bluuebunny/crossref_metadata_2025 huggingface dataset:
Perancangan dan Implementasi Aplikasi Kriptografi Algoritma Hill Cipher dalam Dekripsi Enkripsi Data Keuangan Nasabah Bank Sampoerna Menggunakan Kode ASCII
Authors: Hasibuan Yusna Warni , Isnarto Isnarto , Veronica Rahayu Budhiati
Published: June 2022

Hill Cipher merupakan teknik kriptografi yang menggunakan matriks persegi sebagai kunci digunakan untuk proses enkripsi dan dekripsi. Masalah umum yang muncul pada algoritma ini terletak pada pemilihan kunci. Pemilihan kunci dilakukan secara sebarang dapat menyebabkan kegagalan pada proses dekripsi, karena algoritma ini syarat kunci yang digunakan yaitu matriks persegi dapat dibalik atau memiliki invers (invertible). Penelitian dilakukan pada Bank Sampoerna di Kota Padangsidimpuan Provinsi Sumatera Utara. Tujuan penelitian ini merancang aplikasi berbasis WEB digunakan untuk pendataan nasabah dan melakukan transaksi secara aman dengan penerapan metode autentifikasi nasabah. Sistem keamanan pengguna dibangun dengan menerapkan proses autentifikasi nasabah berupa PIN serta penggunaan metode kriptografi Hill Cipher pada proses transaksi yaitu saat nasabah melakukan transaksi menggunakan aplikasi WEB. Hasil pada penelitian ini adalah terciptanya suatu aplikasi berbasis WEB yang digunakan untuk melakukan transaksi oleh nasabah dan aplikasi server berbasis PHP dan menggunakan database MySQL untuk melakukan administrasi transaksi.
Prediksi Besar Daya Listrik Tenaga Gelombang Laut Metode Oscillating Water Coloumn (PLTGL-OWC) di Banyuwangi Menggunakan Extreme Learning Machine (ELM)
Authors: Phiadelvira Bunga Yuwa , Haq Dina Zatusiva , Novitasari Dian C Rini , Setiawan Fajar
Published: June 2022

Peningkatan kebutuhan listrik harus diiringi dengan pengingkatan sumber daya listrik, namun pada kenyataannya sumber energi listrik yang ada ternyata semakin lama semakin menurun. Menurunnya tingkat sumber energi listrik disebabkan karena sumber energi yang digunakan selama ini berasal dari bahan bakar fosil yang sifatnya tidak dapat diperbaharui, sehingga jika digunakan terus-menerus akan sumber energi tersebut akan habis, maka dari itu dibutuhkan sumber energi alternatif yang sifatnya dapat diperbaharui dan tidak akan habis jika digunakan secara terus-menerus. Sumber energi alternatif yang dapat digunakan untuk sumber energi listrik salah satunya yaitu gelombang laut dengan memanfaatkan sistem PLTGL-OWC yang mampu merubah menghasilkan energi listrik dari gelombang laut. Pada penelitian dilakukan peramalan terhadap energi listrik yang dihasilkan untuk menghindari ketidsksesuaian saat memasok energi listrik ke konsumen. Metode yang digunakan pada penelitian ini adalah metode Extreme Learning Machine (ELM) dengan jumlah node pada hidden layer sebanyak 100 yang menghasilkan nilai MAPE sebesar 2.3367%.
Paediatric differentiated thyroid carcinoma: a UK National Clinical Practice Consensus Guideline
Authors: Howard Sasha R , Freeston Sarah , Harrison Barney , Izatt Louise , Natu Sonali , Newbold Kate , Pomplun Sabine , Spoudeas Helen A , Wilne Sophie , Kurzawinski Tom R , Gaze Mark N
Published: June 2022

This guideline is written as a reference document for clinicians presented with the challenge of managing paediatric patients with differentiated thyroid carcinoma up to the age of 19 years. Care of paediatric patients with differentiated thyroid carcinoma differs in key aspects from that of adults, and there have been several recent developments in the care pathways for this condition; this guideline has sought to identify and attend to these areas. It addresses the presentation, clinical assessment, diagnosis, management (both surgical and medical), genetic counselling, follow-up and prognosis of affected patients. The guideline development group formed of a multi-disciplinary panel of sub-speciality experts carried out a systematic primary literature review and Delphi consensus exercise. The guideline was developed in accordance with The Appraisal of Guidelines Research and Evaluation Instrument II criteria, with input from stakeholders including charities and patient groups. Based on scientific evidence and expert opinion, 58 recommendations have been collected to produce a clear, pragmatic set of management guidelines. It is intended as an evidence base for future optimal management and to improve the quality of clinical care of Paediatric patients with differentiated thyroid carcinoma.
Evaluación de propiedades fisicoquímicas del frijol costarricense (Phaseolus vulgaris) como estrategia de diferenciación y valorización
Authors: Mencía Guevara Alejandra , Villalobos Moya Keylor , Rubí Zeledón Julián , Granados Rojas Leonardo , Hernández Aguirre Carlos , Brenes Monge Hillary
Published: June 2022

Debido a los cambios en patrones de consumo orientados a reconocer atributos de productos locales y diferenciados, el presente estudio tuvo como objetivo evaluar variedades de frijol costarricense con la finalidad de identificar sus propiedades que permitan una mejor valoración y diferenciación dentro del mercado nacional. Se realizó un análisis proximal, fenólico, físico y de calidad e impurezas. Los análisis fueron realizados en el año 2018 en laboratorios de la Universidad Nacional, Costa Rica y el Laboratorio de Calidad e Inocuidad del Consejo Nacional de Producción (CNP). Entre los resultados obtenidos sobresalió la variedad Guaimí (proveniente de Upala) la cual presentó menor dureza, por tanto, podría necesitar menor tiempo de cocción para alcanzar la textura óptima, así como conservar mejor valor textural como sus propiedades funcionales.  De forma general, se obtuvieron valores relevantes de proteína en rangos de 16 a 27 %, de fibra de 3 a 7 %, de extracto etéreo de 3 a 7 % y de cenizas de 4 a 6 %. Los resultados del estudio podrían ser un punto de partida para definir la funcionalidad de cada variedad de frijol de con  sus propiedades físicas y químicas.
Análisis de la regeneración natural de cinco especies forestales de la Finca Ándil UNESUM
Authors: Cabrera Verdesoto César Alberto , Sornoza Briones Leonel , Cantos Cevallos Cristóbal , Pionce Andrade Ginger , Ganchozo Quimis Máximo
Published: June 2022

La investigación estuvo enmarcada en el análisis de cinco especies forestales con mayor regeneración natural en los predios del bosque seco tropical de la granja Ándil de la Universidad Estatal del Sur de Manabí. El principal problema radica en la falta de investigación sobre la regeneración natural en el bosque seco tropical por lo que es de vital importancia estudiar el comportamiento de la regeneración natural de las especies forestales. El trabajo de campo se realizó en la granja Ándil que tiene un total de 86,00 ha, donde 60,2 ha representa la parte del bosque seco tropical y 6,1 ha del bs-T utiliza la carrera de ingeniería forestal para sus trabajos de investigación, se realizó tres parcelas de 50 m x 50 m correspondientes a 2,500 m2 por parcela, dando un total de 7,500 m2 el área donde se desarrolló la investigación. Las variables dasométricas utilizadas en la realización del inventario forestal fueron: diámetro a la base (15,0 cm del suelo hacia la base de la planta, esto incluye las especies menores de 1,0 m de altura total), diámetro a la altura del pecho (DAP). En el comportamiento regenerativo del bosque seco se tomaron como referencia las categorías de regeneración natural de tipo fustal, latizal y brinzal. Se inventarió un total de 1 004 árboles, cifra que representa 26 especies arbóreas y 21 tipos de familias, las cinco especies con mayor regeneración natural fueron el Triplaris cumingiana Fisher y Meyer, Cynophalla mollis (Kunth) J.Presl,  Leucaena leucocephala  (Lam) de Wit, Cordia alliodora Ruiz & Pav., Cedrela odorata L., especies forestales representativas del bosque seco tropical.
El Buen Vivir y los cultivos de uso ilícito en territorios indígenas
Authors: Triana Sarmiento Alejandro Enrique
Published: June 2022

El narcotráfico es una de las economías ilegales más rentables y vigentes en la región. Colombia se ha convertido en uno de los mayores productores de cultivos de uso ilícito en las últimas décadas, debido a las condiciones biofísicas favorables para su establecimiento, así como por el escenario de conflicto armado interno que ha impulsado esta actividad por parte de los grupos armados ilegales que se lucran de sus rendimientos. Estos cultivos se han extendido en distintas zonas rurales del país, incluyendo los resguardos indígenas. Por medio del estudio de caso del Resguardo del pueblo Inga de Aponte, este artículo describe las afectaciones que causa el narcotráfico sobre el Buen Vivir de los indígenas desde el enfoque étnico - territorial, y presenta un análisis sobre la forma en la que la cosmovisión, la institucionalidad y la gobernanza indígenas, constituyeron elementos determinantes para el proceso de abandono autónomo y sostenido de la producción ilícita de amapola. El análisis permite concluir sobre la necesidad de incorporar la perspectiva del Buen Vivir en el diseño de planes de acción contra el problema de las drogas en estos territorios.
El uso de especies medicinales como productos forestales no madereros sitio la Tranca Arriba, Parroquia San Plácido
Authors: Cabrera-Verdesoto César Alberto , Cantos-Zambrano Jennifer Andrea , Jiménez-González Alfredo , Tapia-Zúñiga Mónica Virginia , Briones-Anchundia Gema Stephanya
Published: June 2022

El interés por saber, entender y conocer las ventajas que posee la medicina natural en la salud del ser humano, ha sido un debate constante desde la antigüedad hasta la actualidad. La Tranca Arriba, se encuentra en la parroquia San Plácido, cantón Portoviejo provincia de Manabí, Ecuador. En donde se realizó el estudio con el fin de clasificar el uso de especies medicinales como productos forestales no maderables. Se aplicaron encuestas en un rango de edad entre 20 y 80 años, estimando el nivel de uso de las especies y significativo Tramil. Los pobladores del sector clasificaron el conocimiento de las especies según su forma, uso y consumo medicinal. Se registraron 24 especies, 23 géneros y 18 familias, las cuatro especies con mayor valor de uso fueron Matricaria chamomilla L., Mentha rotundifolia L., Origanum vulgare L, y Mentha sativa L. utilizadas en un 100% para uso medicinal. Las hojas con un 52,5% es la parte más utilizada y el uso más frecuente es la infusión con un 67,65%. Referente al nivel de uso significativo todas las especies registradas obtuvieron un valor superior al 20% con aceptación cultural alta, utilizando las plantas medicinales de forma natural para aliviar sus dolencias y afecciones.
Nexus between Consumption, Income and Price Changes: Asymmetric Evidence from NARDL Mode
Authors: Gohar Raheel , Chang Bisharat Hussain , Derindag Omer Faruk , Abro Zahida
Published: June 2022

Previous research has primarily examined the link between price, income, and consumer spending using linear regression models. On the other hand, the latest evidence shows an asymmetric link among economic and financial variables. We contribute to the literature by employing a novel technique known as the asymmetric ARDL model. This approach is used to investigate the impact of favorable and unfavorable changes in income and prices on household consumption. The results show that higher income has a substantial and beneficial effect on household expenditures in the short term and long term. On the other hand, a fall in income has no impact on consumer spending. Moreover, for most developing countries, price adjustments have a negligible effect on consumer expenditures. Our findings suggest that implementing the same policy initiatives across periods of rising and falling income and prices may result in potential losses.JEL Classification: E31, F31, C22
Do Tax Policies in Nigeria Have Similar Implications for the Manufacturing Sector Output?
Authors: Sakanko Musa Abdullahi , Manomi Jeremiah Nshe , Ijoko Abubakar , Audu Usman Abdulkareem
Published: June 2022

The study examines whether tax policies in Nigeria have similarimplications on the manufacturing sector’s output during the1994Q1-2020Q4 period using the ARDL bounds testing approach.The bounds testing result suggests the presence of cointegrationbetween tax policies and the manufacturing sector output. Further,the estimation results demonstrate that company income tax (CIT)and import tax are positively related to manufacturing sectoroutput. In contrast, value-added tax (VAT) has a negative effecton the manufacturing sector output, both in the short- and longterm.In addition, the results of the Granger causality test indicatea unidirectional causal relationship running from tax policies tothe manufacturing sector output and not vice versa. Thus, policiesand measures are recommended to prioritize the CIT and importtax, review the assortment in the VAT, and ensure accountabilityand transparency in the tax system.JEL Classification: C32, L60, H22
Options Counseling for the Pregnant Adolescent Patient
Authors:   AMERICAN ACADEMY OF PEDIATRICS,   COMMITTEE ON ADOLESCENCE
Published: June 2022

Although teenage pregnancy rates have decreased over the past 30 years, many adolescents become pregnant every year. It is important for pediatricians to have the ability and the resources to make a timely pregnancy diagnosis in their adolescent patients and provide them with nonjudgmental counseling that includes the full range of pregnancy options. Counseling includes an unbiased discussion of the adolescent’s options to continue or terminate the pregnancy, supporting the adolescent in the decision-making process, and referring the adolescent to appropriate resources and services. It is important for pediatricians to be familiar with laws and policies impacting access to abortion care, especially for minor adolescents, as well as laws that seek to limit health care professionals’ provision of unbiased pregnancy options counseling and referrals, either for abortion care or continuation of pregnancy in accordance with the adolescent’s choice. Pediatricians who choose not to provide such discussions should promptly refer pregnant adolescent patients to a health care professional who will offer developmentally appropriate pregnancy options counseling that includes the full range of pregnancy options. Pediatricians should be aware of and oppose policies that restrict their ability to provide pregnant adolescents with unbiased counseling that includes the full range of pregnancy options. This approach to pregnancy options counseling has not changed since the original 1989 American Academy of Pediatrics statement on this issue.
Yacht Tourism on the Azov-Black Sea Coast of Russia as a Factor of Tourist Attractiveness of the Region
Authors: Poddubnaya Tatiana N. , Zadneprovskaya Elena L.
Published: June 2022

Introduction. The relevance of this considered scientific problem is due to the need to assess the recreational preferences of potential consumers of yachting services as a factor of tourist attractiveness and active promotion of the region. The purpose of the article is to determine the prospects for the development of yacht tourism on the Azov-Black Sea coast of Russia on the basis of the conducted analysis, which reflects the tourist attractiveness of the region in modern conditions.

Materials and Methods. The authors analyzed panel statistics of the charter website 2Yachts, the informational and analytical agency SeaNews, the international broker of motor yachts Worldmarine, PKF International. To study the trends in the development of yacht tourism on the Azov-Black Sea coast of Russia, a number of indicators of the state of the yacht tourism industry of individual European countries have been studied, an investment project to create a network of marinas in the Republic of Crimea and the Krasnodar Territory has been considered.

Results. The consumption preferences of clients in yacht tourism services on the Azov-Black Sea coast are revealed, the state of the yachting infrastructure of the region is assessed, the prospects for the development of yachting in the sea area of the Krasnodar Territory as a factor of tourist attractiveness of the region are outlined. The Azov-Black Sea coast of Russia has a significant potential for the development of yacht tourism (natural, socio-economic, cultural and historical resources). The development of yacht tourism should be carried out taking into account consumption preferences according to three basic criteria when choosing a yacht tour in the waters of the Azov and Black Seas – price, excursion program and tourist service.

Discussion and Conclusion. The modernization of the yacht infrastructure and the popularization of yachting will contribute to the additional development of the tourism industry and the tourist attractiveness of the region, and will also raise yacht tourism to a new international level. The results of the study can become the basis for the development of a strategy for the creation and promotion of a tourist product for potential yachting customers at the regional level, and are also taken into account in the development of programs of integrated tourist services.
Integration of the University into the Process of Digital Transformation of the Regional Economy and Social Sphere
Authors: Shchiptsova Anna V. , Poverinov Igor E. , Ilyina Ekaterina A.
Published: June 2022

Introduction. A key barrier in the period of economy digitalization is the lack of qualified personnel having necessary competencies for the development, implementation and the use of digital technologies. The authors analyze the current stage of digital transformation of the Russian economy and that of foreign countries, the role of universities in this process, considering in close connection the concepts of information policy and the strategy of digital transformation of the university; they propose approaches to form the university policy in the field of information technologies in order to develop digital skills of human resources. The purpose of the study is to identify the ways to effectively integrate universities into the process of digital transformation of the regional economy and social sphere.

Materials and Methods. To study the problem, research and survey results on the subject of the study were selected, as well as key legal acts and documents defining and regulating the processes of digital economy transformation in Russia. In the course of the research, the method of analysis was used: to compare the indicators of citizens’ digital literacy during the digital economy transformation; to determine and summarize measures of state and regional policy in the field of education, the role and experience of universities’ participation in the process of overcoming the barriers to the digital transformation of the economy and the social sphere.

Results. As a result of the study, the barriers to the digital economy transformation were identified, among which the problem of personnel shortage and, as a result, personnel training is the leading one. The authors conclude that the effectiveness of the university’s information technology policy can become an indicator of the university’s readiness for digital transformation.

Discussion and Conclusion. The results obtained relate to changes in both the internal educational environment and changes in the content and format of education; they contribute to the development of the training system of highly qualified personnel for the national digital economy, strategic planning of regional development, educational management. The materials of the article can be useful to the heads of educational institutions of higher education when forming the digital transformation strategy in the aspect of three missions: educational, scientific, educative.
Existence of Religious Programmes in Analog Platform: A Case Study in Broadcast Media in West Java, Indonesia
Authors: Hkikmat Mahi M , Firdaus Dadan
Published: June 2022

Religious programmes are important contents which should be inserted in all programmes in broadcast media. Although broadcasting platform can transform, but the religious programmes still exist. How are existences of the religious programmes in analog platform by the broadcast media? This query is described in a study by using interpretive-subjective approach with qualitative method to examine quantitative data via a case study discovered in broadcast media in West Java, Indonesia. Strategic considerations on urgency of religious programmes in broadcast media are normative mandate of laws which turn into basic characteristics of the citizens, and commitment idealism in carrying out the function of education, but the financial benefits are still lower.
SOSOK USEP ROMLI HM DALAM WACANA BUDAYA SUNDA PADA TAHUN 1973-2020
Authors: Rusmana Dadan
Published: June 2022

Tulisan ini membahas tentang sosok Usep Romli HM sebagai pelaku sejarah dalam dakwah Islam. Usep melihat celah dakwah yang berbeda. Beliau mampu menghadirkan risalah kenabian dengan baik dalam kesusastraan Sunda yang kemudian menjadi celah untuknya berdakwah. Teori Orang Besar atau The Great Man Theory dari Thomas Carlyle digunakan dalam penelitian ini, di mana menempatkan Usep Romli HM sebagai pahlawan, individu yang berpengaruh, berkarisma, cerdas, hikmat, dan memiliki keterampilan berpolitik. Sebagai diskursus kesejarahan, penelitian ini ditempuh melalui metodologi penelitian sejarah: Heuristik, kritik, interpretasi, dan historiografi. Atas telaah tersebut, maka Usep Romli HM layak diapresiasi dan disematkan sebagai tokoh sejarah dakwah Islam dalam wacana budaya Sunda.
Regulatory effect of intracellular polysaccharides from Antrodia cinnamomea on the intestinal microbiota of mice with antibiotic-associated diarrhea
Authors: Lu Chun-lei , Li Hua-xiang , Zhu Xiao-yan , Luo Zhi-shan , Rao Sheng-qi , Yang Zhen-quan
Published: June 2022

Antrodia cinnamomea is a valuable and scarce medicinal and edible mushroom. Its main active substances are triterpenoids and polysaccharides with many biological activities, such as liver protection, immunity enhancement, antitumor, anti-inflammatory, and antiviral. The long-term use of antibiotics can easily lead to disorders in the intestinal flora and causes diarrhea, inflammation, immune decline, and other symptoms. In this study, A. cinnamomea intracellular polysaccharides (AIPSs) were extracted from mycelia in submerged fermentation, and the AIPSs with a neutral polysaccharide content of 82.74%±0.63% were obtained. Then, the therapeutic effect of AIPSs on diarrhea in mice caused by lincomycin hydrochloride (LIH), and their regulatory and recovery effects on the intestinal flora, was studied with fructooligosaccharide as the positive control. In vivo experiments in mice showed that fed intragastrically with 0.25 g/kg (bodyweight of mice) of AIPSs significantly reduced the weight loss caused by antibiotics and restored the immune-organ index (P<0.05). The AIPSs also significantly lowered the degrees of the inflammatory cytokines IL-6 and TNF-α in mouse serum (P<0.01). Analysis of mouse intestinal flora showed that AIPSs treated diarrhea caused by LIH and regulated and improved the flora’s structure. These effects were primarily manifested in the increased relative abundance of good microorganisms (e.g., Lachnospiraceae_NK4A136_group, Osllospiraceae, and Lachnospiraceae) and reduced relative abundance of harmful microorganisms (e.g., Enterococcus) (P<0.05). This study further expanded the application value of the submerged fermentation products of A. cinnamomea. A new perspective and theoretical basis for developing new multifunctional prebiotics to regulate antibiotic-induced disorders in the intestinal flora was also provided.
CONSTRUÇÃO E ANÁLISE DE MATERIAIS ADJUVANTES NO PROCESSO DE EDUCAÇÃO EM SAÚDE PARA A DOAÇÃO DE SANGUE
Authors: Siqueira Luciano de Oliveira , Tessele Bianca , Pian Luiza Carla Migliavacca , Zanatta Alexandre Lazaretti , Amarante Victor Grando do , Araujo Cristiane da Silva Rodrigues
Published: June 2022

A insuficiência de doadores de sangue e a limitada oferta de bolsas é uma realidade na maioria dos Serviços de Hemoterapia, principalmente em épocas festivas e em períodos delicados, como a pandemia de COVID-19. Nessa perspectiva, a produção de materiais que incentivem a doação sanguínea e esclareçam as dúvidas sobre tal processo se faz necessária. O objetivo do presente estudo foi transformar uma página estática em uma página web animada e criar vídeos animados com conteúdos informativos e motivacionais sobre a doação de sangue. Para o desenvolvimento, as ferramentas open source utilizadas foram Inkscape para a formatação de imagens vetoriais, Wick Editor para a criação das animações e exportação no formato gif, e Animator para a animação dos vídeos. Para a validação, realizou-se uma pesquisa de satisfação. A análise dos resultados demonstrou que tanto a página web quanto os vídeos apresentam potencial de esclarecer e incentivar a doação de sangue na população.
A Geometric Method to Construct Minimal Peer Prediction Mechanisms
Authors: Frongillo Rafael , Witkowski Jens
Published: June 2022

Minimal peer prediction mechanisms truthfully elicit private information (e.g., opinions or experiences) from rational agents without the requirement that ground truth is eventually revealed. In this paper, we use a geometric perspective to prove that minimal peer prediction mechanisms are equivalent to power diagrams, a type of weighted Voronoi diagram. Using this characterization and results from computational geometry, we show that many of the mechanisms in the literature are unique up to affine transformations, and introduce a general method to construct new truthful mechanisms.
Towards Clause-Learning State Space Search: Learning to Recognize Dead-Ends
Authors: Steinmetz Marcel , Hoffmann Joerg
Published: June 2022

We introduce a state space search method that identifies dead-end states, analyzes the reasons for failure, and learns to avoid similar mistakes in the future. Our work is placed in classical planning. The key technique are critical-path heuristics hC, relative to a set C of conjunctions. These recognize a dead-end state s, returning hC(s) = infty, if s has no solution even when allowing to break up conjunctive subgoals into the elements of C. Our key idea is to learn C during search. Starting from a simple initial C, we augment search to identify unrecognized dead-ends s, where hC(s) < infinity. We design methods analyzing the situation at such s, adding new conjunctions into C to obtain hC(s) = infty, thus learning to recognize s as well as similar dead-ends search may encounter in the future. We furthermore learn clauses phi where s' not satisfying phi implies hC(s') = infty, to avoid the prohibitive overhead of computing hC on every search state. Arranging these techniques in a depth-first search, we obtain an algorithm approaching the elegance of clause learning in SAT, learning to refute search subtrees. Our experiments show that this can be quite powerful. On problems where dead-ends abound, the learning reliably reduces the search space by several orders of magnitude.
DRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networks
Authors: Wang Biao , Chen Ge , Fu Luoyi , Song Li , Wang Xinbing , Liu Xue
Published: June 2022

Rumor blocking is a serious problem in large-scale social networks. Malicious rumors could cause chaos in society and hence need to be blocked as soon as possible after being detected. In this paper, we propose a model of dynamic rumor influence minimization with user experience (DRIMUX). Our goal is to minimize the influence of the rumor (i.e., the number of users that have accepted and sent the rumor) by blocking a certain subset of nodes. A dynamic Ising propagation model considering both the global popularity and individual attraction of the rumor is presented based on realistic scenario. In addition, different from existing problems of influence minimization, we take into account the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the blocking time of each user exceeds that threshold, the utility of the network will decrease. Under this constraint, we then formulate the problem as a network inference problem with survival theory, and propose solutions based on maximum likelihood principle. Experiments are implemented based on large-scale real world networks and validate the effectiveness of our method.
Mapping Action Language BC to Logic Programs: A Characterization by Postulates
Authors: Zhang Haodi , Lin Fangzhen
Published: June 2022

We have earlier shown that the standard mappings from action languages B and C to logic programs under answer set semantics can be captured by sets of properties on transition systems. In this paper, we consider action language BC and show that a standard mapping from BC action descriptions to logic programs can be similarly captured when the action rules in the descriptions do not have consistency conditions.
Temporal Vaccination Games under Resource Constraints
Authors: Adiga Abhijin , Vullikanti Anil
Published: June 2022

The decision to take vaccinations and other protective interventions for avoiding an infection is a natural game-theoretic setting. Most of the work on vaccination games has focused on decisions at the start of an epidemic. However, a lot of people defer their vaccination decisions, in practice. For example, in the case of the seasonal flu, vaccination rates gradually increase, as the epidemic rate increases. This motivates the study of temporal vaccination games, in which vaccination decisions can be made more than once. An important issue in the context of temporal decisions is that of resource limitations, which may arise due to production and distribution constraints. While there has been some work on temporal vaccination games, resource constraints have not been considered. In this paper, we study temporal vaccination games for epidemics in the SI (susceptible-infectious) model, with resource constraints in the form of a repeated game in complex social networks, with budgets on the number of vaccines that can be taken at any time. We find that the resource constraints and the vaccination and infection costs have a significant impact on the structure of Nash equilibria (NE). In general, the budget constraints can cause NE to become very inefficient, and finding efficient NE as well as the social optimum are NP-hard problems. We develop algorithms for finding NE and approximating the social optimum. We evaluate our results using simulations on different kinds of networks.
Collective Noise Contrastive Estimation for Policy Transfer Learning
Authors: Zhang Weinan , Paquet Ulrich , Hofmann Katja
Published: June 2022

We address the problem of learning behaviour policies to optimise online metrics from heterogeneous usage data. While online metrics, e.g., click-through rate, can be optimised effectively using exploration data, such data is costly to collect in practice, as it temporarily degrades the user experience. Leveraging related data sources to improve online performance would be extremely valuable, but is not possible using current approaches. We formulate this task as a policy transfer learning problem, and propose a first solution, called collective noise contrastive estimation (collective NCE). NCE is an efficient solution to approximating the gradient of a log-softmax objective. Our approach jointly optimises embeddings of heterogeneous data to transfer knowledge from the source domain to the target domain. We demonstrate the effectiveness of our approach by learning an effective policy for an online radio station jointly from user-generated playlists, and usage data collected in an exploration bucket.
Graph-without-cut: An Ideal Graph Learning for Image Segmentation
Authors: Gao Lianli , Song Jingkuan , Nie Feiping , Zou Fuhao , Sebe Nicu , Shen Heng Tao
Published: June 2022

Graph-based image segmentation organizes the image elements into graphs and partitions an image based on the graph. It has been widely used and many promising results are obtained. Since the segmentation performance highly depends on the graph, most of existing methods focus on obtaining a precise similarity graph or on designing efficient cutting/merging strategies. However, these two components are often conducted in two separated steps, and thus the obtained graph similarity may not be the optimal one for segmentation and this may lead to suboptimal results. In this paper, we propose a novel framework, Graph-Without-Cut (GWC), for learning the similarity graph and image segmentations simultaneously. GWC learns the similarity graph by assigning adaptive and optimal neighbors to each vertex based on the spatial and visual information. Meanwhile, the new rank constraint is imposed to the Laplacian matrix of the similarity graph, such that the connected components in the resulted similarity graph are exactly equal to the region number. Extensive empirical results on three public data sets (i.e, BSDS300, BSDS500 and MSRC) show that our unsupervised GWC achieves state-of-the-art performance compared with supervised and unsupervised image segmentation approaches.
Learning Step Size Controllers for Robust Neural Network Training
Authors: Daniel Christian , Taylor Jonathan , Nowozin Sebastian
Published: June 2022

This paper investigates algorithms to automatically adapt the learning rate of neural networks (NNs). Starting with stochastic gradient descent, a large variety of learning methods has been proposed for the NN setting. However, these methods are usually sensitive to the initial learning rate which has to be chosen by the experimenter. We investigate several features and show how an adaptive controller can adjust the learning rate without prior knowledge of the learning problem at hand.
Stochastic Parallel Block Coordinate Descent for Large-Scale Saddle Point Problems
Authors: Zhu Zhanxing , Storkey Amos
Published: June 2022

We consider convex-concave saddle point problems with a separable structure and non-strongly convex functions. We propose an efficient stochastic block coordinate descent method using adaptive primal-dual updates, which enables flexible parallel optimization for large-scale problems. Our method shares the efficiency and flexibility of block coordinate descent methods with the simplicity of primal-dual methods and utilizing the structure of the separable convex-concave saddle point problem. It is capable of solving a wide range of machine learning applications, including robust principal component analysis, Lasso, and feature selection by group Lasso, etc. Theoretically and empirically, we demonstrate significantly better performance than state-of-the-art methods in all these applications.
Infinite Plaid Models for Infinite Bi-Clustering
Authors: Ishiguro Katsuhiko , Sato Issei , Nakano Masahiro , Kimura Akisato , Ueda Naonori
Published: June 2022

We propose a probabilistic model for non-exhaustive and overlapping (NEO) bi-clustering. Our goal is to extract a few sub-matrices from the given data matrix, where entries of a sub-matrix are characterized by a specific distribution or parameters. Existing NEO biclustering methods typically require the number of sub-matrices to be extracted, which is essentially difficult to fix a priori. In this paper, we extend the plaid model, known as one of the best NEO bi-clustering algorithms, to allow infinite bi-clustering; NEO bi-clustering without specifying the number of sub-matrices. Our model can represent infinite sub-matrices formally. We develop a MCMC inference without the finite truncation, which potentially addresses all possible numbers of sub-matrices. Experiments quantitatively and qualitatively verify the usefulness of the proposed model. The results reveal that our model can offer more precise and in-depth analysis of sub-matrices.
Co-Regularized PLSA for Multi-Modal Learning
Authors: Wang Xin , Chang MingChing , Ying Yiming , Lyu Siwei
Published: June 2022

Many learning problems in real world applications involve rich datasets comprising multiple information modalities. In this work, we study co-regularized PLSA(coPLSA) as an efficient solution to probabilistic topic analysis of multi-modal data. In coPLSA, similarities between topic compositions of a data entity across different data modalities are measured with divergences between discrete probabilities, which are incorporated as a co-regularizer to augment individual PLSA models over each data modality. We derive efficient iterative learning algorithms for coPLSA with symmetric KL, L2 and L1 divergences as co-regularizers, in each case the essential optimization problem affords simple numerical solutions that entail only matrix arithmetic operations and numerical solution of 1D nonlinear equations. We evaluate the performance of the coPLSA algorithms on text/image cross-modal retrieval tasks, on which they show competitive performance with state-of-the-art methods.
Learning Future Classifiers without Additional Data
Authors: Kumagai Atsutoshi , Iwata Tomoharu
Published: June 2022

We propose probabilistic models for predicting future classifiers given labeled data with timestamps collected until the current time. In some applications, the decision boundary changes over time. For example, in spam mail classification, spammers continuously create new spam mails to overcome spam filters, and therefore, the decision boundary that classifies spam or non-spam can vary. Existing methods require additional labeled and/or unlabeled data to learn a time-evolving decision boundary. However, collecting these data can be expensive or impossible. By incorporating time-series models to capture the dynamics of a decision boundary, the proposed model can predict future classifiers without additional data. We developed two learning algorithms for the proposed model on the basis of variational Bayesian inference. The effectiveness of the proposed method is demonstrated with experiments using synthetic and real-world data sets.
A Scalable and Extensible Framework for Superposition-Structured Models
Authors: Zhao Shenjian , Xie Cong , Zhang Zhihua
Published: June 2022

In many learning tasks, structural models usually lead to better interpretability and higher generalization performance. In recent years, however, the simple structural models such as lasso are frequently proved to be insufficient. Accordingly, there has been a lot of work on "superposition-structured" models where multiple structural constraints are imposed. To efficiently solve these "superposition-structured" statistical models, we develop a framework based on a proximal Newton-type method. Employing the smoothed conic dual approach with the LBFGS updating formula, we propose a scalable and extensible proximal quasi-Newton (SEP-QN) framework. Empirical analysis on various datasets shows that our framework is potentially powerful, and achieves super-linear convergence rate for optimizing some popular "superposition-structured" statistical models such as the fused sparse group lasso.
A Proximal Alternating Direction Method for Semi-Definite Rank Minimization
Authors: Yuan Ganzhao , Ghanem Bernard
Published: June 2022

Semi-definite rank minimization problems model a wide range of applications in both signal processing and machine learning fields. This class of problem is NP-hard in general. In this paper, we propose a proximal Alternating Direction Method (ADM) for the well-known semi-definite rank regularized minimization problem. Specifically, we first reformulate this NP-hard problem as an equivalent biconvex MPEC (Mathematical Program with Equilibrium Constraints), and then solve it using proximal ADM, which involves solving a sequence of structured convex semi-definite subproblems to find a desirable solution to the original rank regularized optimization problem. Moreover, based on the Kurdyka-Lojasiewicz inequality, we prove that the proposed method always converges to a KKT stationary point under mild conditions. We apply the proposed method to the widely studied and popular sensor network localization problem. Our extensive experiments demonstrate that the proposed algorithm outperforms state-of-the-art low-rank semi-definite minimization algorithms in terms of solution quality.
Deep Hashing Network for Efficient Similarity Retrieval
Authors: Zhu Han , Long Mingsheng , Wang Jianmin , Cao Yue
Published: June 2022

Due to the storage and retrieval efficiency, hashing has been widely deployed to approximate nearest neighbor search for large-scale multimedia retrieval. Supervised hashing, which improves the quality of hash coding by exploiting the semantic similarity on data pairs, has received increasing attention recently. For most existing supervised hashing methods for image retrieval, an image is first represented as a vector of hand-crafted or machine-learned features, followed by another separate quantization step that generates binary codes. However, suboptimal hash coding may be produced, because the quantization error is not statistically minimized and the feature representation is not optimally compatible with the binary coding. In this paper, we propose a novel Deep Hashing Network (DHN) architecture for supervised hashing, in which we jointly learn good image representation tailored to hash coding and formally control the quantization error. The DHN model constitutes four key components: (1) a sub-network with multiple convolution-pooling layers to capture image representations; (2) a fully-connected hashing layer to generate compact binary hash codes; (3) a pairwise cross-entropy loss layer for similarity-preserving learning; and (4) a pairwise quantization loss for controlling hashing quality. Extensive experiments on standard image retrieval datasets show the proposed DHN model yields substantial boosts over latest state-of-the-art hashing methods.
Spectral Bisection Tree Guided Deep Adaptive Exemplar Autoencoder for Unsupervised Domain Adaptation
Authors: Shao Ming , Ding Zhengming , Zhao Handong , Fu Yun
Published: June 2022

Learning with limited labeled data is always a challenge in AI problems, and one of promising ways is transferring well-established source domain knowledge to the target domain, i.e., domain adaptation. In this paper, we extend the deep representation learning to domain adaptation scenario, and propose a novel deep model called ``Deep Adaptive Exemplar AutoEncoder (DAE$^2$)''. Different from conventional denoising autoencoders using corrupted inputs, we assign semantics to the input-output pairs of the autoencoders, which allow us to gradually extract discriminant features layer by layer. To this end, first, we build a spectral bisection tree to generate source-target data compositions as the training pairs fed to autoencoders. Second, a low-rank coding regularizer is imposed to ensure the transferability of the learned hidden layer. Finally, a supervised layer is added on top to transform learned representations into discriminant features. The problem above can be solved iteratively in an EM fashion of learning. Extensive experiments on domain adaptation tasks including object, handwritten digits, and text data classifications demonstrate the effectiveness of the proposed method.
An Alternating Proximal Splitting Method with Global Convergence for Nonconvex Structured Sparsity Optimization
Authors: Zhang Shubao , Qian Hui , Gong Xiaojin
Published: June 2022

In many learning tasks with structural properties, structured sparse modeling usually leads to better interpretability and higher generalization performance. While great efforts have focused on the convex regularization, recent studies show that nonconvex regularizers can outperform their convex counterparts in many situations. However, the resulting nonconvex optimization problems are still challenging, especially for the structured sparsity-inducing regularizers. In this paper, we propose a splitting method for solving nonconvex structured sparsity optimization problems. The proposed method alternates between a gradient step and an easily solvable proximal step, and thus enjoys low per-iteration computational complexity. We prove that the whole sequence generated by the proposed method converges to a critical point with at least sublinear convergence rate, relying on the Kurdyka-Łojasiewicz inequality. Experiments on both simulated and real-world data sets demonstrate the efficiency and efficacy of the proposed method.
Toward a Better Understanding of Deep Neural Network Based Acoustic Modelling: An Empirical Investigation
Authors: Wang Xingfu , Wang Lin , Chen Jing , Wu Litao
Published: June 2022

Recently, deep neural networks (DNNs) have outperformed traditional acoustic models on a variety of speech recognition benchmarks.However, due to system differences across research groups, although a tremendous breadth and depth of related work has been established, it is still not easy to assess the performance improvements of a particular architectural variant from examining the literature when building DNN acoustic models. Our work aims to uncover which variations among baseline systems are most relevant for automatic speech recognition (ASR) performance via a series of systematic tests on the limits of the major architectural choices.By holding all the other components fixed, we are able to explore the design and training decisions without being confounded by the other influencing factors. Our experiment results suggest that a relatively simple DNN architecture and optimization technique produces strong results.These findings, along with previous work, not only help build a better understanding towards why DNN acoustic models perform well or how they might be improved, but also help establish a set of best practices for new speech corpora and language understanding task variants.
Reduction Techniques for Graph-Based Convex Clustering
Authors: Han Lei , Zhang Yu
Published: June 2022

The Graph-based Convex Clustering (GCC) method has gained increasing attention recently. The GCC method adopts a fused regularizer to learn the cluster centers and obtains a geometric clusterpath by varying the regularization parameter. One major limitation is that solving the GCC model is computationally expensive. In this paper, we develop efficient graph reduction techniques for the GCC model to eliminate edges, each of which corresponds to two data points from the same cluster, without solving the optimization problem in the GCC method, leading to improved computational efficiency. Specifically, two reduction techniques are proposed according to tree-based and cyclic-graph-based convex clustering methods separately. The proposed reduction techniques are appealing since they only need to scan the data once with negligibly additional cost and they are independent of solvers for the GCC method, making them capable of improving the efficiency of any existing solver. Experiments on both synthetic and real-world datasets show that our methods can largely improve the efficiency of the GCC model.
Decoding Hidden Markov Models Faster Than Viterbi Via Online Matrix-Vector (max, +)-Multiplication
Authors: Cairo Massimo , Farina Gabriele , Rizzi Romeo
Published: June 2022

In this paper, we present a novel algorithm for the maximum a posteriori decoding (MAPD) of time-homogeneous Hidden Markov Models (HMM), improving the worst-case running time of the classical Viterbi algorithm by a logarithmic factor. In our approach, we interpret the Viterbi algorithm as a repeated computation of matrix-vector (max, +)-multiplications. On time-homogeneous HMMs, this computation is online: a matrix, known in advance, has to be multiplied with several vectors revealed one at a time. Our main contribution is an algorithm solving this version of matrix-vector (max,+)-multiplication in subquadratic time, by performing a polynomial preprocessing of the matrix. Employing this fast multiplication algorithm, we solve the MAPD problem in O(mn2/log n) time for any time-homogeneous HMM of size n and observation sequence of length m, with an extra polynomial preprocessing cost negligible for m > n. To the best of our knowledge, this is the first algorithm for the MAPD problem requiring subquadratic time per observation, under the assumption — usually verified in practice — that the transition probability matrix does not change with time.