Semantic Reconstruction Research Articles

Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval.

Deep hashing is the mainstream algorithm for large-scale cross-modal retrieval due to its high retrieval speed and low storage capacity, but the problem of reconstruction of modal semantic information is still very challenging. In order to further solve the problem of unsupervised cross-modal retrieval semantic reconstruction, we propose a novel deep semantic-preserving reconstruction hashing (DSPRH). The algorithm combines spatial and channel semantic information, and mines modal semantic information based on adaptive self-encoding and joint semantic reconstruction loss. The main contributions are as follows: (1) We introduce a new spatial pooling network module based on tensor regular-polymorphic decomposition theory to generate rank-1 tensor to capture high-order context semantics, which can assist the backbone network to capture important contextual modal semantic information. (2) Based on optimization perspective, we use global covariance pooling to capture channel semantic information and accelerate network convergence. In feature reconstruction layer, we use two bottlenecks auto-encoding to achieve visual-text modal interaction. (3) In metric learning, we design a new loss function to optimize model parameters, which can preserve the correlation between image modalities and text modalities. The DSPRH algorithm is tested on MIRFlickr-25K and NUS-WIDE. The experimental results show that DSPRH has achieved better performance on retrieval tasks.

Reconstruction of High-Precision Semantic Map.

We present a real-time Truncated Signed Distance Field (TSDF)-based three-dimensional (3D) semantic reconstruction for LiDAR point cloud, which achieves incremental surface reconstruction and highly accurate semantic segmentation. The high-precise 3D semantic reconstruction in real time on LiDAR data is important but challenging. Lighting Detection and Ranging (LiDAR) data with high accuracy is massive for 3D reconstruction. We so propose a line-of-sight algorithm to update implicit surface incrementally. Meanwhile, in order to use more semantic information effectively, an online attention-based spatial and temporal feature fusion method is proposed, which is well integrated into the reconstruction system. We implement parallel computation in the reconstruction and semantic fusion process, which achieves real-time performance. We demonstrate our approach on the CARLA dataset, Apollo dataset, and our dataset. When compared with the state-of-art mapping methods, our method has a great advantage in terms of both quality and speed, which meets the needs of robotic mapping and navigation.

Object-RPE: Dense 3D reconstruction and pose estimation with convolutional neural networks

We present an approach for recognizing objects present in a scene and estimating their full pose by means of an accurate 3D instance-aware semantic reconstruction. Our framework couples convolutional neural networks (CNNs) and a state-of-the-art dense Simultaneous Localization and Mapping (SLAM) system, ElasticFusion (Whelan et al., 2016), to achieve both high-quality semantic reconstruction as well as robust 6D pose estimation for relevant objects. We leverage the pipeline of ElasticFusion as a backbone, and propose a joint geometric and photometric error function with per-pixel adaptive weights. While the main trend in CNN-based 6D pose estimation has been to infer object’s position and orientation from single views of the scene, our approach explores performing pose estimation from multiple viewpoints, under the conjecture that combining multiple predictions can improve the robustness of an object detection system. The resulting system is capable of producing high-quality instance-aware semantic reconstructions of room-sized environments, as well as accurately detecting objects and their 6D poses. The developed method has been verified through extensive experiments on different datasets. Experimental results confirmed that the proposed system achieves improvements over state-of-the-art methods in terms of surface reconstruction and object pose prediction. Our code and video are available at https://sites.google.com/view/object-rpe.

«Если бы я был живописец…»: проблема интермедиального прочтения повести Н. В. Гоголя «Старосветские помещики»

<p>The article analyzes the problem of the intermedial reading of Gogol's novel <em>The Old World Landowners</em>, presented by the animated film <em>He and She</em> by M. Muat. The novelty of the work is determined by the application of the method of a comprehensive analysis to the texts dialogically connected with each other. The extent of adequacy of the creative reading of Gogol's novel by a contemporary film director is determined by the semantic reconstruction of an idyllic chronotope of the novel. It is noticed that in the film limiting the history of main characters only by mutual relations within a circle of a manor house, Gogol's idea about the confrontation between family and home space and forces of chaos and destruction, is kept. The director's technique of a circular composition is an artistic factor that can be compared to the key “moment” in painting that condenses all previous and subsequent events. A figurative ring formed by the images of an old oak can be seen as a semantic analogue of the idyllic circle of the manor. However, if in the novel this circle is destroyed from the inside, in the film it is preserved and affirmed thanks to the creative efforts of the film director. The idyllic world of M. Muat is conscious of the “evil spirit” of destruction, but its mission is to keep the moment of peace, love and quiet happiness. The director, thus, asserts love as the supreme and permanent value of human existence. Not following literally the text of Gogol's novel, M. Muat offers his own reading of those laws that form the core of Gogol's artistic world.</p>

ТИПИ ТА ШЛЯХИ СЕМАНТИЧНИХ ЗМІН В ЛІНГВІСТИЦІ 1990-Х РОКІВ - ПОЧАТОК XXI СТОЛІТТЯ

У статті розглянуті погляди європейських дослідників мови 90-х років ХХ ст. – початку XXI ст. на типи та способи семантичних змін, семантичної реконструкції та синкретизму, існування європейської прамови та тісного зв’язку семасіології та етимології. Предметом етимологічного аналізу є реконструкція форми та значення слів, первинної семантичної мотивації. Серед найважливіших цілей семасіології – класифікація семантичних змін та пошук певних універсальних закономірностей таких змін. Виконуючи ці завдання, дослідники мови не можуть ігнорувати допомогу різних лінгвістичних та нелінгвістичних наук, які забезпечують семасіологію численними мовними фактами та сприяють формуванню більш-менш повної картини природи смислових змін. Прийняття ідеї існування прамови є вигідним, оскільки дає виняткову можливість простежити зміни значень задовго в історії та дає змогу лінгвістам знайти універсальні закони. Неможливість ігнорування схожих рис у багатьох мовах світу дає лінгвістам право говорити про певну історичну спільноту, яка володіла певною мовою – прамовою. Виведення універсальних законів семантичних змін, які, безперечно, значно полегшили б дослідження у такій царині як компаративістика, так чи інакше захоплювало майже всіх учених, тому що вони виводяться через порівняння багатьох мовних систем, кожна з яких вважається добре дослідженою. Складовими, яким, безперечно, необхідно приділяти велику увагу, є граматика, звукова структура та словниковий склад. Тільки таким чином можна отримати найбільш повну картину дослідження та якомога менше залишити місця для припущень. Серйозною перепоною процесу дослідження розглядався той факт, що, незважаючи на зв’язок, всі три вищезгадані системи мають здатність у більшості випадків змінюватися незалежно одна від одної. Тільки порівнявши всі пам’ятки, індоєвропейська приналежність яких не викликає сумнівів, ми можемо отримати науково достовірні знання про індоєвропейську прамову. На жаль, гіпотетичного характеру деяких результатів не уникнути. Ось чому важливо заглибитись у діахронічне дослідження та обрати найбільш ефективні методи лінгвістичних досліджень.

Deep Semantic Reconstruction Hashing for Similarity Retrieval

Hashing has shown enormous potentials in preserving semantic similarity for large-scale data retrieval. Existing methods widely retain the similarity within two binary codes towards their discrete semantic affinity, i.e., 1 or -1. However, such a discrete reconstruction approach has obvious drawbacks. First, two unrelated dissimilar samples would have similar binary codes when both of them are the most dissimilar with an anchor sample. Second, the fine-grained semantic similarity cannot be shown in the generated binary codes among data with multiple semantic concepts. Furthermore, existing approaches generally adopt a point-wise error-minimizing strategy to enforce the real-valued codes close to its associated discrete codes, resulting in the well-learned paired semantic similarity being unintentionally damaged when performing quantization. To address these issues, we propose a novel deep hashing method with pairwise similarity-preserving quantization constraint, termed Deep Semantic Reconstruction Hashing (DSRH), which defines a high-level semantic affinity within each data pair to learn compact binary codes. Specifically, DSRH is expected to learn the specific binary codes whose similarity can reconstruct their high-level semantic similarity. Besides, we adopt a pairwise similarity-preserving quantization constraint instead of the traditional point-wise quantization technique, which is conducive to maintain the well-learned paired semantic similarity when performing quantization. Extensive experiments are conducted on four representative image retrieval benchmarks, and the proposed DSRH outperforms the state-of-the-art deep-learning methods with respect to different evaluation metrics.

Мясо в рационе севернорусских крестьян: этнолингвистический аспект

The author reconstructs the complex of folk ideas connected with meat in the Russian North (Arkhangelsk and Vologda regions, north of the Kostroma region). The analysis involves Russian dialect vocabulary, folklore texts and folk beliefs. The new data collected by the Toponymic Expedition of the Ural University are introduced into scientific use. The methods of motivational analysis, semantic reconstruction, as well as semantic-motivational parallels between linguistic facts and data of non-linguistic forms of culture are applied. The study characterizes the attitude of the peasants towards the meat of different animals, slaughter ritu-als, methods of storing meat, features, important for meat products (raw – boiled, fresh – har-vested for future use, clean – unclean, etc.), and food preferences of local social groups. The author concludes that meat served not only as food but also as a cultural and linguistic sym-bol. Analysis of the vocabulary, rituals, and folk legends associated with the slaughter of live-stock allows reconstructing the tradition of the sacrifice festivals ( ). The vocabulary associated with meat food reveals a difference in the meat ration of Russians and aborigines of the Russian North, who consumed raw meat, as well as not familiar to Russian reindeer meat, seal meat, and walrus. The paper clarifies the motivation of some lexemes, for example, the designations with component tsarskiy and tserkovnyy. Nicknames, the folk motivation of which is associated with the use of meat, are analyzed (volog. telyatniki, volog. veksheedy, arch. morzheedy, arch. kostogryzy, volog. Samoed, etc.).

Some excerptions of the Tauric Peninsula toponymy. Perekop – Armyansk

Abstract: The main aim of the proposal article is to put in order the data of two indissolubly tied towns – Perekop and Armyansk (both in Krasnoperekopsky district). those towns history is closely tied and reflecting in their names, needing renewed and detailed commentaries. Primarily, Perekop was known as the Tάφρος, Taphræ since the І b.c. and since XVIII A.D. ­ as Or, Or–Аgzy, Or–Каpі, Ferrah–Kerman. This fortress was the only one terrestrial way to the Crimea. But after the main part of Greek and Armenian people resettlement to the situated in five miles trade point in the thirties of the XVIII century, this point (Armyansk town nowadays) obtaines two new names ­ Yeni / Novy–Bazar and Armyansky Bazar (which repels both new townsfolk natonality and those main line of work). at the same time, much of these oiconyms (besides Ferrah–Kerman) demonstrates extreme semantic firmness: at various times and in different languages these ones has been the ‘ditch’ meaning saving: Gr. τάφρος, Lat. Fossa, Old-Ukr., Old-Russ. Перекопъ(ь), Pol. Przekop, Prsecop, Tat. Or / Ор (Or–Аgzy and Or–Каpі), Russ. Rov. There such semantic typology examples in the Crimean oiconymy (and in toponymy in general) are frequent and forming one of the main Tauric toponymy features, reflecting an active centuries-old lingual interaction process. There Armyansk, Yeni / Novy–Bazar and Armyansky Bazar oiconyms have not any features of the typological continuity and reflects the new settlement rising circumstances. Keywords: history, onomatology, oiconymy, semantic reconstruction, historical linguistics.

Images of Russian political leaders as viewed by youth: a semantic reconstruction

This article presents material associated with the semantic reconstruction of the images of Russian political figures as viewed by young students, based on material from an empirical study conducted in 2018. While conducting research, students attending higher and secondary professional education institutions in Primorsky Krai were surveyed. Such an undertaking as a semantic reconstruction of the images of Russian political figures allowed for identifying both the substantive and the structural properties of each image. The categorical structure of the semantic space of views on political leaders was defined. The general categories – selected based on associations – primarily include “leadership”, “moral and human qualities”, “intellectual”, “negative personal traits” and “political symbols”. The aforementioned categories, which were selected from the material on associations, bear specific similarities with those categories which were established using factor analysis of semantic differential data. The general categories for evaluating leaders based on semantic differential data include, first of all, “intellectual and leadership qualities”, “greed and hypocrisy – kindness”, “aggression and rudeness – handsomeness”. Within the structure of the category “negative personal traits” we can trace subcategories which are inverse to (or which mirror) the positive traits (stupidity is the opposite of intelligence and wisdom, aggressiveness is contrary to kindness). Additionally the substantive characteristics of the images of Russia’s four leading political figures (according to young people) were identified. A comparative analysis of the images of Russian politicians shows that positive personal traits, according to the respondents, are most apparently exhibited by Vladimir Putin and Ramzan Kadyrov. Incidentally both of them possess so called “leadership qualities” and high moral characteristics. Such data corresponds with modern Russian society’s demand for a competent, strong-willed and righteous leader. All in all the identified categories and groups of traits allow for reconstructing the organizational structure of subjective experience in perceiving political leaders, as well as the political thinking of young people. The data acquired in this study might be useful in the consequent study of the modern Russian people’s political awareness, and they could be used by political leaders and parties in conducting practical operations.

The Adverb Necessarily (Obyazatelno) in a Whirlwind of the Russian Language

The purpose of the article is to present the history of adverbs as the synonym dominant in the meaning of obligatory. The methods of study are descriptive, comparative-historical methods as well as the method of lexical semantic reconstruction. In the Russian language the adverb necessarily is a lexical word-building calque, which was translated appeared in the XVIII century from the French obligeament or Germ. unverbindlich. It had the meaning 'affectionately, politely, courteously'. From the 1830-1840s, the adverb necessarily started began to be used in the changed meaning of 'certainly, unconditionally' in those genres and styles (law, office work, military science, science), which had the German influence. Both meanings were used as lexical homonyms, belonging to different semantic groups of synonyms. By the 1950s, the adverb necessarily finally took the place of the dominant in the group of synonyms with the meaning of necessity.

Real-Time Semantic Plane Reconstruction on a Monocular Drone Using Sparse Fusion

A semantic map, which can provide important data to facilitate a drone's understanding to the environment, is critical for a fully autonomous drone. However, recent methods for producing such a map implemented on small drones that use low-/middle-grade processors and graphics processing units can hardly achieve real-time performance. In addition, few existing methods can reconstruct semantic planes based on a sparse depth map, which can greatly reduce computational load while encountering challenges in semantic reconstruction. To address this problem, this paper presents a novel on-board approach, called sparse fusion, which demonstrates real-time reconstruction of a semantic plane on a self-designed small drone. The approach combines the sparse depth map derived from a visual-inertial simultaneous localization and mapping with the semantic labels derived from a convolutional neural network for each frame using sparse fusion. Our proposed local plane optimization function greatly improves the accuracy of the semantic plane. Experimental results on various scenarios demonstrate that our sparse fusion module running on the drone platform can update the semantic plane within 1 ms, which is faster and achieves greater accuracy than the state-of-the-art real-time semantic reconstruction method. We also conducted experiments in real environments to demonstrate the performance of our method.

Uncovering historical semantic connections with the help of image schemata

Abstract Standard etymological dictionaries agree that Modern English some, same and their Old English cognate sam- ‘half’ descend from the same etymon. However, while explaining their phonological development from the same proto-form is unproblematic, their divergent meanings make the reconstruction of their semantic evolution more challenging. The paper examines the historical semantic connection between these three morphemes from a cognitive perspective and attempts to provide an explanation of how they are conceptually linked to each other. Based on a cognitive semantic analysis of the meanings of these forms, we propose that all three concepts are understood on the basis of and embedded in one and the same image schematic domain – comprised by the general unity/multiplicity schema – and derive from entailments of its subschemata. Such an image schematic account of the conceptual connections between these meanings provides an explanation for the various paths of semantic development from the original etymon leading to the established later meanings. This approach will also facilitate the semantic reconstruction of the ancestral Proto-Indo-European form and help identify the exact cognate relationships between some, same and sam-.

КОГНІТИВНО-ОНОМАСІОЛОГІЧНА РЕКОНСТРУКЦІЯ АНГЛІЙСЬКИХ ТА НІМЕЦЬКИХ ФІТОНІМІВ АСОЦІАТИВНО-ТЕРМІНАЛЬНОГО ТИПУ МОТИВАЦІЇ

Background. Cognitive onomasiological analysis of language units is one of the most studied theories of linguistic science today, especially in the field of the study of phytonyms as a lexical-semantic class of language units. However, the mechanisms, according to which these names are generated, are still waiting to be explained, which, in its turn, will explain both the complex interrelations between the conceptual and linguistic levels, and processes of obtaining, processing and processing information that is acquired through sensual experiences. Therefore, the article is devoted to the comparative cognitive-onomasiological analysis of the evaluative semantic component of medicinal plants names.Purpose. The purpose of this research is to establish the motivational base of German and English phytonyms with an evaluative semantic component belonging to the associative-terminal type of motivation and to reconstruct evaluative semantics that is inherent in the designator as a result of this type of motivation.Methods. Cognitive-onomasiological analysis, cognitive reconstruction, onomasiological reconstruction, semantic reconstruction and comparative method.Results. The study presents the results of the analysis of 520 names of medicinal plants in two languages of the Germanic group (German and English), onomasiological structures of which contain designators of associative-terminal type with evaluative semantics. It has been indicated that phytonyms with evaluative semantics are motivated by metaphorical signs borrowed from the donor areas such as “natural factor”, “person”, “animal”, “mythological creature” and “artefact”. Conceptual sphere “plant”, which is a part of the broader conceptual sphere “natural factor”, has the largest number of examples of use as a donor area. Correlations between different types of evaluative components and metaphoric donor spheres have been reconstructed.Discussion. Comparing results of cognitive and onomasiological analysis of medicinal plants’ names in German and English revealed donor spheres and mechanisms of associative-terminal motivation, that are common for both languages. Metaphorical motivators have been indicated both in plants’ names motivated according to the associative-terminal type, and in those belonging to mixed motivation type. Since a comparative cognitive-onomasiological analysis of two closely related languages naturally reveals common tendencies rather than differences, we see prospects for further exploration in conducting a similar study with languages belonging to different groups and compare results for languages, that would be unrelated or distantly related.Keywords: phytonym, evaluative component, cognitive onomasiological analysis, associative-terminal motivation, designator, donor sphere.

Новый подход к индоевропейской классификации: фонетико-семантическая реконструкция базисных лексических единиц для промежуточных праязыковых состояний (A New Approach to the Indo-European Classification: Phonetic-Semantic Reconstruction of Basic Lexical Units for Intermediate Original Language States)

Russian Abstract: В настоящем препринте предлагается реконструкция индоевропейской филогении на основе тринадцати 110-словных списков для праязыков ИЕ-подгрупп (протогерманский, протославянский и т. д.) или древних языков соответствующих подгрупп (хеттский, древнегреческий и т. д.). Мы применяем более или менее формальные методы сбора лингвистических данных и последующей обработки (семантическая реконструкция, деривационная дрейфовая элиминация, оптимизация гомоплазий), недавно предложенные или разработанные для настоящего исследования. Мы используем последовательную филогенетическую процедуру и получаем консенсусное дерево, основанное на нескольких алгоритмах (байесовский анализ, максимальная экономность, присоединение соседей, отсутствие применения топологических ограничений). Полученная топология дерева и датировки полностью совместимы с традиционными представлениями экспертов. Нашей главной находкой является мультифуркация внутренней ИЕ клады на четыре ветви ок. 3357-2162 гг. до н.э.: (1) греко-армянскую, (2) албанскую, (3) итало-германо-кельтскую, (4) балто-славяно-индо-иранскую. Предлагаемый сценарий расхождения примиряет друг с другом различные мнения о внутреннем ИЕ ветвлении, высказывавшиеся ранее индоевропеистами. English Abstract: This preprint suggests the reconstruction of Indo-European phylogeny based on thirteen 110-word lists for the proto-languages ​​of the IE subgroups (Proto-German, Proto-Slavonic, etc.) or the ancient languages ​​of the corresponding subgroups (Hittite, Ancient Greek, etc.). We use more or less formal methods for the collection of linguistic data and subsequent processing (semantic reconstruction, derivational drift elimination, optimization of homoplasias) recently proposed or developed for this study. We use a consistent phylogenetic procedure and obtain a consensus tree based on several algorithms (Bayesian analysis, maximum economy, joining of neighbors, lack of application of topological restrictions). The obtained tree topology and dating are fully compatible with the traditional views of experts. Our main finding is the multifurcation of the internal clade AI into four branches approx. 3357-2162 BC: (1) Greek-Armenian, (2) Albanian, (3) Italo-Germanic-Celtic, (4) Balto-Slavic-Indo-Iranian. The proposed scenario of discrepancy reconciles with each other the different opinions on the internal AI branching expressed earlier by the Indo-Europeists.

Scope Reconstruction in A-Movement

This study explores A-movement reconstruction and asserts that A-movement reconstructs as A’-movement does, providing semantic type-based accounts in accordance with semantic reconstruction proposed by Lechner (1998) and Ruys (2015). The current proposal is that quantificational subjects leave a higher type trace ,t> at any immediate landing site and an individual type trace at their theta-position. Based on this hypothesis, three sentences that show no reconstruction effects can be accounted for. The components in the three sentences such as negations, degree modifiers and non-neg-raising predicates induce weak island effects for higher type traces. As resumptive pronouns in island conditions, an individual type trace can obviate island effects, but a higher type trace ,t> cannot.

ObjectFusion: An object detection and segmentation framework with RGB-D SLAM and convolutional neural networks

Given the driving advances on CNNs (Convolutional Neural Networks) [1], deep neural networks being deployed for accurate detection and semantic reconstruction in SLAM (Simultaneous Localization and Mapping) has become a trend. However, as far as we know, almost all existing methods focus on design a specific CNN architecture for single task. In this paper, we propose a novel framework which employs a general object detection CNN to fuse with a SLAM system towards obtaining better performances on both detection and semantic segmentation in 3D space. Our approach first use CNN-based detection network to obtain the 2D object proposals which can be used to establish the local target map. We then use the results estimated from SLAM to update the dynamic global target map based on the local target map obtained by CNNs. Finally, we are able to obtain the detection result for the current frame by projecting the global target map into 2D space. On the other hand, we send the estimation results back to SLAM and update the semantic surfel model in SLAM system. Therefore, we can acquire the segmentation result by projecting the updated 3D surfel model into 2D. Our fusion scheme privileges in object detection and segmentation by integrating with SLAM system to preserve the spatial continuity and temporal consistency. Evaluation performances on four datasets demonstrate the effectiveness and robustness of our method.

Geographic, geometrical and semantic reconstruction of urban scene from high resolution oblique aerial images

An effective approach is proposed for 3D urban scene reconstruction in the form of point cloud with semantic labeling. Starting from high resolution oblique aerial images, our approach proceeds through three main stages: geographic reconstruction, geometrical reconstruction and semantic reconstruction. The absolute position and orientation of all the cameras relative to the real world are recovered in the geographic reconstruction stage. Then, in the geometrical reconstruction stage, an improved multi-view stereo matching method is employed to produce 3D dense points with color and normal information by taking into account the prior knowledge of aerial imagery. Finally the point cloud is classified into three classes &#x0028 building, vegetation, and ground &#x0029 by a rule-based hierarchical approach in the semantic reconstruction step. Experiments on complex urban scene show that our proposed 3-stage approach could generate reasonable reconstruction result robustly and efficiently. By comparing our final semantic reconstruction result with the manually labeled ground truth, classification accuracies from 86.75&#x25 to 93.02&#x25 are obtained.

The Development of Cultural Value of the Activities of Eating in the Communities of Javanese Coastal Area through the Reconstruction of Semantic Components

This study is aimed to develop and enhance the roots of Javanese culture which are oriented towards the activities of eating in the social culture of coastal Javanese communities. Social culture contains the pillars of harmony and politeness. The harmony derives from noble values and politeness derived from the principle of cooperation in the context of speech acts. The research location was in Semarang city, especially in the rural area. This type of research is qualitative. The methods of data collection were observation, in-depth interviews, and focus group discussions (FGD). The methods of data analysis were semantic component reconstruction, lexicon mapping, and the settlement of lexicon elements in Javanese culture. The result of the research is the development of methods in the form of semantic component reconstruction. Reconstruction of semantic components involves four pillars of analysis: value-referent-concept words. Another finding, in the form of the design of the development of cultural values of eating activities, can be done through the hierarchy of speech acts. The enrichment of lexicon eating activities can be done through word alignment or paraphrasing.

Screening out All Valid Aristotelian Modal Syllogisms

It is easy to understand that whether a classical syllogism is valid. That whether a modal syllogism is valid is not so transparent. The prevailing view on Aristotelian modal syllogistic is that the syllogistic is incomprehensible due to its many faults and inconsistencies. Although adequate semantic analysis or reconstruction of the syllogistic have be given by many authors, it is far from obvious how to extend these results so as to consistently cover the whole modal syllogistic developed. The major aim of this paper is to overcome these difficulties, and screen out 384 Aristotelian valid modal syllogisms from 6656 Aristotelian modal syllogisms in natural language. They can be formalized by means of set theory and generalized quantifier theory, and their validity can be proved by possible world semantics and the truth definition of Aristotelian quantifiers defined in generalized quantifier theory. The basic steps of screening out all valid Aristotelian modal syllogisms are as follows: firstly one can get all possible modal syllogisms obtained by adding modal operators to 24 valid classical syllogisms, and secondly eliminate invalid modal syllogisms by characteristic rules of modal syllogisms. It is hoped that these innovative achievements will make contributions to promote the development of Aristotelian and generalized modal syllogistic, natural language information processing, and further research on knowledge representation and knowledge reasoning in computer science.

On the precision of semantic reconstruction of proto-language lexis: The Latin Swadesh list put to test as an example

Only applying data from the Romance languages, the author carries out a linguistic experiment in an attempt to reconstruct the Proto-Romance 110-word list of basic lexis. The received data are then compared with attested Latin forms. Cases where the reconstruction doesn’t correspond to the reality are brought to light, while the reasons behind it are explained. The results of the research can be most useful for lexicostatistics, where reconstructed Swadesh lists are widely applied, but at the same time they are valuable for the methodology of comparative linguistics on the whole.