152 research outputs found
Astragalus wui M. Idrees & Z. Y. Zhang 2021, nom. nov.
No full textAstragalus wui M. Idrees & Z.Y. Zhang, nom. nov. Replaced name:— Astragalus sylvaticus Y.H. Wu (2015: 718), nom. illeg., non A. sylvaticus (Pall.) Willd. (1802: 1300). Type:— CHINA. Xinjiang: Yecheng Country, Sukepiya, in border forest, alt. 3000 m, 15 Aug. 1987, Exped. Qinghai-Tibet Wu Yuhu 1067 (holotype: QTPMB, not seen). Etymology:—The specific epithet honours Prof. Dr. Wu Yuhu (Northwest Institute of Plateau Biology, Chinese Academy of Science, Xining, China), author of the replaced name, who first described this new species.Published as part of Idrees, Muhammad & Zhang, Zhiyong, 2021, Astragalus wui, a new replacement name for A. sylvaticus Y. H. Wu (Galegeae, Papilionoideae, Fabaceae), pp. 210-211 in Phytotaxa 524 (3) on page 210, DOI: 10.11646/phytotaxa.524.3.6, http://zenodo.org/record/564936
Salix diazii M. Idrees & J. M. H. Shaw 1015, nom. nov.
No full textSalix × diazii M. Idrees & J.M.H. Shaw, nom. nov. Replaced name:— Salix × longissima T.E.Díaz & J.Andrés (1987: 132), nom. illeg., non S. longissima P.Wessel (1855: 140). Type:— SPAIN. Léon Province: La Martina, 490 m, 29TPH91, 16 June 1985, T.E. Díaz et al. s.n. (holotype, LEB29538!, isotypes LEB29539!, LEB30605!). Etymology:— The specific epithet honours Prof. Dr. Tomás Emilio Díaz González (University of Oviedo, Oviedo, Spain), author of the replaced name, for his tremendous contributions to the taxonomy of the genus Salix. Distribution:— Spain, Léon Province (La Martina).Published as part of Idrees, Muhammad & Shaw, Julian M. H., 2022, A new name for extant Salix × longissima T. E. Díaz & J. Andrés (Salicaceae), pp. 213-214 in Phytotaxa 550 (2) on page 213, DOI: 10.11646/phytotaxa.550.2.11, http://zenodo.org/record/664103
Large-Scale Image Geo-Localization Using Dominant Sets
No full textThis paper presents a new approach for the challenging problem of geo-localization using image matching in a structured database of city-wide reference images with known GPS coordinates. We cast the geo-localization as a clustering problem of local image features. Akin to existing approaches to the problem, our framework builds on low-level features which allow local matching between images. For each local feature in the query image, we find its approximate nearest neighbors in the reference set. Next, we cluster the features from reference images using Dominant Set clustering, which affords several advantages over existing approaches. First, it permits variable number of nodes in the cluster, which we use to dynamically select the number of nearest neighbors for each query feature based on its discrimination value. Second, this approach is several orders of magnitude faster than existing approaches. Thus, we obtain multiple clusters (different local maximizers) and obtain a robust final solution to the problem using multiple weak solutions through constrained Dominant Set clustering on global image features, where we enforce the constraint that the query image must be included in the cluster. This second level of clustering also bypasses heuristic approaches to voting and selecting the reference image that matches to the query. We evaluate the proposed framework on an existing dataset of 102k street view images as well as a new larger dataset of 300k images, and show that it outperforms the state-of-the-art by 20 and 7 percent, respectively, on the two datasets
Classification of library materials on Islam: a literature survey
No full textPurposeThe purpose of this paper is to develop understanding of the problems of classification, to discover the classification practices of libraries with rich collections on Islam cited in the literature, to find the gaps, and to determine the point from which to start work on further development.Design/methodology/approachPublished and unpublished literature, both print and electronic, that is relevant to the problem was reviewed objectively in the compilation of this paper.FindingsStandard classification systems lack proper space for materials on Islam for two reasons: less awareness on the part of devisers of the depth and variety of Islamic topics; and their bias and lack of interest in Islam. Different indigenous classification systems and expansions have been developed, using either the original notation or alternative notations. Some systems have been developed without following any standards or logic. This study has revealed a need for empirical study of libraries with rich collections on Islam in order to gain a better understanding of the problem and find an optimal solution.Research limitations/implicationsNo empirical field data are included in this study. This is a review of the literature.Originality/valueThe author indicates the current situation of the problem and a potential framework for its solution.</jats:sec
Library Classification Systems and Organization of Islamic Knowledge
No full textStandard library classification systems like Dewey Decimal Classification (DDC), U.S. Library of Congress Classification (LCC), and Universal Decimal Classification (UDC) are internationally known and widely used by libraries as the tools for organizing information. Charles Ammi Cutter’s Expansive Classification (EC), James Duff Brown’s Subject Classification (SC), Henry E. Bliss’ Bibliographic Classification (BC), and S. R. Ranganathan’s Colon Classification (CC) also are standard classification systems, but they are less commonly used compared to aforementioned three systems. All these systems are easy to use and convenient for most general collection libraries. However, these systems are not adequate for some special collections. Libraries with rich collections on Islam also face problems while using these systems, although such libraries often use expansions in the original systems for their collections. This paper examines this problem and presents a potential optimal solution. The author collected data, using a semistructured interview technique, from a representative sample of thirty libraries in eight countries with strong collections in Islam. These data were analyzed employing qualitative methods
Technical Staff Positions and Technology related Tasks: A Study of University Libraries in Pakistan
No full textThis study investigated the current state of technical staff positions in the central libraries of leading universities in Pakistan. Multiple quantitative methods were adopted to carry out this study. Quantitative data about technical staff positions were retrieved from organizational documents such as service structure documents, organizational charts and budgetary documents. Principal author carried out quantitative observations and informal discussions with library executives in order to measure the levels of working of technical staff positions and alternatives adopted by libraries to carry out technical tasks. This study found that fifty percent of the libraries have not created any technical staff positions and automation related tasks are being performed by library professionals with the cooperation of vendors who have provided the library software. Of the nine positions for technical staff sanctioned by the libraries, only two were found to have been filled. Centralized mode of operation has been adopted for technical staff personnel. Only one library has given the additional charge of section head to the technology expert who is also managing technical tasks at organizational level
Needs of Libraries with Rich Islamic Collections: Classification Dilemma and Optimal Solution for Islamic Knowledge
No full textStandard library classification systems (e.g., DDC, LCC, UDC) are convenient information organization tools. Libraries with rich collections on Islam face problems; hence, use expansions/indigenous systems. This poster examines this problem and presents a potential optimal solution, based on data from 30 libraries and 16 LIS Scholars from nine countries.Les systèmes de classification documentaires standards (p. ex. DDC, LCC, UDC) sont des outils d’organisation de l’information pratiques. Les bibliothèques avec de riches collections sur l’Islam font face à des problèmes et elles doivent utiliser des systèmes complémentaires ou ad hoc. Cette affiche examine ce problème et présente une solution optimale potentielle, basée sur les données de 30 bibliothèques et 16 chercheurs en BSI de neuf pays
Visual Analysis of Extremely Dense Crowded Scenes
No full textVisual analysis of dense crowds is particularly challenging due to large number of individuals, occlusions, clutter, and fewer pixels per person which rarely occur in ordinary surveillance scenarios. This dissertation aims to address these challenges in images and videos of extremely dense crowds containing hundreds to thousands of humans. The goal is to tackle the fundamental problems of counting, detecting and tracking people in such images and videos using visual and contextual cues that are automatically derived from the crowded scenes. For counting in an image of extremely dense crowd, we propose to leverage multiple sources of information to compute an estimate of the number of individuals present in the image. Our approach relies on sources such as low confidence head detections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region. Furthermore, we employ a global consistency constraint on counts using Markov Random Field which caters for disparity in counts in local neighborhoods and across scales. We tested this approach on crowd images with the head counts ranging from 94 to 4543 and obtained encouraging results. Through this approach, we are able to count people in images of high-density crowds unlike previous methods which are only applicable to videos of low to medium density crowded scenes. However, the counting procedure just outputs a single number for a large patch or an entire image. With just the counts, it becomes difficult to measure the counting error for a query image with unknown number of people. For this, we propose to localize humans by finding repetitive patterns in the crowd image. Starting with detections from an underlying head detector, we correlate them within the image after their selection through several criteria: in a pre-defined grid, locally, or at multiple scales by automatically finding the patches that are most representative of recurring patterns in the crowd image. Finally, the set of generated hypotheses is selected using binary integer quadratic programming with Special Ordered Set (SOS) Type 1 constraints. Human Detection is another important problem in the analysis of crowded scenes where the goal is to place a bounding box on visible parts of individuals. Primarily applicable to images depicting medium to high density crowds containing several hundred humans, it is a crucial pre-requisite for many other visual tasks, such as tracking, action recognition or detection of anomalous behaviors, exhibited by individuals in a dense crowd. For detecting humans, we explore context in dense crowds in the form of locally-consistent scale prior which captures the similarity in scale in local neighborhoods with smooth variation over the image. Using the scale and confidence of detections obtained from an underlying human detector, we infer scale and confidence priors using Markov Random Field. In an iterative mechanism, the confidences of detections are modified to reflect consistency with the inferred priors, and the priors are updated based on the new detections. The final set of detections obtained are then reasoned for occlusion using Binary Integer Programming where overlaps and relations between parts of individuals are encoded as linear constraints. Both human detection and occlusion reasoning in this approach are solved with local neighbor-dependent constraints, thereby respecting the inter-dependence between individuals characteristic to dense crowd analysis. In addition, we propose a mechanism to detect different combinations of body parts without requiring annotations for individual combinations. Once human detection and localization is performed, we then use it for tracking people in dense crowds. Similar to the use of context as scale prior for human detection, we exploit it in the form of motion concurrence for tracking individuals in dense crowds. The proposed method for tracking provides an alternative and complementary approach to methods that require modeling of crowd flow. Simultaneously, it is less likely to fail in the case of dynamic crowd flows and anomalies by minimally relying on previous frames. The approach begins with the automatic identification of prominent individuals from the crowd that are easy to track. Then, we use Neighborhood Motion Concurrence to model the behavior of individuals in a dense crowd, this predicts the position of an individual based on the motion of its neighbors. When the individual moves with the crowd flow, we use Neighborhood Motion Concurrence to predict motion while leveraging five-frame instantaneous flow in case of dynamically changing flow and anomalies. All these aspects are then embedded in a framework which imposes hierarchy on the order in which positions of individuals are updated. The results are reported on eight sequences of medium to high density crowds and our approach performs on par with existing approaches without learning or modeling patterns of crowd flow. We experimentally demonstrate the efficacy and reliability of our algorithms by quantifying the performance of counting, localization, as well as human detection and tracking on new and challenging datasets containing hundreds to thousands of humans in a given scene
Development of a classification scheme for Islam
No full textDie Bibliotheken, die reichen Sammlungen über den Islam haben, stehen vor den Problemen der Organisation des Wissens während Klassifizierung von Materialien über den Islam. Denn die Standard-Klassifikationssysteme wie DDC-Sachgruppe, Library of Congress Klassifikation, Universal Decimal Klassifikation, Bliss Klassifikation, und Colon Klassifikation, die durch eine große Anzahl von Bibliotheken verwendet werden, nicht genügend Platz und angemessene Zählen oder Hierarchie für islamischen Wissens haben. Anschließend, verschiedene alternative Lösungen wurden von Gelehrten sortiert und von diesen Bibliotheken adoptiert. Diese Lösungen umfassen Erweiterungen im Standard-Klassifikationssysteme mit unterschiedlichen Ansätzen und indigenen Klassifikationssysteme für den Islam gemacht. Trotz dieser Lösungen, die Bibliotheken hatten Probleme und sind immer noch unzufrieden. Dieses Forschungsprojekt untersucht dieses Problem. Es hat die Literatur zu diesem Thema bewertet, und empirischer Daten gesammelt, die im Bibliotheks-und Informationswissenschaft (LIS) Bereich in neun verschiedenen Ländern der Welt relevant sind. Die Forschung verwendet Interviews (persönlich oder per Telefon) als wichtigstes Instrument für die Datenerfassung. Die Durchsicht der Literatur und die Analyse der empirischen Daten bestätigte die Existenz des Problems und die Unzufriedenheit der Bibliotheken. Folglich, die optimale Lösung dass in der Literatur und von recherchierten Bevölkerung angegeben ist, wurde vorgebracht worden in diese Forschung: Entwicklung einer unabhängigen und umfassenden Klassifikationssystem für islamischen Wissens. Dieses System wurde von den Gelehrten der LIS und Islamische Studien überprüft worden.The Libraries that have rich collections on Islam are facing the problems of knowledge organization while classifying materials on Islam. This is because the standard classification systems like Dewey Decimal Classification, Library of Congress Classification, Universal Decimal Classification, Bliss Classification, and Colon Classification, which are used by a huge number of libraries, have not provided with sufficient place and proper enumeration or hierarchy for Islamic knowledge. Subsequently, different alternative solutions have been sorted by scholars and opted by these libraries. These solutions include expansions made in standard clas-sification systems with different approaches and indigenous classification systems for Islam. Despite these solutions, the libraries had problems and were not satisfied. This study has addressed this problem, reviewing litera-ture on the problem and collecting empirical data from relevant libraries and scholars of Library and Information Science (LIS) from nine different countries of the world, using interviewing as data collection instrument. Findings of literature and empirical data confirmed the existence of prob-lem and dissatisfaction of libraries. Consequently, the optimal solution in-dicated in literature and by researched population has been brought for-ward in this research, i.e., development of an independent and comprehensive classification system for Islamic knowledge. This system has been got verified by the scholars of LIS and of Islamic Studies
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