Hasil untuk "Semantics"

Mark Burgess

Some formal aspects of the Semantic Spacetime graph model are presented, with reference to its use for directed knowledge representations and process modelling. A finite $γ(3,4)$ representation is defined to form a closed set of operations that can scale to any degree of semantic complexity. The Semantic Spacetime postulates bring predictability with minimal constraints to pathways in graphs. The ubiquitous appearance of absorbing states in any partial graph means that a graph process leaks information. The issue is closely associated with the issue of division by zero, which signals a loss of closure and the need for manual injection of remedial information. The Semantic Spacetime model (and its Promise Theory) origins help to clarify how such absorbing states are associated with boundary information where intentionality can enter.

Aleksandr Boldachev

This paper presents boldsea, Boldachev's semantic-event approach -- an architecture for modeling complex dynamic systems using executable ontologies -- semantic models that act as dynamic structures, directly controlling process execution. We demonstrate that integrating event semantics with a dataflow architecture addresses the limitations of traditional Business Process Management (BPM) systems and object-oriented semantic technologies. The paper presents the formal BSL (boldsea Semantic Language), including its BNF grammar, and outlines the boldsea-engine's architecture, which directly interprets semantic models as executable algorithms without compilation. It enables the modification of event models at runtime, ensures temporal transparency, and seamlessly merges data and business logic within a unified semantic framework.

Yuyao Zhang, Hongliang Guan, Fuzhou Duan

Water pipeline leak detection in a fast and accurate way is of much importance for water utility companies and the general public. At present, the rapid development of remote sensing and computer technologies makes it possible to detect water pipeline leaks on a large scale efficiently and timely. The leakage will cause an increase in the water content and dielectric constant of the soil around the pipeline, so it is feasible to determine the leakage site by measuring the subsurface soil relative dielectric constant (SSRDC). In this paper, we combine the SAOCOM-1A L-band synthetic-aperture radar (SAR) and the ground-penetrating radar (GPR) data to develop regression models that predict the SSRDC values. The model features are selected with the Boruta wrapper algorithm based on the SAOCOM-1A images after pre-processing, and the SSRDC values at sampling locations within the research area are calculated with the reflected wave method based on the GPR data. We evaluate multiple linear regression (MLR), random forest (RF), and multi-layer perceptron neural network (MLPNN) models for their ability to predict the SSRDC values using the selected features. The experimental results show that the MLPNN model (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.705, RMSE = 1.936, MAE = 1.664) can better estimate the SSRDC values. Further, in the main urban area of Tianjin, China, which has a large water pipeline system, the SSDRC values of the area are obtained with the best model, and the locations where the predicted SSDRC values exceeded a certain threshold were considered potential leak locations. The empirical results indicate an encouraging potential of the proposed method to locate the pipeline leaks. This will provide a new avenue for the monitoring and treatment of water pipeline leaks.

Rubén Juárez, Antonio Hernández-Fernández, Claudia de Barros-Camargo et al.

This article formalizes AI-assisted assessment as a discrete-time <i>policy-level</i> design for iterative feedback and evaluates it in a digitally transformed higher-education setting. We integrate an <i>agentic</i> retrieval-augmented generation (RAG) feedback engine—operationalized through <i>planning</i> (rubric-aligned task decomposition), <i>tool use</i> beyond retrieval (tests, static/dynamic analyzers, rubric checker), and <i>self-critique</i> (checklist-based verification)—into a six-iteration dynamic evaluation cycle. Learning trajectories are modeled with three complementary formulations: (i) an interpretable update rule with explicit parameters <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>η</mi></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula> that links next-step gains to feedback quality and the gap-to-target and yields iteration-complexity and stability conditions; (ii) a logistic-convergence model capturing diminishing returns near ceiling; and (iii) a relative-gain regression quantifying the marginal effect of feedback quality on the fraction of the gap closed per iteration. In a <i>Concurrent Programming</i> course (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>35</mn></mrow></semantics></math></inline-formula>), the cohort mean increased from 58.4 to 91.2 (0–100), while dispersion decreased from 9.7 to 5.8 across six iterations; a Greenhouse–Geisser corrected repeated-measures ANOVA indicated significant within-student change. Parameter estimates show that higher-quality, evidence-grounded feedback is associated with larger next-step gains and faster convergence. Beyond performance, we engage the broader pedagogical question of <i>what to value and how to assess</i> in AI-rich settings: we elevate <i>process and provenance</i>—planning artifacts, tool-usage traces, test outcomes, and evidence citations—to first-class assessment signals, and outline defensible formats (trace-based walkthroughs and oral/code defenses) that our controller can instrument. We position this as a <i>design model for feedback policy</i>, complementary to state-estimation approaches such as knowledge tracing. We discuss implications for instrumentation, equity-aware metrics, reproducibility, and epistemically aligned rubrics. Limitations include the observational, single-course design; future work should test causal variants (e.g., stepped-wedge trials) and cross-domain generalization.

Jeonghun Park, Sung Whan Yoon

Recently, semantic communications have drawn great attention as the groundbreaking concept surpasses the limited capacity of Shannon's theory. Specifically, semantic communications probably become crucial in realizing visual tasks that demand massive network traffic. Although highly distinctive forms of visual semantics exist for computer vision tasks, a thorough investigation of what visual semantics can be transmitted in time and which one is required for completing different visual tasks has not yet been reported. To this end, we first scrutinize the achievable throughput in transmitting existing visual semantics through the limited wireless communication bandwidth. In addition, we further demonstrate the resulting performance of various visual tasks for each visual semantic. Based on the empirical testing, we suggest a task-adaptive selection of visual semantics is crucial for real-time semantic communications for visual tasks, where we transmit basic semantics (e.g., objects in the given image) for simple visual tasks, such as classification, and richer semantics (e.g., scene graphs) for complex tasks, such as image regeneration. To further improve transmission efficiency, we suggest a filtering method for scene graphs, which drops redundant information in the scene graph, thus allowing the sending of essential semantics for completing the given task. We confirm the efficacy of our task-adaptive semantic communication approach through extensive simulations in wireless channels, showing more than 45 times larger throughput over a naive transmission of original data. Our work can be reproduced at the following source codes: https://github.com/jhpark2024/jhpark.github.io

Nicolò Zamperlin

In this paper I introduce a generalized version of Richard Epstein's set-assignment semantics ([Epstein, 1990]). As a case study, I consider how this framework can be used to characterize William Parry's logic of analytic implication and some of its recent variations proposed by [Ferguson, 2023a]. In generalized Epstein semantics the parallel use of two algebras, one for extensional and the other for intensional values, allows to account for various forms of content sharing between formulae, which motivates the choice to investigate Parry systems. Hilbert-style axiomatizations and completeness proofs will be presented for all the considered calculi, in particular as main result I provide a set-assignment semantics for Parry's logic.

Marc Aiguier, Romain Pascual

Los's theorem, also known as the fundamental result of ultraproducts, states that the ultraproduct over a family of structures for the same language satisfies a first-order formula if and only if the set of indices for which the structures satisfy the formula belongs to the underlying ultrafilter. The associated notion of satisfaction is the Tarskian one via the elements of the set-theoretic structure that allow interpreting the formula. In the context of topoi, Kripke-Joyal semantics extends Tarski's notion to categorical logic. In this article, we propose to extend Los's theorem to first-order structures on elementary topoi for Kripke-Joyal semantics. We also show that the extension entails its set-theoretic version. As is customary, we use the categorical version of Los's theorem to obtain a proof of the compactness theorem for Kripke-Joyal semantics.

Alena Galilee, Lisa J. Beck, Clara J. Lownie et al.

IntroductionAs children become independent readers, they regularly encounter new words whose meanings they must infer from context, and whose spellings must be learned for future recognition. The self-teaching hypothesis proposes orthographic learning skills are critical in the transition to fluent reading, while the lexical quality hypothesis further emphasizes the importance of semantics. Event-related potential (ERP) studies of reading development have focused on effects related to the N170 component—print tuning (letters vs. symbols) and lexical tuning (real words vs. consonant strings)—as well as the N400 reflecting semantic processing, but have not investigated the relationship of these components to word learning during independent reading.MethodsIn this study, children in grade 3 independently read short stories that introduced novel words, then completed a lexical decision task from which ERPs were derived.ResultsLike real words, newly-learned novel words evoked a lexical tuning effect, indicating rapid establishment of orthographic representations. Both real and novel words elicited significantly smaller N400s than pseudowords, suggesting that semantic representations of the novel words were established. Further, N170 print tuning predicted accuracy on identifying the spellings of the novel words, while the N400 effect for novel words was associated with reading comprehension.DiscussionExposure to novel words during self-directed reading rapidly establishes neural markers of orthographic and semantic processing. Furthermore, the ability to rapidly filter letter strings from symbols is predictive of orthographic learning, while rapid establishment of semantic representations of novel words is associated with stronger reading comprehension.

Clotilde Bizière, Erich Grädel, Matthias Naaf

Semiring semantics of first-order logic generalises classical Boolean semantics by permitting truth values from a commutative semiring, which can model information such as costs or access restrictions. This raises the question to what extent classical model theoretic properties still apply, and how this depends on the algebraic properties of the semiring. In this paper, we study this question for the classical locality theorems due to Hanf and Gaifman. We prove that Hanf's Locality Theorem generalises to all semirings with idempotent operations, but fails for many non-idempotent semirings. We then consider Gaifman normal forms and show that for formulae with free variables, Gaifman's Theorem does not generalise beyond the Boolean semiring. Also for sentences, it fails in the natural semiring and the tropical semiring. Our main result, however, is a constructive proof of the existence of Gaifman normal forms for min-max and lattice semirings. The proof implies a stronger version of Gaifman's classical theorem in Boolean semantics: every sentence has a Gaifman normal form which does not add negations.

Yicheng Xiao, Yue Ma, Shuyan Li et al.

In this paper, we propose SemanticAC, a semantics-assisted framework for Audio Classification to better leverage the semantic information. Unlike conventional audio classification methods that treat class labels as discrete vectors, we employ a language model to extract abundant semantics from labels and optimize the semantic consistency between audio signals and their labels. We verify that simple textual information from labels and advanced pretraining models enable more abundant semantic supervision for better performance. Specifically, we design a text encoder to capture the semantic information from the text extension of labels. Then we map the audio signals to align with the semantics of corresponding class labels via an audio encoder and a similarity calculation module so as to enforce the semantic consistency. Extensive experiments on two audio datasets, ESC-50 and US8K demonstrate that our proposed method consistently outperforms the compared audio classification methods.

Aihua Wu, Songyu Ding

Research on few-shot action recognition has received widespread attention recently. However, there are some blind spots in the current research: (1) The prevailing practice in many models is to assign uniform weights to all samples; nevertheless, such an approach may yield detrimental consequences for the model in the presence of high-noise samples. (2) Samples with similar features but different classes make it difficult for the model to be distinguished. (3) Skeleton data harbors rich temporal features, but most encoders face challenges in effectively extracting them. In response to these challenges, this study introduces a reconstructed prototype network (RC-PN) based on a prototype network framework and a novel spatiotemporal encoder. The RC-PN comprises two enhanced modules: Sample coefficient reconstruction (SCR) and a reconstruction loss function (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>L</mi></mrow><mrow><mi>R</mi><mi>C</mi></mrow></msub></mrow></semantics></math></inline-formula>). SCR leverages cosine similarity between samples to reassign sample weights, thereby generating prototypes robust to noise interference and more adept at conveying conceptual essence. Simultaneously, the introduction of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>L</mi></mrow><mrow><mi>R</mi><mi>C</mi></mrow></msub></mrow></semantics></math></inline-formula> enhances the feature similarity among samples of the same class while increasing feature distinctiveness between different classes. In the encoder aspect, this study introduces a novel spatiotemporal convolutional encoder called CDC-TAGCN. The temporal convolution operator is redefined in CDC-TAGCN. The vanilla temporal convolution operator can only capture the surface-level characteristics of action samples. Drawing inspiration from differential convolution (CDC), this research enhances TCN to CDC-TGCN. CDC-TGCN allows for the fusion of discrepant features from action samples into the features extracted by the vanilla convolutional operator. Extensive feasibility and ablation experiments are performed on the skeleton action dataset NTU-RGB + D 120 and Kinetics and compared with recent research.

Horiyeh Kokabi Dana, Ali Saedavi

Keywords: Book of Al-Ayyam, Taha Hossein, Khadiv Jam, Al-Ayyam Translating, Referential Meaning, Slippage in theReferential Meaning.IntroductionWhether we consider the translation unit as a word, a sentence, a text, or even a concept, the quest for finding the appropriate equivalent for vocabulary remains essential and effective. Understanding the meaning of sentences or texts does not occur in isolation; it is inherently connected to the vocabulary used. Therefore, when translating a text from one language to another, the translator’s initial task should be to assess the meaning of individual words.Peter Newmark contends that translators who dismiss the translation of individual words in favor of focusing solely on sentences and messages deceive themselves. After all, every sentence or text comprises words, each of which independently carries a meaning that reflects a tangible or abstract reality from the external world. Consequently, many experts prioritize the referential meaning—the genuine and concrete sense of words—over other potential meanings. As long as a translator can convey a word’s referential meaning accurately in the target language, there is no need to resort to alternative interpretations.Nevertheless, it is observed that translators occasionally falter when translating the referential meaning. In this essay, the authors aim to analyze and evaluate the translation of the second volume of Hossein Khadiojam’s book Al-Ayyam, specifically addressing the factors that led to deviations from the intended referential meaning.Literature ReviewNumerous studies have explored various aspects of meaning and the quest for equivalence in translation. Among these, the following noteworthy works can be highlighted:“Pragmatics of Referential Meanings of Words in the Translation Process of Nahj al-Balaghah” (1396-2016)by Seyyed Mehdi Masbooq: In this article, Masbooq and colleagues meticulously examine one hundred and twelve words from Nahj al-Balaghah across translations by Mr. Jafari, Dashti, Faqihi, and Faiz al-Islam. The study sheds light on challenges faced by translators, including issues related to vocabulary structure, as well as formal and spiritual nuances of the words.“Research on Translation from Arabic to Persian Based on the Process of ‘Spiritual Equivalence’ (Case Study of the Novel Al-Sakriye)” (1393-2013)by Adnan Tahmasabi and Siddiqa Jafari: This article delves into the translation process, exploring lexical equivalence and language structures across different lexical and semantic layers, with a focus on Al-Sukariyyeh’s novel.“Types of Meaning in Translation” (1393-2013)by Alireza Khan Jan: Khan Jan addresses the critical issue of distinguishing between various types of meaning, emphasizing its significance from Halliday’s perspective.“Criticism on the Translation of ‘Al-Ibarat’: A Testimony to the Necessity of Proficiency in Source and Target Languages” (2012)by Shahriar Gitti and colleagues: Through a critical examination of the translation of the textbook Al-Ibrate, the authors discuss common errors and their underlying causes, emphasizing the challenges faced by translators due to insufficient fluency in both the source and target languages.“Etymology of Words in Nahj al-Balagha: An Emphasis on Ibn-Faris’s Method” (1391-2013): Hossein Mu-yadi’s thesis delves into the etymology process of words found in Nahj al-Balagha. By analyzing the semantics and roots of the vocabulary, the study explores Ibn-Faris’s approach and its impact on vocabulary comprehension.“Pragmatics of ‘Translation Equivalence’ for Words in Quran Translation” (2011)by Hamidreza Mirhaji and colleagues: This article emphasizes the necessity of considering different semantic layers to achieve translation equivalence. The authors examine the process of equivalence and equality at the word level, highlighting that translations from the Quran often lack sufficient attention to the principle of “translation equivalence.” Translators tend to focus primarily on transferring the referential meaning, often overlooking other semantic layers.“Text, Metatext, and Analysis of Basic and Relative Meaning: A Comparative Study with Interpretation” (1386-2006)by Dr. Mohammad Baqer Saeedi Roshan: In this article, the author compares the perspectives of Muslim scholars and contemporary semantic approaches regarding situational meaning and pragmatics as the primary meaning of words, both within and outside the context. The study also explores the relative meaning of words, as understood from the context and text.Although referential meaning—one of the types of meaning—has been analyzed and evaluated in various books and research, there appears to be a gap in independent research regarding its importance, impact on understanding other meanings, and the factors contributing to translator errors in handling referential meaning during translation.On the contrary, the focus of this research lies on the referential meaning within the context of equivalence-seeking during the translation of the second volume of Al-Ayam. The authors aim to address the following questions:What factors contributed to Khadiojam’s errors in translating the referential meaning of certain words?Which factor occurred most frequently?To achieve this, the authors have extracted 69 examples from the second volume of Al-Ayam, where they believe Khadiojam deviated from the intended referential meaning. Due to space constraints, the article provides a select few examples for each factor, followed by an evaluation and analysis using a comparative approach.Conclusion

Abdulrahman Mallah, Mourad Debbichi, Mohamed Houcine Dhaou et al.

In the present work, the noncentrosymmetric 2D ternary Janus monolayers Al<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>XX’(X/X’ = S, Se, Te and O), Si<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>XX’(X/X’ = P, As, Sb and Bi), and A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>PAs(A = Ge, Sn and Pb) have been studied based on first-principles calculations. We find that all the monolayers exhibit in-plane d<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>12</mn></msub></semantics></math></inline-formula>, and out-of-plane d<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>13</mn></msub></semantics></math></inline-formula> piezoelectric coefficients due to the lack of reflection symmetry with respect to the central A atoms. Moreover, our calculations show that Al<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>OX(T = S, Se, Te) chalcogenide monolayers have higher absolute in-plane piezoelectric coefficients. However, the highest out-of-plane values are achieved in the Si<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>PBi monolayer, larger than those of some advanced piezoelectric materials, making them very promising transducer materials for lightweight and high-performance piezoelectric nanodevices.

Xiwen Qu, Hao Che, Jun Huang et al.

Multi-label image classification (MLIC) is a fundamental and practical task, which aims to assign multiple possible labels to an image. In recent years, many deep convolutional neural network (CNN) based approaches have been proposed which model label correlations to discover semantics of labels and learn semantic representations of images. This paper advances this research direction by improving both the modeling of label correlations and the learning of semantic representations. On the one hand, besides the local semantics of each label, we propose to further explore global semantics shared by multiple labels. On the other hand, existing approaches mainly learn the semantic representations at the last convolutional layer of a CNN. But it has been noted that the image representations of different layers of CNN capture different levels or scales of features and have different discriminative abilities. We thus propose to learn semantic representations at multiple convolutional layers. To this end, this paper designs a Multi-layered Semantic Representation Network (MSRN) which discovers both local and global semantics of labels through modeling label correlations and utilizes the label semantics to guide the semantic representations learning at multiple layers through an attention mechanism. Extensive experiments on four benchmark datasets including VOC 2007, COCO, NUS-WIDE, and Apparel show a competitive performance of the proposed MSRN against state-of-the-art models.

Xiaowei Chen

Generalized zero-shot learning (GZSL) is one of the most realistic but challenging problems due to the partiality of the classifier to supervised classes, especially under the class-inductive instance-inductive (CIII) training setting, where testing data are not available. Instance-borrowing methods and synthesizing methods solve it to some extent with the help of testing semantics, but therefore neither can be used under CIII. Besides, the latter require the training process of a classifier after generating examples. In contrast, a novel non-transductive regularization under CIII called Semantic Borrowing (SB) for improving GZSL methods with compatibility metric learning is proposed in this paper, which not only can be used for training linear models, but also nonlinear ones such as artificial neural networks. This regularization item in the loss function borrows similar semantics in the training set, so that the classifier can model the relationship between the semantics of zero-shot and supervised classes more accurately during training. In practice, the information of semantics of unknown classes would not be available for training while this approach does NOT need it. Extensive experiments on GZSL benchmark datasets show that SB can reduce the partiality of the classifier to supervised classes and improve the performance of generalized zero-shot classification, surpassing inductive GZSL state of the arts.

Yanou Ramon, R.A. Farrokhnia, Sandra C. Matz et al.

Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights, however, often remain opaque. In this paper, we show how Explainable AI (XAI) can help domain experts and data subjects validate, question, and improve models that classify psychological traits from digital footprints. We elaborate on two popular XAI methods (rule extraction and counterfactual explanations) in the context of Big Five personality predictions (traits and facets) from financial transactions data (<i>N</i> = <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6408</mn></mrow></semantics></math></inline-formula>). First, we demonstrate how global rule extraction sheds light on the spending patterns identified by the model as most predictive for personality, and discuss how these rules can be used to explain, validate, and improve the model. Second, we implement local rule extraction to show that individuals are assigned to personality classes because of their unique financial behavior, and there exists a positive link between the model’s prediction confidence and the number of features that contributed to the prediction. Our experiments highlight the importance of both global and local XAI methods. By better understanding how predictive models work in general as well as how they derive an outcome for a particular person, XAI promotes accountability in a world in which AI impacts the lives of billions of people around the world.

Hao Jiang, Caiwen Ma, Ming Li et al.

Ultrafast laser drilling has been proven to effectively reduce the heat-affected zone (HAZ) of carbon fiber-reinforced polymer (CFRP) composites. However, previous research mainly focused on the effects of picosecond laser parameters on CFRP drilling. Compared with a picosecond laser, a femtosecond laser can achieve higher quality CFRP drilling due to its smaller pulse width, but there are few studies on the effects of femtosecond laser parameters on CFRP drilling. Moreover, the cross-sectional taper of CFRP produced by laser drilling is very large. This paper introduces the use of the femtosecond laser to drill cylindrical holes in CFRP. The effect of laser power, rotational speed of the laser, and number of spiral passes on HAZ and ablation depth in circular laser drilling and spiral laser drilling mode was studied, respectively. It also analyzed the forming process of the drilling depth in the spiral drilling mode and studied the influence of laser energy and drilling feed depth on the holes’ diameters and the taper. The experimental results show that the cylindrical hole of CFRP with a depth-to-diameter ratio of about 3:1 (taper < 0.32<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>, HAZ < 10 m) was obtained by using femtosecond laser and a spiral drilling apparatus.

Emilia Hilgert

With the massive entry into the use of the name “emotion” as a generic name for all psychological manifestations, due to the popularization of specialist discourse in psychology, the arts, etc., there is the temptation to consider this name, from the point linguistically, only as a hyperonym and even as a "general name", of the top or the foundations of the lexicon. This article presents a new point of view: it shows that, by its morphological, syntactic and anaphoric properties, the name emotion and also (if not especially) a basic name, on the same lexical level as fear, joy, sadness, etc., that we could consider as its hyponyms.

Maria Ryskina, Ella Rabinovich, Taylor Berg-Kirkpatrick et al.

We perform statistical analysis of the phenomenon of neology, the process by which new words emerge in a language, using large diachronic corpora of English. We investigate the importance of two factors, semantic sparsity and frequency growth rates of semantic neighbors, formalized in the distributional semantics paradigm. We show that both factors are predictive of word emergence although we find more support for the latter hypothesis. Besides presenting a new linguistic application of distributional semantics, this study tackles the linguistic question of the role of language-internal factors (in our case, sparsity) in language change motivated by language-external factors (reflected in frequency growth).

Liang Lu, Carlos Redondo, Pascual Campoy

Aerial robots are widely used in search and rescue applications because of their small size and high maneuvering. However, designing an autonomous exploration algorithm is still a challenging and open task, because of the limited payload and computing resources on board UAVs. This paper presents an autonomous exploration algorithm for the aerial robots that shows several improvements for being used in the search and rescue tasks. First of all, an RGB-D sensor is used to receive information from the environment and the OctoMap divides the environment into obstacles, free and unknown spaces. Then, a clustering algorithm is used to filter the frontiers extracted from the OctoMap, and an information gain based cost function is applied to choose the optimal frontier. At last, the feasible path is given by A* path planner and a safe corridor generation algorithm. The proposed algorithm has been tested and compared with baseline algorithms in three different environments with the map resolutions of <inline-formula><math display="inline"><semantics><mrow><mn>0.2</mn></mrow></semantics></math></inline-formula> m, and <inline-formula><math display="inline"><semantics><mrow><mn>0.3</mn></mrow></semantics></math></inline-formula> m. The experimental results show that the proposed algorithm has a shorter exploration path and can save more exploration time when compared with the state of the art. The algorithm has also been validated in the real flight experiments.