Representation Scheme Research Articles

Mystery and artistic veil – Widma [Phantoms] by Stanisław Moniuszko and Paweł Passini

The purpose of this paper is to analyse Moniuszko’s Phantoms with a particular focus on the relationships between music and libretto in order to compare them later with modern strategies of performances staged recently in Polish operas and concert halls. The source-historical context referring to cultural and perceptive aspects of Phantoms serves as a starting point. The question is also raised concerning the relationship and fusion of arts constituting that intriguing work. A search for dramaturgic-theatrical aspects of Moniuszko’s work assumes an attempt to capture a so-called ‘intentio operis’, created at the intersection of Mickiewicz’s text and musical intentions of the composer. The analysis was centered on selected aspects of dramaturgy of a few performances of ‘Phantoms’ staged at the Wratislavia Cantans Festival (2017, dir. P. Passini), the Wrocław Opera, (prem. 2018, dir. J. Fret), and the Polish Royal Opera (prem. 2017, dir. R. Peryt). All stagings were an interesting attempt to reinterpret a starting form of cantata bringing it closer to theatrical work. This paper intends to answer the following questions: what dramaturgic and theatrical strategies are generated by Moniuszko’s cantata? To what extent does the cantata nature of this music-textual work contributes to new theatrical interpretations? To what extent are modern stage productions the answer to an artistic potential present in its music-textual layer? Do the new projects of its visualization and dramatisation result, even indirectly, from the structural and staging potential of the work, or whether they attempt to add new artistic ideas in order to make Phantoms more attractive as a starting, incomplete, music-textual structure?

Corporeal Representations of Lenin in Post‑Soviet Ideological Games

This article discusses the significance of the study of Lenin's corporeality in the context of mass and elite culture of the post-Soviet space. The author highlights the importance of understanding the role of Lenin's images in the ideological and political context and suggests analyzing them using theoretical tools. The article also shows that interest in Lenin's images persists in contemporary mass and elite culture, being realized in such strategies of representation of Lenin's bodily aspects as phantasmagoric mystification, “skomoroshchestvo”, annihilation and dehumanization.
 The phantasmagoric strategy of representing the leader of the world proletariat includes attempts to depict Lenin in unusual and paradoxical bodily images, often with elements of fantasy and mystification. The strategy of “skomoroshchestvo” allows researchers and artists to play with Lenin's image and reinterpret it in a comical and absurd way. The strategy of annihilation emphasizes the contrasting views and emotions associated with Lenin's image in the contemporary world and provokes discussions about the boundaries of art, symbolism and respect for historical figures. The strategy of dehumanizing Lenin in the representation of his corporeal image is expressed in transformations that take him away from his human historical context and reduce him to an object of irony, satire and symbolic associations.

Exploring graph representation strategies for text classification

Since 2005, the deep learning community has had access to input graphs to their models. So, the natural language processing (NLP) community started using this technique to process text. However, a challenge that the graph neural networks (GNN) may encounter is the sensibility to representation format. Since different graphs can represent the same text, the model’s performance may change depending on the representation used. Even though many practitioners have this intuition, only some works touch on this aspect of GNN. Therefore, we explore twelve different text representation strategies that build graphs from text and apply them to the same GNN to investigate how different graphs may affect the results. We divide these strategies into four groups: reading order, dependency-based, binary tree, and graph of words. From these groups, we created the binary tree group for this paper. Nevertheless, in our tests, we observed that the dependency-based representations tend to achieve better performance: The dependency-based methods allow us to stay competitive in five relevant datasets and beat the state-of-the-art in another dataset. These results suggest that performing representation tuning can be a valuable technique to improve a deep learning model.

Racial rhetoric in black and white: situational whiteness in Francoist Spanish Guinea through Misión blanca

ABSTRACT This essay analyzes through the interactions of its racially encoded characters under the lens of whiteness. The objective is to locate the representational strategies that Francoist cultural production used to convey moral superiority through racial teachings. The study specifically examines Francoist practices of racial representation to suggest how Spanish whiteness may have been traditionally situational; that is, conceived as a highly rhetorical cultural tactic to assimilate non-whites into the fringes of Spanish whiteness, which in this historical period led to the totalization and silencing of racial dissidence. As a result, the essay will demonstrate how Francoist cinema displayed rhetorical race notions and situational whiteness in Africa to build relationships of moral superiority with regard to Spain’s European counterparts.

Evolving Multi-valued Regulatory Networks on Tunable Fitness Landscapes

Random Boolean networks have been used widely to explore aspects of gene regulatory networks. As the name implies, traditionally the model has used a binary representation scheme. This paper uses a modified form of the model to systematically explore the effects of increasing the number of gene states. These random multi-valued networks are evolved within rugged fitness landscapes to explore their behavior. Results suggest the basic properties of the original model remain, regardless of the update scheme or fitness sampling method. Changes are seen in sensitivity to high levels of connectivity, the mutation rate and the ability to vary network size.

Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach

Most deployed data discovery systems, such as Google Datasets, and open data portals only support keyword search. Keyword search is geared towards general audiences but limits the types of queries the systems can answer. We propose a new system that lets users write natural language questions directly. A major barrier to using this learned data discovery system is it needs expensive-to-collect training data, thus limiting its utility. In this paper, we introduce a self-supervised approach to assemble training datasets and train learned discovery systems without human intervention. It requires addressing several challenges, including the design of self-supervised strategies for data discovery, table representation strategies to feed to the models, and relevance models that work well with the synthetically generated questions. We combine all the above contributions into a system, Solo, that solves the problem end to end. The evaluation results demonstrate the new techniques outperform state-of-the-art approaches on well-known benchmarks. All in all, the technique is a stepping stone towards building learned discovery systems.

Remanufacturing system scheduling of batch products with the consideration of dynamic changes in machine efficiency using an improved artificial bee colony algorithm

Remanufacturing has the ability to bring the quality and condition of the recycled product back to the level of or even better than the new product. As remanufacturing continues to receive more and more attention from industry and academia, the scheduling of remanufacturing systems (RMS) has been extensively studied. However, few studies have not only focused on the dynamic changes (deterioration and learning) in machine efficiency during RMS scheduling but also considered that the three subsystems (disassembly, reprocessing and reassembly) of RMS collaborate with each other during scheduling. Therefore, this study proposes a new RMS scheduling model of batch recycled products with the consideration of dynamic changes in machine efficiency for handling the batch recycled products. It considers not only the collaboration among the three subsystems comprehensively, but also the dynamic changes in machine efficiency, i.e., deterioration effects and learning effects, during the scheduling. To solve this model, an improved artificial bee colony algorithm with a new representation scheme and a new population generating method is proposed. The effectiveness and practicality of this algorithm are examined through simulation experiments and comparison with other baseline algorithms.

Deep Isolation Forest for Anomaly Detection

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard anomalies that are difficult to isolate in high-dimensional/non-linear-separable data space, and (ii) notorious algorithmic bias that assigns unexpectedly lower anomaly scores to artefact regions. These issues contribute to high false negative errors. Several iForest extensions are introduced, but they essentially still employ shallow, linear data partition, restricting their power in isolating true anomalies. Therefore, this paper proposes deep isolation forest. We introduce a new representation scheme that utilises casually initialised neural networks to map original data into random representation ensembles, where random axis-parallel cuts are subsequently applied to perform the data partition. This representation scheme facilitates high freedom of the partition in the original data space (equivalent to non-linear partition on subspaces of varying sizes), encouraging a unique synergy between random representations and random partition-based isolation. Extensive experiments show that our model achieves significant improvement over state-of-the-art isolation-based methods and deep detectors on tabular, graph and time series datasets; our model also inherits desired scalability from iForest.

Toward Intelligent Cross-Domain Resource Coordinate Scheduling for Satellite Networks

The new generation satellite network is a comprehensive service system that can provide communication, observation, navigation, and other functions. Typically, a system with a specific service function is defined as a domain and the supply and demand relationship of resources, such as communication, storage, and energy resources, are unbalanced among domains. Therefore, it is nontrivial to accurately characterize the cross-domain resource state and coordinate the data transmission policy of interrelated satellites from different domains to make full use of the resources in each domain aiming at improving the resource utilization ratio (RUR) of the whole network. To this end, this paper investigates the cross-domain resource scheduling (CDRS) problem in satellite networks aiming at maximizing the total amount of downloaded transmitted data. We start with the orbit motion law of satellites and construct the satellite orbit feature matrix of each domain to design a hierarchical sparse resource representation (HSRR) scheme to accurately characterize the resource state of each domain in real-time with low complexity. Further, based on the HSRR, we develop a cross-domain dynamic multi-resource scheduling algorithm to solve the CDRS problem by introducing the advantage factor and policy-oriented hyper-parameter. The algorithm can make full use of the resources in each domain to achieve efficient CDRS by dynamically adjusting the data transmission policy of satellites among domains. Simulation results show that the greater the service demand and resource difference among domains, the more significant the performance improvement brought by CDRS, and compared with the existing algorithms, the RUR and resource utilization efficiency have been significantly improved.

Coherent category training enhances generalization in prototype-based categories.

A major question for the study of learning and memory is how to tailor learning experiences to promote knowledge that generalizes to new situations. In two experiments, we used category learning as a representative domain to test two factors thought to influence the acquisition of conceptual knowledge: the number of training examples (set size) and the similarity of training examples to the category average (set coherence). Across participants, size and coherence of category training sets were varied in a fully crossed design. After training, participants demonstrated the breadth of their category knowledge by categorizing novel examples varying in their distance from the category center. Results showed better generalization following more coherent training sets, even when categorizing items furthest from the category center. Training set size had limited effects on performance. We also tested the types of representations underlying categorization decisions by fitting formal prototype and exemplar models. Prototype models posit abstract category representations based on the category's central tendency, whereas exemplar models posit that categories are represented by individual category members. In Experiment 1, low coherence training led to fewer participants relying on prototype representations, except when training length was extended. In Experiment 2, low coherence training led to chance performance and no clear representational strategy for nearly half of the participants. The results indicate that highlighting commonalities among exemplars during training facilitates learning and generalization and may also affect the types of concept representations that individuals form. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Репрезентация темы граффити в городских виртуальных сообществах: итоги контент-анализа

The paper is devoted to the conclusions of an empirical research that was carried out by the authors for sys-tematization of the most common strategies of representation of the topic of graffiti in contemporary urban vir-tual communities. Communication in urban virtual communities is considered by the authors as one of the most important variables that influence both the awareness of contemporary citizens about various phenome-na of urban life, and the modality of their attitude towards these phenomena. Using the data of their own empir-ical research, the authors strive to identify and systematize specific strategies, that appear in urban virtual communities when reflecting the controversial topic of graffiti. The analysis allows the authors to identify the three most common strategies for representing this phenomenon in urban virtual communities: information, conceptualization, and marginalization. The remarkable thing is that these strategies characterize different trends in the way graffiti is presented in the Internet environment limited to a certain urban space, but they form the mainstream of this process.

Local but not global graph theoretic measures of semantic networks generalize across tasks.

"Dogs" are connected to "cats" in our minds, and "backyard" to "outdoors." Does the structure of this semantic knowledge differ across people? Network-based approaches are a popular representational scheme for thinking about how relations between different concepts are organized. Recent research uses graph theoretic analyses to examine individual differences in semantic networks for simple concepts and how they relate to other higher-level cognitive processes, such as creativity. However, it remains ambiguous whether individual differences captured via network analyses reflect true differences in measures of the structure of semantic knowledge, or differences in how people strategically approach semantic relatedness tasks. To test this, we examine the reliability of local and global metrics of semantic networks for simple concepts across different semantic relatedness tasks. In four experiments, we find that both weighted and unweighted graph theoretic representations reliably capture individual differences in local measures of semantic networks (e.g., how related pot is to pan versus lion). In contrast, we find that metrics of global structural properties of semantic networks, such as the average clustering coefficient and shortest path length, are less robust across tasks and may not provide reliable individual difference measures of how people represent simple concepts. We discuss the implications of these results and offer recommendations for researchers who seek to apply graph theoretic analyses in the study of individual differences in semantic memory.

Depression, anxiety, and burnout in academia: topic modeling of PubMed abstracts

The problem of mental health in academia is increasingly discussed in literature, and to extract meaningful insights from the growing amount of scientific publications, text mining approaches are used. In this study, BERTopic, an advanced method of topic modeling, was applied to abstracts of 2,846 PubMed articles on depression, anxiety, and burnout in academia published in years 1975–2023. BERTopic is a modular technique comprising a text embedding method, a dimensionality reduction procedure, a clustering algorithm, and a weighing scheme for topic representation. A model was selected based on the proportion of outliers, the topic interpretability considerations, topic coherence and topic diversity metrics, and the inevitable subjectivity of the criteria was discussed. The selected model with 27 topics was explored and visualized. The topics evolved differently with time: research papers on students' pandemic-related anxiety and medical residents' burnout peaked in recent years, while publications on psychometric research or internet-related problems are yet to be presented more amply. The study demonstrates the use of BERTopic for analyzing literature on mental health in academia and sheds light on areas in the field to be addressed by further research.

Rethinking Extractivism and Masculinity: Aesthetics of the Environment in There Will Be Blood (2007) and First Cow (2019)

As today we face the urgency of thinking film history through the implications of the ecological emergency, the aesthetics of oil extraction in cinema can be linked to representations of petro-masculinity and to discourses of capitalist exploitation of nature. Considering the ecocritical trend in film studies, this paper analyzes the subgenre of the oil film, which is inextricably related to the genre of the western historical tradition and its revisions, and presents There Will Be Blood (Paul Thomas Anderson, 2007) as a key case. The comparative analysis discovers that the critical and aesthetic representational strategies of landscape and character of the US founding myth in First Cow (Kelly Reichardt, 2019) manifest a proposal that deconstructs the genre with the intention of filming the relationships between the human and more-than-human from a viewpoint that approaches environmental consciousness.
 Keywords: 
 oil, masculinity, western, Paul Thomas Anderson, Kelly Reichard, nature aesthetic, ecological emergency, film history

Human-Robot Shared Assembly Taxonomy: A step toward seamless human-robot knowledge transfer

Future manufacturing will witness a shift in human-robot relationships toward collaboration, compassion, and coevolution. This will require seamless human-robot knowledge transfer. Differences in language and knowledge representation hinder the transfer of knowledge between humans and robots. Thus, a unified knowledge representation system that can be shared by humans and robots is essential. Driven by this need in a product assembly scenario, we propose the Human-Robot Shared Assembly Taxonomy (HR-SAT). With HR-SAT, any comprehensive assembly task can be represented as a knowledge graph that both humans and robots can understand. To ensure consistency in task decomposition and representation, we define the key elements of HR-SAT. HR-SAT incorporates rich assembly information and provides necessary information for diverse applications, e.g., process planning, quality checking, and human-robot collaboration. The usage and practicality of HR-SAT are demonstrated through two case studies. As a unified assembly process representation schema, HR-SAT constitutes a critical step toward seamless human-robot knowledge transfer. The specifications of HR-SAT and the two case studies are available at: https://iai-hrc.github.io/hr-sat.

Altered neural encoding of vowels in noise does not affect behavioral vowel discrimination in gerbils with age-related hearing loss.

Understanding speech in a noisy environment, as opposed to speech in quiet, becomes increasingly more difficult with increasing age. Using the quiet-aged gerbil, we studied the effects of aging on speech-in-noise processing. Specifically, behavioral vowel discrimination and the encoding of these vowels by single auditory-nerve fibers were compared, to elucidate some of the underlying mechanisms of age-related speech-in-noise perception deficits. Young-adult and quiet-aged Mongolian gerbils, of either sex, were trained to discriminate a deviant naturally-spoken vowel in a sequence of vowel standards against a speech-like background noise. In addition, we recorded responses from single auditory-nerve fibers of young-adult and quiet-aged gerbils while presenting the same speech stimuli. Behavioral vowel discrimination was not significantly affected by aging. For both young-adult and quiet-aged gerbils, the behavioral discrimination between /eː/ and /iː/ was more difficult to make than /eː/ vs. /aː/ or /iː/ vs. /aː/, as evidenced by longer response times and lower d' values. In young-adults, spike timing-based vowel discrimination agreed with the behavioral vowel discrimination, while in quiet-aged gerbils it did not. Paradoxically, discrimination between vowels based on temporal responses was enhanced in aged gerbils for all vowel comparisons. Representation schemes, based on the spectrum of the inter-spike interval histogram, revealed stronger encoding of both the fundamental and the lower formant frequencies in fibers of quiet-aged gerbils, but no qualitative changes in vowel encoding. Elevated thresholds in combination with a fixed stimulus level, i.e., lower sensation levels of the stimuli for old individuals, can explain the enhanced temporal coding of the vowels in noise. These results suggest that the altered auditory-nerve discrimination metrics in old gerbils may mask age-related deterioration in the central (auditory) system to the extent that behavioral vowel discrimination matches that of the young adults.

Multi-level knowledge-driven feature representation and triplet loss optimization network for image–text retrieval

Image–text retrieval plays a considerable role in associating vision and language. Existing mainstream approaches focus on fine-grained alignment while ignoring the influence of prior knowledge on model performance and the limitation of using a fixed margin in the triplet loss. In this paper, we propose a Multi-level Knowledge-driven feature representation and Triplet Loss Optimization Network (MKTLON) that exploits prior knowledge to enhance visual and textual feature representations and utilizes an adaptive margin of the triplet loss to optimize network training. Specifically, we first present an Enhanced feature Representation scheme based on the Self-Attention (ERSA) module, which incorporates the prior knowledge randomly initialized by uniform distribution into the matrices K and V in the self-attention mechanism. Subsequently, we adopt cascaded ERSA modules to encode images and texts to obtain multi-level visual and textual features with prior knowledge. Furthermore, we develop an adaptive margin optimization strategy that models the relevance scores of positive and negative samples as two independent Gaussian distributions, and obtain the optimized margin by minimizing the intersection of these two distributions. Extensive experiments on two benchmarks, Flickr30K (155,000 image–text pairs) and MSCOCO (616,435 image–text pairs), show the proposed MKTLON achieves 5.7% and 4.3% improvements on rSum, respectively, compared to the state-of-the-art method. The source code will be released at https://github.com/FlyCuteBird/MKTLON.

Variation in Mathematics Content Coverage, Instructional Grouping, and Representational Strategies

Variation in Mathematics Content Coverage, Instructional Grouping, and Representational Strategies

Exploring visual representations of computer mouse movements for bot detection using deep learning approaches

Nowadays, bots can be seen everywhere on the Internet and are responsible for a large percentage of website traffic. The problem of bot detection has increasingly gained attention since more and more bots have been abused from click fraud in online advertisements to launching credential stuffing attacks for harvesting user accounts at a large scale. In this work, we present an end-to-end deep framework for bot detection based on computer mouse movements. Specifically, we propose a novel visual representation scheme that can simultaneously encode spatial and kinematic information in mouse movements into an image which can then be used as the input to Convolutional Neural Networks (CNN). Various strategies to encode kinematic features into images are investigated to obtain a better scheme of visual representation. Experimental results show that the proposed representation scheme in combination with CNN outperforms several baseline models with a TPR of 99.34% in detecting known bots and can be generalized to unknown bots with a highest accuracy of 99.20%. We also demonstrate that the proposed approach can reach acceptable performance levels even for models trained with a small number of training samples. This makes the deployment of our approach easier in real-world scenarios.

#EndSARS Protests and the Search for Social Justice in Nigeria: Examining Activist Discourses on Police Brutality, Politics and Human Security

Abstract On October 3, 2020, members of the Special Anti-Robbery Squad (sars) of the Nigerian Police Force arrested a young man at Wetland Hotel, Ughelli in Delta State, and later shot and killed him, and threw him out of a moving police van. In addition to eye witnesses of what happened, the video of this incident was also trending on social media. Again, on October 5, another video went viral of sars officers’ killing a young musician popularly known as “Sleek,” in unjustifiable circumstances. This was the last straw that broke the camel’s back. What initially began as spontaneous peaceful protests soon turned bloody and by October 8, the protests had spread to almost all Nigerian cities demanding the disbandment of sars. This empirical study examines the operational structure of sars, the formation of the EndSARS movement and its first main protests in 2020 in the search for social justice. Methods in qualitative discourse analysis and multimodality, are used to analyze representational strategies in the protest discourse of the #EndSARS protesters. Other issues bordering on police brutality, injustice, corruption and state failure, which form the main narratives of the protests are analyzed.