Formal Specification of Non-Functional Properties of Context-Aware Systems

During the recent years, there is an increasing demand for software systems that dynamically adapt their behavior at run-time in response to changes in user preferences, execution environment, and system requirements, being thus context-aware. Authors are referring here to requirements related to both functional and non-functional aspects of system behavior since changes can also be induced by failures or unavailability of parts of the software system itself. To ensure the coherence and correctness of the proposed model, all relevant properties of system entities are precisely and formally described. This is especially true for non-functional properties, such as performance, availability, and security. This article discusses semantic concepts for the specification of non-functional requirements, taking into account the specific needs of a context-aware system. Based on these semantic concepts, we present a specification language that integrates non-functional requirements design and validation in the development process of context-aware self-adaptive systems.

Remote-Sensing Image Tile-Level Annotation Based on Discriminative Features and Expressive Visual Word Descriptors

Remote-sensing images play a crucial role in a wide range of applications and have been receiving significant attention. In recent years, great efforts have been made in developing various methods for intelligent interpretation of remote-sensing images. Generally speaking, machine learning-based methods of remote-sensing image interpretation require a large number of labeled samples and there are still not enough annotated datasets in the field of remote sensing. However, manual annotation of remote-sensing images is usually labor-intensive and requires expert knowledge and the accuracy of annotation results is relatively low. The goal of this paper is to propose a novel tile-level annotation method of remote-sensing images to obtain remote-sensing datasets which are well-labeled and contain accurate semantic concepts. Firstly, we use a set of images with defined semantic concepts to represent the training set and divide them into several nonoverlapping regions. Secondly, the color features, texture features, and spatial features of each region are extracted, and discriminative features are obtained by the weight optimization feature fusion method. Then, the features are quantized into visual words by applying a density-based clustering center selection method and an isolated feature point elimination method. And the remote-sensing images can be represented by a series of visual words. Finally, the LDA model is used to calculate the probabilities of semantic categories for each region. The experiments are conducted on remote-sensing images which demonstrate that our proposed method can achieve good performance on remote-sensing image tile-level annotation. The implications of our research can obtain annotated datasets with accurate semantic concepts for intelligent interpretation of remote-sensing images.

Iranian innovations in mosque lighting techniques: A historical survey

From the viewpoint of preserving the values of sustainable architecture, daylight in the interiors of mosques in hot and dry climates has always faced limitations. The need to use daylight to provide the required lighting and, in contrast, to prevent the scorching desert sun from entering the spaces led to innovative techniques in Iranian architecture. These techniques have gradually evolved along with the development of semantic concepts of space in different periods of Islamic architecture, which has resulted in slight differences in their application in mosque buildings. In this article, while analysing the place of light in mosque architecture, the standard techniques in lighting mosques located in Iran’s hot and dry climate are studied. The employed research method is a combination of qualitative and quantitative approaches. As the current historic-architectural research cannot be based solely on perception-based definitions, authors had to convert the conceptual features into a measurable index. To achieve this, a numerical index with the scale from 0 to 3 has been defined. The scoring was based on documents such as plans, images, etc. Although most case study objects were built over the centuries, they have general characteristics that distinguish them from a specific historical era. The authors studied the application of these techniques in some examples of selected mosques from four periods of Islamic architecture and present the results in the form of trend charts. Furthermore, they observed the principle of continuity in Iranian architecture from the historical period from the beginning of the Islamic period to the Qajar period, and, in accordance with the theoretical foundations of research, analysed the reasons for the ups and downs of each of the techniques.

Interpreting and Comparing Convolutional Neural Networks: A Quantitative Approach

A convolutional neural network (CNN) is sometimes understood as a black box in the sense that while it can approximate any function, studying its structure will not give us any insights into the nature of the function being approximated. In other terms, the discriminative ability does not reveal much about the latent representation of a network. This research aims to establish a framework for interpreting the CNNs by profiling them in terms of interpretable visual concepts and verifying them by means of Integrated Gradient. We also ask the question, "Do different input classes have a relationship or are they unrelated?" For instance, could there be an overlapping set of highly active neurons to identify different classes? Could there be a set of neurons that are useful for one input class whereas misleading for a different one? Intuition answers these questions positively, implying the existence of a structured set of neurons inclined to a particular class. Knowing this structure has significant values; it provides a principled way for identifying redundancies across the classes. Here the interpretability profiling has been done by evaluating the correspondence between individual hidden neurons and a set of human-understandable visual semantic concepts. We also propose an integrated gradient-based class-specific relevance mapping approach that takes the spatial position of the region of interest in the input image. Our relevance score verifies the interpretability scores in terms of neurons tuned to a particular concept/class. Further, we perform network ablation and measure the performance of the network based on our approach.

Elastic CRFs for Open-Ontology Slot Filling

Slot filling is a crucial component in task-oriented dialog systems that is used to parse (user) utterances into semantic concepts called slots. An ontology is defined by the collection of slots and the values that each slot can take. The most widely used practice of treating slot filling as a sequence labeling task suffers from two main drawbacks. First, the ontology is usually pre-defined and fixed and therefore is not able to detect new labels for unseen slots. Second, the one-hot encoding of slot labels ignores the correlations between slots with similar semantics, which makes it difficult to share knowledge learned across different domains. To address these problems, we propose a new model called elastic conditional random field (eCRF), where each slot is represented by the embedding of its natural language description and modeled by a CRF layer. New slot values can be detected by eCRF whenever a language description is available for the slot. In our experiment, we show that eCRFs outperform existing models in both in-domain and cross-domain tasks, especially in predicting unseen slots and values.

The Importance of Being Familiar: The Role of Semantic Knowledge in the Activation of Emotions and Factual Knowledge from Music in the Semantic Variant of Primary Progressive Aphasia

Background: The role of semantic knowledge in emotion recognition remains poorly understood. The semantic variant of primary progressive aphasia (svPPA) is a degenerative disorder characterized by progressive loss of semantic knowledge, while other cognitive abilities remain spared, at least in the early stages of the disease. The syndrome is therefore a reliable clinical model of semantic impairment allowing for testing the propositions made in theoretical models of emotion recognition. Objective: The main goal of this study was to investigate the role of semantic memory in the recognition of basic emotions conveyed by music in individuals with svPPA. Methods: The performance of 9 individuals with svPPA was compared to that of 32 control participants in tasks designed to investigate the ability: a) to differentiate between familiar and non-familiar musical excerpts, b) to associate semantic concepts to musical excerpts, and c) to recognize basic emotions conveyed by music. Results: Results revealed that individuals with svPPA showed preserved abilities to recognize familiar musical excerpts but impaired performance on the two other tasks. Moreover, recognition of basic emotions and association of musical excerpts with semantic concepts was significantly better for familiar than non-familiar musical excerpts in participants with svPPA. Conclusion: Results of this study have important implications for theoretical models of emotion recognition and music processing. They suggest that impairment of semantic memory in svPPA affects both the activation of emotions and factual knowledge from music and that this impairment is modulated by familiarity with musical tunes.

Visions of lexicography of a semantic European

In this essay, it is assumed that the languages of Latin Europe do have many semantic features in common, which contradicts the prevailing view of a general semantic particularity of every individual language and thus the exploitation for national-political purposes arising from that view. However, the proposition made here requires a summary and the assessment of different semantic concepts led by the idea of commonality. By means of individual cases that can be understood as relevant examples, a vision of lexicography will follow that aims at replacing the biologistic concept of a genetic explanation for contrastive semantics by the concept of a comparative semantics that is based on socio-historical, cultural-historcial and textual-historical arguments. In doing so, a historiography relating to the subject-matter of “semantics” will be suggested that assigns a semantic bridging function to Late Antiquity / Early Medieval Latin in relation to all languages of Latin Europe. The logic of the argument implies that a new era of semantic history begins upon the development of a structure of national languages in Europe, whose historical basis can still be recognised in the semantic communalities.

Lexico-semantic meanings of the word Sufi

The article examines the semantic nature of the word “Sufi”, widely used in thehistory of language. The based linguistic materials are differentiated by scientific data. The analysislooks at terminological, religious, linguistic concepts of the word, its semantic use and changes inhistorical periods, lexical and semantic concepts and functions of our language. It is also thefoundations of the formation of the word Sufi in accordance with the norms of public speech, itsentry into circulation, the period of its activity, etc. The definition of the word as a character, theconclusions of those who have completely sacrificed themselves to religion and accepted it as a wayof life, are well presented in the historical data. Periodic and systematic lines of work areconsistently offered. The life principles of those who have glorified and promoted Sufism, paved aparticular path, and formed a school of Sufism are also presented with compelling facts. Theinformation that these famous schools had their own principles, ways, and structure is based on theopinions and conclusions of scholars. In fact, if the cognitive characteristics of the worshipper,worship, action, and piety, which have special significance in human life, are subjected to specialanalysis, the method, purpose, and mission of those who follow this path are considered veryimportant. At the same time, such actions of those who make Sufism their pillar, aim at it, and set asa principle to live only by love to the Creator alone, inevitably requires special study. Therefore, theimportance of this work is reflected here, and special attention is paid to the etymological study ofthe history and semantic nature of the word. The first meaning of the word Sufi, the second name,the official meaning, has been scientifically established. The fact that from the first appearance ofthe word such issues as submission to the Creator's will, directing all thoughts and actions to theCreator, have influenced the history of the word's origin cannot be overlooked. The author revealsthe basic principles and pillars of the word, its concepts, and therefore its status in worship. In aword, the official nature of the word Sufi was defined, which determined the role and place in theformation of values in the spiritual cognition of the peoples of the Kazakh steppes.

A New Approach for Video Concept Detection Based on User Comments

Video concept detection means describing a video with semantic concepts that correspond to the content of the video. The concepts help to retrieve video quickly. These semantic concepts describe high-level elements that depict the key information present in the content. In recent years, many efforts have been done to automate this task because the manual solution is time-consuming. Nowadays, videos come with comments. Therefore, in addition to the content of the videos, the comments should be analyzed because they contain valuable data that help to retrieve videos. This paper focused especially on videos shared on social media. The specificity of these videos was the presence of massive comments. This paper attempted to exploit comments by extracting concepts from them. This would support the research effort that works only on the visual content. Natural language processing techniques were used to analyze comments and to filter words to retain only the ones that could be considered as concepts. The proposed approach was tested on YouTube videos. The results demonstrated that the proposed approach was able to extract accurate data and concepts from the comments that could be used to ease the retrieval of videos. The findings supported the research effort of working on the visual and audio contents of the videos.