bluuebunny/crossref_metadata_2025 huggingface dataset:
A drone-based survey for large, basking freshwater turtle species
Authors: Bogolin Amy P. , Davis Drew R. , Kline Richard J. , Rahman Abdullah F.
Published: October 2021

Conservation concerns are increasing for numerous freshwater turtle species, includingPseudemys gorzugi, which has led to a call for more research. However, traditional sampling methodologies are often time consuming, labor intensive, and invasive, restricting the amount of data that can be collected. Biases of traditional sampling methods can further impair the quality of the data collected, and these shortfalls may discourage their use. The use of unmanned aerial vehicles (UAVs, drones) for conducting wildlife surveys has recently demonstrated the potential to bridge gaps in data collection by offering a less labor intensive, minimally invasive, and more efficient process. Photographs and video can be obtained by camera attachments during a drone flight and analyzed to determine population counts, abundance, and other types of data. In this study we developed a detailed protocol to survey for large, freshwater turtle species in an arid, riverine landscape. This protocol was implemented with a DJI Matrice 600 Pro drone and a SONY ILCE α6000 digital camera to determineP.gorzugiand sympatric turtle species occurrence across 42 sites in southwestern Texas, USA. The use of a large drone and high-resolution camera resulted in high identification percentages, demonstrating the potential of drones to survey for large, freshwater turtle species. Numerous advantages to drone-based surveys were identified as well as some challenges, which were addressed with additional refinement of the protocol. Our data highlight the utility of drones for conducting freshwater turtle surveys and provide a guideline to those considering implementing drone-mounted high-resolution cameras as a survey tool.
Invincibility threatens vaccination intentions during a pandemic
Authors: Leonhardt James M. , Ridinger Garret , Rong Yu , Talaei-Khoe Amir
Published: October 2021

Some people feel they are invincible to the novel coronavirus SARS-CoV-2 (COVID-19). They believe that being infected with COVID-19 would not be a serious threat to their health. While these people may or may not be correct in their personal risk assessment, we find that such perceived invincibility may undermine community efforts to achieve herd immunity. Multi-level analysis of survey respondents across 51 countries finds that perceived invincibility from COVID-19 is negatively associated with believing there is a need to prevent the spread of COVID-19 in one’s community (n= 218,956) and one’s willingness to inoculate against the disease (n= 71,148). These effects are most pronounced among individuals from countries lower in cultural collectivism (e.g., USA, UK, Canada) and highlight the need to consider the interplay of individual and cultural factors in our efforts to understand, predict, and promote preventative health behavior during a pandemic.
Rational design of chimeric Multiepitope Based Vaccine (MEBV) against human T-cell lymphotropic virus type 1: An integrated vaccine informatics and molecular docking based approach
Authors: Tariq Muhammad Hamza , Bhatti Rashid , Ali Nida Fatima , Ashfaq Usman Ali , Shahid Farah , Almatroudi Ahmad , Khurshid Mohsin
Published: October 2021

Human T-cell lymphotropic virus type 1 (HTLV-1) is an infectious virus that has been linked to adult T cell leukemia /lymphoma, aggressive CD4-T cell malignancy and many other immune-related medical illnesses. So far, no effective vaccine is known to combat HTLV-1, hence, the current research work was performed to design a potential multi-epitope-based subunit vaccine (MEBV) by adopting the latest methodology of reverse vaccinology. Briefly, three highly antigenic proteins (Glycoprotein, Accessory protein, and Tax protein) with no or minimal (<37%) similarity with human proteome were sorted out and potential B- and T-cell epitopes were forecasted from them. Highly antigenic, immunogenic, non-toxic, non-allergenic and overlapping epitopes were short-listed for vaccine development. The chosen T-cell epitopes displayed a strong binding affinity with their corresponding Human Leukocyte Antigen alleles and demonstrated 95.8% coverage of the world’s population. Finally, nine Cytotoxic T Lymphocytes, six Helper T Lymphocytes and five Linear B Lymphocytes epitopes, joint through linkers and adjuvant, were exploited to design the final MEBV construct, comprising of 382 amino acids. The developed MEBV structure showed highly antigenic properties while being non-toxic, soluble, non-allergenic, and stable in nature. Moreover, disulphide engineering further enhanced the stability of the final vaccine protein. Additionally, Molecular docking analysis and Molecular Dynamics (MD) simulations confirmed the strong association between MEBV construct and human pathogenic immune receptor TLR-3. Repeated-exposure simulations and Immune simulations ensured the rapid antigen clearance and higher levels of cell-mediated immunity, respectively. Furthermore, MEBV codon optimization and in-silico cloning was carried out to confirm its augmented expression. Results of our experiments suggested that the proposed MEBV could be a potential immunogenic against HTLV-1; nevertheless, additional wet lab experiments are needed to elucidate our conclusion.
Pillar 3: Does banking regulation support stakeholders’ interest in banks financial and risk profile?
Authors: Pilková Anna , Munk Michal , Benko Ľubomír , Blažeková Petra , Kapusta Jozef
Published: October 2021

The paper examines the interest of the commercial banks’ stakeholders in Pillar 3 disclosures and their behaviour during the timing of serious market turbulence. The aim is to discover to which extent current banking regulation supports stakeholders’ interest in the information required by regulators to be disclosed. The examined data consists of log files that were pre-processed using web mining techniques and from which were extracted frequent item sets by quarters and evaluated in terms of quantity. The authors have proposed a methodology to evaluate frequent item sets of web parts over a dedicated time. Based on the verification of applied methodology on two commercial banks, the results show that stakeholders’ interest in disclosures is highest in the first quarter at each year and after turbulent times in 2009 their interests decreased. Moreover, the results suggest that stakeholders expressed higher interest than in regulatory required Pillar 3 information in the following group of information: Pillar3 related information, Annual reports, Information on Group. Following our results, the paper contributes to cover the gap in the research by analysing Pillar 3 disclosures and their compliance with regulatory requirements, which also increase the interest of the relevant stakeholders to conduce them as an effective market discipline tool.
The multifaceted care-seeking practices among caregivers of children with cerebral palsy: Perspectives from mothers and providers in Ghana
Authors: Fonzi Victoria , Sheriff Blessed , Dalglish Sarah , Anum Adote , Dwomo Agyei Emmanuella , Diggs Devin , Eboigbe Loretta , Owusu Prince Gyebi , Sakyi Kwame S.
Published: October 2021

Background

Research on cerebral palsy (CP) has lacked emphasis on knowledge and treatment practices among caregivers, particularly in low- and middle-income countries where socio-cultural contexts encourage a variety of treatment alternatives. In this study, we explored the beliefs and experiences that motivate care-seeking practices among caregivers of children with CP in Ghana.

Methods

Semi-structured interviews were conducted with 25 caregivers, 10 medical providers, and 5 alternative providers in the Greater Accra Region. Participant interviews were analyzed using principles adapted from grounded theory. A conceptual model was constructed to illustrate salient patterns and motivational factors influencing care-seeking practices.

Results

Participants’ experiences showed that caregivers initially sought physiotherapy and prescription medications from medical providers. Many of them then transitioned to alternative methods to search for a cure or address specific CP symptoms. Over time, most caregivers discontinued both medical and alternative care in favor of at-home treatment. A few withdrew completely from all forms of care. Cost of treatment, caregiver burden, and stigma strongly inhibited care-seeking outside the home.

Conclusion

Although caregivers were open to exploring a variety of treatment options, at-home treatment was preferred by long-time caregivers for its convenience, low cost, and adaptability to patient and caregiver needs.
Relationship between obesity-related anthropometric indicators and cognitive function in Chinese suburb-dwelling older adults
Authors: Ma Weibo , Zhang Hui , Wu Ning , Liu Yuewen , Han Peipei , Wang Feng , Wang Jingru , Xie Fandi , Niu Shumeng , Hu Hao , Zhang Chenyu , Chen Nuo , Zhang Yichen , Guo Qi , Yu Ying
Published: October 2021

Background

Studies relating obesity to cognition in older people show conflicting results, which may be explained by the choice of obesity indicators.

Objectives

This study aimed to investigate the relationship between obesity-related indicators and cognitive impairment, especially between different age or gender subgroups, and explore whether obesity-related indicators were related to specific cognitive domains.

Methods

This was a cross-sectional study on 1753 participants aged ≥ 60 years (41.0% men; aged 71.36 ± 5.96 years). Obesity-related indicators included body mass index (BMI), waist circumference (WC), calf circumference (CC), waist to hip ratio (WHR), waist to calf circumstance ratio (WCR), fat to fat-free mass ratio (FM/FFM). The Mini-Mental State Examination scale (MMSE) was used to assess cognitive function. Cognitive impairment was defined as a score ≤ 17 for illiterates, ≤ 20 for participants with primary school education, and ≤ 24 for those with junior high school degrees or above. Multiple logistic regression analysis was used to estimate multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Restricted cubic splines were used to analyze and visualize the linear relationships.

Results

The prevalence of cognitive impairment was 18.77%. In the fully adjusted model, CC was negatively associated with cognitive impairment (OR = 0.94, 95% CI: 0.90−0.98). Further analysis showed that CC correlated positively with recall and place orientation. A higher FM/FFM was found to be associated with a higher prevalence of cognitive impairment (OR: 1.44, 95%CI: 0.88–2.35, P for trend = 0.029); this association was notable in women (P for trend = 0.002) and the oldest (P for trend = 0.009), and so did the potential effect of BMI on cognitive impairment (70–80 years: P for trend = 0.011; ≥ 80 years: P for trend = 0.013). No statistically significant association was found between cognitive impairment and WC, WHR, or WCR.

Conclusion

CC and FM/FFM were associated with cognitive impairment in older people. Future research needs to distinguish the effects of fat and muscle mass on cognitive function, with special attention to different ages and genders.
The impact of the traditional male role norms on the posttraumatic stress disorder among Polish male firefighters
Authors: Jakubowski Tomasz Daniel , Sitko-Dominik Magdalena Maja
Published: October 2021

Background

The aim of this study was to explore potential associations between compliance with the traditional male role norms and posttraumatic stress disorder (PTSD) symptoms.

Methods

The study was conducted on 135 male Polish firefighters. The study used the Impact Event Scale-Revised, the Relations/Social Support Scale and the Male Role Norms Scale.

Results

The study revealed that the firefighters that suffered from probable PTSD (over 34% of all the respondents) seemed to comply with the male role norms more strictly than those without PTSD. The reverse was found to be true for the perceived social support. Social status norms and toughness norms understood as expectations that men should achieve high status by means of successful career, etc., and be tough and resilient at all costs to be perceived as “manly” were found to be positively associated with the development of PTSD, while in the case of perceived social support, a negative association was confirmed. The results might yield important clinical implications—traditional male role norms pertaining to toughness and social status might be associated with the increase in chances of developing PTSD after the exposure to potentially traumatic events.

Conclusions

Firefighting as a stereotypically masculine occupation may be associated with the reinforcement of stereotypically masculine behaviors, which in turn is associated with a decreased ability to cope with potentially traumatic stimuli and favoring maladaptive behaviors. The results might suggest that addressing the beliefs about masculinity during psychological intervention in the case of PTSD might be beneficial especially among such masculinized groups as firefighters. The main limitations of the study are: participation of those more eager to participate and reveal personal information; the recall and report bias; the relatively small sample size; sociodemographic data omissions; the study group almost exclusively consisting of firefighters from large urban centers.
Automated color detection in orchids using color labels and deep learning
Authors: Apriyanti Diah Harnoni , Spreeuwers Luuk J. , Lucas Peter J. F. , Veldhuis Raymond N. J.
Published: October 2021

The color of particular parts of a flower is often employed as one of the features to differentiate between flower types. Thus, color is also used in flower-image classification. Color labels, such as ‘green’, ‘red’, and ‘yellow’, are used by taxonomists and lay people alike to describe the color of plants. Flower image datasets usually only consist of images and do not contain flower descriptions. In this research, we have built a flower-image dataset, especially regarding orchid species, which consists of human-friendly textual descriptions of features of specific flowers, on the one hand, and digital photographs indicating how a flower looks like, on the other hand. Using this dataset, a new automated color detection model was developed. It is the first research of its kind using color labels and deep learning for color detection in flower recognition. As deep learning often excels in pattern recognition in digital images, we applied transfer learning with various amounts of unfreezing of layers with five different neural network architectures (VGG16, Inception, Resnet50, Xception, Nasnet) to determine which architecture and which scheme of transfer learning performs best. In addition, various color scheme scenarios were tested, including the use of primary and secondary color together, and, in addition, the effectiveness of dealing with multi-class classification using multi-class, combined binary, and, finally, ensemble classifiers were studied. The best overall performance was achieved by the ensemble classifier. The results show that the proposed method can detect the color of flower and labellum very well without having to perform image segmentation. The result of this study can act as a foundation for the development of an image-based plant recognition system that is able to offer an explanation of a provided classification.
Phenoloxidase activity and organic carbon dynamics in historic Anthrosols in Scotland, UK
Authors: Esiana Benneth O. I. , Coates Christopher J. , Adderley W. Paul , Berns Anne E. , Bol Roland
Published: October 2021

Phenolic compounds are chemical precursor building blocks of soil organic matter. Their occurrence can be inhibitory to certain enzymes present in soil, thereby influencing the rate of decomposition of soil organic matter. Microbe-derived phenoloxidases (laccases) are extracellular enzymes capable of degrading recalcitrant polyphenolic compounds. In this study, our aim was to investigate the relationships between phenoloxidase enzyme activity, organic carbon content and microbial abundance in the context of long-term anthropogenically amended soils. To achieve this, we used a series of complementary biochemical analytical methods including gas chromatography, enzyme assays and solid-state Carbon-13 Cross Polarisation Magic-Angle Spinning Nuclear Magnetic Resonance Spectroscopy (13C CPMAS NMR). Using several anthrosols found in St Andrews (Scotland, UK) that had been subjected to intense anthropogenic modification since the medieval period (11th century AD) to present-day, we were able to scope the impact of past waste disposal on soils. The long-term anthropogenic impact led to organic matter-rich soils. Overall, phenoloxidase activity increased by up to 2-fold with soil depth (up to 100 cm) and was inversely correlated with microbial biomass. Solid-state 13C NMR characterisation of carbon species revealed that the observed decline in soil organic matter with depth corresponded to decreases in the labile organic carbon fractions as evidenced by changes in the O/N-alkyl C region of the spectra. The increase in phenoloxidase activity with depth would appear to be a compensatory mechanism for the reduced quantities of organic carbon and lower overall nutrient environment in subsoils. By enzymatically targeting phenolic compounds, microbes can better utilise recalcitrant carbon when other labile soil carbon sources become limited, thereby maintaining metabolic processes.
Comparative quantitation of aquaporin-2 and arginine vasopressin receptor-2 localizations among chronic kidney disease and healthy kidney in dogs
Authors: Matchimakul Pitchaya , Pongkan Wanpitak , Kongtung Piyamat , Mektrirat Raktham
Published: October 2021

Background and Aim: Aquaporin-2 (AQP2) and arginine vasopressin receptor-2 (AVPR2) are proteins that control water homeostasis in principal cells. Chronic kidney disease (CKD) is defined as the impairment and irreversible loss of kidney function and/or structure, which causes water imbalances and polyuria. The study aimed to know the expression of AQPs and AVPR2 in the kidneys of a canine with CKD.

Materials and Methods: The kidneys were collected from two dog carcasses from Small Animal Teaching Hospital, Faculty of Veterinary Medicine, Chiang Mai University. The kidney tissue was prepared for immunohistochemistry and investigated the expression and localization of tissue's AQP2 and AVPR2. For statistical analysis, the Mann–Whitney U-test was applied to the data.

Results: By immunohistochemistry, AQP2 was expressed strongly in the basolateral and apical membranes of the principal cells, whereas AVPR2 was localized in the principal cell's basolateral membrane in both renal cortex and renal medulla. In the normal kidney, the semi-quantitative immunohistochemistry for the percentage of protein expression of AQP2 and AVPR2 was 5.062±0.4587 and 4.306±0.7695, respectively. In contrast, protein expression of AQP2 and AVPR2 in CKD was found to be 1.218±0.1719 and 0.8536±0.1396, respectively. The data shows that the percentage of AQP2 and AVPR2 expression was decreased, corresponding to a 4-fold and 5-fold in CKD (p<0.001).

Conclusion: Our findings revealed that CKD was a marked decrease in AQP2 and AVPR2 expression. The central role of specific AQP2 and AVPR2 in regulating water homeostasis will provide correlations in case of CKD with polyuria.
Perineal Hygienic Practices in Patients of Himalayan Foothills - A Descriptive Study
Authors: Prateek Shashi , Yadav Ankita , Chawla Latika , Chaudhary Deepti , Kochhar Neetu , Sharma Shailja , Gaur Rakhi , Grover Nitika
Published: October 2021

BACKGROUND It has been said that health is wealth and when it comes to reproductive health of a women it has a significant value indeed. There is lot of loops and holes in perineal hygiene practices due to incomplete knowledge and various myth that is transferred from generation to generation in a family. The aim of the study was to study the perineal hygiene practices in women of Himalayan foothills. METHODS This is an observational study performed between March 2019 to February 2020 at AIIMS Rishikesh. Data was extracted from OPD records of patients which included details on perineal hygiene, routinely asked in one of the three units in department of obstetrics and gynaecology at AIIMS Rishikesh. RESULTS Total of 2400 women participated in the study. The mean age of subjects was 32± 6.9(Range 12 - 80 years). Maximum patients were married (92 %), mostly Hindus (48 %) belonging to upper lower class (37.5 %) according to modified Kuppuswamy socioeconomic scale. In this study we studied the routine perineal cleaning practices, Attention is being drawn to the fact that only 15 % of the subjects cleaned the genital area with water after urination, 5 % while changing pads during menstruation, only 1.5 % washed the genitalia immediately after sexual intercourse In present study the commonest method removal of perineal hair was shaving (38.4 %). While analysing menstrual hygiene, in present study, 42.5 % of study population was using sanitary pads from market and 35 % used homemade cloth pads. Ninety eight point seven percent of women avoided sexual intercourse during menstruation in present study. CONCLUSIONS Female genital hygiene is a vital topic. Awareness should be increased regarding maintenance of hygienic practices through health education programmes. More studies need to be performed in order to provide directions for proper female genital hygiene, as well as guidelines. KEY WORDS Perineal Hygiene, Pubic Hair Grooming, Menstrual Hygiene, Perineal Itching, Perineal Rashes, Long Mirror for Self Examination.
Efeitos da prática de tanqueamento na segurança das operações aéreas: o caso da aviação geral
Authors: Del Fiaco Reinaldo Moreira , Maestrelli Leobons Camila , Braytiner de Souza Silva Wesley , Lopes da Silva Paulo Afonso
Published: October 2021

O gasto de combustível representa a maior parcela do custo operacional na aviação, e a sua falta, ou pane seca, é um dos fatores que mais contribuem contribuintes para os acidentes aeronáuticos. O presente trabalho tem o objetivo de desenvolver um modelo de programação não linear para minimizar o custo total com abastecimento por meio da prática de tanqueamento, observando as restrições de regra de voo, operacionais e físicas da aeronave. Foi realizada uma simulação de quatro voos para encontrar qual a quantidade de combustível a ser abastecida em cada etapa, e o resultado mostrou uma economia de 1,7% quando comparado com o abastecimento mínimo necessário para a quantidade regulamentar. A partir deste estudo, aplicativos online podem ser desenvolvidos para facilitar a inserção dos dados necessários e simplificar os processos envolvidos no planejamento de voos.
Light pollution: A landscape-scale issue requiring cross-realm consideration
Authors: Mayer-Pinto Mariana , Jones Theresa M. , Swearer Stephen E. , Robert Kylie A. , Bolton Damon , Aulsebrook Anne E. , Dafforn Katherine A. , Dickerson Ashton L. , Dimovski Alicia M. , Hubbard Nikki , McLay Lucy K. , Pendoley Kellie , Poore Alistair G.B. , Thums Michelle , Willmott Nikolas J. , Yokochi Kaori , Fobert Emily K.
Published: October 2021

Terrestrial, marine, and freshwater realms are inherently linked through ecological, biogeochemical and/or physical processes. An understanding of these connections is critical to optimise management strategies and ensure the ongoing resilience of ecosystems. Artificial light at night (ALAN) is a global stressor that can profoundly affect a wide range of organisms and habitats and impact multiple realms. Despite this, current management practices for light pollution rarely consider connectivity between realms. Here we discuss the ways in which ALAN can have cross-realm impacts and provide case studies for each example discussed. We identified three main ways in which ALAN can affect two or more realms: 1) impacts on species that have life cycles and/or stages on two or more realms, such as diadromous fish that cross realms during ontogenetic migrations and many terrestrial insects that have juvenile phases of the lifecycle in aquatic realms; 2) impacts on species interactions that occur across realm boundaries, and 3) impacts on transition zones or ecosystems such as mangroves and estuaries. We then propose a framework for cross-realm management of light pollution and discuss current challenges and potential solutions to increase the uptake of a cross-realm approach for ALAN management. We argue that the strengthening and formalisation of professional networks that involve academics, lighting practitioners, environmental managers and regulators that work in multiple realms is essential to provide an integrated approach to light pollution. Networks that have a strong multi-realm and multi-disciplinary focus are important as they enable a holistic understanding of issues related to ALAN.
EVOLUCIÓN TERRITORIAL DE LA TRANSICIÓN EPIDEMIOLÓGICA EN EL ESTADO DE MÉXICO, 1980-2015
Authors: Hernández Bernal Jesus Emilio , Juárez Marcela Virginia Santana , Jaimes Noel Bonfilio Pineda , Cedillo Jesús Gastón Gutiérrez , Barroso Diana Gómez
Published: October 2021

El propósito de este artículo es analizar la distribución territorial de la transición epidemiológica (únicamente mortalidad) en el Estado de México, durante el período de 1980 a 2015. Esta variable presenta un progreso evolutivo, diferenciado a lo largo del tiempo y del territorio. Los indicadores tomados en cuenta en este estudio son las tasas de mortalidad que se deben a alguna de las enfermedades incluidas en la lista GBD 165 (Global Burden Disease, o Carga Global de Enfermedad) desarrollada por la OMS. Estas tasas se calcularon con base en la población total y las defunciones hospitalarias registradas del periodo 1980-2015. Posteriormente se analizaron estadísticamente empleando como métodos el Valor Índice Medio (VIM) y el análisis bivariado, cuya interpretación permitió determinar el comportamiento espacio temporal de la transición epidemiológica y elaborar la cartografía correspondiente. Algunos hallazgos señalan que en el Estado de México la transición epidemiológica de la mortalidad presenta cuatro etapas: dos de ellas en regiones extensas en el territorio y otras dos en pequeñas regiones, de forma aleatoria.
PENGAKUAN IDENTITAS WARIA DI INDONESIA (STUDI KASUS WARIA YANG BERHADAPAN DENGAN HUKUM DI YOGYAKARTA)
Authors: Jati Fardi Prabowo , Mukhosiyah Cucu , Febriani Hanifah
Published: October 2021

Kendati waria sudah tidak digolongkan sebagai gangguan jiwa dan memiliki kontribusi sosiakultural di masyarakat, faktanya komunitas ini masih memiliki kerentanannya. Kerentanan tersebut termasuk ketika waria berhadapan dengan hukum. Oleh karena itu penelitian ini mencoba membahas dua hal. Pertama, pengakuan identitas waria dalam peraturan perundang-undangan di Indonesia. Kedua, pengakuan identitas waria dalam kasus waria berhadapan dengan hukum di Yogyakarta. Penelitian ini merupakan penelitian yuridis normatif empiris. Hasil penelitian ini menunjukkan sudah ada pengakuan dalam konvensi internasional. Meskipun demikian, dalam hukum Indonesia masih terdapat diskriminasi atas identitas dan karakteristik yang melekat terhadap waria. Dalam beberapa kasus waria yang berhadapan dengan hukum, waria sudah diterima identitas dan karakteristiknya oleh aparat penegak hukum meskipun belum sepenuhnya.
REKONSTRUKSI PENCABUTAN HAK ATAS TANAH DAN KONSINYASI DALAM PENGADAAN TANAH UNTUK KEPENTINGAN UMUM
Authors: Joesoef Iwan Erar
Published: October 2021

Penelitian ini bertujuan mengkaji penggunaan konsep pencabutan hak atas tanah dan ganti rugi dengan penitipan di pengadilan (konsinyasi). Ruang lingkup penelitian adalah pengadaan tanah untuk kepentingan umum dalam pembangunan insfrastruktur jalan tol di Indonesia. Dalam undang-undang pengadaan tanah untuk kepentingan umum secara eksplisit tidak menyebut pencabutan hak atas tanah, namun penerapan konsep ganti rugi pengadaan tanah untuk kepentingan umum yang dititipkan di pengadilan berakibat hilangnya hak atas tanah dan tanah jatuh kepada negara. Metode penelitian yang digunakan adalah yuridis normatif. Hasil penelitian  bahwa, pencabutan hak atas tanah dan konsinyasi urgensinya lebih tepat diterapkan pada pembangunan infrastruktur publik oleh pemerintah. Hal ini sesuai dengan Pasal 33 (3) UUD NRI Tahun 1945. Sedangkan pembangunan infrastruktur publik oleh swasta dalam model kerjasama pemerintah swasta tidak adil untuk diterapkan karena bersifat komersial.
PATSQL
Authors: Takenouchi Keita , Ishio Takashi , Okada Joji , Sakata Yuji
Published: October 2021

SQL is one of the most popular tools for data analysis, and it is now used by an increasing number of users without having expertise in databases. Several studies have proposed programming-by-example approaches to help such non-experts to write correct SQL queries. While existing methods support a variety of SQL features such as aggregation and nested query, they suffer a significant increase in computational cost as the scale of example tables increases. In this paper, we propose an efficient algorithm utilizing properties known in relational algebra to synthesize SQL queries from input and output tables. Our key insight is that a projection operator in a program sketch can be lifted above other operators by applying transformation rules in relational algebra, while preserving the semantics of the program. This enables a quick inference of appropriate columns in the projection operator, which is an essential component in synthesis but causes combinatorial explosions in prior work. We also introduce a novel form of constraints and its top-down propagation mechanism for efficient sketch completion. We implemented this algorithm in our tool PATSQL and evaluated it on 226 queries from prior benchmarks and Kaggle's tutorials. As a result, PATSQL solved 68% of the benchmarks and found 89% of the solutions within a second. Our tool is available at https://naist-se.github.io/patsql/.
A comprehensive survey and experimental comparison of graph-based approximate nearest neighbor search
Authors: Wang Mengzhao , Xu Xiaoliang , Yue Qiang , Wang Yuxiang
Published: October 2021

Approximate nearest neighbor search (ANNS) constitutes an important operation in a multitude of applications, including recommendation systems, information retrieval, and pattern recognition. In the past decade, graph-based ANNS algorithms have been the leading paradigm in this domain, with dozens of graph-based ANNS algorithms proposed. Such algorithms aim to provide effective, efficient solutions for retrieving the nearest neighbors for a given query. Nevertheless, these efforts focus on developing and optimizing algorithms with different approaches, so there is a real need for a comprehensive survey about the approaches' relative performance, strengths, and pitfalls. Thus here we provide a thorough comparative analysis and experimental evaluation of 13 representative graph-based ANNS algorithms via a new taxonomy and fine-grained pipeline. We compared each algorithm in a uniform test environment on eight real-world datasets and 12 synthetic datasets with varying sizes and characteristics. Our study yields novel discoveries, offerings several useful principles to improve algorithms, thus designing an optimized method that outperforms the state-of-the-art algorithms. This effort also helped us pinpoint algorithms' working portions, along with rule-of-thumb recommendations about promising research directions and suitable algorithms for practitioners in different fields.
Butterfly-core community search over labeled graphs
Authors: Dong Zheng , Huang Xin , Yuan Guorui , Zhu Hengshu , Xiong Hui
Published: October 2021

Community search aims at finding densely connected subgraphs for query vertices in a graph. While this task has been studied widely in the literature, most of the existing works only focus on finding homogeneous communities rather than heterogeneous communities with different labels. In this paper, we motivate a new problem of cross-group community search, namely Butterfly-Core Community (BCC), over a labeled graph, where each vertex has a label indicating its properties and an edge between two vertices indicates their cross relationship. Specifically, for two query vertices with different labels, we aim to find a densely connected cross community that contains two query vertices and consists of butterfly networks, where each wing of the butterflies is induced by a k-core search based on one query vertex and two wings are connected by these butterflies. We first develop a heuristic algorithm achieving 2-approximation to the optimal solution. Furthermore, we design fast techniques of query distance computations, leader pair identifications, and index-based BCC local explorations. Extensive experiments on seven real datasets and four useful case studies validate the effectiveness and efficiency of our BCC and its multi-labeled extension models.
LES
3
Authors: Li Yifan , Yu Xiaohui , Koudas Nick
Published: October 2021

Set similarity search is a problem of central interest to a wide variety of applications such as data cleaning and web search. Past approaches on set similarity search utilize either heavy indexing structures, incurring large search costs or indexes that produce large candidate sets. In this paper, we design a learning-based exact set similarity search approach, LES
3
. Our approach first partitions sets into groups, and then utilizes a light-weight bitmap-like indexing structure, called token-group matrix (TGM), to organize groups and prune out candidates given a query set. In order to optimize pruning using the TGM, we analytically investigate the optimal partitioning strategy under certain distributional assumptions. Using these results, we then design a learning-based partitioning approach called L2P and an associated data representation encoding, PTR, to identify the partitions. We conduct extensive experiments on real and synthetic datasets to fully study LES
3
, establishing the effectiveness and superiority over other applicable approaches.
Approximating median absolute deviation with bounded error
Authors: Chen Zhiwei , Song Shaoxu , Wei Ziheng , Fang Jingyun , Long Jiang
Published: October 2021

The median absolute deviation (MAD) is a statistic measuring the variability of a set of quantitative elements. It is known to be more robust to outliers than the standard deviation (SD), and thereby widely used in outlier detection. Computing the exact MAD however is costly, e.g., by calling an algorithm of finding median twice, with space cost
O
(
n
) over
n
elements in a set. In this paper, we propose the first fully mergeable approximate MAD algorithm, OP-MAD, with one-pass scan of the data. Remarkably, by calling the proposed algorithm at most twice, namely TP-MAD, it guarantees to return an (ϵ, 1)-accurate MAD, i.e., the error relative to the exact MAD is bounded by the desired ϵ or 1. The space complexity is reduced to
O
(
m
) while the time complexity is
O
(
n
+
m
log
m
), where
m
is the size of the sketch used to compress data, related to the desired error bound ϵ. To get a more accurate MAD, i.e., with smaller ϵ, the sketch size
m
will be larger, a trade-off between effectiveness and efficiency. In practice, we often have the sketch size
m
≪
n
, leading to constant space cost
O
(1) and linear time cost
O
(
n
). The extensive experiments over various datasets demonstrate the superiority of our solution, e.g., 160000× less memory and 18x faster than the aforesaid exact method in datasets
pareto
and
norm
. Finally, we further implement and evaluate the parallelizable TP-MAD in Apache Spark, and the fully mergeable OP-MAD in Structured Streaming.
An experimental evaluation and guideline for path finding in weighted dynamic network
Authors: Zhang Mengxuan , Li Lei , Zhou Xiaofang
Published: October 2021

Shortest path computation is a building block of various network applications. Since real-life networks evolve as time passes, the
Dynamic Shortest Path (DSP)
problem has drawn lots of attention in recent years. However, as
DSP
has many factors related to network topology, update patterns, and query characteristics, existing works only test their algorithms on limited situations without sufficient comparisons with other approaches. Thus, it is still hard to choose the most suitable method in practice. To this end, we first identify the
determinant dimensions and constraint dimensions
of the
DSP
problem and create a complete problem space to cover all possible situations. Then we evaluate the state-of-the-art
DSP
methods under the same implementation standard and test them systematically under a set of synthetic dynamic networks. Furthermore, we propose the concept of
dynamic degree
to classify the dynamic environments and use
throughput
to evaluate their performance. These results can serve as a guideline to find the best solution for each situation during system implementation and also identify research opportunities. Finally, we validate our findings on real-life dynamic networks.
LANCET
Authors: Zhang Huayi , Cao Lei , Madden Samuel , Rundensteiner Elke
Published: October 2021

Cutting-edge machine learning techniques often require millions of labeled data objects to train a robust model. Because relying on humans to supply such a huge number of labels is rarely practical, automated methods for label generation are needed. Unfortunately, critical challenges in auto-labeling remain unsolved, including the following research questions: (1) which objects to ask humans to label, (2) how to automatically propagate labels to other objects, and (3) when to stop labeling. These three questions are not only each challenging in their own right, but they also correspond to tightly interdependent problems. Yet existing techniques provide at best isolated solutions to a subset of these challenges. In this work, we propose the first approach, called LANCET, that successfully addresses all three challenges in an integrated framework. LANCET is based on a theoretical foundation characterizing the properties that the labeled dataset must satisfy to train an effective prediction model, namely the Covariate-shift and the Continuity conditions. First, guided by the Covariate-shift condition, LANCET maps raw input data into a semantic feature space, where an unlabeled object is expected to share the same label with its near-by labeled neighbor. Next, guided by the Continuity condition, LANCET selects objects for labeling, aiming to ensure that unlabeled objects always have some sufficiently close labeled neighbors. These two strategies jointly maximize the accuracy of the automatically produced labels and the prediction accuracy of the machine learning models trained on these labels. Lastly, LANCET uses a distribution matching network to verify whether both the Covariate-shift and Continuity conditions hold, in which case it would be safe to terminate the labeling process. Our experiments on diverse public data sets demonstrate that LANCET consistently outperforms the state-of-the-art methods from Snuba to GOGGLES and other baselines by a large margin - up to 30 percentage points increase in accuracy.
ByShard
Authors: Hellings Jelle , Sadoghi Mohammad
Published: October 2021

The emergence of blockchains has fueled the development of resilient systems that can deal with
Byzantine failures
due to crashes, bugs, or even malicious behavior. Recently, we have also seen the exploration of
sharding
in these resilient systems, this to provide the scalability required by very large data-based applications. Unfortunately, current sharded resilient systems all use system-specific specialized approaches toward sharding that do not provide the flexibility of traditional sharded data management systems.

To improve on this situation, we fundamentally look at the design of sharded resilient systems. We do so by introducing BYSHARD, a unifying framework for the study of sharded resilient systems. Within this framework, we show how
two-phase commit
and
two-phase locking
---two techniques central to providing
atomicity
and
isolation
in traditional sharded databases---can be implemented efficiently in a Byzantine environment, this with a minimal usage of costly Byzantine resilient primitives. Based on these techniques, we propose
eighteen
multi-shard transaction processing protocols. Finally, we practically evaluate these protocols and show that each protocol supports high transaction throughput and provides scalability while each striking its own trade-off between
throughput, isolation level, latency
, and
abort rate.
As such, our work provides a strong foundation for the development of ACID-compliant general-purpose and flexible sharded resilient data management systems.
SetSketch
Authors: Ertl Otmar
Published: October 2021

MinHash and HyperLogLog are sketching algorithms that have become indispensable for set summaries in big data applications. While HyperLogLog allows counting different elements with very little space, MinHash is suitable for the fast comparison of sets as it allows estimating the Jaccard similarity and other joint quantities. This work presents a new data structure called SetSketch that is able to continuously fill the gap between both use cases. Its commutative and idempotent insert operation and its mergeable state make it suitable for distributed environments. Fast, robust, and easy-to-implement estimators for cardinality and joint quantities, as well as the ability to use SetSketch for similarity search, enable versatile applications. The presented joint estimator can also be applied to other data structures such as MinHash, HyperLogLog, or Hyper-MinHash, where it even performs better than the corresponding state-of-the-art estimators in many cases.
Scalable community detection via parallel correlation clustering
Authors: Shi Jessica , Dhulipala Laxman , Eisenstat David , Łăcki Jakub , Mirrokni Vahab
Published: October 2021

Graph clustering and community detection are central problems in modern data mining. The increasing need for analyzing billion-scale data calls for faster and more scalable algorithms for these problems. There are certain trade-offs between the quality and speed of such clustering algorithms. In this paper, we design scalable algorithms that achieve high quality when evaluated based on ground truth.

We develop a generalized sequential and shared-memory parallel framework based on the LAMBDACC objective (introduced by Veldt et al.), which encompasses modularity and correlation clustering. Our framework consists of highly-optimized implementations that scale to large data sets of billions of edges and that obtain high-quality clusters compared to ground-truth data, on both unweighted and weighted graphs. Our empirical evaluation shows that this framework improves the state-of-the-art trade-offs between speed and quality of scalable community detection. For example, on a 30-core machine with two-way hyper-threading, our implementations achieve orders of magnitude speedups over other correlation clustering baselines, and up to 28.44× speedups over our own sequential baselines while maintaining or improving quality.
SlimChain
Authors: Xu Cheng , Zhang Ce , Xu Jianliang , Pei Jian
Published: October 2021

Blockchain technology has emerged as the cornerstone of many decentralized applications operating among otherwise untrusted peers. However, it is well known that existing blockchain systems do not scale well. Transactions are often executed and committed sequentially in order to maintain the same view of the total order. Furthermore, it is necessary to duplicate both transaction data and their executions in every node in the blockchain network for integrity assurance. Such storage and computation requirements put significant burdens on the blockchain system, not only limiting system scalability but also undermining system security and robustness by making the network more centralized. To tackle these problems, in this paper, we propose SlimChain, a novel blockchain system that scales transactions through off-chain storage and parallel processing. Advocating a stateless design, SlimChain maintains only the short commitments of ledger states on-chain while dedicating transaction executions and data storage to off-chain nodes. To realize SlimChain, we propose new schemes for off-chain smart contract execution, on-chain transaction validation, and state commitment. We also propose optimizations to reduce network transmissions and a new sharding technique to improve system scalability further. Extensive experiments are conducted to validate the performance of the proposed SlimChain system. Compared with the existing systems, SlimChain reduces the on-chain storage requirements by 97% ~ 99%, while also improving the peak throughput by 1.4× ~ 15.6×.
GRAIN
Authors: Zhang Wentao , Yang Zhi , Wang Yexin , Shen Yu , Li Yang , Wang Liang , Cui Bin
Published: October 2021

Data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets. However, recent deep learning models have moved forward from independent and identically distributed data to graph-structured data, such as social networks, e-commerce user-item graphs, and knowledge graphs. This evolution has led to the emergence of Graph Neural Networks (GNNs) that go beyond the models existing data selection methods are designed for. Therefore, we present GRAIN, an efficient framework that opens up a new perspective through connecting data selection in GNNs with
social influence maximization.
By exploiting the common patterns of GNNs, GRAIN introduces a novel feature propagation concept, a diversified influence maximization objective with novel influence and diversity functions, and a greedy algorithm with an approximation guarantee into a unified framework. Empirical studies on public datasets demonstrate that GRAIN significantly improves both the performance and efficiency of data selection (including active learning and core-set selection) for GNNs. To the best of our knowledge, this is the first attempt to bridge two largely parallel threads of research, data selection, and social influence maximization, in the setting of GNNs, paving new ways for improving data efficiency.
Database technology for the masses
Authors: Bandle Maximilian , Giceva Jana
Published: October 2021

A wealth of technology has evolved around relational databases over decades that has been successfully tried and tested in many settings and use cases. Yet, the majority of it remains overlooked in the pursuit of performance (e.g., NoSQL) or new functionality (e.g., graph data or machine learning). In this paper, we argue that a wide range of techniques readily available in databases are crucial to tackling the challenges the IT industry faces in terms of hardware trends management, growing workloads, and the overall complexity of a rapidly changing application and platform landscape.

However, to be truly useful, these techniques must be freed from the legacy component of database engines: relational operators. Therefore, we argue that to make databases more flexible as platforms and to extend their functionality to new data types and operations requires exposing a lower level of abstraction: instead of working with SQL it would be desirable for database engines to compile, optimize, and run a collection of
sub-operators
for manipulating and managing data, offering them as an external interface. In this paper, we discuss the advantages of this, provide an initial list of such sub-operators, and show how they can be used in practice.