Search results for: text processing

Authors: Artem Surkov

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This work is devoted to a problem of aesthetic quality determinism in the context of contemporary art. The object of study is photography regarding as a kind of art which demands specific system of quality marking. Objective: To define aesthetic criteria in photography art. For current searching different kind of texts by such powerful authors like Clement Greenberg and Rosalind Krauss, Theodor Adorno and Herbert Marcuse, Charlott Cotton and Boris Groys, Viktor Miziano and Ekaterina Degot' were analyzed. Before all, there are two different kinds of photography: the classic art photography (by Ansel Adams) and the photography as kind of art (by Andreas Gursky). In this text we are talking about the photography as kind of art. The main principle of current searching is synthesis of two different approaches: modernism and postmodernism. This method helps us to define uniform criteria of aesthetic quality in photography as kind of art. The criteria mentioned in conclusion paragraph are: aesthetic rationality, aesthetic economy, awareness (using photographic technics or references), and intention to go beyond form, practice and method.

Keywords: aesthetic, art, criteria of quality, photography, visually

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Authors: Arth J. Shah, Madhu R. Kamble

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Voice biometric liveness detection is customized to certify an authentication process of the voice data presented is genuine and not a recording or synthetic voice. With the rise of deepfakes and other equivalently sophisticated spoofing generation techniques, it’s becoming challenging to ensure that the person on the other end is a live speaker or not. Voice Liveness Detection (VLD) system is a group of security measures which detect and prevent voice spoofing attacks. Motivated by the recent development of the Kolmogorov-Arnold Network (KAN) based on the Kolmogorov-Arnold theorem, we proposed KAN for the VLD task. To date, multilayer perceptron (MLP) based classifiers have been used for the classification tasks. We aim to capture not only the compositional structure of the model but also to optimize the values of univariate functions. This study explains the mathematical as well as experimental analysis of KAN for VLD tasks, thereby opening a new perspective for scientists to work on speech and signal processing-based tasks. This study emerges as a combination of traditional signal processing tasks and new deep learning models, which further proved to be a better combination for VLD tasks. The experiments are performed on the POCO and ASVSpoof 2017 V2 database. We used Constant Q-transform, Mel, and short-time Fourier transform (STFT) based front-end features and used CNN, BiLSTM, and KAN as back-end classifiers. The best accuracy is 91.26 % on the POCO database using STFT features with the KAN classifier. In the ASVSpoof 2017 V2 database, the lowest EER we obtained was 26.42 %, using CQT features and KAN as a classifier.

Keywords: Kolmogorov Arnold networks, multilayer perceptron, pop noise, voice liveness detection

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Authors: Mballa Abanda Serge, Henda Gnakate Biba, Romaric Guemno Kuate, Akono Rufine Nicole, Petfiang Sidonie, Bella Sidonie

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The main hypotheses proposed around the definition of the syllable and of music, of the common origin of music and language, should lead the reader to reflect on the cross-cutting questions raised by the debate on the notion of universals in linguistics and musicology. These are objects of controversy, and there lies its interest: the debate raises questions that are at the heart of theories on language. It is an inventive, original and innovative research thesis. The main hypotheses proposed around the definition of the syllable and of music, of the common origin of music and language, should lead the reader to reflect on the cross-cutting questions raised by the debate on the notion of universals in linguistics and musicology. These are objects of controversy, and there lies its interest: the debate raises questions that are at the heart of theories on language. It is an inventive, original and innovative research thesis. A contribution to the theoretical, musicological, ethno musicological and linguistic conceptualization of languages, giving rise to the practice of interlocution between the social and cognitive sciences, the activities of artistic creation and the question of modeling in the human sciences: mathematics, computer science, translation automation and artificial intelligence. When you apply this theory to any text of a folksong of a world-tone language, you do not only piece together the exact melody, rhythm, and harmonies of that song as if you knew it in advance but also the exact speaking of this language. The author believes that the issue of the disappearance of tonal languages and their preservation has been structurally resolved, as well as one of the greatest cultural equations related to the composition and creation of tonal, polytonal and random music. The experimentation confirming the theorization, It designed a semi-digital, semi-analog application which translates the tonal languages of Africa (about 2,100 languages) into blues, jazz, world music, polyphonic music, tonal and anatonal music and deterministic and random music). To test this application, I use a music reading and writing software that allows me to collect the data extracted from my mother tongue, which is already modeled in the musical staves saved in the ethnographic (semiotic) dictionary for automatic translation ( volume 2 of the book). Translation is done (from writing to writing, from writing to speech and from writing to music). Mode of operation: you type a text on your computer, a structured song (chorus-verse), and you command the machine a melody of blues, jazz and world music or variety etc. The software runs, giving you the option to choose harmonies, and then you select your melody.

Keywords: music, entanglement, langauge, science

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Authors: Jiří Klečka, Dagmar Čámská

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This paper is focused on the investigation of productivity (total productivity and partial productivity). The value productivity is an indicator of level and changes in technical economic efficiency of production factors. It represents an important factor in achieving corporate objectives. This text works with the contemporary concept of value productivity that means that indicators of the productivity express the effect of economic efficiency not only of inputs consumption, but also of inputs binding efficiency. This approach is based on principles of the economic profit, respectively the economic value added (EVA). The research is done on the sample of Czech enterprises operating in the automotive industry in the regions of Liberec and the Central Bohemia. The data sample covers the time period 2006-2011 which allows the comparison of development before crisis and during crisis period. It enables to discover the companies' reaction during crises and the regional comparison allows to showing if there are significant differences between regions.

Keywords: automotive industry, Czech Republic, economic efficiency, regional comparison, value productivity

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Authors: Amna Javed, Hisashi Masuda, Youji Kohda

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This study examines the value analysis in Islamic and conventional banking services in Pakistan. Many scholars have focused on co-creation of values in services but mainly economic values not non-economic. As Islamic banking is based on Islamic principles that are more concerned with non-economic values (well-being, partnership, fairness, trust worthy, and justice) than economic values as money in terms of interest. This study is important to know the providers point of view about the co-created values, because, it may be more sustainable and appropriate for today’s unpredictable socioeconomic environment. Data were collected from 4 banks (2 Islamic and 2 conventional banks). Text mining technique is applied for data analysis, and values with 100% occurrences in Islamic banking are chosen. The results reflect that Islamic banking is more centric towards non-economic values than economic values and it promotes team work and partnership concept by applying Islamic spirit and trust worthiness concept.

Keywords: economic values, Islamic banking, non-economic values, value system

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Authors: Aiza Dalman

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Words are powerful when these are used wisely and strategically. In this study, twelve (12) campaign speeches of President Donald J. Trump were analyzed as to frequently used words and ethos, pathos and logos being employed. The speeches were read thoroughly, analyzed and interpreted. With the use of Word Counter Tool and Text Analyzer software accessible online, it was found out that the word ‘will’ has the highest frequency of 121, followed by Hillary (58), American (38), going (35), plan and Clinton (32), illegal (30), government (28), corruption (26) and criminal (24). When the speeches were analyzed as to ethos, pathos and logos, on the other hand, it revealed that these were all employed in his speeches. The statements under these pointed out against Hillary or in his favor. The unique strategy of President Donald J. Trump as to frequently used words and ethos, pathos and logos in persuading people perhaps lead the way to his victory.

Keywords: campaign speeches, corpus analysis, ethos, logos and pathos, power of words

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Authors: Sikiru Ademola Adewale, Joe Thomas, Bolanle Hafiz Matti, Tosin Ige

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This project demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness.

Keywords: decoder, encoder, many-to-many mapping, video captioning, 2-gram BLEU

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Authors: Mi Sun Park

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Biotechnology provides us with technological solutions to resource-based challenges facing the global society. A bioeconomy or bio-based economy emerged as all economic activities derived from biotechnology. This paper aims to understand discourses on bioeconomy in the sector of agriculture with three dimensions; media discourse, science discourse, and policy discourse. For achieving research goals, content analysis was applied to this research. Media articles, academic journal articles and policy documents published from 2000 to 2016 were collected in South Korea. The text was coded and analyzed with the categories of speakers and their arguments. The research findings indicate that powerful actors and key messages of bioeconomy in South Korean agriculture. Differences and similarities among media, science, and policy were examined. Therefore this case study can contribute to understanding dynamic interaction and interfaces of media, science and policy discourse on biotechnology in the sector of agriculture.

Keywords: media, discourse, bioeconomy, agriculture

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Authors: Dina Sabirova, Madina Moldagali

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The rapid development of new media, especially the Internet, has led to major changes in folk culture. The net space is increasingly becoming a creation of the ‘folk’ imagination, saturated with multimedia stories with collective authorship, like traditional folklore. Moreover, the Internet picks up and changes old folklore traditions, such as the form of publication, the way of storytelling, or gave a new morality to the ‘old tales’. In this article, the similarities and differences between Internet folklore/ cyber-folklore/ digital folklore and oral folk art were examined by using the material of modern Kazakh authors. The relationship between tradition and innovation was studied in order to interpret the sequence of the authors' research taking into account the realities. The material of the article was the prose texts of Kazakh writers published in internet magazines and social networks. An immanent and intertextual analysis of the text was carried out. Thus, the new forms of Internet folklore lead to new forms of expression and social morality in society

Keywords: internet literature, modern Kazakhstani authors, net folklore, oral folk art

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Authors: Anam Alam, Rahim Jamaluddin Kanji

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With the rapid growth of event in everywhere, event extraction has now become an important matter to retrieve the information from the unstructured data. One of the challenging problems is to extract the event from it. An event is an observable occurrence of interaction among entities. The paper investigates the effectiveness of event extraction capabilities of three software tools that are Wandora, Nitro and SPSS. We performed standard text mining techniques of these tools on the data sets of (i) Afghan War Diaries (AWD collection), (ii) MUC4 and (iii) WebKB. Information retrieval measures such as precision and recall which are computed under extensive set of experiments for Event Extraction. The experimental study analyzes the difference between events extracted by the software and human. This approach helps to construct an algorithm that will be applied for different machine learning methods.

Keywords: event extraction, Wandora, nitro, SPSS, event analysis, extraction method, AFG, Afghan War Diaries, MUC4, 4 universities, dataset, algorithm, precision, recall, evaluation

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Authors: Igor Guatelli

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The universe of aesthetic perception entails impasses about sensitive divergences that each text or visual object may be subjected to. If approached through intertextuality that is not based on the misleading notion of kinships or similarities a priori admissible, the possibility of anachronistic, heterogeneous - and non-diachronic - assemblies can enhance the emergence of interval movements, intermediate, and conflicting, conducive to a method of reading, interpreting, and assigning meaning that escapes the rigid antinomies of the mere being and non-being of things. In negative, they operate in a relationship built by the lack of an adjusted meaning set by their positive existences, with no remainders; the generated interval becomes the remnant of each of them; it is the opening that obscures the stable positions of each one. Without the negative of absence, of that which is always missing or must be missing in a text, concept, or image made positive by history, nothing is perceived beyond what has been already given. Pairings or binary oppositions cannot lead only to functional syntheses; on the contrary, methodological disturbances accumulated by the approximation of signs and entities can initiate a process of becoming as an opening to an unforeseen other, transformation until a moment when the difficulties of [re]conciliation become the mainstay of a future of that sign/entity, not envisioned a priori. A counter-history can emerge from these unprecedented, misadjusted approaches, beginnings of unassigned injunctions and disjunctions, in short, difficult alliances that open cracks in a supposedly cohesive history, chained in its apparent linearity with no remains, understood as a categorical historical imperative. Interstices are minority fields that, because of their opening, are capable of causing opacity in that which, apparently, presents itself with irreducible clarity. Resulting from an incomplete and maladjusted [at the least dual] marriage between the signs/entities that originate them, this interval may destabilize and cause disorder in these entities and their own meanings. The interstitials offer a hyphenated relationship: a simultaneous union and separation, a spacing between the entity’s identity and its otherness or, alterity. One and the other may no longer be seen without the crack or fissure that now separates them, uniting, by a space-time lapse. Ontological, semantic shifts are caused by this fissure, an absence between one and the other, one with and against the other. Based on an improbable approximation between some conceptual and semantic shifts within the design production of architect Rem Koolhaas and the textual production of the philosopher Jacques Derrida, this article questions the notion of unity, coherence, affinity, and complementarity in the process of construction of thought from these ontological, epistemological, and semiological fissures that rattle the signs/entities and their stable meanings. Fissures in a thought that is considered coherent, cohesive, formatted are the negativity that constitutes the interstices that allow us to move towards what still remains as non-identity, which allows us to begin another story.

Keywords: clearing, interstice, negative, remnant, spectrum

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Authors: S. Djaziri, F. Sket, A. Hynowska, S. Milenkovic

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The use of Fe-Al based intermetallics as an alternative to Cr/Ni based stainless steels is very promising for industrial applications that use critical raw materials parts under extreme conditions. However, the development of advanced Fe-Al based intermetallics with appropriate mechanical properties presents several challenges that involve appropriate processing and microstructure control. A processing strategy is being developed which aims at producing a net-shape porous Fe-based preform that is infiltrated with molten Al or Al-alloy. In the present work, porous Fe-based preforms produced by two different methods (selective laser melting (SLM) and Kochanek-process (KE)) are studied during infiltration with molten aluminum. In the objective to elucidate the mechanisms underlying the formation of Fe-Al intermetallic phases during infiltration, an in-house furnace has been designed for in situ observation of infiltration at synchrotron facilities combining x-ray radiography (XR) and x-ray diffraction (XRD) techniques. The feasibility of this approach has been demonstrated, and information about the melt flow front propagation has been obtained. In addition, reactive infiltration has been achieved where a bi-phased intermetallic layer has been identified to be formed between the solid Fe and liquid Al. In particular, a tongue-like Fe₂Al₅ phase adhering to the Fe and a needle-like Fe₄Al₁₃ phase adhering to the Al were observed. The growth of the intermetallic compound was found to be dependent on the temperature gradient present along the preform as well as on the reaction time which will be discussed in view of the different obtained results.

Keywords: combined synchrotron radiography and diffraction, Fe-Al intermetallic compounds, in-situ molten Al infiltration, porous solid Fe preforms

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Authors: Albert W. K. Tan

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This research consolidates a comprehensive array of publications from peer-reviewed journals, case studies, and seminar reports focused on reverse logistics and network design. By synthesizing this secondary knowledge, our objective is to identify and articulate key decision factors crucial to reverse logistics network design for e-commerce. Through this exploration, we aim to present a refined mathematical model that offers valuable insights for companies seeking to optimize their reverse logistics operations. The primary goal of this research endeavor is to develop a comprehensive framework tailored to advising organizations and companies on crafting effective networks for their reverse logistics operations, thereby facilitating the achievement of their organizational goals. This involves a thorough examination of various network configurations, weighing their advantages and disadvantages to ensure alignment with specific business objectives. The key objectives of this research include: (i) Identifying pivotal factors pertinent to network design decisions within the realm of reverse logistics across diverse supply chains. (ii) Formulating a structured framework designed to offer informed recommendations for sound network design decisions applicable to relevant industries and scenarios. (iii) Propose a mathematical model to optimize its reverse logistics network. A conceptual framework for designing a reverse logistics network has been developed through a combination of insights from the literature review and information gathered from company websites. This framework encompasses four key stages in the selection of reverse logistics operations modes: (1) Collection, (2) Sorting and testing, (3) Processing, and (4) Storage. Key factors to consider in reverse logistics network design: I) Centralized vs. decentralized processing: Centralized processing, a long-standing practice in reverse logistics, has recently gained greater attention from manufacturing companies. In this system, all products within the reverse logistics pipeline are brought to a central facility for sorting, processing, and subsequent shipment to their next destinations. Centralization offers the advantage of efficiently managing the reverse logistics flow, potentially leading to increased revenues from returned items. Moreover, it aids in determining the most appropriate reverse channel for handling returns. On the contrary, a decentralized system is more suitable when products are returned directly from consumers to retailers. In this scenario, individual sales outlets serve as gatekeepers for processing returns. Considerations encompass the product lifecycle, product value and cost, return volume, and the geographic distribution of returns. II) In-house vs. third-party logistics providers: The decision between insourcing and outsourcing in reverse logistics network design is pivotal. In insourcing, a company handles the entire reverse logistics process, including material reuse. In contrast, outsourcing involves third-party providers taking on various aspects of reverse logistics. Companies may choose outsourcing due to resource constraints or lack of expertise, with the extent of outsourcing varying based on factors such as personnel skills and cost considerations. Based on the conceptual framework, the authors have constructed a mathematical model that optimizes reverse logistics network design decisions. The model will consider key factors identified in the framework, such as transportation costs, facility capacities, and lead times. The authors have employed mixed LP to find the optimal solutions that minimize costs while meeting organizational objectives.

Keywords: reverse logistics, supply chain management, optimization, e-commerce

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Authors: Wararit Panichkitkosolkul

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The unit root tests based on the robust estimator for the first-order autoregressive process are proposed and compared with the unit root tests based on the ordinary least squares (OLS) estimator. The percentiles of the null distributions of the unit root test are also reported. The empirical probabilities of Type I error and powers of the unit root tests are estimated via Monte Carlo simulation. Simulation results show that all unit root tests can control the probability of Type I error for all situations. The empirical power of the unit root tests based on the robust estimator are higher than the unit root tests based on the OLS estimator.

Keywords: autoregressive, ordinary least squares, type i error, power of the test, Monte Carlo simulation

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Authors: Alexander Wachtel, Michael T. Franzen, Walter F. Tichy

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Programming requires years of training. With natural language and end user development methods, programming could become available to everyone. It enables end users to program their own devices and extend the functionality of the existing system without any knowledge of programming languages. In this paper, we describe an Interactive Spreadsheet Processing Module (ISPM), a natural language interface to spreadsheets that allows users to address ranges within the spreadsheet based on inferred table schema. Using the ISPM, end users are able to search for values in the schema of the table and to address the data in spreadsheets implicitly. Furthermore, it enables them to select and sort the spreadsheet data by using natural language. ISPM uses a machine learning technique to automatically infer areas within a spreadsheet, including different kinds of headers and data ranges. Since ranges can be identified from natural language queries, the end users can query the data using natural language. During the evaluation 12 undergraduate students were asked to perform operations (sum, sort, group and select) using the system and also Excel without ISPM interface, and the time taken for task completion was compared across the two systems. Only for the selection task did users take less time in Excel (since they directly selected the cells using the mouse) than in ISPM, by using natural language for end user software engineering, to overcome the present bottleneck of professional developers.

Keywords: natural language processing, natural language interfaces, human computer interaction, end user development, dialog systems, data recognition, spreadsheet

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Authors: Anuta Mukherjee, Saswati Mukherjee

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Data is growing everyday. Social networking sites such as Twitter are becoming an integral part of our daily lives, contributing a large increase in the growth of data. It is a rich source especially for sentiment detection or mining since people often express honest opinion through tweets. However, although sentiment analysis is a well-researched topic in text, this analysis using Twitter data poses additional challenges since these are unstructured data with abbreviations and without a strict grammatical correctness. We have employed collision theory to achieve sentiment analysis in Twitter data. We have also incorporated discourse analysis in the collision theory based model to detect accurate sentiment from tweets. We have also used the retweet field to assign weights to certain tweets and obtained the overall weightage of a topic provided in the form of a query. Hadoop has been exploited for speed. Our experiments show effective results.

Keywords: sentiment analysis, twitter, collision theory, discourse analysis

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Authors: Nidhi Kaushal, Sanjit Mishra

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Narrative writings in literature are ample source of knowledge and easily understandable. In every old tradition, we found that people learn ethics from oral tales. They had their leaders and lessons of leadership in their stories. In India, we have sufficient amount of stories of leaders. Whether the story is of an ordinary person or a corporate leader of large firm, it always has a unique message of motivation. The objective of this paper is to elaborate the story lines in literature and get the leadership lessons from them, so that we can set up a new concept of leadership based on scholarship of literature. This is our hypothesis that leadership lessons can be learned from the study of literary writings and it can also act an innovative way of learning the management skills through literature. The role of the leader can be familiarly communicated in the form of the tales. Describing a positive psychological narrative from the text is the best way to manifesting an idea into the minds of people. We accomplished this paper that leadership as an attribute can be learned from the folk psychological literary writings.

Keywords: leadership, literature, management, psychology

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Authors: Laura Rueda-Gensini, Jader Rodríguez, Juan C. Cruz, Carolina Munoz-Camargo

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There is an emerging interest in botanicals and plant extracts for medicinal practices due to their widely reported health benefits. A large variety of phytochemicals found in plants have been correlated with antioxidant, immunomodulatory, and analgesic properties, which makes plant-derived products promising candidates for modulating the progression and treatment of numerous diseases. Non-centrifugal cane sugar (NCS), in particular, has been known for its high antioxidant and nutritional value, but composition-wise variability due to changing environmental and processing conditions have considerably limited its use in the nutraceutical and biomedical fields. This work is therefore aimed at assessing the effect of thermal exposure during NCS production over its bioactive composition and, in turn, its therapeutic value. Accordingly, two modified dehydration methods are proposed that employ: (i) vacuum-aided evaporation, which reduces the necessary temperatures to dehydrate the sample, and (ii) window refractance evaporation, which reduces thermal exposure time. The biochemical composition of NCS produced under these two methods was compared to traditionally-produced NCS by estimating their total polyphenolic and protein content with Folin-Ciocalteu and Bradford assays, as well as identifying the major phenolic compounds in each sample via HPLC-coupled mass spectrometry. Their antioxidant activities were also compared as measured by their scavenging potential of ABTS and DPPH radicals. Results show that the two modified production methods enhance polyphenolic and protein yield in resulting NCS samples when compared to traditional production methods. In particular, reducing employed temperatures with vacuum-aided evaporation demonstrated to be superior at preserving polyphenolic compounds, as evidenced both in the total and individual polyphenol concentrations. However, antioxidant activities were not significantly different between these. Although additional studies should be performed to determine if the observed compositional differences affect other therapeutic activities (e.g., anti-inflammatory, analgesic, and immunoprotective), these results suggest that reducing thermal exposure holds great promise for the production of natural products with enhanced nutritional value.

Keywords: non-centrifugal cane sugar, polyphenolic compounds, thermal processing, antioxidant activity

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Authors: Asma Ben Abdallah

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Language has a powerful influence over people and their actions. The language of advertising has a very great impact on the consumer. It makes use of different features from the linguistic continuum. The present paper attempts to apply the theories of stylistics to the analysis of advertising texts. In order to decipher the stylistic features of the advertising discourse, 30 advertising text samples designed by MA Business students have been selected. These samples have been analyzed at the level of design and content. The study brings insights into the use of stylistic devices in advertising, and it reveals that both linguistic and non-linguistic features of advertisements are frequently employed to develop a well-thought-out design and content. The practical significance of the study is to highlight the specificities of the advertising genre so that people interested in the language of advertising (Business students and ESP teachers) will have a better understanding of the nature of the language used and the techniques of writing and designing ads. Similarly, those working in the advertising sphere (ad designers) will appreciate the specificities of the advertising discourse.

Keywords: the language of advertising, advertising discourse, ad design, stylistic features

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Authors: B. Elshafei, X. Mao

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The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.

Keywords: data fusion, Gaussian process regression, signal denoise, temporal extrapolation

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Authors: Lien-Fa Lin, Yung-Ming Li, Fu-Shun Hsieh

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With the advancement of information technology and development of group commerce, people have obviously changed in their lifestyle. However, group commerce faces some challenging problems. The products or services provided by vendors do not satisfactorily reflect customers’ opinions, so that the sale and revenue of group commerce gradually become lower. On the other hand, the process for a formed customer group to reach group-purchasing consensus is time-consuming and the final decision is not the best choice for each group members. In this paper, we design a social decision support mechanism, by using group discussion message to recommend suitable options for group members and we consider social influence and personal preference to generate option ranking list. The proposed mechanism can enhance the group purchasing decision making efficiently and effectively and venders can provide group products or services according to the group option ranking list.

Keywords: social network, group decision, text mining, group commerce

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Authors: Tannistha Dasgupta

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This study explores the aspects of second language (L2) spoken fluency that are related to L2 linguistic knowledge and processing skill. It draws on Levelt’s ‘blueprint’ of the L2 speaker which discusses the cognitive issues underlying the act of speaking. However, L2 speaking assessments have largely neglected the underlying mechanism involved in language production; emphasis is given on the relationship between subjective ratings of L2 speech sample and objectively measured aspects of fluency. Hence, in this study, the relation between L2 linguistic knowledge and processing skill i.e. Cognitive Fluency (CF), and objectively measurable aspects of L2 spoken fluency i.e. Utterance Fluency (UF) is examined. The participants of the study are L2 learners of English, studying at high school level in Hyderabad, India. 50 participants with intermediate level of proficiency in English performed several lexical retrieval tasks and attention-shifting tasks to measure CF, and 8 oral tasks to measure UF. Each aspect of UF (speed, pause, and repair) were measured against the scores of CF to find out those aspects of UF which are reliable indicators of CF. Quantitative analysis of the data shows that among the three aspects of UF; speed is the best predictor of CF, and pause is weakly related to CF. The study suggests that including the speed aspect of UF could make L2 fluency assessment more reliable, valid, and objective. Thus, incorporating the assessment of psycholinguistic mechanisms into L2 spoken fluency testing, could result in fairer evaluation.

Keywords: attention-shifting, cognitive fluency, lexical retrieval, utterance fluency

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Authors: Andrei Nosov

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This paper discusses the development directions of dialogue systems within the digitalisation of the railway industry, where technologies based on conversational AI are already potentially applied or will be applied. Conversational AI is one of the popular natural language processing (NLP) tasks, as it has great prospects for real-world applications today. At the same time, it is a challenging task as it involves many areas of NLP based on complex computations and deep insights from linguistics and psychology. In this review, we focus on dialogue systems and their implementation in the railway domain. We comprehensively review the state-of-the-art research results on dialogue systems and analyse them from three perspectives: type of problem to be solved, type of model, and type of system. In particular, from the perspective of the type of tasks to be solved, we discuss characteristics and applications. This will help to understand how to prioritise tasks. In terms of the type of models, we give an overview that will allow researchers to become familiar with how to apply them in dialogue systems. By analysing the types of dialogue systems, we propose an unconventional approach in contrast to colleagues who traditionally contrast goal-oriented dialogue systems with open-domain systems. Our view focuses on considering retrieval and generative approaches. Furthermore, the work comprehensively presents evaluation methods and datasets for dialogue systems in the railway domain to pave the way for future research. Finally, some possible directions for future research are identified based on recent research results.

Keywords: digitalisation, railway, dialogue systems, conversational AI, natural language processing, natural language understanding, natural language generation

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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Authors: Sadeq Al Yaari, Ayman Al Yaari, Adham Al Yaari, Montaha Al Yaari, Aayah Al Yaari, Sajedah Al Yaari

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Aims: To summarize and critically review involved articles for the purpose of investigating the research ethics in them. It also tests the hypothesis, identifying causal relationship, association between variables and differences between/ among groups of participants Design: This is quasi experimental study wherein scientific models were included. It starts from the ideas before the researchers draw the questions, formulate the hypothesis and seek for the solutions. Hypothesis was brief and to the point. A data collection form was constructed. The researchers made use of speculative, presumptive, stipulated and conclusive propositions. Data are statistically analyzed and visualized and are treated objectively in light of the characteristics of a good research. Outcomes: Results and discussion are relevant to the statement of the problem and research objectives. Principles of ethical research were met where the researchers ensured high ethical standards. Variables’ types are scientifically analyzed.

Keywords: research, method, analysis, speech, text

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Authors: Prasanna Haddela

Abstract:

Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets.

Keywords: evolved search queries, Sinhala document classification, Lucene Sinhala analyzer, interpretable text classification, genetic algorithm

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Authors: Zunaira Zaman, David Bohannon

Abstract:

This study explores the application of machine learning (ML) for detecting security vulnerabilities in source code. The research aims to assist organizations with large application portfolios and limited security testing capabilities in prioritizing security activities. ML-based approaches offer benefits such as increased confidence scores, false positives and negatives tuning, and automated feedback. The initial approach using natural language processing techniques to extract features achieved 86% accuracy during the training phase but suffered from overfitting and performed poorly on unseen datasets during testing. To address these issues, the study proposes using the abstract syntax tree (AST) for Java and C++ codebases to capture code semantics and structure and generate path-context representations for each function. The Code2Vec model architecture is used to learn distributed representations of source code snippets for training a machine-learning classifier for vulnerability prediction. The study evaluates the performance of the proposed methodology using two datasets and compares the results with existing approaches. The Devign dataset yielded 60% accuracy in predicting vulnerable code snippets and helped resist overfitting, while the Juliet Test Suite predicted specific vulnerabilities such as OS-Command Injection, Cryptographic, and Cross-Site Scripting vulnerabilities. The Code2Vec model achieved 75% accuracy and a 98% recall rate in predicting OS-Command Injection vulnerabilities. The study concludes that even partial AST representations of source code can be useful for vulnerability prediction. The approach has the potential for automated intelligent analysis of source code, including vulnerability prediction on unseen source code. State-of-the-art models using natural language processing techniques and CNN models with ensemble modelling techniques did not generalize well on unseen data and faced overfitting issues. However, predicting vulnerabilities in source code using machine learning poses challenges such as high dimensionality and complexity of source code, imbalanced datasets, and identifying specific types of vulnerabilities. Future work will address these challenges and expand the scope of the research.

Keywords: code embeddings, neural networks, natural language processing, OS command injection, software security, code properties

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Authors: Jana Teplá

Abstract:

This text focuses on the visual construction of youth in press photographs in socialist Czechoslovakia. It deals with photographs in a magazine for young readers, Mladý svět, published by the Socialist Union of Youth of Czechoslovakia. The aim of this study was to develop a methodological tool for uncovering the values and the ideological messages in the strategies used in the visual construction of reality in the socialist press. Two methods of visual analysis were applied to the photographs, a quantitative content analysis and a social semiotic analysis. The social semiotic analysis focused on images representing youth in their free time. The study shows that the meaning of a socialist press photograph is a result of a struggle for ideological power between formal and informal ideologies. This struggle takes place within the process of production of the photograph and also within the process of interpretation of the photograph.

Keywords: ideology, press photography, socialist regime, social semiotics, youth

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Authors: Mohammad A. Bani-Khaled

Abstract:

In this work we use the Discrete Proper Orthogonal Decomposition transform to characterize the properties of coupled dynamics in thin-walled beams by exploiting numerical simulations obtained from finite element simulations. The outcomes of the will improve our understanding of the linear and nonlinear coupled behavior of thin-walled beams structures. Thin-walled beams have widespread usage in modern engineering application in both large scale structures (aeronautical structures), as well as in nano-structures (nano-tubes). Therefore, detailed knowledge in regard to the properties of coupled vibrations and buckling in these structures are of great interest in the research community. Due to the geometric complexity in the overall structure and in particular in the cross-sections it is necessary to involve computational mechanics to numerically simulate the dynamics. In using numerical computational techniques, it is not necessary to over simplify a model in order to solve the equations of motions. Computational dynamics methods produce databases of controlled resolution in time and space. These numerical databases contain information on the properties of the coupled dynamics. In order to extract the system dynamic properties and strength of coupling among the various fields of the motion, processing techniques are required. Time- Proper Orthogonal Decomposition transform is a powerful tool for processing databases for the dynamics. It will be used to study the coupled dynamics of thin-walled basic structures. These structures are ideal to form a basis for a systematic study of coupled dynamics in structures of complex geometry.

Keywords: coupled dynamics, geometric complexity, proper orthogonal decomposition (POD), thin walled beams

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

Abstract:

An essential task in the field of artificial intelligence is to allow computers to interact with people through natural language. Therefore, researches such as virtual assistants and dialogue systems have received widespread attention from industry and academia. The response generation plays a crucial role in dialogue systems, so to push forward the research on this topic, this paper surveys various methods for response generation. We sort out these methods into three categories. First one includes finite state machine methods, framework methods, and instance methods. The second contains full-text indexing methods, ontology methods, vast knowledge base method, and some other methods. The third covers retrieval methods and generative methods. We also discuss some hybrid methods based knowledge and deep learning. We compare their disadvantages and advantages and point out in which ways these studies can be improved further. Our discussion covers some studies published in leading conferences such as IJCAI and AAAI in recent years.

Keywords: deep learning, generative, knowledge, response generation, retrieval

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