Reviewerly/exHarmony huggingface dataset:
Face Recognition Model Optimization Research Based on Embedded Platform

ABSTRACTThe development of information technology has promoted the expansion of the application field of facial recognition technology.Its mainstream recognition methods rely on deep learning algorithms for calculation, but the problem of large data computation brought by its system makes it difficult to apply in embedded platform devices.As a result, this study focuses on improving recognition systems built on lightweight backend networks and builds an embedded platform system environment using multi-scale feature fusion, anchor box size optimization, the addition of channel attention mechanism weighted features, affine face alignment, and file compilation.The experimental results showed that when the number of iterations was 300, the loss value (0.46) of the improved embedded algorithm was much smaller than that of other comparison algorithms (1.42, 1.73, 2.01), and its ACC value (0.924) was significantly better than other comparison algorithms (0.915, 0.909, 0.894).The minimum system testing time consumption was 7 ms.This deep learning embedded facial recognition algorithm has high recognition accuracy and fast running speed, and is less limited by environmental conditions and data types.It is ideally suited for use in embedded hardware devices, broadening the scope of equipment matching and facial recognition algorithms' applications.As a result, it is better suited to satisfy the demands of embedded devices and massive data processing jobs.
Prediction of Productivity Improvement Applying Simulation and FOM based on Final Machining Process Data of Large Chamber

Because of the acceleration of DX and the influence of the Fourth Industrial Revolution, productivity improvement is required for manufacturing companies to increase efficiency, reduce costs, and rapidly adapt to changing market conditions. Therefore, efficient evaluation and verification are possible if the simulation of the FOM(smart-factory operation management) and CPS(cyberphysical system) -based 3D process optimization model is analyzed by field manufacturing data as a method of predicting the effect of the improvement plan. Therefore, we analyze the process data of a large chamber using FOM, and sample the data, we determine the effect of enhancement through a simulation model, and predict the effectiveness of productivity improvement by feedback and verification. Consequently, many small and medium-sized manufacturing companies can achieve optimization of data-driven smart manufacturing sites and improve profitability through loss factor analysis and production forecasting, creating a foundation for growth as a competitive enterprise.
Digital image correlation through surface patterning of skins to monitor knee motion

Digital Image Correlation (DIC) systems have been used to measure surface displacement fields by tracking a pattern applied to a surface. Offering many benefits in comparison to conventional strain-measurement devices, the use of DIC has been reported in the study of tissue biomechanics and performance of joints underneath. In this study, a new method was developed to apply a quality speckle pattern on the skin of knees. Temporary tattoo paper provided a safe, easy, and quick way for pattern application to the skin without hindering its natural behavior. A pattern was developed for analyzing the knee during a squatting motion in which the field of view, system set up, and curvature of the knee were contributing factors to the development. This research is designed to prove the feasibility of using tattoo papers as patent indicators for DIC measurement. The software was able to map the entire region of the knee and measure the displacement of each area independent of other parts, thus making it easy to identify twisting and bending of the joint. Experimental results indicated that it is an effective method to accurately analyze the motion of the knee without the need of sophisticated equipment. This methodology can be used to understand how strain affects the knee during action and thus will be useful for various sports-related activities such as training and injury management.
Generalizing max pooling via <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si656.svg" display="inline" id="d1e8388"><mml:mrow><mml:mo>(</mml:mo><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>b</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math>-grouping functions for Convolutional Neural Networks

Due to their high adaptability to varied settings and effective optimization algorithm, Convolutional Neural Networks (CNNs) have set the state-of-the-art on image processing jobs for the previous decade. CNNs work in a sequential fashion, alternating between extracting significant features from an input image and aggregating these features locally through "pooling" functions, in order to produce a more compact representation. Functions like the arithmetic mean or, more typically, the maximum are commonly used to perform this downsampling operation. Despite the fact that many studies have been devoted to the development of alternative pooling algorithms, in practice, "max-pooling" still equals or exceeds most of these possibilities, and has become the standard for CNN construction. In this paper we focus on the properties that make the maximum such an efficient solution in the context of CNN feature downsampling and propose its replacement by grouping functions, a family of functions that share those desirable properties. In order to adapt these functions to the context of CNNs, we present (a,b)-grouping functions, an extension of grouping functions to work with real valued data. We present different construction methods for (a,b)-grouping functions, and demonstrate their empirical applicability for replacing max-pooling by using them to replace the pooling function of many well-known CNN architectures, finding promising results.
Interval-based reasoning over continuous variables using independent component analysis and Bayesian networks

Bayesian networks (BNs) can be used for probabilistic reasoning over discrete variables. Their extension to the continuous domain, however, remains an open research issue. In this work, we address the Bayesian inference problem for continuous variables when negative evidence is available. This situation is faced when, because of unreliable or imperfect sensors, some variables are observed to be at specific intervals. We propose two solutions to this issue. The first one, dubbed dynamic discrete BN (DD-BN), is simply an adaptation of the dynamic discretization method; the second, referred to as SICA, is based on spline functions and the independent component analysis (ICA) method. SICA can represent a wide range of general multivariate distributions and possesses some integration characteristics that facilitate the inference process. Empirical trials on public datasets and simulated data yielded promising results regarding inference time and accuracy. Indeed, SICA achieved the highest accuracy at the expense of increased inference time with increasing dimensions.
Exploring the potential of Siamese network for RGBT object tracking

Siamese tracking is one of the most promising object tracking methods today due to its balance of performance and speed. However, it still performs poorly when faced with some challenges such as low light or extreme weather. This is caused by the inherent limitations of visible images, and a common way to cope with it is to introduce infrared data as an aid to improve the robustness of tracking. However, most of the existing RGBT trackers are variants of MDNet (Hyeonseob Nam and Bohyung Han, Learning multi-domain convolutional neural networks for visual tracking, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 4293–4302.), which have significant limitations in terms of operational efficiency. On the contrary, the potential of Siamese tracking in the field of RGBT tracking has not been effectively exploited due to the reliance on large-scale training data. To solve this dilemma, in this paper, we propose an end-to-end Siamese RGBT tracking framework that is based on cross-modal feature enhancement and self-attention (SiamFEA). We draw on the idea of migration learning and employ local fine-tuning to reduce the dependence on large-scale RGBT data and verify the feasibility of this approach, and then we propose a reliable fusion approach to efficiently fuse the features of different modalities. Specifically, we first propose a cross-modal feature enhancement module to exploit the complementary properties of dual-modality, followed by capturing non-local attention in channel and spatial dimensions for adaptive weighted fusion, respectively. Our network was trained end-to-end on the LasHeR (Chenglong Li, Wanlin Xue, Yaqing Jia, Zhichen Qu, Bin Luo, Jin Tang, LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking, CoRR abs/2104.13202, 2021) training set and reached new SOTAs on GTOT (C. Li, H. Cheng, S. Hu, X. Liu, J. Tang, L. Lin, Learning collaborative sparse representation for grayscale-thermal tracking, IEEE Trans. Image Process, 25 (12) (2016) 5743–5756.), RGBT234 (C. Li, X. Liang, Y. Lu, N. Zhao, and J. Tang, “Rgb-t object tracking: Benchmark and baseline,” Pattern Recognition, vol. 96, p. 106977, 2019.), and LasHeR (Chenglong Li, Wanlin Xue, Yaqing Jia, Zhichen Qu, Bin Luo, Jin Tang, LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking, CoRR abs/2104.13202, 2021) while running in real-time.
An data augmentation method for source code summarization

Code comments improve the readability and intelligibility of codes, Unfortunately, code comments are often missing, or outdated in software projects, which negatively affects the efficiency of developers to infer the functionality from source code and affect the efficiency of software maintenance and evolution. To solve this problem, many source code summarization algorithms have been proposed, which automatically generate code comments from source code. However, these methods usually try to collect a large data set which contains the mapping between code comments and source code to train models. However, there are two limitations for the training sets: the insufficient data collection limitation (i.e., generate a large amount of noises-free training data) and data distribution bias limitation (i.e., generate training data for infrequently used methods). To address this issues, we have proposed a data augmentation method for code comments, named CDA-CS. Training models on the augmented dataset, the state-of-the-art algorithms can easily get a further 1.37% to 2.24% improvement in terms of different evaluation metrics (i.e., BLUE-4, METEOR, ROUGH_L).
Analysis of Factors Subjacent to the Implementation of ICTS in Higher Education

Nowadays, technology is pervasive in society and, therefore, in the economy and businesses. The so-called fourth industrial revolution, marked by the digital economy, has the potential to be enormously disruptive. Therefore, higher education must change and adapt, increasing the use of information and communication technologies (ICTs). It is necessary to detect the needs and make proposals to higher education institutions that will also be transferred to the real economy, since today's university students are tomorrow's professionals. To this end, the authors have carried out a survey of university students of different economics and business administration subjects. The answers have been treated using a non-metric multidimensional scaling. The main results obtained indicate that the response profile is very homogeneous, with a minority rejection of ICTs, predictably caused by learning difficulties or economic inequalities, issues that should be addressed in future research.
Translating Russian Literature into Italian: A Case Study between Language and Culture

Abstract: The present article focuses on some reflectionsabout the process of translation into Italian of the well-known Russian fairy tale by Aleksey Nikolaevich Tolstoy(1882-1945) Zolotoy klyuchik, ili Priklyucheniya Buratino(The Little Golden Key, or the Adventures of Burattino, 1935). Basing from the methodological point of view on some recent acquisitions of international TranslationTheory, we will examine the main “difficulties” or “challenges” for the translator present in the source text, such as anisomorphisms, linguistic connotations, neologisms, cultural realia and phraseologisms. We willalso consider the employment of different stylistic registersin the tale, often connected to the characters’ “expressiveaura” and to thick intertextual allusions to Russian Silver Age. Our lexical and stylistic choices in the translated text will be analyzed with regard to the dominants of the original work and to the specific characteristics of Russian(and Soviet) children literature of the time. Some solutionsproposed in the two published Italian versions of the 1980swill be also taken into account, questioning the opportunityand the main orientations of a new translation in relation to the kind of “implied readers” expected.
On Some Syntactic Peculiarities of the Armenian Translation of Faulkner’s Absalom, Absalom!

The grandiose novel of William Faulkner “Absalom!Absalom!” is a majestic example of a piece of writing which encompasses diverse layers of plots, social layers, and dramatic collisions, revealed through intricate linguistic apparatus, particularly a very specific syntactic organization. The paper is an attempt to make a close comparison of the original and the Armenian texts of the novel, translated by Levon Mkrtchyan. The analysis of the syntactic complexity of the novel and its appropriate rendering into the Armenian language has been conducted within the framework of three sets of translation methods, elaborated by J.P.Vinay’s and J. Darbelnet and M.Baker. The results of the investigation manifest a striking deviation from the original text on the syntactic and textual levels. Scrupulous quantitative and qualitative data and interpretation are provided in the conclusion.
Interactive Dynamic Presentation (IDP) and Semantic Faceted Search and Browsing (SFB) of the Wittgenstein <i>Nachlass</i>

Abstract In 2000 the Wittgenstein Archives at the University of Bergen (WAB) published the CD-ROM edition of Wittgenstein’s Nachlass: The Bergen Electronic Edition (BEE). Moreover, since then WAB has worked towards complementing the static CD-ROM edition with an interactive web platform that additionally allows more user-specific and more user-tailored utilizations of WAB’s Nachlass resources. The paper describes two specific web service tools of this platform: Interactive Dynamic Presentation (IDP) of the Wittgenstein Nachlass and Semantic Faceted Search and Browsing (SFB) of Wittgenstein domain metadata. The paper argues that it is only when these two tools are fully implemented and functional that WAB can adequately serve the scholarly needs of the Wittgenstein Nachlass user community. The paper discusses some selected features and functionalities of the two tools in detail. An earlier version of this paper was published as „Complementing Static Scholarly Editions with Dynamic Research Platforms: Interactive Dynamic Presentation (IDP) and Semantic Faceted Search and Browsing (SFB) for the Wittgenstein Nachlass“ in Selected Papers from the CLARIN Annual Conference 2020 , ed. by C. Navarretta &amp; M. Eskevich (Utrecht: CLARIN ERIC). In writing this paper I have benefitted from comments by K. De Smedt, N. Gangopadhyay, Ø. Gjesdal, J. Hendrickson, C. Huitfeldt, H. Al Ruweh, the Clarin 2020 conference reviewers and editors as well as Jasmin Trächtler.
Comparative Analysis of Machine Learning Algorithms in Breast Cancer Classification

Now days breast cancer has emerged as a diseases effecting women to suffer a life threating phase and eventually lead to death world wide. The prediction of breast cancer in woman at the initial stage can aggrandize recovery and chance of abidance considerably as the essential medical treatments can be adapted on time and stop its further growth. Moreover the precise categorization of tumor eliminates the avoidable treatments and patients skips from witnessing the medical emergencies. Thus the exact categorization of breast cancer either benign or malignant and the precised analysis of each is a matter of important exploration. Machine learning have extensively beneficial aspects in critical feature extraction from the breast cancer dataset. Thus the machine learning can be astronomically honored as a alternative methodology in breast cancer pattern categorization and forecast modeling. In this paper ML techniques namely Support vector machines, logistic Regression, Random forest tree and k-nearest neighbours (k-Nns) are over viewed and later performance measures compared for breast cancer analysis and prognosis.
Going with the Flow: Real-Time Max-Flow on Asynchronous Dynamic Graphs

Processing graphs that evolve over time has seen renewed attention. Processing solutions on dynamic graphs (often dubbed "graph streaming" solutions) aim to maintain the state for a graph query as the graph evolves over time, and to timely offer a solution (approximate, or precise) when requested by the user. In this space, and in the context of shared-nothing platforms, solutions have been proposed only for relatively simple problems (e.g., BFS, SSSP, PageRank), and some are limited to incremental-only evolutions traces. Support for more complex problems remains rather unexplored.
Graph Feature Management: Impact, Challenges and Opportunities

Graph features are crucial to many applications such as recommender systems and risk management systems. The process to obtain useful graph features involves ingesting data from various upstream data sources, defining the desired graph features for the required applications, constructing a feature engineering workflow to compute the features, and storing and managing the resulting features for downstream tasks (e.g., graph AI and graph BI) and for future reuse. To the majority of users, especially SMEs and non-tech companies, this process poses daunting challenges as it requires users to not only learn various methods (e.g., graph analytical algorithms, non-GNN graph embeddings, GNNs) to define graph features and program their computation, but also learn many infrastructures (e.g., upstream databases, downstream ML systems, graph analytics systems) to compute, manage and use the graph features in production. These challenges have significantly restricted the wider applications of graph technologies such as graph AI and graph BI currently in industry. The current solution provided by major graph database vendors (e.g., Amazon Neptune, Neo4j, Tiger-Graph) is to connect various upstream and downstream systems to their own graph database, which is used to compute and manage graph features. However, such a solution ties users to a specific graph infrastructure that may not be the preferred infrastructure and may even require them to re-develop their applications on a new infrastructure. In addition, a specific graph database or infrastructure often does not have the best performance for all workloads and certainly does not support the computation of all types of graph features. As a result, the existing solution limits users' flexibility in choosing their own infrastructure and their productivity in developing their applications.
Stakeholders’ involvement in social design practices in Turkey

This paper scrutinizes a selection of social design initiatives functional in the last 15 years in Turkey. By aiming to understand the motivations and values, the roles of various stakeholders, the approaches and strategies of involvement and the local requirements on these issues are explored with a critical, locally-based approach. To achieve this, the author conducted semi-structured, face-to-face interviews for thirteen initiatives with various local initiators. Analysis of these interviews was made by template analysis with descriptive and in-vivo coding methods. In final, the paper makes local-based key inferences and recommendations regarding the overall characteristics and involvement procedures of social design applications.
Towards a Unified Theory of Illocutionary Normativity

Speech acts are governed by a variety of illocutionary norms. This chapter attempts to develop a common framework to study them, building on Sbisà’s (2019) work. Four families of illocutionary rules are identified: (i) Validity rules set conditions for (actual) performance; (ii) Cooperative rules set conditions for cooperative performance; (iii) Illocutionary goals set conditions for successful performance; (iv) Illocutionary obligations set conditions for compliance. Illocutionary rules are often taken to play a constitutive role: speech acts are said to be constituted by the unique set of rules that regulates them. Against this view, it is argued that many illocutionary rules are instead best construed as rationally derivable expectations of cooperation. This alternative paradigm provides fertile ground to reconcile disagreement between speech act theorists, and yields a promising explanation of how illocutionary norms are learned and evolve through time.
Presupposition and Propaganda: A Socially Extended Analysis

Drawing on work from Marina Sbisà’s “Ideology and the Persuasive Use of Presupposition” (1999), Rae Langton has developed a powerful account of the subtle mechanisms through which hate speech and propaganda spread. However, this model has a serious limitation: it focuses too strongly on individual speech acts isolated from their wider context, rendering its applicability to a broader range of cases suspect. In this chapter, I consider the limits of presupposition accommodation to clarify the audience’s role in helping hate speakers, and claim the necessary mechanisms are much more active and social than Langton suggests, with effects not capturable in the analysis of isolated speech acts. I revisit Sbisà’s work on presupposition and develop an alternative model that (1) improves upon the Langtonian model for explaining the power of hate propaganda, and (2) is more in line with Sbisà’s original approach to ideology and the normative aspects of presupposition.
Technology and Lawyering: On Legal Practice and Value in a Digital Age

In 2010, Richard Susskind documented the impact of digital transformation and development of Artificial Intelligence (AI) in the legal industry. He portentously queried, are we on the precipice of witnessing ‘The End of Lawyers’? It is now a decade since Susskind’s seminal publication on the future of the legal profession, and certainly, many of his proclamations ring true. The changes to the legal profession have been immense: robo judges, ‘newlaw’, online dispute resolution and blockchain technologies, to name a few. A fear that robots will replace lawyers preoccupies the profession. And yet, in 2021, the legal profession has not been transformed by digital technology and lawyers have not become extinct. There are still judges hearing cases and handing down judgements, and there are still bricks and mortar courts in which lawyers appear, paper brief in hand. So what does this say about the continued relevance of legal professionals in the digital world? How can we understand the human value of solicitors, barristers and judges in the face of digital transformation? What is it about the human element in the practice of law that pushes against its replacement by technologies often lauded as better, cheaper and faster? This chapter uses the legal profession as a prism to explore these themes, specifically through the narratives of five legal actors: a barrister, a law firm partner, an in-house lawyer, an ethics lawyer and a law student. It asks them to reflect upon their human value, which perhaps the world of law can’t do without. In tracking the changing nature of the legal profession and interweaving the rich narratives of central protagonists in the legal world, this chapter contributes to the broader understanding of the digital human condition with which this book is concerned.