1,721,056 research outputs found
Nikos Nikolaidis de Chypre
Full text linkAlthough Nikos Nikolaidis, the Cypriot (1884-1956), was in his time acclaimed as a master short-story writer and a distinguished painter, he often in his older age complained to be neglected, if not forgotten, by critics his peers and the general public. My research proves the contrary. Nikolaidis was admitted to the Pantheon of Modern Greek literature already during his lifetime and his literary work continues to be published, discussed, translated and honoured in many countries.As I hope to have shown in this paper, if Nikolaidis is still remembered it is not only because he was an excellent writer but equally because of his personality, the kindness, integrity and noblesse of his character, all qualities that permeated his writings, his paintings and his attitude towards all human beings.Nikos Nikolaidis de Chypre (1884-1956) fût tôt dans sa carrière salué comme un maître du récit et un excellent peintre. Pourtant il se plaignait souvent d’être négligé, sinon oublié, des critiques, de ses pairs et du public en général. Ma recherche, cependant prouve le contraire. Nikolaidis, fut admis au Panthéon de la littérature néo-hellénique déjà de son vivant et ses ouvrages continuent toujours d’être publiés, traduits, étudiés et honorés dans nombre de pays.Comme j’espère avoir démontré dans mon article, si Nikolaidis a laissé son souvenir ce n’est pas uniquement parce qu’il était un excellent écrivain mais également à cause de sa personnalité, la gentillesse, l’intégrité et la noblesse de son caractère, qualités qui se reflétaient aussi bien dans ses écrits et ses tableaux que dans ses rapports avec tous les hommes
The cinematic work of Nikos Nikolaidis and female representation
Full text linkThis thesis examines the work of Greek postmodern filmmaker Nikos Nikolaidis with a specific focus on female representation. I examine Nikolaidis as an auteur and I trace elements throughout his oeuvre that contribute to the formation of his authorial signature. Nikolaidis’s work is autobiographical and highly political. Nikolaidis’s cinema does not abide by the traditional theories of ‘Greekness’, and his main influences are American cinema, and specifically for film noir, rock ‘n’ roll culture and his antiauthoritarian ideology. All these elements are combined together within his work through the use of pastiche.
I examine Nikolaidis’s work according to Richard Dyer’s notion of pastiche. Through pastiche he expresses nostalgia for rock ‘n’ roll culture and film noir, but also he expresses his concern for the future. Nikolaidis pastiches a selection of film genres and specific films in order to appropriate the elements that interest him. His pastiche work shows that the filmmaker addresses cineliterate audiences that would ideally understand his dialogue with the different genres and films he pastiches.
With regards to female representation in Nikolaidis’s films, women are given leading roles, exhibit varying degrees of agency, and are presented as stronger and more powerful than men. However, their representations remain paradoxical, complex and misogynistic. While on the one hand, women are portrayed as powerful, independent, and able to subvert patriarchy, on the other hand, they are often used as props, rendering their representation inconsistent and problematic. Nikolaidis differentiates and juxtaposes two types of women throughout his work: the powerful women versus the unimportant women. Those who do not conform to the powerful female characteristics are characterised within the second category. Since Nikolaidis was highly influenced by film noir, his female protagonists pastiche the classic film noir figure of the femme fatale
Decadence and the Necrophilic Intertext of Film Noir: Nikos Nikolaidis’ Singapore Sling
Full text linkAn erudite, inveterate cineaste, dedicated auteur and provocateur, Nikos Nikolaidis (1939–2007) became one of the most distinctive and uncompromising voices of Greek and world cinema. Singapore Sling: Ο Άνθρωπος που Αγάπησε ένα Πτώμα [Singapore Sling: The Man Who Loved a Corpse] (1990) is his chef d’œuvre, a bold, independent film that has acquired cult status internationally. The film is an elitist shocker that, as the exotic cocktail of its title suggests, blends genres and styles: black comedy, Grand Guignol, splatter horror, Gothic melodrama, tragedy, and, most of all, film noir. In fact, classic film noir is not only referenced but is the very skin that gives form and shape to Singapore Sling. This is a film whose narrative and visual motifs rely on allusions to other films. Shot in lush black and white, it is a quasi-prequel and tempestuous cinematic love letter of sorts to Otto Preminger’s Laura (1944) that also gestures towards Billy Wilder’s Sunset Boulevard (1950). Its exquisitely photographed, polished, and highly baroque mise en scène is replete with heavy furnishings, bibelots, objets d’art, vintage costumes, fabrics, and ostentatious jewellery. Its materiality blends with a fetishistic presentation of the female body in gorgeous, tactile textures and a geometry of dramatic contours. Its ambience of Gothic luxury and decay recalls Norma Desmond’s mansion in Sunset Boulevard and even Paul Mangin’s mansion in Terence Young’s debut feature, the noirish Gothic melodrama, Corridor of Mirrors (1948)
ActiveFace
No full text<h2><strong>Dataset Description</strong></h2><p>ActiveFace is a synthetic face image dataset was generated using Unity's Perception package. </p><p>It consists of 175428 face images taken at different environments, lighting conditions, camera distances and angles. In total, the dataset contains images for 8 environments, 33 humans, 4 lighting conditions, 7 camera distances (1m-4m) and 36 camera angles (0-360 at 10-degree intervals).</p><p>The dataset does not include images at every single combination of available camera distances and angles, since for some values the camera would collide with another object or go outside the confines of an environment. As a result, some combinations of camera distances and angles do not exist in the dataset.</p><h2><strong>How to Download</strong></h2><p>You can download the dataset <a href="https://cicloud.csd.auth.gr/owncloud/s/OG6Bkgf9Hn5kpT9">here</a>.</p><h2><strong>Folder Configuration</strong></h2><p>The dataset consists of 33 main folders each one containing all the face images for one human. Each main folder consists of 32 subfolders, each one containing that person's face images for one combination of environment and lighting condition. Each subfolder is named "x_y", where "x" denotes the id of the environment and "y" denotes the id of the lighting condition.</p><h2><strong>Naming Conventions</strong></h2><p>Each image is named "e_h_l_d_r.jpg", where:</p><ul><li>e denotes the id of the environment.</li><li>h denotes the id of the person.</li><li>l denotes the id of the lighting condition.</li><li>d denotes the camera distance at which the image was captured.</li><li>r denotes the camera angle at which the image was captured.</li></ul><p>You can download the dataset <a href="https://cicloud.csd.auth.gr/owncloud/s/OG6Bkgf9Hn5kpT9">here</a>.</p>
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
ActiveHuman Part 2
No full text<p><strong>This is Part 2/2 of the ActiveHuman dataset! Part 1 can be found </strong><a href="https://zenodo.org/record/8359766"><strong>here</strong></a><strong>.</strong></p><p><strong>Dataset Description</strong></p><p>ActiveHuman was generated using Unity's Perception package.</p><p>It consists of 175428 RGB images and their semantic segmentation counterparts taken at different <strong>environments</strong>, <strong>lighting conditions</strong>, <strong>camera distances</strong> and <strong>angles</strong>. In total, the dataset contains images for 8 environments, 33 humans, 4 lighting conditions, 7 camera distances (1m-4m) and 36 camera angles (0-360 at 10-degree intervals).</p><p>The dataset does not include images at every single combination of available camera distances and angles, since for some values the camera would collide with another object or go outside the confines of an environment. As a result, some combinations of camera distances and angles do not exist in the dataset.</p><p>Alongside each image, <strong>2D Bounding Box</strong>, <strong>3D Bounding Box</strong> and <strong>Keypoint</strong> ground truth annotations are also generated via the use of Labelers and are stored as a JSON-based dataset. These Labelers are scripts that are responsible for capturing ground truth annotations for each captured image or frame. Keypoint annotations follow the COCO format defined by the COCO keypoint annotation template offered in the perception package.</p><p> </p><p><strong>Folder configuration</strong></p><p>The dataset consists of 3 folders:</p><ul><li><strong>JSON Data</strong>: Contains all the generated JSON files.</li><li><strong>RGB Images</strong>: Contains the generated RGB images.</li><li><strong>Semantic Segmentation Images</strong>: Contains the generated semantic segmentation images.</li></ul><p> </p><p><strong>Essential Terminology</strong></p><ul><li><strong>Annotation</strong>: Recorded data describing a single capture.</li><li><strong>Capture</strong>: One completed rendering process of a Unity sensor which stored the rendered result to data files (e.g. PNG, JPG, etc.).</li><li><strong>Ego</strong>: Object or person on which a collection of sensors is attached to (e.g., if a drone has a camera attached to it, the drone would be the ego and the camera would be the sensor).</li><li><strong>Ego coordinate system</strong>: Coordinates with respect to the ego.</li><li><strong>Global coordinate system</strong>: Coordinates with respect to the global origin in Unity.</li><li><strong>Sensor</strong>: Device that captures the dataset (in this instance the sensor is a camera).</li><li><strong>Sensor coordinate system</strong>: Coordinates with respect to the sensor.</li><li><strong>Sequence</strong>: Time-ordered series of captures. This is very useful for video capture where the time-order relationship of two captures is vital.</li><li><strong>UIID</strong>: Universal Unique Identifier. It is a unique hexadecimal identifier that can represent an individual instance of a capture, ego, sensor, annotation, labeled object or keypoint, or keypoint template.</li></ul><p> </p><p><strong>Dataset Data</strong></p><p>The dataset includes 4 types of JSON annotation files files:</p><ul><li><strong>annotation_definitions.json</strong>: Contains annotation definitions for all of the active Labelers of the simulation stored in an array. Each entry consists of a collection of key-value pairs which describe a particular type of annotation and contain information about that specific annotation describing how its data should be mapped back to labels or objects in the scene. Each entry contains the following key-value pairs:<ul><li><strong>id</strong>: Integer identifier of the annotation's definition.</li><li><strong>name</strong>: Annotation name (e.g., keypoints, bounding box, bounding box 3D, semantic segmentation).</li><li><strong>description</strong>: Description of the annotation's specifications.</li><li><strong>format</strong>: Format of the file containing the annotation specifications (e.g., json, PNG).</li><li><strong>spec</strong>: Format-specific specifications for the annotation values generated by each Labeler.</li></ul></li></ul><p> </p><p>Most Labelers generate different annotation specifications in the spec key-value pair:</p><ul><li><strong>BoundingBox2DLabeler/BoundingBox3DLabeler</strong>:<ul><li><strong>label_id</strong>: Integer identifier of a label.</li><li><strong>label_name</strong>: String identifier of a label.</li></ul></li><li><strong>KeypointLabeler</strong>:<ul><li><strong>template_id</strong>: Keypoint template UUID.</li><li><strong>template_name</strong>: Name of the keypoint template.</li><li><strong>key_points</strong>: Array containing all the joints defined by the keypoint template. This array includes the key-value pairs:<ul><li><strong>label</strong>: Joint label.</li><li><strong>index</strong>: Joint index.</li><li><strong>color</strong>: RGBA values of the keypoint.</li><li><strong>color_code</strong>: Hex color code of the keypoint</li></ul></li><li><strong>skeleton</strong>: Array containing all the skeleton connections defined by the keypoint template. Each skeleton connection defines a connection between two different joints. This array includes the key-value pairs:<ul><li><strong>label1</strong>: Label of the first joint.</li><li><strong>label2</strong>: Label of the second joint.</li><li><strong>joint1</strong>: Index of the first joint.</li><li><strong>joint2</strong>: Index of the second joint.</li><li><strong>color</strong>: RGBA values of the connection.</li><li><strong>color_code</strong>: Hex color code of the connection.</li></ul></li></ul></li><li><strong>SemanticSegmentationLabeler</strong>:<ul><li><strong>label_name</strong>: String identifier of a label.</li><li><strong>pixel_value</strong>: RGBA values of the label.</li><li><strong>color_code</strong>: Hex color code of the label.</li></ul></li></ul><p> </p><ul><li><strong>captures_xyz.json</strong>: Each of these files contains an array of ground truth annotations generated by each active Labeler for each capture separately, as well as extra metadata that describe the state of each active sensor that is present in the scene. Each array entry in the contains the following key-value pairs:<ul><li><strong>id</strong>: UUID of the capture.</li><li><strong>sequence_id</strong>: UUID of the sequence.</li><li><strong>step</strong>: Index of the capture within a sequence.</li><li><strong>timestamp</strong>: Timestamp (in ms) since the beginning of a sequence.</li><li><strong>sensor</strong>: Properties of the sensor. This entry contains a collection with the following key-value pairs:<ul><li><strong>sensor_id</strong>: Sensor UUID.</li><li><strong>ego_id</strong>: Ego UUID.</li><li><strong>modality</strong>: Modality of the sensor (e.g., camera, radar).</li><li><strong>translation</strong>: 3D vector that describes the sensor's position (in meters) with respect to the global coordinate system.</li><li><strong>rotation</strong>: Quaternion variable that describes the sensor's orientation with respect to the ego coordinate system.</li><li><strong>camera_intrinsic</strong>: matrix containing (if it exists) the camera's intrinsic calibration.</li><li><strong>projection</strong>: Projection type used by the camera (e.g., orthographic, perspective).</li></ul></li><li><strong>ego</strong>: Attributes of the ego. This entry contains a collection with the following key-value pairs:<ul><li><strong>ego_id</strong>: Ego UUID.</li><li><strong>translation</strong>: 3D vector that describes the ego's position (in meters) with respect to the global coordinate system.</li><li><strong>rotation</strong>: Quaternion variable containing the ego's orientation.</li><li><strong>velocity</strong>: 3D vector containing the ego's velocity (in meters per second).</li><li><strong>acceleration</strong>: 3D vector containing the ego's acceleration (in ).</li></ul></li><li><strong>format</strong>: Format of the file captured by the sensor (e.g., PNG, JPG).</li><li><strong>annotations</strong>: Key-value pair collections, one for each active Labeler. These key-value pairs are as follows:<ul><li><strong>id</strong>: Annotation UUID .</li><li><strong>annotation_definition</strong>: Integer identifier of the annotation's definition.</li><li><strong>filename</strong>: Name of the file generated by the Labeler. This entry is only present for Labelers that generate an image.</li><li><strong>values</strong>: List of key-value pairs containing annotation data for the current Labeler.</li></ul></li></ul></li></ul><p> </p><p>Each Labeler generates different annotation specifications in the <strong>values</strong> key-value pair:</p><ul><li><strong>BoundingBox2DLabeler</strong>:<ul><li><strong>label_id</strong>: Integer identifier of a label.</li><li><strong>label_name</strong>: String identifier of a label.</li><li><strong>instance_id</strong>: UUID of one instance of an object. Each object with the same label that is visible on the same capture has different <strong>instance_id</strong> values.</li><li><strong>x</strong>: Position of the 2D bounding box on the X axis.</li><li><strong>y</strong>: Position of the 2D bounding box position on the Y axis.</li><li><strong>width</strong>: Width of the 2D bounding box.</li><li><strong>height</strong>: Height of the 2D bounding box.</li></ul></li><li><strong>BoundingBox3DLabeler</strong>:<ul><li><strong>label_id</strong>: Integer identifier of a label.</li><li><strong>label_name</strong>: String identifier of a label.</li><li><strong>instance_id</strong>: UUID of one instance of an object. Each object with the same label that is visible on the same capture has different <strong>instance_id</strong> values.</li><li><strong>translation</strong>: 3D vector containing the location of the center of the 3D bounding box with respect to the sensor coordinate system (in meters).</li><li><strong>size</strong>: 3D vector containing the size of the 3D bounding box (in meters)</li><li><strong>rotation</strong>: Quaternion variable containing the orientation of the 3D bounding box.</li><li><strong>velocity</strong>: 3D vector containing the velocity of the 3D bounding box (in meters per second).</li><li><strong>acceleration</strong>: 3D vector containing the acceleration of the 3D bounding box acceleration (in ).</li></ul></li><li><strong>KeypointLabeler</strong>:<ul><li><strong>label_id</strong>: Integer identifier of a label.</li><li><strong>instance_id</strong>: UUID of one instance of a joint. Keypoints with the same joint label that are visible on the same capture have different <strong>instance_id</strong> values.</li><li><strong>template_id</strong>: UUID of the keypoint template.</li><li><strong>pose</strong>: Pose label for that particular capture.</li><li><strong>keypoints</strong>: Array containing the properties of each keypoint. Each keypoint that exists in the keypoint template file is one element of the array. Each entry's contents have as follows:<ul><li><strong>index</strong>: Index of the keypoint in the keypoint template file.</li><li><strong>x</strong>: Pixel coordinates of the keypoint on the X axis.</li><li><strong>y</strong>: Pixel coordinates of the keypoint on the Y axis.</li><li>state: State of the keypoint.</li></ul></li></ul></li></ul><p> </p><p>The SemanticSegmentationLabeler does not contain a <strong>values</strong> list.</p><ul><li><strong>egos.json</strong>: Contains collections of key-value pairs for each ego. These include:<ul><li><strong>id</strong>: UUID of the ego.</li><li><strong>description</strong>: Description of the ego.</li></ul></li><li><strong>sensors.json</strong>: Contains collections of key-value pairs for all sensors of the simulation. These include:<ul><li><strong>id</strong>: UUID of the sensor.</li><li><strong>ego_id</strong>: UUID of the ego on which the sensor is attached.</li><li><strong>modality</strong>: Modality of the sensor (e.g., camera, radar, sonar).</li><li><strong>description</strong>: Description of the sensor (e.g., camera, radar).</li></ul></li></ul><p> </p><p><strong>Image names</strong></p><p>The RGB and semantic segmentation images share the same image naming convention. However, the semantic segmentation images also contain the string <i>Semantic_</i> at the beginning of their filenames.</p><p>Each RGB image is named "e_h_l_d_r.jpg", where:</p><ul><li><strong>e </strong>denotes the id of the environment.</li><li><strong>h </strong>denotes the id of the person.</li><li><strong>l </strong>denotes the id of the lighting condition.</li><li><strong>d </strong>denotes the camera distance at which the image was captured.</li><li><strong>r </strong>denotes the camera angle at which the image was captured.</li></ul><p>This is Part 2/2 of the ActiveHuman dataset</p>
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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