bluuebunny/crossref_metadata_2025 huggingface dataset:
PENGEMBANGAN EKONOMI PEDESAAN DALAM RANGKA MENGENTAS KEMISKINAN DI PEDESAAN
Authors: Nasfi Nasfi
Published: August 2021

The rural community economic development program in order to reduce poverty in rural areas is carried out with the development of a balanced industry, improving the structure of agriculture and programs to improve the education of rural communities, so as to maintain urbanization. The next program is to increase rural community empowerment programs by environmental preservation programs and strengthening of rural institutions and facilities and infrastructure. By realizing the horizontal top-down policy that includes rural communities, the government is only the general policy holder. Pilot project program by supporting rural production at a competitive level, by strengthening rural institutionalism and improving information and management for rural communities. Various ways in which countries reduce poverty and hunger through sustainable food production and consumption have been established within the framework of global sustainable development goals. This type of research is a qualitative study, where qualitative research by describing activities, phenomena, opinions / thoughts / views and documents, with data analysis techniques systematically compile the data obtained.
MODEL KONSEPTUAL KEPEMIMPINAN, GENDER, DAN DIVERSITAS
Authors: Nurmayanti Poppy , Suryawati Evi , Firzal Yohannes , Ramaiyanti Sinta , Maulida Yusni
Published: August 2021

This paper presents a conceptual model on leadership, gender, and diversity toward decision making with using role congruity theory. This paper also explore generally whether and why gender may matter for leadership and evaluate views on a feminine/masculine in effectiveness leadership. Recently, considerations of gender and diversity have predominantly focused on differences and similarities between female and male leaders that results from the difference between stereotypes and leadership stereotype. These difference exist worldwide and globalization of management brings to the need to examine this stereotype phenomenon in cross cultural area.
DEMOKRASI PINCANG: ANALISIS TERHADAP INDEKS DEMOKRASI PROVINSI SUMATERA BARAT PASCA REFORMASI
Authors: Fajri Hidayatul , Wahyuni Nila , Saputra Boni , Maani Karjuni Dt.
Published: August 2021

Demokrasi dipercaya sebagai sebuah pilihan terbaik dari sistem politik modern. Namun begitu di dalam prakteknya, demokrasi seringkali dimaknai dalam posisi yang berbeda dari normatifnya dan seringkali dipengaruhi oleh lingkungan politik praktis. Oleh sebab itu perlu suatu ukuran yang dilakukan secara terus menerus untuk melihat apakah demokrasi tersebut bergerak maju. Indonesia memiliki Indeks Demokrasi Indonesia (IDI) yang bertujuan untuk memotret kondisi demokrasi Indonesia yang berbasis pada data di tingkat provinsi. Pekerjaan kami dalam artikel ini adalah menganalisis kondisi demokrasi di Provinsi Sumatera Barat yang belakangan sering mendapat sorotan publik akibat dianggap memiliki aturan-aturan yang kurang plural. Dengan mengolah data IDI dari tahun 2009 sampai 2019 secara statistik deskriptif, kami berupaya melihat lebih dalam tentang kondisi demokrasi di Sumatera Barat dan  mendapati bahwa penyebab rendahnya indeks demokrasi Sumatera Barat bukan saja diakibatkan oleh adanya aturan tertulis yang membatasi kebebasan atau mengharuskan masyarakat dalam menjalankan agamanya yang menjadi sorotan publik namun juga diakibatkan oleh dominasi eksekutif dan lemahnya peran legislatif terutama dalam hal menginisiasi kebijakan yang kemudian ditetapkan menjadi peraturan. Dan kami berhipotesis jika eksekutif sering memanfaatkan kondisi itu untuk membuat kebijakan-kebijakan populis yang salah satunya adalah dengan kebijakan-kebijakan berbau “agamis” tersebut.
SPORT ACCOMPANIMENT OF SELF DEFENSE PENCAK SILAT BANJAR RAGA INSANI CLUB AT ARA PAYUNG VILLAGE
Authors: Ratno Puji , Fadli Zen , Irwansyah Irwansyah
Published: August 2021

This community service activity is one form of the implementation of the Tri Dharma of Higher Education. This activity was carried out in order to help preserve the pencak silat banjar culture which is in the Village of Ara Payung. The main problem faced by partners is that in carrying out the training of pencak silat trainers, they do not yet have the ability to prepare training programs, variations in the practice of pencak silat in the art of motion are not many. And the lack of training facilities that exist in supporting a good training process, and supporting facilities. The output targets in this activity are: Trainers are able to make a good and correct training program in accordance with the rules of coaching. Availability of standard martial arts training facilities in accordance with the provisions. The method used to achieve the objectives of this activity is. Coaching clinic for the preparation of training programs for pencak silat coach, and mentoring for banjar pencak silat training. This service activity has resulted in an exercise program that is used in training at tha club, the trainer is able to arrange an exercise program that can be applied in training, increasing the variety of training in the Pencak Silat Raga Insani club. With the training and mentoring activities, the enthusiasm of the teenagers in the village of Ara Payung was higher in participating in the martial arts training in his village.Keywords: Instrument, Talent, Karate.
AN ANALYSIS OF PARROT FISH SUPPLY CHAIN IN TOBA SAMOSIR REGENCY
Authors: Marisa Julia , Sitepu Sukma Aditya
Published: August 2021

Proper supply chain management in tilapia commodities in Toba Samosir Regency is needed to add value, maintain stock and quality, and can be easily reached by consumers/ communities. This study aims to determine the production process, analyze the actors involved, and analyze product flow, money flow and information flow in the tilapia fish supply chain in Toba Samosir Regency. This research is a qualitative descriptive study. Data analysis method is carried out in several steps: 1) Data Collection, 2) Data Reduction, 3) Data Display, 4) Drawing Conclusion and Verification. The results of this study are 1) Tilapia fish production process in Toba SamosirRegency is carried out with two systems, namely the first fish cultivation is done starting from the nursery and the second is cultivation for enlargement. 2) The actors involved in the supply chain system for the freshwater fish industry in Toba Samosir Regency are Fish Cultivators, Collectors, Diluents, Fish Processing Industries, Consumers. 3) There are product flow, money flow and information flow in the supply chain of the freshwater fishing industry in Toba Samosir Regency. Product flow flows from upstream to downstream, financial flow flows from downstream to upstream and information flows flow in both directions.Keywords: Industry; Supply Chain; Tilapia; Toba Samosir Regency.
TOWARD ESTABLISHING A SUSTAINABLE TOURISM ON THE TALIMBARU VILLAGE IN THE KARO REGENCY
Authors: Herliani Rini , Zainal Andri
Published: August 2021

This article aims to present a descriptive analysis related to the Embung Talimbaru Ecotourism Area (ETEA). The development of this ETEA refers to the actualization of the Talimbaru Village Mid-Term Development Plan (RPJMD) for 2016-2021 which has the potential for the realization of an ecotourism area that is not only suitable to support the Lake Toba Geopark Area, but also as the next New Bali program. Previous studies have highlighted the importance of strengthening the capacity of local communities in the development of community-based tourism that was characteristic of the previous era of tourism in Karo District. The priority issues that underline the importance of implementing business management and good governance in Village Owned Enterprise (known as BUMDES) activities, especially with the acceleration of the management of the ETEA as a leading local nature tourism object. In addition, the success of the sustainable management of the ETEA will be largely determined by the competence and professionalism of the human resources and infrastructure of the associated tourism village management ecosystem. Strategic actualization of the embodiment of a descriptive approach to the development of ETEA is presented in detail in the discussion segment toward preparing for Community Service program in both mono and multi-year schemes. Keywords: community-based tourism; descriptive approach; Embung Talimbaru Ecotourism Area; Community Service Program; Karo.
The Influence of Competence and Independence to Work Effectiveness of Auditor at Regional Inspectorate of Gorontalo Province
Authors: Talib Novianty , Ilato Rosman , Sulila Ismet
Published: August 2021

The research objective was to find out the influence of competence and independence on the work effectiveness of auditor at Regional Inspectorate of Gorontalo Province either partially or simultaneously. The approach used in this research was quantitative. The research method used expost facto. The research design applied causality. The technique of data analysis used multiple regression.The research finding showed that (1) competence of auditor had a positive and significant influence on the work effectiveness of auditor at Regional Inspectorate of Gorontalo Province by the determinant value of 23,50%. The meaning of the positive coefficient indicated that the higher the competence of auditor, the more effective work result of auditor at Regional Inspectorate of Gorontalo Province. (2) Independence of auditor had a positive and significant influence on the work effectiveness of auditor at Regional Inspectorate of Gorontalo Province by determinant value of 56,60%. The meaning of positive coefficient indicated that the more independent of auditor in doing inspection and functional supervision function, the more optimum work effectiveness of auditor at Regional Inspectorate of Gorontalo Province. (3) The competence and independence of auditor, simultaneously, had a significant influence on the work effectiveness of auditor at Regional Inspectorate of Gorontalo Province by determinant value of 61,70%. Meanwhile, the rest 38,30% could be explained by other variables such as auditor education, auditor integrity, auditor work motivation, work discipline, and auditor professionalism attitude.Keywords: Work Effectiveness, Auditor Competence, Auditor Independence
Using the digital potential of education (based on the working experience)
Authors: Chechetka Valentina I. , Lukina Lyudmila V. , Buzinova Lyudmila M. , Pashkovskaia Natalia D.
Published: August 2021

The paper analyses the information openness of universities in accordance with the requirements provided by the current legislation of the Russian Federation in the field of education. It also considers the organization of educational activities carried out by universities and providing access to information and educational resources for students and scientific-pedagogical staff. The stages of digital learning units’ development within the framework of university courses are considered. Functional strategies for planning and conducting remote and in-class learning are investigated by the example of teaching foreign languages at a non-linguistic/technical university.
Reducing plastic packaging at the Prince George Farmers' Market
Authors:   , Tanubamrungsuk Waranuch
Published: August 2021

Plastic pollution, a global problem, contributes to detrimental impacts on the environment. To mitigate plastic pollution locally,farmers’ market scan play a role in reducing plastic consumption. Many studies have suggested that customers and vendors are aware of environmental problems. Yet vendor environmental awareness does not always result in appropriate actions, as many vendors still rely on plastic packaging. To investigate the reasons for such reliance, I conducted vendor interviews and a customer survey at the Prince George (B.C.) Farmers’ Market. The results show that the barriers to reducing plastic include packaging availability, characteristics, functions, and price. Based on interview results, I developed a manual to support vendors interested in reducing plastic packaging at their stalls. I also offer recommendations as to how policy makers can motivate vendors and customers to reduce plastic consumption, such as offering customers reusable bags and organizing plastic-free events.
Demographic Data for SCSP Mobile Health Care Programme Conducted in the Rural Area of Bhopal District, Madhya Pradesh
Authors:   Research Associate (Unani), Clinical Research Unit Bhopal, Under CCRUM, Ministry of AYUSH Govt. of India, Fatima Yasmin
Published: August 2021

Introduction: Demography is the statistical and mathematical study of the size composition and spatial distribution of human populations. In this article we focused on some demographic data such as age, gender, education, caste, religion, marital status, occupation, addiction and dietary habits which may help to assess the socio economic and health status of the population. Methods: This is an observational and descriptive study, where data collected using a pretested, predesigned questionnaire/screening form to assess the demographic information i.e. age, gender, education, caste, religion, marital status, occupation, addiction and dietary habits, provided by Central Council for Research in Unani Medicine(CCRUM), Ministry of AYUSH, Govt. of India, Delhi. Sample size was not calculated as all the patients who were coming to OPD were included in the study and all patients were assured of confidentiality of the details collected and patients provided data willingly. Inclusion criteria for the study included all the patients of either sex of any age group attending OPDs in five adopted villages, i.e. Kurana, Kalkheda, Bagoniya, Toomda and Dhammarra during July 2019 to March 2020 at SCSP Mobile Healthcare Programme, Clinical Research Unit, Bhopal. Results and Conclusion: Collected and compiled data exhibits that the Scheduled Castes population visited the OPDs belongs to a low socio-economic group and are mainly small farmers and landless labourers. The percentage of the female population who visited the OPDs was more which reveals that due to the excessive burden of work and poor nutritional diet they are more susceptible to have an illness. Literacy level was fair among population with 19.16 % illiterate. Tobacco chewing is seen more prevalent among the participants. Poverty and less availability of food have led to unsatisfactory dietary habit, though literacy is considerable in these villages.
Vietnam’s Meat Import Market Under Impacts Of The European -Vietnam Free Trade Agreement And Quality Management Demand
Authors: Minh Doan , Nga Le , Huy Dinh , Dat Pham
Published: August 2021

The impact of Free Trade Agreement (FTA) on commercial business of the member could be assessed by the potential and tangible effects. This paper is adopted by Partial equilibrium theory and SMART tool to measure the impact of EVFTA on the Vietnamese meat import (HS code 02). The result of this model is claimed that EVFTA has a huge impact on boosting the meat import from EU to Vietnam. However, the value of import in this category from European nations in each country and good fluctuated significantly. This study also proposes some measures for domestic businesses and the government to ensure the benefits on Vietnam’s livestock industry. Last but not least, meat quality management is one of vital issues under EFVTA and global competitiveness to meet higher expectation of consumers. Good food (meat) manufacturing practices need to be applied. That is the social contribution value of this paper.
Management Issues in Medical Industry in Vietnam
Authors: Dat Pham , Huy Dinh
Published: August 2021

Stock exchange has impacts on activities and capitals of medical companies in Vietnam market. However, medical firms still need researches to address some management issues including good practices of governance, good resources allocation and better risk management policies.

Authors uses quantitative analysis and statistics, together with qualitative methods including synthesis and explanatory methods. We figure out CPI and stock price has negative relationship while there is positive correlation between stock price and Risk free rate (Rf).

In conclusion, authors also propose total quality management perspective as well as recommend macro policies that affect company performance.

For example, exchange rate policy could support USD/VND just little bit while inflation policy need to be controlled at low level.
The impact of the COVID-19 pandemic on the activities of small enterprises on the example of business customers of a manufacturing company
Authors: Danielak Wiesław
Published: August 2021

The aim of the article is to show the impact of the COVID-19 pandemic on the activities of small enterprises on the example of business customers of a manufacturing company. The article contains current issues and presents problems regarding the global pandemic caused by COVID-19. which started in March 2020. The theoretical part shows the impact of the pandemic crisis on the activities of enterprises. The empirical part presents the results of research showing the decisions of company managers during the COVID-19 pandemic affecting the behavior of employees. suppliers. customers and the paper producer. The results of the research can be a source of knowledge for top and middle management. but also for employees whose safety of life and health is particularly important in the times of the COVID-19 pandemic.
Use of Geospatial Tools in Morphometric Analysis and Prioritisation of Sub-catchments of the Soungrougrou (Casamance Basin)
Authors: Faye Cheikh , Ndiaye Modou
Published: August 2021

The prioritisation of catchments, particularly in the context of catchment plans and management programmes, is part of water resources development. In fact, morphometric analysis assisted by geospatial technology is carried out by prioritising sub-catchments according to their natural resource availability characteristics. Information on the geomorphology and erosion factors of the study area is used in the area in the preparation of local models of ungauged sub-catchments, which otherwise lack an adequate hydrological database. The objective of this paper is to use geographic information systems (GISs) in morphometric analysis to prioritise sub-catchments of the Soungrougrou (a tributary of the Casamance River). In this respect, the integrated methodology involving morphometric aspects from geospatial technology is used. To carry out the geospatial research, basic mathematical equations used in a GIS environment were used to measure a series of aspects of hydrology such as flow length, flow length ratio, bifurcation ratio, drainage density, drainage texture, flow frequency, elongation rate, circularity ratio, form factor, relief and relief ratio. The results divided the whole catchment into three priority areas, namely high, medium and low. The results are relevant for establishing soil and water conservation plans in the Soungrougrou basin, as well as adequate groundwater production and management. The high category (sub-basins 6, 8, 14, 17 and 18) is subject to maximum soil erosion, which requires immediate intervention to avoid possible natural hazards.
Непрофессионализм в отечественном общем образовании: педагогика, которую мы потеряли в образовании
Authors: Невзоров М.Н. , Кравцов В.В.
Published: August 2021

Цель данной монографии – вскрыть причины отторжения педагогики образованием и по вертикали администрирования, и по горизонтали учитель –ученики, осмыслить, почему образование прекрасно себя чувствует в условиях дикого педагогического непрофессионализма. В работе представлена объективная печальная действительность массовой школы, рассмотрена ситуация в образовании в аспекте триады философии образования: становление человека, профессиональная деятельность, социальный институт (управление), показаны пути трансформации массовой школы.
Проблемы описания интернет-жанров на примере жанра «личный блог» в сети Instagram
Authors: Пилюгина Н. Ю.
Published: August 2021

Статья посвящена проблемам, возникающим при исследовании интернет-жанров. Обсуждаются основные направления исследования виртуальных жанров, подходы к изучению главных признаков таких жанров, возможности создания модели их описания. Проводится разграничение понятий формата и виртуального гипержанра. Представлен подход, учитывающий влияние медийных, прагматических, структурных и стилистико-языковых особенностей на природу интернет-жанра. Обобщается опыт последних исследований в области составления модели жанра блога, который представлен как гипержанровое образование, характеризующееся полимодальностью и дискурсивной неоднородностью. Важнейшими медийными характеристиками интернет-жанров являются креолизованность, гипертекстуальность и интерактивность. Особое внимание акцентируется на том, что современные виртуальные жанры – это особая форма организации текста, которая характеризуется процессом нелинейного чтения и письма. Отмечается, что рост текстовой креолизации ведет к изменению способов и типов общения, переопределению функций единиц разных языковых уровней, тенденции к использованию упрощенных конструкций и языковых средств. В статье выделена ведущая особенность жанра блога на основе коммуникативной функции: личный блог ориентирует автора на позиционирование, обусловленное различными коммуникативными действиями публично-частной языковой личности. Приводится пример анализа взаимодействия невербальных и вербальных составляющих блогов в реализации функций социального позиционирования и самопрезентации автора блога (на примере блога В. Шаповаловой). Приведенные примеры доказывают сложность, многомерность исследуемого типа жанра, неразрывную связь визуального и вербального кода в его оформлении.
Loyalty in Cardinal Hedonic Games
Authors: Bullinger Martin , Kober Stefan
Published: August 2021

A common theme of decision making in multi-agent systems is to assign utilities to alternatives, which individuals seek to maximize. This rationale is questionable in coalition formation where agents are affected by other members of their coalition. Based on the assumption that agents are benevolent towards other agents they like to form coalitions with, we propose loyalty in hedonic games, a binary relation dependent on agents' utilities. Given a hedonic game, we define a loyal variant where agents' utilities are defined by taking the minimum of their utility and the utilities of agents towards which they are loyal. This process can be iterated to obtain various degrees of loyalty, terminating in a locally egalitarian variant of the original game.

We investigate axioms of group stability and efficiency for different degrees of loyalty. Specifically, we consider the problem of finding coalition structures in the core and of computing best coalitions, obtaining both positive and intractability results. In particular, the limit game possesses Pareto optimal coalition structures in the core.
Planning with Learned Dynamic Model for Unsupervised Point Cloud Registration
Authors: Jiang Haobo , Qian Jianjun , Xie Jin , Yang Jian
Published: August 2021

Point cloud registration is a fundamental problem in 3D computer vision. In this paper, we cast point cloud registration into a planning problem in reinforcement learning, which can seek the transformation between the source and target point clouds through trial and error. By modeling the point cloud registration process as a Markov decision process (MDP), we develop a latent dynamic model of point clouds, consisting of a transformation network and evaluation network. The transformation network aims to predict the new transformed feature of the point cloud after performing a rigid transformation (i.e., action) on it while the evaluation network aims to predict the alignment precision between the transformed source point cloud and target point cloud as the reward signal. Once the dynamic model of the point cloud is trained, we employ the cross-entropy method (CEM) to iteratively update the planning policy by maximizing the rewards in the point cloud registration process. Thus, the optimal policy, i.e., the transformation between the source and target point clouds, can be obtained via gradually narrowing the search space of the transformation. Experimental results on ModelNet40 and 7Scene benchmark datasets demonstrate that our method can yield good registration performance in an unsupervised manner.
Picking Sequences and Monotonicity in Weighted Fair Division
Authors: Chakraborty Mithun , Schmidt-Kraepelin Ulrike , Suksompong Warut
Published: August 2021

We study the problem of fairly allocating indivisible items to agents with different entitlements, which captures, for example, the distribution of ministries among political parties in a coalition government. Our focus is on picking sequences derived from common apportionment methods, including five traditional divisor methods and the quota method. We paint a complete picture of these methods in relation to known envy-freeness and proportionality relaxations for indivisible items as well as monotonicity properties with respect to the resource, population, and weights. In addition, we provide characterizations of picking sequences satisfying each of the fairness notions, and show that the well-studied maximum Nash welfare solution fails resource- and population-monotonicity even in the unweighted setting. Our results serve as an argument in favor of using picking sequences in weighted fair division problems.
Text-based Person Search via Multi-Granularity Embedding Learning
Authors: Wang Chengji , Luo Zhiming , Lin Yaojin , Li Shaozi
Published: August 2021

Most existing text-based person search methods highly depend on exploring the corresponding relations between the regions of the image and the words in the sentence. However, these methods correlated image regions and words in the same semantic granularity. It 1) results in irrelevant corresponding relations between image and text, 2) causes an ambiguity embedding problem. In this study, we propose a novel multi-granularity embedding learning model for text-based person search. It generates multi-granularity embeddings of partial person bodies in a coarse-to-fine manner by revisiting the person image at different spatial scales. Specifically, we distill the partial knowledge from image scrips to guide the model to select the semantically relevant words from the text description. It can learn discriminative and modality-invariant visual-textual embeddings. In addition, we integrate the partial embeddings at each granularity and perform multi-granularity image-text matching. Extensive experiments validate the effectiveness of our method, which can achieve new state-of-the-art performance by the learned discriminative partial embeddings.
Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head Motion
Authors: Wang Suzhen , Li Lincheng , Ding Yu , Fan Changjie , Yu Xin
Published: August 2021

We propose an audio-driven talking-head method to generate photo-realistic talking-head videos from a single reference image. In this work, we tackle two key challenges: (i) producing natural head motions that match speech prosody, and (ii)} maintaining the appearance of a speaker in a large head motion while stabilizing the non-face regions. We first design a head pose predictor by modeling rigid 6D head movements with a motion-aware recurrent neural network (RNN). In this way, the predicted head poses act as the low-frequency holistic movements of a talking head, thus allowing our latter network to focus on detailed facial movement generation. To depict the entire image motions arising from audio, we exploit a keypoint based dense motion field representation. Then, we develop a motion field generator to produce the dense motion fields from input audio, head poses, and a reference image. As this keypoint based representation models the motions of facial regions, head, and backgrounds integrally, our method can better constrain the spatial and temporal consistency of the generated videos. Finally, an image generation network is employed to render photo-realistic talking-head videos from the estimated keypoint based motion fields and the input reference image. Extensive experiments demonstrate that our method produces videos with plausible head motions, synchronized facial expressions, and stable backgrounds and outperforms the state-of-the-art.
Micro-Expression Recognition Enhanced by Macro-Expression from Spatial-Temporal Domain
Authors: Xia Bin , Wang Shangfei
Published: August 2021

Facial micro-expression recognition has attracted much attention due to its objectiveness to reveal the true emotion of a person. However, the limited micro-expression datasets have posed a great challenge to train a high performance micro-expression classifier. Since micro-expression and macro-expression share some similarities in both spatial and temporal facial behavior patterns, we propose a macro-to-micro transformation framework for micro-expression recognition. Specifically, we first pretrain two-stream baseline model from micro-expression data and macro-expression data respectively, named MiNet and MaNet. Then, we introduce two auxiliary tasks to align the spatial and temporal features learned from micro-expression data and macro-expression data. In spatial domain, we introduce a domain discriminator to align the features of MiNet and MaNet. In temporal domain, we introduce relation classifier to predict the correct relation for temporal features from MaNet and MiNet. Finally, we propose contrastive loss to encourage the MiNet to give closely aligned features to all entries from the same class in each instance. Experiments on three benchmark databases demonstrate the superiority of the proposed method.
Tool- and Domain-Agnostic Parameterization of Style Transfer Effects Leveraging Pretrained Perceptual Metrics
Authors: Yakura Hiromu , Koyama Yuki , Goto Masataka
Published: August 2021

Current deep learning techniques for style transfer would not be optimal for design support since their "one-shot" transfer does not fit exploratory design processes. To overcome this gap, we propose parametric transcription, which transcribes an end-to-end style transfer effect into parameter values of specific transformations available in an existing content editing tool. With this approach, users can imitate the style of a reference sample in the tool that they are familiar with and thus can easily continue further exploration by manipulating the parameters. To enable this, we introduce a framework that utilizes an existing pretrained model for style transfer to calculate a perceptual style distance to the reference sample and uses black-box optimization to find the parameters that minimize this distance. Our experiments with various third-party tools, such as Instagram and Blender, show that our framework can effectively leverage deep learning techniques for computational design support.
Improving Multi-agent Coordination by Learning to Estimate Contention
Authors: Danassis Panayiotis , Wiedemair Florian , Faltings Boi
Published: August 2021

We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. We circumvent the traditional pitfalls of multi-agent learning (e.g., the moving target problem, the curse of dimensionality, or the need for mutually consistent actions) by relying on the ALMA heuristic as a coordination mechanism for each stage game. ALMA-Learning is decentralized, observes only own action/reward pairs, requires no inter-agent communication, and achieves near-optimal (<5% loss) and fair coordination in a variety of synthetic scenarios and a real-world meeting scheduling problem. The lightweight nature and fast learning constitute ALMA-Learning ideal for on-device deployment.
Context-Aware Image Inpainting with Learned Semantic Priors
Authors: Zhang Wendong , Zhu Junwei , Tai Ying , Wang Yunbo , Chu Wenqing , Ni Bingbing , Wang Chengjie , Yang Xiaokang
Published: August 2021

Recent advances in image inpainting have shown impressive results for generating plausible visual details on rather simple backgrounds. However, for complex scenes, it is still challenging to restore reasonable contents as the contextual information within the missing regions tends to be ambiguous. To tackle this problem, we introduce pretext tasks that are semantically meaningful to estimating the missing contents. In particular, we perform knowledge distillation on pretext models and adapt the features to image inpainting. The learned semantic priors ought to be partially invariant between the high-level pretext task and low-level image inpainting, which not only help to understand the global context but also provide structural guidance for the restoration of local textures. Based on the semantic priors, we further propose a context-aware image inpainting model, which adaptively integrates global semantics and local features in a unified image generator. The semantic learner and the image generator are trained in an end-to-end manner. We name the model SPL to highlight its ability to learn and leverage semantic priors. It achieves the state of the art on Places2, CelebA, and Paris StreetView datasets
Rescuing Deep Hashing from Dead Bits Problem
Authors: Zhao Shu , Wu Dayan , Zhou Yucan , Li Bo , Wang Weiping
Published: August 2021

Deep hashing methods have shown great retrieval accuracy and efficiency in large-scale image retrieval. How to optimize discrete hash bits is always the focus in deep hashing methods. A common strategy in these methods is to adopt an activation function, e.g. sigmoid() or tanh(), and minimize a quantization loss to approximate discrete values. However, this paradigm may make more and more hash bits stuck into the wrong saturated area of the activation functions and never escaped. We call this problem "Dead Bits Problem (DBP)". Besides, the existing quantization loss will aggravate DBP as well. In this paper, we propose a simple but effective gradient amplifier which acts before activation functions to alleviate DBP. Moreover, we devise an error-aware quantization loss to further alleviate DBP. It avoids the negative effect of quantization loss based on the similarity between two images. The proposed gradient amplifier and error-aware quantization loss are compatible with a variety of deep hashing methods. Experimental results on three datasets demonstrate the efficiency of the proposed gradient amplifier and the error-aware quantization loss.
Decomposition Strategies to Count Integer Solutions over Linear Constraints
Authors: Ge Cunjing , Biere Armin
Published: August 2021

Counting integer solutions of linear constraints has found interesting applications in various fields. It is equivalent to the problem of counting integer points inside a polytope. However, state-of-the-art algorithms for this problem become too slow for even a modest number of variables. In this paper, we propose new decomposition techniques which target both the elimination of variables as well as inequalities using structural properties of counting problems. Experiments on extensive benchmarks show that our algorithm improves the performance of state-of-the-art counting algorithms, while the overhead is usually negligible compared to the running time of integer counting.
Online Selection of Diverse Committees
Authors: Do Virginie , Atif Jamal , Lang Jérôme , Usunier Nicolas
Published: August 2021

Citizens' assemblies need to represent subpopulations according to their proportions in the general population. These large committees are often constructed in an online fashion by contacting people, asking for the demographic features of the volunteers, and deciding to include them or not. This raises a trade-off between the number of people contacted (and the incurring cost) and the representativeness of the committee. We study three methods, theoretically and experimentally: a greedy algorithm that includes volunteers as long as proportionality is not violated; a non-adaptive method that includes a volunteer with a probability depending only on their features, assuming that the joint feature distribution in the volunteer pool is known; and a reinforcement learning based approach when this distribution is not known a priori but learnt online.
Heterogeneous Graph Information Bottleneck
Authors: Yang Liang , Wu Fan , Zheng Zichen , Niu Bingxin , Gu Junhua , Wang Chuan , Cao Xiaochun , Guo Yuanfang
Published: August 2021

Most attempts on extending Graph Neural Networks (GNNs) to Heterogeneous Information Networks (HINs) implicitly take the direct assumption that the multiple homogeneous attributed networks induced by different meta-paths are complementary. The doubts about the hypothesis of complementary motivate an alternative assumption of consensus. That is, the aggregated node attributes shared by multiple homogeneous attributed networks are essential for node representations, while the specific ones in each homogeneous attributed network should be discarded. In this paper, a novel Heterogeneous Graph Information Bottleneck (HGIB) is proposed to implement the consensus hypothesis in an unsupervised manner. To this end, information bottleneck (IB) is extended to unsupervised representation learning by leveraging self-supervision strategy. Specifically, HGIB simultaneously maximizes the mutual information between one homogeneous network and the representation learned from another homogeneous network, while minimizes the mutual information between the specific information contained in one homogeneous network and the representation learned from this homogeneous network. Model analysis reveals that the two extreme cases of HGIB correspond to the supervised heterogeneous GNN and the infomax on homogeneous graph, respectively. Extensive experiments on real datasets demonstrate that the consensus-based unsupervised HGIB significantly outperforms most semi-supervised SOTA methods based on complementary assumption.
Knowledge-based Residual Learning
Authors: Zheng Guanjie , Liu Chang , Wei Hua , Jenkins Porter , Chen Chacha , Wen Tao , Li Zhenhui
Published: August 2021

Small data has been a barrier for many machine learning tasks, especially when applied in scientific domains. Fortunately, we can utilize domain knowledge to make up the lack of data. Hence, in this paper, we propose a hybrid model KRL that treats domain knowledge model as a weak learner and uses another neural net model to boost it. We prove that KRL is guaranteed to improve over pure domain knowledge model and pure neural net model under certain loss functions. Extensive experiments have shown the superior performance of KRL over baselines. In addition, several case studies have explained how the domain knowledge can assist the prediction.
Bounded-cost Search Using Estimates of Uncertainty
Authors: Fickert Maximilian , Gu Tianyi , Ruml Wheeler
Published: August 2021

Many planning problems are too hard to solve optimally. In bounded-cost search, one attempts to find, as quickly as possible, a plan that costs no more than a user-provided absolute cost bound. Several algorithms have been previously proposed for this setting, including Potential Search (PTS) and Bounded-cost Explicit Estimation Search (BEES). BEES attempts to improve on PTS by predicting whether nodes will lead to plans within the cost bound or not. This paper introduces a relatively simple algorithm, Expected Effort Search (XES), which uses not just point estimates but belief distributions in order to estimate the probability that a node will lead to a plan within the bound. XES's expansion order minimizes expected search time in a simplified formal model. Experimental results on standard planning and search benchmarks show that it consistently exhibits strong performance, outperforming both PTS and BEES. We also derive improved variants of BEES that can exploit belief distributions. These new methods advance the recent trend of taking advantage of uncertainty estimates in deterministic single-agent search.
Best-Effort Synthesis: Doing Your Best Is Not Harder Than Giving Up
Authors: Aminof Benjamin , De Giacomo Giuseppe , Rubin Sasha
Published: August 2021

We study best-effort synthesis under environment assumptions specified in LTL, and show that this problem has exactly the same computational complexity of standard LTL synthesis: 2EXPTIME-complete. We provide optimal algorithms for computing best-effort strategies, both in the case of LTL over infinite traces and LTL over finite traces (i.e., LTLf). The latter are particularly well suited for implementation.
Cardinality Queries over DL-Lite Ontologies
Authors: Bienvenu Meghyn , Manière Quentin , Thomazo Michaël
Published: August 2021

Ontology-mediated query answering (OMQA) employs structured knowledge and automated reasoning in order to facilitate access to incomplete and possibly heterogeneous data. While most research on OMQA adopts (unions of) conjunctive queries as the query language, there has been recent interest in handling queries that involve counting. In this paper, we advance this line of research by investigating cardinality queries (which correspond to Boolean atomic counting queries) coupled with DL-Lite ontologies. Despite its apparent simplicity, we show that such an OMQA setting gives rise to rich and complex behaviour. While we prove that cardinality query answering is tractable (TC0) in data complexity when the ontology is formulated in DL-Lite-core, the problem becomes coNP-hard as soon as role inclusions are allowed. For DL-Lite-pos-H (which allows only positive axioms), we establish a P-coNP dichotomy and pinpoint the TC0 cases; for DL-Lite-core-H (allowing also negative axioms), we identify new sources of coNP complexity and also exhibit L-complete cases. Interestingly, and in contrast to related tractability results, we observe that the canonical model may not give the optimal count value in the tractable cases, which led us to develop an entirely new approach based upon exploring a space of strategies to determine the minimum possible number of query matches.
How Hard to Tell? Complexity of Belief Manipulation Through Propositional Announcements
Authors: Eiter Thomas , Hunter Aaron , Schwarzentruber Francois
Published: August 2021

Consider a set of agents with initial beliefs and a formal operator for incorporating new information. Now suppose that, for each agent, we have a formula that we would like them to believe. Does there exist a single announcement that will lead all agents to believe the corresponding formula? This paper studies the problem of the existence of such an announcement in the context of model-preference definable revision operators. First, we provide two characterisation theorems for the existence of announcements: one in the general case, the other for total partial orderings. Second, we exploit the characterisation theorems to provide upper bound complexity results. Finally, we also provide matching optimal lower bounds for the Dalal and Ginsberg operators.
Multi-Agent Belief Base Revision
Authors: Lorini Emiliano , Schwarzentruber Francois
Published: August 2021

We present a generalization of belief base revision

to the multi-agent case. In our approach agents

have belief bases containing both propositional beliefs

and higher-order beliefs about their own beliefs

and other agents’ beliefs. Moreover, their belief

bases are split in two parts: the mutable part,

whose elements may change under belief revision,

and the core part, whose elements do not change.

We study a belief revision operator inspired by

the notion of screened revision. We provide complexity

results of model checking for our approach

as well as an optimal model checking algorithm.

Moreover, we study complexity of epistemic planning

formulated in the context of our framework.
On the Relation Between Approximation Fixpoint Theory and Justification Theory
Authors: Marynissen Simon , Bogaerts Bart , Denecker Marc
Published: August 2021

Approximation Fixpoint Theory (AFT) and Justification Theory (JT) are two frameworks to unify logical formalisms. AFT studies semantics in terms of fixpoints of lattice operators, and JT in terms of so-called justifications, which are explanations of why certain facts do or do not hold in a model. While the approaches differ, the frameworks were designed with similar goals in mind, namely to study the different semantics that arise in (mainly) non-monotonic logics. The First contribution of our current paper is to provide a formal link between the two frameworks. To be precise, we show that every justification frame induces an approximator and that this mapping from JT to AFT preserves all major semantics. The second contribution exploits this correspondence to extend JT with a novel class of semantics, namely ultimate semantics: we formally show that ultimate semantics can be obtained in JT by a syntactic transformation on the justification frame, essentially performing some sort of resolution on the rules.
Compressing Exact Cover Problems with Zero-suppressed Binary Decision Diagrams
Authors: Nishino Masaaki , Yasuda Norihito , Nakamura Kengo
Published: August 2021

Exact cover refers to the problem of finding subfamily

F of a given family of sets S whose universe

is D, where F forms a partition of D. Knuth’s Algorithm

DLX is a state-of-the-art method for solving

exact cover problems. Since DLX’s running

time depends on the cardinality of input S, it can be

slow if S is large. Our proposal can improve DLX

by exploiting a novel data structure, DanceDD,

which extends the zero-suppressed binary decision

diagram (ZDD) by adding links to enable efficient

modifications of the data structure. With DanceDD,

we can represent S in a compressed way and perform

search in linear time with the size of the structure

by using link operations. The experimental results

show that our method is an order of magnitude

faster when the problem is highly compressed.
Modeling Precomputation In Games Played Under Computational Constraints
Authors: Orton Thomas
Published: August 2021

Understanding the properties of games played under computational constraints remains challenging. For example, how do we expect rational (but computationally bounded) players to play games with a prohibitively large number of states, such as chess? This paper presents a novel model for the precomputation (preparing moves in advance) aspect of computationally constrained games. A fundamental trade-off is shown between randomness of play, and susceptibility to precomputation, suggesting that randomization is necessary in games with computational constraints. We present efficient algorithms for computing how susceptible a strategy is to precomputation, and computing an $\epsilon$-Nash equilibrium of our model. Numerical experiments measuring the trade-off between randomness and precomputation are provided for Stockfish (a well-known chess playing algorithm).
Efficient PAC Reinforcement Learning in Regular Decision Processes
Authors: Ronca Alessandro , De Giacomo Giuseppe
Published: August 2021

Recently regular decision processes have been proposed as a well-behaved form of non-Markov decision process. Regular decision processes are characterised by a transition function and a reward function that depend on the whole history, though regularly (as in regular languages). In practice both the transition and the reward functions can be seen as finite transducers. We study reinforcement learning in regular decision processes. Our main contribution is to show that a near-optimal policy can be PAC-learned in polynomial time in a set of parameters that describe the underlying decision process. We argue that the identified set of parameters is minimal and it reasonably captures the difficulty of a regular decision process.