bluuebunny/crossref_metadata_2025 huggingface dataset:
Bangladesh and SAARC Countries: Bilateral Trade and Flaring of Economic Cooperation
Authors: Gazi Md. Abu Issa , Al Masud Abdullah , Shuvro Rezuan Ahmed , Hossain Abu Ishaque , Rahman Muhammad Khalilur
Published: June 2022

The primary purpose of this study is to observe the economic andinternational trade relationships between Bangladesh and SAARCcountries during the Fiscal Years 2015-2016 to 2020-2021. Thisresearch uses panel data to examine the relationship betweenthe nature and direction of exports and economic growth.The researcher found that economic cooperation with SAARCcountries impacted economic growth, but there was insufficientexport adaptation. The study also showed that economic relationshave strengthened in recent epochs, and bilateral trade hasincreased compared to previous years with SAARC countries.The obtained results also showed that Bangladesh has sufferedfrom a long-standing trade imbalance with India and is in agood position with Afghanistan, Sri Lanka, Bhutan, Maldives,Nepal, and Pakistan. Our findings are beneficial for internationaltrade stakeholders and suggest that steps should be taken toincrease exports to reduce the trade deficit. Concern authoritiesshould be more aware of the expansion of trade facilitation andinfrastructure development.JEL Classification: F5, F18, F23, F50, M16
Climate Change Impacts on San Francisco Estuary Aquatic Ecosystems: A Review
Authors:   Private consultant, Herbold Bruce , Bush Eva ,   Delta Stewardship Council, Castillo Gonzalo ,   US Fish and Wildlife Service, Colombano Denise ,   University of California, Berkeley, Hartman Rosemary ,   California Department of Water Resources, Lehman Peggy ,   California Department of Fish and Wildlife, Mahardja Brian ,   US Bureau of Reclamation, Sommer Ted ,   California Department of Water Resources
Published: June 2022

Climate change is intensifying the effects of multiple interacting stressors on aquatic ecosystems, particularly in estuaries. In the San Francisco Estuary, signals of climate change are apparent in the long-term monitoring record. Here we synthesize current and potential future climate change effects on three main ecosystems (floodplain, tidal marsh, and open water) in the upper estuary and two representative native fishes that commonly occur in these ecosystems (anadromous Chinook Salmon, Oncorhynchus tshawytscha and estuarine resident Sacramento Splittail, Pogonichthys macrolepidotus). Based on our review, we found that the estuary is experiencing shifting baseline environmental conditions, amplification of extremes, and restructuring of physical habitats and biological communities. We present priority topics for research and monitoring, and a conceptual model of how the estuary currently functions in relation to climate variables. In addition, we discuss four tools for management of climate change effects: regulatory, water infrastructure, habitat development, and biological measures. We conclude that adapting to climate change requires fundamental changes in management.
Human Development and Alcohol Consumption: State and Relationship in Russian Regions
Authors: Skokov Roman Yu. , Rogachev Aleksey F.
Published: June 2022

Introduction. The study of the relationship between the costs of purchasing a typical addictive product – alcoholic beverages and the human development index in the regions of Russia is due to the need to overcome population decline, improve the level of education and life of the population. The purpose of the article is to analyze human development in the Russian regions on the basis of the results obtained under the influence of spending on the consumption of alcoholic beverages.

Materials and Methods. The initial research data are United Nations Development Programme and Rosstat statistics for Russian regions for 2000–2017. A regression analysis of the relationship between the share of expenses on alcoholic beverages in the structure of the household final consumption expenses and the human development index in the regions of Russia was carried out. For each region, the coefficients of determination and correlation are calculated, the trends of change over the period, the direction and strength of the relationship between the human development index and the cost of alcoholic beverages are determined.

Results. The analysis has showed that in 77 Russian regions in 2000–2017, the share of expenses on alcoholic beverages in the structure of the household final consumption expenses is decreasing against the backdrop of an increase in the human development index and there is an inverse relationship between these indicators. To reduce the risk of negative health impacts from the consumption of addictive products in Russian regions, the implementation of local policies to reduce inequality and increase welfare is recommended. A toolkit for methodological, analytical and predictive support of regional regulatory policy has been developed and tested. Measures to reduce the consumption of alcoholic products, the risks of non-communicable diseases and mortality in the Russian regions are proposed.

Discussion and Conclusion. The data obtained indicate a steady downward trend in the share of expenses on the purchase of alcoholic beverages against the backdrop of a steady increase in the human development index in 2000–2017. The materials of the paper can be used to monitor and adjust the approaches of the state authorities to the directions of reducing the degradation of human capital from the consumption of addictive goods in troubled regions.
The Influence of Men’s Religious Identity on Fatherhood Institute Formation
Authors: Ildarkhanova Chulpan I. , Gnevasheva Vera A. , Abdulzyanov Artur R. , Pyatinina Oksana R.
Published: June 2022

Introduction. Scientific study of religious aspect of fatherhood is actual due to the significance of the man’s role in sustainability of parental and family relations in conditions of a demographic crisis. The aim of the article is to analyze a range of reproductive, generative and marital behavior of men defined by their religious identification as a factor that influences the formation of a family image that is shared in traditional religions.

Materials and Methods. Empirical basis of the study includes data of social survey held by authors in two subjects of Russian Federation – in the Republic of Tatarstan and Vologda Region. The object of the study – men at the age of 18‒49, the sample – 1 353 men. Use of comparative, economic-statistical and factor methods of analysis allowed to define the level of the influence of men’s religious identity on their generative behavior.

Results. The calculation of average values has defined comparative superiority of the value of the family in comparison with the value of work in all subgroups of the surveyed men, regardless of their involvement in the religious context, however, among religious men the family in the structure of life values is more vivid. Some peculiarities of the role distribution in families, expectations towards the number of children in the family, orientation towards upbringing and complicity in the growing up of children in the family, determination of the image of a father and correspondingly behavior of the respondents were determined. Men’s religious identity influences their desire to have bigger number of children: Christians tend to have two children while Muslims want to have more than two children. The percentage of men who desire to become a father of many children is less among those who don’t believe in God.

Discussion and Conclusion. The study of the influence of men’s religious identity on generative behavior allowed to reveal factors causing formation of reproductive behavior. This data will help to work out measures to increase the birth rate into account the role of fatherhood institute and confessional identity of a man. Results of the research can be used in the process of developing measures for the implementation of demographic and family policy.
Robusta coffee (Coffea canephora) value chain in East Java, Indonesia
Authors: Winarno Sri Tjondro , Harijani Wiwik Sri
Published: June 2022

Introduction. Coffee plantations are one of the growing agriculture sub-sectors and a high coffee production should be based on proper processing and marketing. Objective. To analyze the updates of products, processes, functions, and upgrading of the robusta coffee value chain in East Java. Materials and methods. This study was conducted in the Robusta coffee plantations in the Bondowoso, Jember, Lumajang, Pasuruan, Malang, and Ngawi districts in East Java, Indonesia, between October 2019 and March 2020. The quota sampling technique was used, followed by surveys and interviews. The number of respondents was 120 people who were actors in the coffee value chain, such as farmers, coffee cherry collectors, coffee cherry grinders, green bean grinders, and consumers. Results. The results showed that product, function, and channel upgrading had a significant effect on the coffee value chain, but the upgrading process did not significantly affect it in several regions on East Java. Conclusions. There is a need for innovation to enhance the coffee value chain in order to deliver future benefits to all stakeholders in this market.
TINGKAT KEMAMPUAN BAHASA INGGRIS DOSEN TETAP PERGURUAN TINGGI SWASTA DI JAWA BARAT BERDASARKAN SKOR TOEFL; STUDI KASUS DI UNIVERSITAS AL-GHIFARI BANDUNG
Authors: Hartono Hartono , Sakinah R. Myrna Nur , Nirwana Ria
Published: June 2022

Penelitian ini mempunyai tujuan utama untuk mengetahui tingkat kemampuan Bahasa inggris para dosen tetap di Jawa Barat khususnya di Universitas Alghifari. Universitas Al-Ghifari adalah salah satu perguruan tinggi yang berlokasi di Jln.Cisaranten Kulon no.140 Bandung, JawaBarat. Berdasarkan Data kepegawaian, Universitas Al-ghifari memiliki 63 Dosen tetap yang tentu saja  mempunyai kemampuan Bahasa inggris yang berbeda-beda. Dengan adanya penelitian ini diharapkan nantinya akan terpetakan secara detail tingkat kemampuan Bahasa inggris dari skor  TOEFL yang di hasilkan masing-masing dosen tetap maupun kemampuan secara keseluruhan dosen tetap dengan cara diambil rata-rata. Dalam mengklasifikasikan tingkat atau level kemampuan tersebut peneliti akan berlandaskan pada tingkatan kemampuan  dari CEFR ( Common European Framework of Reference for Languages) yang dikeluarkan oleh ETS (Educational Testing System). Metode yang digunakan adalah metode penelitian diskriptif dengan pendekatan kuantitatif. Sedangkan metode pengambilan data berupa test TOEFL peneliti menggunakan aplikasi google form quiz. Pengolahan menggunakan pendekatan kuantitatif, yaitu dengan statistik deskriptif yaitu mengelola, meringkas dan menggambarkan hasil penelitian. Selain itu dengan statistic deskriptif ditujukan untuk mengetahui rata-rata hasil skor TOEFL sehingga akan diketahui gambaran jelas tentang tingkat kemampuan Bahasa inggris para dosen tetap Universitas Al-ghifari.  Hasil penelitian kami sajikan per fakultas, dan hasilnya adalah sebagai berikut: Fakultas Sastra rata-rata skor adalah 564 level B2 atau Upper Intermediate, Fakultas Ekonomi rata-rata skor adalah 468 level B1 atau intermediate, Fakultas ISIP rata-rata skor adalah 437 level A2  atau elementary, Fakultas MIPA rata-rata skor 491 level B1 atau intermediate dan Fakultas TEKPER rata-rata skor 464 level B1 atau Intermediate. TINGKAT KEMAMPUAN BAHASA INGGRIS DOSEN TETAP PERGURUAN TINGGI SWASTA DI JAWA BARAT BERDASARKAN SKOR TOEFL; STUDI KASUS DI UNIVERSITAS AL-GHIFARI BANDUNG
El estudio de la cerámica medieval en Navarra. Un estado de la cuestión
Authors: Aznar-Auzmendi Jaime
Published: June 2022

Los estudios sobre cerámica medieval en Navarra han experimentado un notable desarrollo desde la segunda mitad del siglo XX. Hoy en día contamos con gran número de publicaciones que nos permiten observar la evolución de dicha disciplina. Precisamente por ello se hace necesario realizar un análisis crítico, basado en criterios geográficos, cronológicos y técnicos, a fin de poder establecer un marco de referencia. Por otro lado, es conveniente generar un debate sobre las nuevas metodologías que marcarán el camino de las próximas décadas.
Próba klasyfikacji typologicznej glinianych przęślików tarnobrzeskiej kultury łużyckiej
Authors:   Instytut Archeologii UR, Rzeszów, Polska, Jabłkowska Aleksandra
Published: June 2022

The article deals with the issues of clay spindle whorls of the Tarnobrzeg Lusatian culture. It is a category of artefacts closely related to the textile manufacturing, but still quite often marginalized by archaeologists in the studies of the excavation materials. It is probably the effect of their commonness and obvious purpose. They are usually mentioned in short in the form of descriptions and figures. The article presents the first attempt at typological classification of clay spindle whorls of the Tarnobrzeg Lusatian culture, which was developed on the basis of the variability of the shapes of cross-sections of spindle whorls. In that way it was possible to distinguish eight basic types – spindle whorls with a rectangular crosssection (type I), spindle whorls with a lenticular cross-section (type II), spindle whorls with a biconvex cross-section (type III), spindle whorls with a plano-convex cross-section (type IV), spindle whorls with a convexo-concave cross-section (type V), spindle whorls with a lenticular-rectangular crosssection (type VI), spool-shaped spindle whorls (type VII) and miniature vessel shaped spindle whorls (type VIII). The material scope of the present research includes the finds of 129 spindle whorls coming from 41 sites of the Tarnobrzeg Lusatian culture. The analysed category of artefacts includes items discovered both at settlement and sepulchral sites, however, it should be noted that the items recorded in the second context are much rarer. The conclusions drawn from the research presented in the article are the issue of standardizing and systematizing the descriptions of spindle whorls in archaeological publications. In general, the descriptions should take into account the following data: the shape of the cross-section, the diameter, the height measured at the opening, the weight and any features visible on the external surfaces of the spindle whorls in the form of ornaments or damage. The secondary features are colour and an admixture tempering the clay. All the abovementioned parameters allow us to obtain a relatively complete image of an individual item and therefore they are helpful for archaeologists whose field of interest is prehistoric textiles. Moreover, remarkably valuable information is also the listing of this category of artefacts together with the materials found in a given feature, which allows drawing conclusions regarding their chronology. In addition, the article discusses the issues in the broadest possible range, aimed at presenting and developing various aspects related to spindle whorls. Due to detailed analyses, it has been proved that the spindle whorls can be a starting point for research on the importance of spindle whorls as a source for analysing and reconstructing the weaving of the population of the Tarnobrzeg Lusatian culture, research on the chronology and cultural relations of the described taxonomic unit as well as research on its rituals and social structures.
Gliniane przedmioty zoomorficzne ze stanowiska w Korczowej 22, pow. jarosławski
Authors:   Instytut Archeologii UR, Rzeszów, Polska, Jabłkowski Michał
Published: June 2022

In 2010, in connection with the planned construction of the A4 motorway, the Radymno-Korczowa junction, rescue excavations were undertaken, which provided the discovery of two clay zoomorphic objects, which are the subject of the article. These are: a fragment of a small, solid, longitudinal and cylindrical zoomorphic pendant and a miniature, fragmentarily preserved zoomorphic figurine. Figural art is rare in the Tarnobrzeg Lusatian culture. In the archaeological literature, this category of artefacts is dated from IV period of the Bronze Age to the Hallstatt period. The article shows that the most analogies can be found to the east of the area occupied by the Tarnobrzeg Lusatian culture. A common feature of all figural representations is the schematicism and simplicity of their performance as well as the dominance of images of farm animals. Figural art discovered in archaeological sites is identified with all forms of art. Such items are interpreted as objects of cult significance, children’s toys or manifestations of the magic sphere. The popularity of these representations and the influence of the „Eastern” cultures, especially the foreststepped zone of the Scythian cultural circle, on the Tarnobrzeg Lusatian culture suggest that both the pendant and the figurine uncovered at the site 22 in Korczowa should be associated with this cultural provenance.
O papel do enfermeiro na prevenção do delirium no paciente adulto/idoso crítico
Authors:   Universidade Católica Portuguesa, Oliveira Cláudia , Garnacho Martins Nobre Cátia Filipa ,   Centro Hospitalar Universitário Lisboa Norte, EPE, Dourado Marques Rita Margarida ,   Universidade Católica Portuguesa, Madureira Lebre Mendes Maria Manuela ,   Universidade Católica Portuguesa, Cruz Pontífice Sousa Patrícia ,   Universidade Católica Portuguesa
Published: June 2022

Introdução: Delirium é uma disfunção cerebral aguda, associado ao aumento da mortalidade e morbilidade, que atinge frequentemente o paciente adulto/idoso crítico. O enfermeiro tem um papel determinante na prevenção/controlo do delirium, através da implementação de intervenções não farmacológicas. Objetivo: Conhecer as intervenções de enfermagem na identificação, prevenção e controlo do delirium no paciente adulto/idoso crítico. Materiais e métodos: Realizada uma Revisão Integrativa da Literatura de artigos publicados entre 2014 e 2018, que identificaram intervenções de enfermagem dirigidas à prevenção e controlo do delirium no paciente adulto/idoso crítico. Foram realizadas quatro pesquisas, nas bases de dados electrónicas da EBSCOhost e na B-on. Resultados: Identificaram-se 13 estudos, que apresentam intervenções de enfermagem, maioritariamente não farmacológicas, para prevenção e controlo do delirium no paciente adulto/idoso crítico. Destas, evidenciam-se intervenções relacionadas com o ambiente, promoção do sono, intervenção terapêutica precoce, avaliação cognitiva e orientação dos pacientes, intervenções sistematizadas em protocolos, bem como intervenções direcionadas à participação dos familiares, à formação dos enfermeiros e ao ensino dos pacientes. Foram também identificados fatores de risco para o desenvolvimento do delirium e instrumentos de avaliação. Discussão: A prevenção do delirium é importante e imperativa, já que nos pacientes críticos a sua ocorrência está associada ao aumento da mortalidade, morbilidade, do tempo de internamento e a um elevado custo hospitalar. A identificação dos fatores de risco para a ocorrência do delirium devem estar incluídos nos protocolos de abordagem do delirium. Conclusão: As evidências demonstraram que o enfermeiro é fundamental na identificação precoce, prevenção e controlo do delirium, evitando a progressão da doença, contribuindo para a diminuição da morbilidade e mortalidade. A intervenção de enfermagem deve incluir a identificação de fatores predisponentes e/ou precipitantes de modo a contribuir para a diminuição da ocorrência e/ou resolução do quadro de delirium.
Scientific Ranking over Heterogeneous Academic Hypernetwork
Authors: Liang Ronghua , Jiang Xiaorui
Published: June 2022

Ranking is an important way of retrieving authoritative papers from a large scientific literature database. Current state-of-the-art exploits the flat structure of the heterogeneous academic network to achieve a better ranking of scientific articles, however, ignores the multinomial nature of the multidimensional relationships between different types of academic entities. This paper proposes a novel mutual ranking algorithm based on the multinomial heterogeneous academic hypernetwork, which serves as a generalized model of a scientific literature database. The proposed algorithm is demonstrated effective through extensive evaluation against well-known IR metrics on a well-established benchmarking environment based on the ACL Anthology Network.
Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference
Authors: Chen Quanjun , Song Xuan , Yamada Harutoshi , Shibasaki Ryosuke
Published: June 2022

With the rapid development of urbanization and public transportation system, the number of traffic accidents have significantly increased globally over the past decades and become a big problem for human society. Facing these possible and unexpected traffic accidents, understanding what causes traffic accident and early alarms for some possible ones will play a critical role on planning effective traffic management. However, due to the lack of supported sensing data, research is very limited on the field of updating traffic accident risk in real-time. Therefore, in this paper, we collect big and heterogeneous data (7 months traffic accident data and 1.6 million users' GPS records) to understand how human mobility will affect traffic accident risk. By mining these data, we develop a deep model of Stack denoise Autoencoder to learn hierarchical feature representation of human mobility. And these features are used for efficient prediction of traffic accident risk level. Once the model has been trained, our model can simulate corresponding traffic accident risk map with given real-time input of human mobility. The experimental results demonstrate the efficiency of our model and suggest that traffic accident risk can be significantly more predictable through human mobility.
Multi-Defender Strategic Filtering Against Spear-Phishing Attacks
Authors: Laszka Aron , Lou Jian , Vorobeychik Yevgeniy
Published: June 2022

Spear-phishing attacks pose a serious threat to sensitive computer systems, since they sidestep technical security mechanisms by exploiting the carelessness of authorized users. A common way to mitigate such attacks is to use e-mail filters which block e-mails with a maliciousness score above a chosen threshold. Optimal choice of such a threshold involves a tradeoff between the risk from delivered malicious emails and the cost of blocking benign traffic. A further complicating factor is the strategic nature of an attacker, who may selectively target users offering the best value in terms of likelihood of success and resulting access privileges. Previous work on strategic threshold-selection considered a single organization choosing thresholds for all users. In reality, many organizations are potential targets of such attacks, and their incentives need not be well aligned. We therefore consider the problem of strategic threshold-selection by a collection of independent self-interested users. We characterize both Stackelberg multi-defender equilibria, corresponding to short-term strategic dynamics, as well as Nash equilibria of the simultaneous game between all users and the attacker, modeling long-term dynamics, and exhibit a polynomial-time algorithm for computing short-term (Stackelberg) equilibria. We find that while Stackelberg multi-defender equilibrium need not exist, Nash equilibrium always exists, and remarkably, both equilibria are unique and socially optimal.
A Security Game Combining Patrolling and Alarm-Triggered Responses Under Spatial and Detection Uncertainties
Authors: Basilico Nicola , De Nittis Giuseppe , Gatti Nicola
Published: June 2022

Motivated by a number of security applications, among which border patrolling, we study, to the best of our knowledge, the first Security Game model in which patrolling strategies need to be combined with responses to signals raised by an alarm system, which is spatially uncertain (i.e., it is uncertain over the exact location the attack is ongoing) and is affected by false negatives (i.e., the missed detection rate of an attack may be positive). Ours is an infinite-horizon patrolling scenario on a graph, where a single patroller moves. We study the properties of the game model in terms of computational issues and form of the optimal strategies and we provide an approach to solve it. Finally, we provide an experimental analysis of our techniques.
Multi-Attribute Proportional Representation
Authors: Lang Jérôme , Skowron Piotr
Published: June 2022

We consider the following problem in which a given number of items has to be chosen from a predefined set. Each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should fit. We look for a set that fits as much as possible the desired distributions on all attributes. Examples of applications include choosing members of a representative committee, where candidates are described by attributes such as sex, age and profession, and where we look for a committee that for each attribute offers a certain representation, i.e., a single committee that contains a certain number of young and old people, certain number of men and women, certain number of people with different professions, etc. With a single attribute the problem boils down to the apportionment problem for party-list proportional representation systems (in such case the value of the single attribute is the political affiliation of a candidate). We study some properties of the associated subset selection rules, and address their computation.
Multiwinner Analogues of the Plurality Rule: Axiomatic and Algorithmic Perspectives
Authors: Faliszewski Piotr , Skowron Piot , Slinko Arkadii , Talmon Nimrod
Published: June 2022

We characterize the class of committee scoring rules that satisfy the fixed-majority criterion. In some sense, the committee scoring rules in this class are multiwinner analogues of the single-winner Plurality rule, which is uniquely characterized as the only single-winner scoring rule that satisfies the simple majority criterion. We find that, for most of the rules in our new class, the complexity of winner determination is high (i.e., the problem of computing the winners is NP-hard), but we also show some examples of polynomial-time winner determination procedures, exact and approximate.
Price of Pareto Optimality in Hedonic Games
Authors: Elkind Edith , Fanelli Angelo , Flammini Michele
Published: June 2022

Price of Anarchy measures the welfare loss caused by selfish behavior: it is defined as the ratio of the social welfare in a socially optimal outcome and in a worst Nash equilibrium. A similar measure can be derived for other classes of stable outcomes. In this paper, we argue that Pareto optimality can be seen as a notion of stability, and introduce the concept of Price of Pareto Optimality: this is an analogue of the Price of Anarchy, where the maximum is computed over the class of Pareto optimal outcomes, i.e., outcomes that do not permit a deviation by the grand coalition that makes all players weakly better off and some players strictly better off. As a case study, we focus on hedonic games, and provide lower and upper bounds of the Price of Pareto Optimality in three classes of hedonic games: additively separable hedonic games, fractional hedonic games, and modified fractional hedonic games; for fractional hedonic games on trees our bounds are tight.
Fast Optimal Clearing of Capped-Chain Barter Exchanges
Authors: Plaut Benjamin , Dickerson John , Sandholm Tuomas
Published: June 2022

Kidney exchange is a type of barter market where patients exchange willing but incompatible donors. These exchanges are conducted via cycles---where each incompatible patient-donor pair in the cycle both gives and receives a kidney---and chains, which are started by an altruist donor who does not need a kidney in return. Finding the best combination of cycles and chains is hard. The leading algorithms for this optimization problem use either branch and price — a combination of branch and bound and column generation — or constraint generation. We show a correctness error in the leading prior branch-and-price-based approach [Glorie et al. 2014]. We develop a provably correct fix to it, which also necessarily changes the algorithm's complexity, as well as other improvements to the search algorithm. Next, we compare our solver to the leading constraint-generation-based solver and to the best prior correct branch-and-price-based solver. We focus on the setting where chains have a length cap. A cap is desirable in practice since if even one edge in the chain fails, the rest of the chain fails: the cap precludes very long chains that are extremely unlikely to execute and instead causes the solution to have more parallel chains and cycles that are more likely to succeed. We work with the UNOS nationwide kidney exchange, which uses a chain cap. Algorithms from our group autonomously make the transplant plans for that exchange. On that real data and demographically-accurate generated data, our new solver scales significantly better than the prior leading approaches.
Rules for Choosing Societal Tradeoffs
Authors: Conitzer Vincent , Freeman Rupert , Brill Markus , Li Yuqian
Published: June 2022

We study the societal tradeoffs problem, where a set of voters each submit their ideal tradeoff value between each pair of activities (e.g., "using a gallon of gasoline is as bad as creating 2 bags of landfill trash"), and these are then aggregated into the societal tradeoff vector using a rule. We introduce the family of distance-based rules and show that these can be justified as maximum likelihood estimators of the truth. Within this family, we single out the logarithmic distance-based rule as especially appealing based on a social-choice-theoretic axiomatization. We give an efficient algorithm for executing this rule as well as an approximate hill climbing algorithm, and evaluate these experimentally.
Intelligent Advice Provisioning for Repeated Interaction
Authors: Levy Priel , Sarne David
Published: June 2022

This paper studies two suboptimal advice provisioning methods ("advisors") as an alternative to providing optimal advice in repeated advising settings. Providing users with suboptimal advice has been reported to be highly advantageous whenever the optimal advice is non-intuitive, hence might not be accepted by the user. Alas, prior methods that rely on suboptimal advice generation were designed primarily for a single-shot advice provisioning setting, hence their performance in repeated settings is questionable. Our methods, on the other hand, are tailored to the repeated interaction case. Comprehensive evaluation of the proposed methods, involving hundreds of human participants, reveals that both methods meet their primary design goal (either an increased user profit or an increased user satisfaction from the advisor), while performing at least as good with the alternative goal, compared to having people perform with: (a) no advisor at all; (b) an advisor providing the theoretic-optimal advice; and (c) an effective suboptimal-advice-based advisor designed for the non-repeated variant of our experimental framework.
Fast Proximal Linearized Alternating Direction Method of Multiplier with Parallel Splitting
Authors: Lu Canyi , Li Huan , Lin Zhouchen , Yan Shuicheng
Published: June 2022

The Augmented Lagragian Method (ALM) and Alternating Direction Method of Multiplier (ADMM) have been powerful optimization methods for general convex programming subject to linear constraint. We consider the convex problem whose objective consists of a smooth part and a nonsmooth but simple part. We propose the Fast Proximal Augmented Lagragian Method (Fast PALM) which achieves the convergence rate O(1/K2), compared with O(1/K) by the traditional PALM. In order to further reduce the per-iteration complexity and handle the multi-blocks problem, we propose the Fast Proximal ADMM with Parallel Splitting (Fast PL-ADMM-PS) method. It also partially improves the rate related to the smooth part of the objective function. Experimental results on both synthesized and real world data demonstrate that our fast methods significantly improve the previous PALM and ADMM
Abstract Zobrist Hashing: An Efficient Work Distribution Method for Parallel Best-First Search
Authors: Jinnai Yuu , Fukunaga Alex
Published: June 2022

Hash Distributed A* (HDA*) is an efficient parallel best first algorithm that asynchronously distributes work among the processes using a global hash function. Although Zobrist hashing, the standard hash function used by HDA*, achieves good load balance for many domains, it incurs significant communication overhead since it requires many node transfers among threads. We propose Abstract Zobrist hashing, a new work distribution method for parallel search which reduces node transfers and mitigates communication overhead by using feature projection functions. We evaluate Abstract Zobrist hashing for multicore HDA*, and show that it significantly outperforms previous work distribution methods.
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty
Authors: Song Changkyu , Yoon Sejong , Pavlovic Vladimir
Published: June 2022

We propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning. The proposed method accelerates the speed of convergence by automatically deciding the constraint penalty needed for parameter consensus in each iteration. In addition, we also propose an extension of the method that adaptively determines the maximum number of iterations to update the penalty. We show that this approach effectively leads to an adaptive, dynamic network topology underlying the distributed optimization. The utility of the new penalty update schemes is demonstrated on both synthetic and real data, including an instance of the probabilistic matrix factorization task known as the structure from motion problem.
CAPReS: Context Aware Persona Based Recommendation for Shoppers
Authors: Banerjee Joydeep , Raravi Gurulingesh , Gupta Manoj , Ernala Sindhu , Kunde Shruti , Dasgupta Koustuv
Published: June 2022

Nowadays, brick-and-mortar stores are finding it extremely difficult to retain their customers due to the ever increasing competition from the online stores. One of the key reasons for this is the lack of personalized shopping experience offered by the brick-and-mortar stores. This work considers the problem of persona based shopping recommendation for such stores to maximize the value for money of the shoppers. For this problem, it proposes a non-polynomial time-complexity optimal dynamic program and a polynomial time-complexity non-optimal heuristic, for making top-k recommendations by taking into account shopper persona and her time and budget constraints. In our empirical evaluations with a mix of real-world data and simulated data, the performance of the heuristic in terms of the persona based recommendations (quantified by similarity scores and items recommended) closely matched (differed by only 8% each with) that of the dynamic program and at the same time heuristic ran at least twice faster compared to the dynamic program.
Solving the Station Repacking Problem
Authors: Fréchette Alexandre , Newman Neil , Leyton-Brown Kevin
Published: June 2022

We investigate the problem of repacking stations in the FCC's upcoming, multi-billion-dollar "incentive auction". Early efforts to solve this problem considered mixed-integer programming formulations, which we show are unable to reliably solve realistic, national-scale problem instances. We describe the result of a multi-year investigation of alternatives: a solver, SATFC, that has been adopted by the FCC for use in the incentive auction. SATFC is based on a SAT encoding paired with a wide range of techniques: constraint graph decomposition; novel caching mechanisms that allow for reuse of partial solutions from related, solved problems; algorithm configuration; algorithm portfolios; and the marriage of local-search and complete solver strategies. We show that our approach solves virtually all of a set of problems derived from auction simulations within the short time budget required in practice.
Minimizing User Involvement for Learning Human Mobility Patterns from Location Traces
Authors: Alharbi Basma , Qahtan Abdulhakim , Zhang Xiangliang
Published: June 2022

Utilizing trajectories for modeling human mobility often involves extracting descriptive features for each individual, a procedure heavily based on experts' knowledge. In this work, our objective is to minimize human involvement and exploit the power of community in learning `features' for individuals from their location traces. We propose a probabilistic graphical model that learns distribution of latent concepts, named motifs, from anonymized sequences of user locations. To handle variation in user activity level, our model learns motif distributions from sequence-level location co-occurrence of all users. To handle the big variation in location popularity, our model uses an asymmetric prior, conditioned on per-sequence features. We evaluate the new representation in a link prediction task and compare our results to those of baseline approaches.
Ontology-Mediated Queries for NOSQL Databases
Authors: Mugnier Marie-Laure , Rousset Marie-Christine , Ulliana Federico
Published: June 2022

Ontology-Based Data Access has been studied so far for relational structures and deployed on top of relational databases. This paradigm enables a uniform access to heterogeneous data sources, also coping with incomplete information. Whether OBDA is suitable also for non-relational structures, like those shared by increasingly popular NOSQL languages, is still an open question. In this paper, we study the problem of answering ontology-mediated queries on top of key-value stores. We formalize the data model and core queries of these systems, and introduce a rule language to express lightweight ontologies on top of data. We study the decidability and data complexity of query answering in this setting.
A Comparative Study of Ranking-Based Semantics for Abstract Argumentation
Authors: Bonzon Elise , Delobelle Jérôme , Konieczny Sébastien , Maudet Nicolas
Published: June 2022

Argumentation is a process of evaluating and comparing a set of arguments. A way to compare them consists in using a ranking-based semantics which rank-order arguments from the most to the least acceptable ones. Recently, a number of such semantics have been pro- posed independently, often associated with some desirable properties. However, there is no comparative study which takes a broader perspective. This is what we propose in this work. We provide a general comparison of all these semantics with respect to the proposed proper- ties. That allows to underline the differences of behavior between the existing semantics.
Emergence of Social Punishment and Cooperation through Prior Commitments
Authors: Han The Anh
Published: June 2022

Social punishment, whereby cooperators punish defectors, has been suggested as an important mechanism that promotes the emergence of cooperation or maintenance of social norms in the context of the one-shot (i.e. non-repeated) interaction. However, whenever antisocial punishment, whereby defectors punish cooperators, is available, this antisocial behavior outperforms social punishment, leading to the destruction of cooperation. In this paper, we use evolutionary game theory to show that this antisocial behavior can be efficiently restrained by relying on prior commitments, wherein agents can arrange, prior to an interaction, agreements regarding posterior compensation by those who dishonor the agreements. We show that, although the commitment mechanism by itself can guarantee a notable level of cooperation, a significantly higher level is achieved when both mechanisms, those of proposing prior commitments and of punishment, are available in co-presence. Interestingly, social punishment prevails and dominates in this system as it can take advantage of the commitment mechanism to cope with antisocial behaviors. That is, establishment of a commitment system helps to pave the way for the evolution of social punishment and abundant cooperation, even in the presence of antisocial punishment.
ConTaCT: Deciding to Communicate during Time-Critical Collaborative Tasks in Unknown, Deterministic Domains
Authors: Unhelkar Vaibhav , Shah Julie
Published: June 2022

Communication between agents has the potential to improve team performance of collaborative tasks. However, communication is not free in most domains, requiring agents to reason about the costs and benefits of sharing information. In this work, we develop an online, decentralized communication policy, ConTaCT, that enables agents to decide whether or not to communicate during time-critical collaborative tasks in unknown, deterministic environments. Our approach is motivated by real-world applications, including the coordination of disaster response and search and rescue teams. These settings motivate a model structure that explicitly represents the world model as initially unknown but deterministic in nature, and that de-emphasizes uncertainty about action outcomes. Simulated experiments are conducted in which ConTaCT is compared to other multi-agent communication policies, and results indicate that ConTaCT achieves comparable task performance while substantially reducing communication overhead.
Implicit Coordination in Crowded Multi-Agent Navigation
Authors: Godoy Julio , Karamouzas Ioannis , Guy Stephen , Gini Maria
Published: June 2022

In crowded multi-agent navigation environments, the motion of the agents is significantly constrained by the motion of the nearby agents. This makes planning paths very difficult and leads to inefficient global motion. To address this problem, we propose a new distributed approach to coordinate the motions of agents in crowded environments. With our approach, agents take into account the velocities and goals of their neighbors and optimize their motion accordingly and in real-time. We experimentally validate our coordination approach in a variety of scenarios and show that its performance scales to scenarios with hundreds of agents.
Convolutional Neural Networks over Tree Structures for Programming Language Processing
Authors: Mou Lili , Li Ge , Zhang Lu , Wang Tao , Jin Zhi
Published: June 2022

Programming language processing (similar to natural language processing) is a hot research topic in the field of software engineering; it has also aroused growing interest in the artificial intelligence community. However, different from a natural language sentence, a program contains rich, explicit, and complicated structural information. Hence, traditional NLP models may be inappropriate for programs. In this paper, we propose a novel tree-based convolutional neural network (TBCNN) for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information. TBCNN is a generic architecture for programming language processing; our experiments show its effectiveness in two different program analysis tasks: classifying programs according to functionality, and detecting code snippets of certain patterns. TBCNN outperforms baseline methods, including several neural models for NLP.
Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks
Authors: Chen Hao , Dou Qi , Wang Xi , Qin Jing , Heng Pheng
Published: June 2022

The number of mitoses per tissue area gives an important aggressiveness indication of the invasive breast carcinoma.However, automatic mitosis detection in histology images remains a challenging problem. Traditional methods either employ hand-crafted features to discriminate mitoses from other cells or construct a pixel-wise classifier to label every pixel in a sliding window way. While the former suffers from the large shape variation of mitoses and the existence of many mimics with similar appearance, the slow speed of the later prohibits its use in clinical practice.In order to overcome these shortcomings, we propose a fast and accurate method to detect mitosis by designing a novel deep cascaded convolutional neural network, which is composed of two components. First, by leveraging the fully convolutional neural network, we propose a coarse retrieval model to identify and locate the candidates of mitosis while preserving a high sensitivity.Based on these candidates, a fine discrimination model utilizing knowledge transferred from cross-domain is developed to further single out mitoses from hard mimics.Our approach outperformed other methods by a large margin in 2014 ICPR MITOS-ATYPIA challenge in terms of detection accuracy. When compared with the state-of-the-art methods on the 2012 ICPR MITOSIS data (a smaller and less challenging dataset), our method achieved comparable or better results with a roughly 60 times faster speed.
Linear Submodular Bandits with a Knapsack Constraint
Authors: Yu Baosheng , Fang Meng , Tao Dacheng
Published: June 2022

Linear submodular bandits has been proven to be effective in solving the diversification and feature-based exploration problems in retrieval systems. Concurrently, many web-based applications, such as news article recommendation and online ad placement, can be modeled as budget-limited problems. However, the diversification problem under a budget constraint has not been considered. In this paper, we first introduce the budget constraint to linear submodular bandits as a new problem called the linear submodular bandits with a knapsack constraint. We then define an alpha-approximation unit-cost regret considering that submodular function maximization is NP-hard. To solve this problem, we propose two greedy algorithms based on a modified UCB rule. We then prove these two algorithms with different regret bounds and computational costs. We also conduct a number of experiments and the experimental results confirm our theoretical analyses.
Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction
Authors: Phan NhatHai , Wang Yue , Wu Xintao , Dou Dejing
Published: June 2022

In recent years, deep learning has spread beyond both academia and industry with many exciting real-world applications. The development of deep learning has presented obvious privacy issues. However, there has been lack of scientific study about privacy preservation in deep learning. In this paper, we concentrate on the auto-encoder, a fundamental component in deep learning, and propose the deep private auto-encoder (dPA). Our main idea is to enforce ε-differential privacy by perturbing the objective functions of the traditional deep auto-encoder, rather than its results. We apply the dPA to human behavior prediction in a health social network. Theoretical analysis and thorough experimental evaluations show that the dPA is highly effective and efficient, and it significantly outperforms existing solutions.
Unsupervised Feature Selection with Structured Graph Optimization
Authors: Nie Feiping , Zhu Wei , Li Xuelong
Published: June 2022

Since amounts of unlabelled and high-dimensional data needed to be processed, unsupervised feature selection has become an important and challenging problem in machine learning. Conventional embedded unsupervised methods always need to construct the similarity matrix, which makes the selected features highly depend on the learned structure. However real world data always contain lots of noise samples and features that make the similarity matrix obtained by original data can't be fully relied. We propose an unsupervised feature selection approach which performs feature selection and local structure learning simultaneously, the similarity matrix thus can be determined adaptively. Moreover, we constrain the similarity matrix to make it contain more accurate information of data structure, thus the proposed approach can select more valuable features. An efficient and simple algorithm is derived to optimize the problem. Experiments on various benchmark data sets, including handwritten digit data, face image data and biomedical data, validate the effectiveness of the proposed approach.