakhan and Volgograd regions, Stavropol region, which are large agricultural producers. Collective farms in these regions could not avoid the disastrous consequences of the agrarian reforms of the 1990s, which resulted in a huge number of "abandoned" villages left without village-forming enterprises. The researcher analyzes some results of sociological research conducted in summer–autumn 2018 in the studied South Russian regions. 400 rural residents, 100 people in each subject were interviewed. The results of the study demonstrate that the way of private farms (36 %) is common in all analyzed regions. This is followed by PFEs (peasant farm enterprises) (15 %), agricultural enterprises (9 %). The number of agricultural associations and the number of their employees have significantly decreased. Small farms of the population are the basis of the agricultural society of the regions under consideration. The author defines three urgent problems of four regions. They are the following: a decent wage, a good leader suggesting the way out of a difficult situation, consolidation of young people in the countryside. The analysis shows that interaction between collective and family patterns of life is gradually dimming. The owners of private farms are becoming more autonomous, their dependence on the farm is gradually disappearing.
Transformation of States in the Era of Globalization: Synergistic Aspect
Authors: Rodukov Alexey
Published: September 2019

Social synergy considers the historical process as a movement along a logarithmic (narrowing and going to infinity) spiral that is a "golden spiral", obeying the law of differentiating and integrating of social ideals (V.P. Branskiy). The totalitarian ideal expresses the cult of responsibility (order) and the anarchist ideal expresses the cult of freedom (chaos). The society is committed to an optimal balance of chaos and order, freedom and responsibility. Thus, in the concept of "regimes with exacerbation" by S.P. Kurdyumov the phase transition (from one state to another) is associated with an increasing tendency to speeding up the process. The history shows rather "oscillation" tendencies of strengthening or weakening of the state. Extremes are deconstructive. Thus the processes of establishing the optimal ratio between chaos and order in society are reflected in the law of hierarchical compensation by E.A. Sedov. However, the self-organization of the state as such is carried out as an endless tendency towards the common to all mankind ideal and it is non-linear, contrary to the classics of Marxism. Nowadays, the fateful for the state contradiction between the democratic process of "degovernmentalization", on the one hand, and the "capture" of supranational institutions and functions first by the national and then transnational corporations, on the other, is being actualized. The privatization of property and the non-admission of the "rest" of population to this process leads to the de-socialization of the state. The goal of a corporation-state is to weaken or even eliminate the national identity and sovereignty of a nation-state. The state rejection of social functions and guarantees means its strengthening. On the contrary, the development and functioning of the institutions of the societal state and civil society goes under state patronage and leads to the removal of the state monopoly from the several sides of the life of society.
Buddhism in the Feminist Context: Historical Experience and Modern Discourse
Authors: Ulanov Mergen
Published: September 2019

The author considers the problems of women's place in Buddhist culture in the context of feminist discourse. He notes that Buddhism is distinguished by a tolerant and respectful attitude to the female. Buddhism admits that women, along with men, are able to achieve enlightenment and find Nirvana. However, the relationship between male and female monastic orders in Buddhism was not fully equal. The order of nuns was considered to be the youngest in comparison with the order of monks, and the rules restricting the behavior of the nuns were more than for the monks, which was probably a forced step aimed at taking into account the realities of society. Despite this, the Foundation of the women's monastic organization, which opened the way for women to religious knowledge and spiritual rank, was in its essence a radical social revolution for that time. The emergence of the female monastic community was an example of a fundamentally new view of women and their position in society. With the release of Buddhism outside India female monasticism became widespread in many Asian countries. Later, however, in the countries of South, South-East Asia and Tibet, the Institute of full female monasticism disappeared. In the second half of the twentieth century the attempts to revive the Institute that have led to the emergence of the phenomenon of neonuns. As a result of the spread of Buddhism in the West, it was included in the field of gender studies and feminist discourse. The question of equality between women and men in Buddhism has been actively developed by Western female Buddhists in the feminist discourse, that has formed a statement about the original equality of the sexes in Buddhism. The theme of the status of women in society and their rights has become an important part of the social concept of Western Buddhism. The result was the emergence of the international women's Buddhist Association "Sakyadhita".
Sociological Monitoring: Experience and Practice of the Institute of Sociology of the National Academy of Sciences of Belarus
Authors: Leonov Nikolai , Borodacheva Elena , Zrazikova Vera , Malinovskaya Nadezhda
Published: September 2019

The authors describe and analyze the experience of monitoring research having been conducted by Institute of Sociology of National Academy of Sciences of Belarus over the past 17 years. They state the main features of monitoring as a type of sociological research and under this general point of view analyze the monitoring of the Institute. The researchers specify goals and objectives of the monitoring study, list the procedures included in it and the corresponding functions. The scientific and theoretical base on which the research is conducted is described in the article, in particular, the authors characterize the scientific schools and directions created at the Institute. The paper sets out the methodological principles that form the basis of this monitoring study. The article describesinstrumentarium, the groups of studied indicators in the form of blocks of questions indicating the frequency of inclusion of these blocks in the questionnaire. The authors note that the questionnaire also includes one-time questions in accordance with the problems of interest at the moment. They present the procedure of sample design developed at the Institute in detail. The procedure uses the concept of the so-called zone. This concept refers to settlements of a lower administrative level than district centers and includes both the location of the settlement relative to the district center (distance, transport accessibility), and certain socio-economic characteristics of the settlement, which allow more accurately stratify settlements. In particular, the zone is included in the number of quotas. The authors point out features of mathematical analysis of the data, the development of the original mathematical software providing an increase in both the efficiency of data analysis and its methodological level. Using a specific example the study shows the advantages of monitoring as a means of studying social problems. On the basis of the analysis the authors make conclusions regarding the research tasks that can be solved by this monitoring study.
Destructive Civic Activity of Young People: Theoretical and Methodological Conceptualization
Authors: Loginova Larisa , Sheblanova Veronika
Published: September 2019

The authors analyze the interdisciplinary discourse and functional-content specificity of the phenomenon of youth civic activity, study the theoretical and methodological positions, categorical signs of destructive civic activity, as well as the contexts of possible manifestations of destructive youth civic practices. There is an increase in the need to study the civic youth activity, which acquires various forms of expression of positive and destructive orientation, reflecting the current political, economic, socio-cultural values of society. The processes of destabilization in society, negatively affecting the level of social protection of young people, due to youth maximalism, instability of life priorities, push to engage in socially dangerous activities, including extremist orientation. The potential space for the implementation of destructive practices of young people is expanding at the expense of social Internet networks, where their informal self-organization is activated. Internet activism is developing – purposeful actions in the online space, built around the situation-problems and aimed at its solution. The authors perform the categorization of civic activism of the youth through the analysis of cause-effect relations, which are necessary contiguous concepts as a response of young people to any social injustice, dysfunction of the economic and political system, an important condition for the normal functioning of society. The researchers define destructive behavior as a type of human activity that changes social objects through their destruction. Destructive civic activity of young people is understood as a sophisticated, multi-level, thematically diverse manifestation of social activity with a certain potential. The authors assume that destructive youth activism is expressed in aggressive individual and collective, verbal and non-verbal actions (including the form of destructive civic content in the Internet space). The paper states that destructiveness of civic activity is manifested in delinquent socially dangerous, aggressive forms. However, the authors conclude that destructive civil initiatives can also be socially useful, aimed at solving urgent problems related to social inequality, injustice, corruption, non-compliance with laws, and under certain conditions become the driving force of the "recovery" of society.
DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing
Authors: Liu Xiao , Li Xiaoting , Prajapati Rupesh , Wu Dinghao
Published: September 2019

Compilers are among the most fundamental programming tools for building software. However, production compilers remain buggy. Fuzz testing is often leveraged with newlygenerated, or mutated inputs in order to find new bugs or security vulnerabilities. In this paper, we propose a grammarbased fuzzing tool called DEEPFUZZ. Based on a generative Sequence-to-Sequence model, DEEPFUZZ automatically and continuously generates well-formed C programs. We use this set of new C programs to fuzz off-the-shelf C compilers, e.g., GCC and Clang/LLVM. We present a detailed case study to analyze the success rate and coverage improvement of the generated C programs for fuzz testing. We analyze the performance of DEEPFUZZ with three types of sampling methods as well as three types of generation strategies. Consequently, DEEPFUZZ improved the testing efficacy in regards to the line, function, and branch coverage. In our preliminary study, we found and reported 8 bugs of GCC, all of which are actively being addressed by developers.
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective
Authors: Lu Chengqiang , Liu Qi , Wang Chao , Huang Zhenya , Lin Peize , He Lixin
Published: September 2019

Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density functional theory (DFT) in physics are proved to be time-consuming for predicting large number of molecules. Recently, the machine learning methods, which consider much rule-based information, have also shown potentials for this issue. However, the complex inherent quantum interactions of molecules are still largely underexplored by existing solutions. In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction. Specifically, we represent each molecule as a graph to preserve its internal structure. Moreover, the well-designed hierarchical graph neural network directly extracts features from the conformation and spatial information followed by the multilevel interactions. As a consequence, the multilevel overall representations can be utilized to make the prediction. Extensive experiments on both datasets of equilibrium and off-equilibrium molecules demonstrate the effectiveness of our model. Furthermore, the detailed results also prove that MGCN is generalizable and transferable for the prediction.
Subtask Gated Networks for Non-Intrusive Load Monitoring
Authors: Shin Changho , Joo Sunghwan , Yim Jaeryun , Lee Hyoseop , Moon Taesup , Rhee Wonjong
Published: September 2019

Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household’s aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous household appliances can be avoided, and only one device needs to be installed. The problem has been well-known since Hart’s seminal paper in 1992, and recently significant performance improvements have been achieved by adopting deep networks. In this work, we focus on the idea that appliances have on/off states, and develop a deep network for further performance improvements. Specifically, we propose a subtask gated network that combines the main regression network with an on/off classification subtask network. Unlike typical multitask learning algorithms where multiple tasks simply share the network parameters to take advantage of the relevance among tasks, the subtask gated network multiply the main network’s regression output with the subtask’s classification probability. When standby-power is additionally learned, the proposed solution surpasses the state-of-the-art performance for most of the benchmark cases. The subtask gated network can be very effective for any problem that inherently has on/off states.
Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification
Authors: Wang Mingliang , Huang Jiashuang , Liu Mingxia , Zhang Daoqiang
Published: September 2019

Brain network analysis can help reveal the pathological basis of neurological disorders and facilitate automated diagnosis of brain diseases, by exploring connectivity patterns in the human brain. Effectively representing the brain network has always been the fundamental task of computeraided brain network analysis. Previous studies typically utilize human-engineered features to represent brain connectivity networks, but these features may not be well coordinated with subsequent classifiers. Besides, brain networks are often equipped with multiple hubs (i.e., nodes occupying a central position in the overall organization of a network), providing essential clues to describe connectivity patterns. However, existing studies often fail to explore such hubs from brain connectivity networks. To address these two issues, we propose a Connectivity Network analysis method with discriminative Hub Detection (CNHD) for brain disease diagnosis using functional magnetic resonance imaging (fMRI) data. Specifically, we incorporate both feature extraction of brain networks and network-based classification into a unified model, while discriminative hubs can be automatically identified from data via ℓ1-norm and ℓ2,1-norm regularizers. The proposed CNHD method is evaluated on three real-world schizophrenia datasets with fMRI scans. Experimental results demonstrate that our method not only outperforms several state-of-the-art approaches in disease diagnosis, but also is effective in automatically identifying disease-related network hubs in the human brain.
Hierarchical Macro Strategy Model for MOBA Game AI
Authors: Wu Bin
Published: September 2019

The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an action space much larger than that of GO. Mastering RTS games requires both strong macro strategies and delicate micro level execution. Recently, great progress has been made in micro level execution, while complete solutions for macro strategies are still lacking. In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games. Trained by the Hierarchical Macro Strategy model, agents explicitly make macro strategy decisions and further guide their micro level execution. Moreover, each of the agents makes independent strategy decisions, while simultaneously communicating with the allies through leveraging a novel imitated crossagent communication mechanism. We perform comprehensive evaluations on a popular 5v5 Multiplayer Online Battle Arena (MOBA) game. Our 5-AI team achieves a 48% winning rate against human player teams which are ranked top 1% in the player ranking system.
Optimal Interdiction of Urban Criminals with the Aid of Real-Time Information
Authors: Zhang Youzhi , Guo Qingyu , An Bo , Tran-Thanh Long , Jennings Nicholas R.
Published: September 2019

Most violent crimes happen in urban and suburban cities. With emerging tracking techniques, law enforcement officers can have real-time location information of the escaping criminals and dynamically adjust the security resource allocation to interdict them. Unfortunately, existing work on urban network security games largely ignores such information. This paper addresses this omission. First, we show that ignoring the real-time information can cause an arbitrarily large loss of efficiency. To mitigate this loss, we propose a novel NEtwork purSuiT game (NEST) model that captures the interaction between an escaping adversary and a defender with multiple resources and real-time information available. Second, solving NEST is proven to be NP-hard. Third, after transforming the non-convex program of solving NEST to a linear program, we propose our incremental strategy generation algorithm, including: (i) novel pruning techniques in our best response oracle; and (ii) novel techniques for mapping strategies between subgames and adding multiple best response strategies at one iteration to solve extremely large problems. Finally, extensive experiments show the effectiveness of our approach, which scales up to realistic problem sizes with hundreds of nodes on networks including the real network of Manhattan.
On Geometric Alignment in Low Doubling Dimension
Authors: Ding Hu , Ye Mingquan
Published: September 2019

In real-world, many problems can be formulated as the alignment between two geometric patterns. Previously, a great amount of research focus on the alignment of 2D or 3D patterns, especially in the field of computer vision. Recently, the alignment of geometric patterns in high dimension finds several novel applications, and has attracted more and more attentions. However, the research is still rather limited in terms of algorithms. To the best of our knowledge, most existing approaches for high dimensional alignment are just simple extensions of their counterparts for 2D and 3D cases, and often suffer from the issues such as high complexities. In this paper, we propose an effective framework to compress the high dimensional geometric patterns and approximately preserve the alignment quality. As a consequence, existing alignment approach can be applied to the compressed geometric patterns and thus the time complexity is significantly reduced. Our idea is inspired by the observation that high dimensional data often has a low intrinsic dimension. We adopt the widely used notion “doubling dimension” to measure the extents of our compression and the resulting approximation. Finally, we test our method on both random and real datasets; the experimental results reveal that running the alignment algorithm on compressed patterns can achieve similar qualities, comparing with the results on the original patterns, but the running times (including the times cost for compression) are substantially lower.
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization
Authors: Huang Feihu , Gu Bin , Huo Zhouyuan , Chen Songcan , Huang Heng
Published: September 2019

Proximal gradient method has been playing an important role to solve many machine learning tasks, especially for the nonsmooth problems. However, in some machine learning problems such as the bandit model and the black-box learning problem, proximal gradient method could fail because the explicit gradients of these problems are difficult or infeasible to obtain. The gradient-free (zeroth-order) method can address these problems because only the objective function values are required in the optimization. Recently, the first zeroth-order proximal stochastic algorithm was proposed to solve the nonconvex nonsmooth problems. However, its convergence rate is O(1/√T) for the nonconvex problems, which is significantly slower than the best convergence rate O(T1) of the zerothorder stochastic algorithm, where T is the iteration number. To fill this gap, in the paper, we propose a class of faster zeroth-order proximal stochastic methods with the variance reduction techniques of SVRG and SAGA, which are denoted as ZO-ProxSVRG and ZO-ProxSAGA, respectively. In theoretical analysis, we address the main challenge that an unbiased estimate of the true gradient does not hold in the zerothorder case, which was required in previous theoretical analysis of both SVRG and SAGA. Moreover, we prove that both ZO-ProxSVRG and ZO-ProxSAGA algorithms have O(T1) convergence rates. Finally, the experimental results verify that our algorithms have a faster convergence rate than the existing zeroth-order proximal stochastic algorithm.
Abduction-Based Explanations for Machine Learning Models
Authors: Ignatiev Alexey , Narodytska Nina , Marques-Silva Joao
Published: September 2019

The growing range of applications of Machine Learning (ML) in a multitude of settings motivates the ability of computing small explanations for predictions made. Small explanations are generally accepted as easier for human decision makers to understand. Most earlier work on computing explanations is based on heuristic approaches, providing no guarantees of quality, in terms of how close such solutions are from cardinality- or subset-minimal explanations. This paper develops a constraint-agnostic solution for computing explanations for any ML model. The proposed solution exploits abductive reasoning, and imposes the requirement that the ML model can be represented as sets of constraints using some target constraint reasoning system for which the decision problem can be answered with some oracle. The experimental results, obtained on well-known datasets, validate the scalability of the proposed approach as well as the quality of the computed solutions.
Automatic Construction of Parallel Portfolios via Explicit Instance Grouping
Authors: Liu Shengcai , Tang Ke , Yao Xin
Published: September 2019

Exploiting parallelism is becoming more and more important in designing efficient solvers for computationally hard problems. However, manually building parallel solvers typically requires considerable domain knowledge and plenty of human effort. As an alternative, automatic construction of parallel portfolios (ACPP) aims at automatically building effective parallel portfolios based on a given problem instance set and a given rich configuration space. One promising way to solve the ACPP problem is to explicitly group the instances into different subsets and promote a component solver to handle each of them. This paper investigates solving ACPP from this perspective, and especially studies how to obtain a good instance grouping. The experimental results on two widely studied problem domains, the boolean satisfiability problems (SAT) and the traveling salesman problems (TSP), showed that the parallel portfolios constructed by the proposed method could achieve consistently superior performances to the ones constructed by the state-of-the-art ACPP methods, and could even rival sophisticated hand-designed parallel solvers.
Bayesian Functional Optimisation with Shape Prior
Authors: Vellanki Pratibha , Rana Santu , Gupta Sunil , Rubin de Celis Leal David , Sutti Alessandra , Height Murray , Venkatesh Svetha
Published: September 2019

Real world experiments are expensive, and thus it is important to reach a target in a minimum number of experiments. Experimental processes often involve control variables that change over time. Such problems can be formulated as functional optimisation problem. We develop a novel Bayesian optimisation framework for such functional optimisation of expensive black-box processes. We represent the control function using Bernstein polynomial basis and optimise in the coefficient space. We derive the theory and practice required to dynamically adjust the order of the polynomial degree, and show how prior information about shape can be integrated. We demonstrate the effectiveness of our approach for short polymer fibre design and optimising learning rate schedules for deep networks.
Learning to Write Stories with Thematic Consistency and Wording Novelty
Authors: Li Juntao , Bing Lidong , Qiu Lisong , Chen Dongmin , Zhao Dongyan , Yan Rui
Published: September 2019

Automatic story generation is a challenging task, which involves automatically comprising a sequence of sentences or words with a consistent topic and novel wordings. Although many attention has been paid to this task and prompting progress has been made, there still exists a noticeable gap between generated stories and those created by humans, especially in terms of thematic consistency and wording novelty. To fill this gap, we propose a cache-augmented conditional variational autoencoder for story generation, where the cache module allows to improve thematic consistency while the conditional variational autoencoder part is used for generating stories with less common words by using a continuous latent variable. For combing the cache module and the autoencoder part, we further introduce an effective gate mechanism. Experimental results on ROCStories and WritingPrompts indicate that our proposed model can generate stories with consistency and wording novelty, and outperforms existing models under both automatic metrics and human evaluations.
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search
Authors: Rao Jinfeng , Yang Wei , Zhang Yuhao , Ture Ferhan , Lin Jimmy
Published: September 2019

Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have mostly been applied to “standard” ad hoc retrieval tasks over web pages and newswire articles. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network), a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A poolingbased similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011–2014 show that our model significantly outperforms prior feature-based as well as existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models. Our code and data are publicly available.1
Surveys without Questions: A Reinforcement Learning Approach
Authors: Sinha Atanu R , Jain Deepali , Sheoran Nikhil , Khosla Sopan , Sasidharan Reshmi
Published: September 2019

The ‘old world’ instrument, survey, remains a tool of choice for firms to obtain ratings of satisfaction and experience that customers realize while interacting online with firms. While avenues for survey have evolved from emails and links to pop-ups while browsing, the deficiencies persist. These include - reliance on ratings of very few respondents to infer about all customers’ online interactions; failing to capture a customer’s interactions over time since the rating is a one-time snapshot; and inability to tie back customers’ ratings to specific interactions because ratings provided relate to all interactions. To overcome these deficiencies we extract proxy ratings from clickstream data, typically collected for every customer’s online interactions, by developing an approach based on Reinforcement Learning (RL). We introduce a new way to interpret values generated by the value function of RL, as proxy ratings. Our approach does not need any survey data for training. Yet, on validation against actual survey data, proxy ratings yield reasonable performance results. Additionally, we offer a new way to draw insights from values of the value function, which allow associating specific interactions to their proxy ratings. We introduce two new metrics to represent ratings - one, customer-level and the other, aggregate-level for click actions across customers. Both are defined around proportion of all pairwise, successive actions that show increase in proxy ratings. This intuitive customer-level metric enables gauging the dynamics of ratings over time and is a better predictor of purchase than customer ratings from survey. The aggregate-level metric allows pinpointing actions that help or hurt experience. In sum, proxy ratings computed unobtrusively from clickstream, for every action, for each customer, and for every session can offer interpretable and more insightful alternative to surveys.
Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System
Authors: Wen Hong , Zhang Jing , Lin Quan , Yang Keping , Huang Pipei
Published: September 2019

Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms. Generally speaking, two essential modules named “ClickThrough Rate Prediction” (CTR) and “Conversion Rate Prediction” (CVR) are included, where CVR module is a crucial factor that affects the final purchasing volume directly. However, it is indeed very challenging due to its sparseness nature. In this paper, we tackle this problem by proposing multiLevel Deep Cascade Trees (ldcTree), which is a novel decision tree ensemble approach. It leverages deep cascade structures by stacking Gradient Boosting Decision Trees (GBDT) to effectively learn feature representation. In addition, we propose to utilize the cross-entropy in each tree of the preceding GBDT as the input feature representation for next level GBDT, which has a clear explanation, i.e., a traversal from root to leaf nodes in the next level GBDT corresponds to the combination of certain traversals in the preceding GBDT. The deep cascade structure and the combination rule enable the proposed ldcTree to have a stronger distributed feature representation ability. Moreover, inspired by ensemble learning, we propose an Ensemble ldcTree (E-ldcTree) to encourage the model’s diversity and enhance the representation ability further. Finally, we propose an improved Feature learning method based on EldcTree (F-EldcTree) for taking adequate use of weak and strong correlation features identified by pretrained GBDT models. Experimental results on off-line data set and online deployment demonstrate the effectiveness of the proposed methods.
Data Augmentation Based on Adversarial Autoencoder Handling Imbalance for Learning to Rank
Authors: Yu Qian , Lam Wai
Published: September 2019

Data imbalance is a key limiting factor for Learning to Rank (LTR) models in information retrieval. Resampling methods and ensemble methods cannot handle the imbalance problem well since none of them incorporate more informative data into the training procedure of LTR models. We propose a data generation model based on Adversarial Autoencoder (AAE) for tackling the data imbalance in LTR via informative data augmentation. This model can be utilized for handling two types of data imbalance, namely, imbalance regarding relevance levels for a particular query and imbalance regarding the amount of relevance judgements in different queries. In the proposed model, relevance information is disentangled from the latent representations in this AAE-based model in order to reconstruct data with specific relevance levels. The semantic information of queries, derived from word embeddings, is incorporated in the adversarial training stage for regularizing the distribution of the latent representation. Two informative data augmentation strategies suitable for LTR are designed utilizing the proposed data generation model. Experiments on benchmark LTR datasets demonstrate that our proposed framework can significantly improve the performance of LTR models.
Robust Online Matching with User Arrival Distribution Drift
Authors: Zhou Yu-Hang , Liang Chen , Li Nan , Yang Cheng , Zhu Shenghuo , Jin Rong
Published: September 2019

Recently, online matching problems have attracted much attention due to its emerging applications in internet advertising. Most existing online matching methods have adopted either adversarial or stochastic user arrival assumption, while on both of them significant limitation exists. The adversarial model does not exploit existing knowledge of the user sequence, and thus can be pessimistic in practice. On other hands, the stochastic model assumes that users are drawn from a stationary distribution, which may not be true in real applications. In this paper, we consider a novel user arrival model where users are drawn from drifting distribution, which is a hybrid case between the adversarial and stochastic model, and propose a new approach RDLA to deal with such assumption. Instead of maximizing empirical total revenues on the revealed users, RDLA leverages distributionally robust optimization techniques to learn dual variables via a worst-case consideration over an ambiguity set on the underlying user distribution. Experiments on a real-world dataset exhibit the superiority of our approach.
HireNet: A Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews
Authors: Hemamou Léo , Felhi Ghazi , Vandenbussche Vincent , Martin Jean-Claude , Clavel Chloé
Published: September 2019

New technologies drastically change recruitment techniques. Some research projects aim at designing interactive systems that help candidates practice job interviews. Other studies aim at the automatic detection of social signals (e.g. smile, turn of speech, etc...) in videos of job interviews. These studies are limited with respect to the number of interviews they process, but also by the fact that they only analyze simulated job interviews (e.g. students pretending to apply for a fake position). Asynchronous video interviewing tools have become mature products on the human resources market, and thus, a popular step in the recruitment process. As part of a project to help recruiters, we collected a corpus of more than 7000 candidates having asynchronous video job interviews for real positions and recording videos of themselves answering a set of questions. We propose a new hierarchical attention model called HireNet that aims at predicting the hirability of the candidates as evaluated by recruiters. In HireNet, an interview is considered as a sequence of questions and answers containing salient socials signals. Two contextual sources of information are modeled in HireNet: the words contained in the question and in the job position. Our model achieves better F1-scores than previous approaches for each modality (verbal content, audio and video). Results from early and late multimodal fusion suggest that more sophisticated fusion schemes are needed to improve on the monomodal results. Finally, some examples of moments captured by the attention mechanisms suggest our model could potentially be used to help finding key moments in an asynchronous job interview.
A Model-Free Affective Reinforcement Learning Approach to Personalization of an Autonomous Social Robot Companion for Early Literacy Education
Authors: Park Hae Won , Grover Ishaan , Spaulding Samuel , Gomez Louis , Breazeal Cynthia
Published: September 2019

Personalized education technologies capable of delivering adaptive interventions could play an important role in addressing the needs of diverse young learners at a critical time of school readiness. We present an innovative personalized social robot learning companion system that utilizes children’s verbal and nonverbal affective cues to modulate their engagement and maximize their long-term learning gains. We propose an affective reinforcement learning approach to train a personalized policy for each student during an educational activity where a child and a robot tell stories to each other. Using the personalized policy, the robot selects stories that are optimized for each child’s engagement and linguistic skill progression. We recruited 67 bilingual and English language learners between the ages of 4–6 years old to participate in a between-subjects study to evaluate our system. Over a three-month deployment in schools, a unique storytelling policy was trained to deliver a personalized story curriculum for each child in the Personalized group. We compared their engagement and learning outcomes to a Non-personalized group with a fixed curriculum robot, and a baseline group that had no robot intervention. In the Personalization condition, our results show that the affective policy successfully personalized to each child to boost their engagement and outcomes with respect to learning and retaining more target words as well as using more target syntax structures as compared to children in the other groups.