Brain Strategy Algorithm for Multiple Object Tracking Based on Merging Semantic Attributes and Appearance Features

The human brain can effortlessly perform vision processes using the visual system, which helps solve multi-object tracking (MOT) problems. However, few algorithms simulate human strategies for solving MOT. Therefore, devising a method that simulates human activity in vision has become a good choice for improving MOT results, especially occlusion. Eight brain strategies have been studied from a cognitive perspective and imitated to build a novel algorithm. Two of these strategies gave our algorithm novel and outstanding results, rescuing saccades and stimulus attributes. First, rescue saccades were imitated by detecting the occlusion state in each frame, representing the critical situation that the human brain saccades toward. Then, stimulus attributes were mimicked by using semantic attributes to reidentify the person in these occlusion states. Our algorithm favourably performs on the MOT17 dataset compared to state-of-the-art trackers. In addition, we created a new dataset of 40,000 images, 190,000 annotations and 4 classes to train the detection model to detect occlusion and semantic attributes. The experimental results demonstrate that our new dataset achieves an outstanding performance on the scaled YOLOv4 detection model by achieving a 0.89 mAP 0.5.

Intuitive Contrasting Map for Antonym Embeddings

This paper shows that modern word embeddings contain information that distinguishes synonyms and antonyms despite small cosine similarities between corresponding vectors. This information is implicitly encoded in the geometry of the embeddings and could be extracted with a straightforward manifold learning procedure or a contrasting map. Such a map is trained on a small labeled subset of the data and can produce new embeddings that explicitly highlight specific semantic attributes of the word. The new embeddings produced by the map are shown to improve the performance on downstream tasks.

Разновидности Текстовой Динамики В Свете Феномена Признаковости В Повести Ф. М. Достоевского «Двойник» (Статья Первая)

В настоящей статье изучаются поэтические принципы и функции двух типов структурно-семантической логики, влияющей на возникновение текстовой динамики в «Двойнике». Парадоксально, что главная линия развития сводится к структуре высокой повторяемости рекуррентных / рекурсивных фигурирований стабильных нарративных единиц, мотивов, лексических элементов текста. Их постоянное возвращение (см. события, характерные черты персонажа, семантические признаки, компоненты устойчивых фразеологических выражений) создает впечатление модели неизменного мира, в котором не происходит никаких смен. Такой феномен можно условно назвать динамикой рекуррентности под знаком статичности. Транспозиционная динамика, с другой стороны, определяется в качестве второго направления текстового развертывания, формирующего механизм порождения смысла. Транспозиция в данной работе понимается в широком смысле, со специальным сосредоточением на 1) трансфигурации и проек\xD1\x86ии смысла путем сдвига от однородности семантических признаков к наделению означающих или означаемых новыми признаками (проблема референтности); 2) процессах семантической интеграции, касающейся включения мелких смысловых единиц в более крупные формации (семантический признак → мотив → литературный персонаж). Цель статьи состоит в выяснении взаимоотношения (своеобразной интеракции) двух типов динамики. Рассматриваемое смысловое образование, воплощаемое в минимальной единице моторной (моторно-словесной) «экспрессии» Голядкина, обладает двойной ориентацией, основанной на корреляции движения и остановки, которую В. B. Виноградов интерпретировал в своей знаменитой работе. С точки зрения объекта изучения в настоящей статье указанная нарративная и лингвистическая единица подвергается толкованию в качестве рекурсивной пары мотивов, обладающей особенной способностью перевоплощаться путем транспозиции. Применением метода «close reading» в пятой главе повести, где появляется двойник господина Голядкина, подробно продемонстрировано, как транспозиция действует в рамках процесса смысловых интеграций. Значение мотива врага претерпевает сдвиг от смысла человеческого врага к смыслу петербургской погоды и человеческой судьбы как новых субъектов. Они ассоциируются с мифологией и литературной культурой (интертексты), подводящими к толкованию личности Голядкина. Образ главного героя проецируется на его двойника многими семантическими признаками. «Тоскливая побежка» в смысле столкновения Голядкина с самим собой представляет его в качестве прозревающего героя, который как л\xD0\xB8тературный персонаж развивается постепенно, включением в этот процесс и образ двойника как транспозицию фигуры, также представляющей собой динамическое смысловое образование. Соотношение Голядкина и его двойника в аспекте проблемы первичности и вторичности поставлено также в контекст семиотики Пирса. The paper examines the poetic principles and functions of two sorts of structural-semantic logic influencing the emergence of textual dynamics in The Double. Paradoxically, the main developmental line consists in a highly repetitive structure of permanent reiteration of fixed narrative units, motifs, and lexical items in the text. Their constant recurrence (cf. events, character traits, semantic attributes, and elements of stable idiomatic phrases) creates the impression of a static world model in which no changes occur. This may be called recursive dynamics under the sign of a static state. Transpositional dynamics, on the other hand, can be regarded as the second direction of the textual developmental movement, producing the mechanism for meaning-generation. Transposition is interpreted broadly, in the paper, with a special accent on 1) meaning transfigurations and projections through the shift from the uniformity of semantic attributes to the acquiring of new attributes by the signifier or the signified (the problem of reference); 2) processes of semantic integration concerning the inclusion of smaller semantic units into larger ones (semantic attribute → motif → character figure). The purpose of the paper is to clarify the reciprocity (the special interaction) of the two types of dynamics. The semantic formation under scrutiny embodied by the smallest unit of Golyadkin’s motoric-verbal expression has a double orientation based on the correlation of motion and stop interpreted by V. Vinogradov in his famous study. From the perspective of the paper’s research object, this narrative and linguistic unit is examined as a recursive motif pair with a special capacity of transformation through transposition. It is demonstrated in detail, within the close reading of the fift h chapter, where Golyadkin’s double appears, how transposition works through a process of semantic integrations. The meaning of enemy is shift ed from human rivals to Petersburg weather and human fate as new subjects, associated with mythology and literary culture (intertexts), leading up to the interpretation of Golyadkin’s personality. The protagonist’s figure is projected upon his double through many semantic attributes. The “melancholy flight” as his encounter with himself presents him as an awakening hero, who as a character is developed gradually, including his double as his figure-transposition which also embodies a dynamic semantic pattern. The correlation of Golyadkin and his double in the context of firstness and secondness is also put into the context of Peircean semiotics.

Detection of Hate Speech and Offensive Language in Twitter Using Sentiment Analysis

The dramatic development of online media, for example, Twitter and local area gatherings has upset correspondence and content distributing, but at the same time is progressively misused for the spread of disdain discourse and the association of disdain based exercises. The secrecy and portability managed by such media has made the rearing and spread of disdain discourse – in the long run prompting disdain wrongdoing – easy in a virtual land scape past the domains of conventional law requirement. Existing techniques in the identification of disdain discourse principally cast the issue as a regulated report grouping task [33]. These can be partitioned into two classifications: one depends on manual element designing that are then devoured by calculations, for example, SVM, Naive Bayes, and Logistic Regression [3, 9, 11, 15, 19, 23, 35–39] (exemplary techniques); the other addresses the later profound learning worldview that utilizes neural organizations to consequently learn multi-facets of dynamic highlights from crude information [13, 26, 30, 34] (profound learning strategies). In this technique We show that it is a significantly more testing task, as our examination of the language in the commonplace datasets shows that disdain discourse needs interesting, discriminative highlights and hence is found in the 'long tail' in a dataset that is hard to find. We then, at that point propose Deep Neural Network structures filling in as highlight extractors that are especially powerful for catching the semantics of disdain discourse. Our techniques are assessed on the biggest assortment of disdain discourse datasets dependent on Twitter, and are demonstrated to have the option to beat best in class by up to 6 rate focuses in large scale normal F1, or 9 rate focuses in the seriously difficult instance of recognizing derisive substance. As an intermediary to evaluate and think about the semantic attributes of disdain and non-disdain Tweets, we additionally propose to contemplate the 'uniqueness' of the jargon for each class. Keywords: Classic Methods; DNN; Detection of hate speech and offensive language in Twitter; Sentimental Analysis

The entropy of morphological systems in natural languages is modulated by functional and semantic properties.

In natural languages, morphological systems such as number and gender can encode semantic attributes of referents, like numerosity or animacy. Notwithstanding the salience of such attributes, morphological systems are not structured to unambiguously encode them, both within and across languages.A partial explanation for this relies on the functional facet of morphology, which sustains sentence processing. For instance, in a language marking feminine and masculine grammatical gender values, the occurrence of a feminine determiner allows to rule out non-feminine nouns from possible upcoming competitors. Even though experimental research has acknowledged the role of morphological cues in prediction, it is not clear whether the distribution of words in languages are structured to systematically exploit them.In a study on Italian, we measured the distributions of the nominal lexicon across the morphological features, and found that they are optimized to sustain discrimination and prediction processes. Though, in a subset of the lexicon denoting animate referents, the semantic interpretability of the features significantly alters the distribution, dropping the overall system’s entropy. We discussed these results in the light of current accounts on natural language efficiency.

Precious Metals in the Worldview System of Ancient Iranian-Speaking Peoples on the Texts of Avesta

An overview and contextual-semantic analysis of the cases of usage the lexeme ‘gold’ (zaraniia-) and ‘silver’ (ərəzata-) in corpus of Avestan sources (Yasna, Yashts, Videvdat, Aogəmadaēca, etc.) are provided in the article. ‘Gold’ is used in the Avesta 101 times. ‘Silver’ — only 12 uses, while this metal is always contextually linked with gold. Silver has a semantic connection with the aquatic sphere. Gold is the material from which, according to the texts, the garments of several deities are made (Vayu, Aredvi Sura (partially)). Gold attributes or decorated with this metal tools have a number of gods and heroes (Yima, Mithra, Verethragna, Tishtria, Sraosha) are made completely or partially from it. In the myth of Yima, the divine instruments, the golden suβrā and gilded aštrā, are endowed with reproductive features and help to create the first kingdom (Videvdat 2.6—38). This ideal mythical world turns into the Afterworld. A set of semantic attributes show that gold is directly related to the Otherworld, where the souls of the righteous deceased receive gold places, golden or silver clothes (Videvdat 19.31—32; Aog. 12, 17). The fact that silver and other «colors» are added to gold may indicate the expansion of the spectrum of precious metals and their penetration into the sphere of funeral beliefs. In the treatise of Aogəmadaēca (84), silver-gold (a metaphor of wealth), along with cattle, horses and bravery, is included in the system of concepts related to the Thanatological worldview of the pre-Zoroastrian representations of ancient Iranian tribes.

Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes With Applications to Anomaly Detection

Our work focuses on unsupervised and generative methods that address the following goals: (1) learning unsupervised generative representations that discover latent factors controlling image semantic attributes, (2) studying how this ability to control attributes formally relates to the issue of latent factor disentanglement, clarifying related but dissimilar concepts that had been confounded in the past, and (3) developing anomaly detection methods that leverage representations learned in the first goal. For goal 1, we propose a network architecture that exploits the combination of multiscale generative models with mutual information (MI) maximization. For goal 2, we derive an analytical result, lemma 1 , that brings clarity to two related but distinct concepts: the ability of generative networks to control semantic attributes of images they generate, resulting from MI maximization, and the ability to disentangle latent space representations, obtained via total correlation minimization. More specifically, we demonstrate that maximizing semantic attribute control encourages disentanglement of latent factors. Using lemma 1 and adopting MI in our loss function, we then show empirically that for image generation tasks, the proposed approach exhibits superior performance as measured in the quality and disentanglement of the generated images when compared to other state-of-the-art methods, with quality assessed via the Fréchet inception distance (FID) and disentanglement via mutual information gap. For goal 3, we design several systems for anomaly detection exploiting representations learned in goal 1 and demonstrate their performance benefits when compared to state-of-the-art generative and discriminative algorithms. Our contributions in representation learning have potential applications in addressing other important problems in computer vision, such as bias and privacy in AI.

Dual discriminative auto-encoder network for zero shot image recognition

Zero Shot learning (ZSL) aims to use the information of seen classes to recognize unseen classes, which is achieved by transferring knowledge of the seen classes from the semantic embeddings. Since the domains of the seen and unseen classes do not overlap, most ZSL algorithms often suffer from domain shift problem. In this paper, we propose a Dual Discriminative Auto-encoder Network (DDANet), in which visual features and semantic attributes are self-encoded by using the high dimensional latent space instead of the feature space or the low dimensional semantic space. In the embedded latent space, the features are projected to both preserve their original semantic meanings and have discriminative characteristics, which are realized by applying dual semantic auto-encoder and discriminative feature embedding strategy. Moreover, the cross modal reconstruction is applied to obtain interactive information. Extensive experiments are conducted on four popular datasets and the results demonstrate the superiority of this method.