Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Gesture Recognition System Based on Flex Sensors

Sign language is the main means of communication for hearing impaired and mute individuals, special populations, and special situations. However, the current low popularity of sign language has also hindered the deep contact between hearing impaired and mute people and the current society. In order to further promote the application level and service ability of sign language, establish a convenient communication bridge for special populations and scenes, and meet the application requirements of portability, real-time, reliability, and stability, this article designs a gesture recognition system based on multiple sensors. This system combines transfer learning and has functions such as Bluetooth communication, data acquisition and processing, gesture recognition, and result display. The transfer experiment results showed that the accuracy of gesture recognition reached 94.07% and 92.53%, respectively, which were 9.41% and 11.73% higher than before the transfer. Moreover, the accuracy and stability of gesture recognition were improved through transfer learning, and the universality and robustness of the model were improved. Through the testing and gesture recognition experiments of the gesture recognition system, it has been shown that the recognition system can recognize gesture movements of different groups of people, with good recognition rate and stability.
Edge Devices Friendly Self-Supervised Monocular Depth Estimation via Knowledge Distillation

Self-supervised monocular depth estimation (MDE) has great potential for deployment in a wide range of applications, including virtual reality, autonomous driving, and robotics. Nevertheless, most previous studies focused on complex architectures to pursue better performance in MDE. In this letter, we aim to develop a lightweight yet highly effective self-supervised MDE model that can deliver competitive performance in edge devices. We introduce a novel MobileViT-based depth (MViTDepth) network that can effectively capture both local features and global information by leveraging the strengths of convolutional neural networks (CNNs) and a vision transformer (ViT). To further compress the proposed MViTDepth model, we employ knowledge distillation, which leads to improved depth estimation performance. Specifically, the self-supervised MDE MonoViT is used as a teacher model to construct the knowledge distillation loss for optimizing a student model. Experimental results on benchmark datasets demonstrate that the proposed MViTDepth significantly outperforms Monodepth2 in terms of parameters and accuracy, thereby indicating its superiority in application to edge devices.
Computer Processing of Language: Where Archaeological Discourse and Computers Meet

Archaeological practice produces a vast amount of documentation about our past in form of archaeological narratives in free-format texts (internal reports, academic publications or dissemination activities). This huge amount of unstructured textual documentation has produced in recent years an increasingly interest in the application of computational processing of natural language as part of the archaeological research and practice. Advancing in the understanding, analysis, processing and exploitation of these archaeological narratives by machines requires an in-depth training work in methods and techniques in natural language processing from other areas, such as linguistics, software engineering or artificial intelligence. This chapter provides a briefly historical and typological review of the different computational approaches for natural language processing (which will be addressed in subsequent chapters of the volume), to later focus on the computational processing of archaeological discourse and its possibilities. The archaeological discourse in form of free-textual narratives, constitutes the expression and reflection of the archaeological knowledge produced. Thus, a computational discursive treatment is necessary to advance in the archaeologist-computers relation. The different computational approaches adopted at the discourse level and what kind of applications in archaeology are possible will be seen here.
Introduction to Discourse Analysis and Argumentation Theory

Discourses analysis is an explicit and systematic study of the structures, strategies and manoeuvres of texts or talks in terms of a given theoretical framework. The current stage of computational technologies allows us to tackle this task from different perspectives. Along this chapter, I explore how an argument can be characterised and analysed from three theoretical perspectives (logic, pragmatic and cognitive). Each of these approaches lead us to different types of discourse analysis, emphasizing different angles of the same text, which shows the richness of this analytical framework. After that, I describe the main steps for transforming raw text into an annotated corpus, essential to draw any reliable conclusions from it. Annotation is a complex task, essential for a good quality analysis of discourse, but it can be split into doable steps. Finally, the chapter concludes with some ideas for the exploitation of these results and how they can be disseminated.
What Archaeological Texts Argue About: Denotations and Ontological Proxies

Argumentation-oriented discourse analysis usually focuses on what is being said and how, following the text under analysis quite literally, and paying little attention to the things in the world to which the text refers. However, to perform argumentation-oriented discourse analysis, one must assume certain conceptualisations by the author in order to interpret and reconstruct propositions and argumentation structures. These conceptualisations are rarely captured as a product of the analysis process. In this chapter, we argue that considering the ontology to which a discourse refers as well as the text itself provides a richer and more useful representation of the discourse and its argumentation structures, facilitates intertextual analysis, and improves understandability of the analysis products. To this end, we propose the notions of ontological proxies and denotations, i.e. the conceptual artefacts that connect elements in the argumentation structure to the associated ontology elements, and the propositional segments that anchor these to the text, respectively.
Dealing with Vagueness in Archaeological Discourses

Vagueness is an intriguing topic, especially in the humanities. It has been treated as a problem that contaminates information and makes research harder, but also as an expression of human subjectivity that enriches our accounts of the world. Vagueness is studied by philosophers, treated by computer scientists, and used by archaeologists intentionally or unintentionally. This chapter aims to provide a comprehensive overview of how vagueness has been treated from philosophy and computer science, and offer a synthetic theoretical framework to operationalise vagueness on archaeological discourses that can be applied for practical purposes. To illustrate this, an empirical study is described. Vagueness is everywhere, and archaeology is no exception. From quantitative measurements or datings to uncertain function or use assessments, archaeologists deal with imprecise, inaccurate and uncertain information all the time. In addition, vagueness is strongly embedded in language. Human language contains a number of mechanisms to express vagueness, such as hedges (“approximately”, “might”) or ranges (“between 12 and 15”). This means that any archaeological discourse is likely to employ devices like these to describe relevant information. In addition, vagueness is a computational challenge. Common representations of knowledge that are stored on computer systems discard vagueness, thus losing nuance and richness. Computer scientists have tried to incorporate these aspects into data through approaches such as fuzzy logic or protoforms, which provide richer accounts of what is being represented, but at the cost of higher complexity. This chapter begins with a philosophical introduction to vagueness, and then it describes how vagueness has been treated from computer science. Then, a vagueness framework is proposed based on the previous, and an empirical study is described to illustrate practical applications.
Argument Mining and Analytics in Archaeology

The ever increasing volume of textual data ripe for analysis has driven computational efforts to unlock the wealth of information contained within. The automated reconstruction of the argumentative structure of texts, Argument Mining, meets this challenge by not only showing what claims are being advanced (conclusion), but also why (premises). In this chapter, we start by surveying some of the foundations and state-of-the-art of argument mining and how they can be applied in domain-specific tasks in different research contexts, such as archaeology. After that, we discuss two central themes in argumentation critical for argument mining: argument schemes (common patterns of reasoning) and discourse markers (that function as argumentative indicators). Next, we describe how to create specific datasets for argument mining systems by means of annotated text corpora and how to store it using the Argument Interchange Format ontology. We conclude explaining Argument Analytics, a visual way to deliver the output of argument mining systems to its potential users.
Information Extraction and Machine Learning for Archaeological Texts

Archaeologists are creating ever-increasing amounts of textual data. So much in fact, that manual reading and inspection has become practically impossible. By leveraging computational approaches, it is possible to extract relevant information from this big data, allowing for more efficient research and new analyses. In this chapter, methods and techniques to extract information from archaeological texts through Machine Learning are introduced and discussed, with a focus on practical examples. After reading the chapter, you should have a clear grasp on the possibilities of text mining in archaeology, the current state of research, and enough information to start your own text analyses.
A New Approach to Interoperable Argumentation Documentation

The chapter outlines the development of support for inference chains in the CIDOC Conceptual Reference Model family of standards. It illustrates the capabilities available in CRMbase and notes the limitation that the evolution of any assertion or knowledge revision cannot be adequately documented. It then continues by detailing the extended facilities delivered by CRMinf that address this short-coming. Next it considers the added potential to document the scholarly reading of texts that are considered specious and finally looks to future work on the extension.
Low 3D-HEVC Depth Map Intra Modes Selection Complexity Based on Clustering Algorithm and an Efficient Edge Detection

In 2016, ITU-T Video Coding Experts Group (VCEG) and ISO/IEC MPEG finalize the recent 3-dimensional video coding standard founded on Multi-view texture Videos plus Depth maps (MVD) format called, the 3D extension of High-Efficiency Video Coding (3D-HEVC). In the current design of 3D-HEVC, the conventional intra-prediction modes and Depth Modeling Modes are exploited sophistically to improve the depth map coding performance. However, the coding efficiency improvement is achieved at the cost of computational complexity load, which obstructs 3D-High Efficiency Video Coding standards from practical world applications. In the 3D-HEVC, the depth maps are described by enormous homogeneous regions, and it is observed that for most of the cases, the DMMs and all or the majority of angular intra modes could be skipped for the homogenous area. Thus, we propose an efficient intra-coding model decision for a depth map founded on the clustering algorithm and edge detection to resolve the above-mentioned complexity increase. Experimental results demonstrate that the proposed intra-coding model decision for depth map can decrease the encoding time up to 41.35%, with insignificant loss of rate-distortion performance.
Pectoral Muscle Segmentation Using Mammogram Images in Medio Lateral Oblique View

Mammography is a prominent and important imaging technique that is recognized as the industry benchmark for detecting breast cancer at an early stage. Computer-aided diagnosis systems (CAD) have made it possible to perform diagnostic procedures more quickly and efficiently; otherwise, if only radiologists were involved and engaged in the process, that may take weeks. These CAD systems, owing to their intensities being close to those of abnormal intensity tissue, pectoral muscles (PM), which are positioned in the upper corner of the mammography in the Medio-Lateral-Oblique view (MLO), must be separated from breast tissue. In order to accomplish our goal, we must first start the preprocessing phase to remove artifacts, labels and digitized lines and to enhance the quality of boundaries and structures. This paper suggests and evaluates a method for removing PM from MLO mammograms. This approach is based on semantic segmentation using DeeplabV3Plus. The suggested methodology has been tested on digital mammography from the MIAS database for implementation, experimentation, and verification.
On the deflationary nature of Bitcoin

Bitcoin was presented at the end of 2008 but the question still remains whether it is a form of money or something entirely different. Bitcoin was not designed with the aim to create money in a strict sense but primarily with the intention to make the transfer of value as effective as possible. Yet, Bitcoin has a capacity to take on the role of money, and that capacity was recognized in court cases. In this regard, the paper presents the results of the primarily empirical but also theoretical research conducted previously on the volatile but still very deflationary nature of Bitcoin and its effect on monetary obligations. The idea that cryptocurrencies can be also used as a hedging instrument to prevent the negative effects of domestic currency depreciation might be controversial for a number of reasons, one of which is certainly the volatile nature of bitcoin "price". We stress that periodic depreciation of its value does not mean that bitcoin is inflationary. On the contrary, bitcoin is deflationary by nature, which is evident in different in-built mechanisms and new ways of application. In this paper, the author uses different analytical method techniques to single out and describe various deflatory mechanisms, both preprogramed and factual ones. The author also applies the synthetical method and its techniques, primarily abstraction and generalization, to sum up the data confirming the main hypothesis that bitcoin is by nature deflationary despite its volatility and, therefore, it can be used as a hedging mechanism.
Multivariate Time Series Data Prediction Based on Social Sentiments Community and LSTM Method (S-S-LSTM)

In this paper, we propose an S-S-LSTM prediction method that predicts a dependent variable based on dominant sentiments of influential social communities combined with other external sources. In the experimental study, we chose the LSTM method to vectorize the heterogeneous input data versus eliminate irrelevant data that may influence the number of vaccinations during the covid-19. Sentiment analysis of dominant communities on Twitter interested in the vaccination topic was developed to identify the correlation between the number of vaccinations and the mood of the general public. Next, a comparative study between predicting the number of vaccinations with and without using the results of the sentiment analysis. The performance of the system is evaluated by the root mean square error (RMSE). The results show that performance improves using community sentiment on Covid-19 vaccines based on the LSTM prediction model.
An Image Compression Approach Based on Convolutional AutoEncoder

In recent years, neural networks have demonstrated their robustness and effectiveness in many fields mainly in computer vision tasks. Hence, recently researchers in image processing domain exploit neural networks, powerful ability of representing data, to develop different compression schemes. These schemes yield images with great visual quality and high compression ratio. In this paper, we propose a compression scheme that takes advantages of convolutional Auto-Encoder (CAE) to improve image compression performance. First, an RGB image is converted to the luminance/chrominance space YCbCr then the luminance component Y is compressed using an auto-encoder based convolutional neural network (CNN) whereas the chrominance components CbCr are sub-scaled. Different parameters used to evaluate the efficiency of our proposed method are: Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Multi Scale Structural Similarity Index Measure (MS-SSIM). The results obtained show the effectiveness of our proposed approach.
DFMCloudsim: an extension of cloudsim for modeling and simulation of data fragments migration over distributed data centers

Due to the increasing volume of data for applications running on geographically distributed Cloud systems, the need for efficient data management has emerged as a crucial performance factor. Alongside basic task scheduling, the management of input data on distributed Cloud systems has become a genuine challenge, particularly with data-intensive applications. Ideally, each dataset should be stored in the same data center as its consumer tasks so as to lead to local data accesses only. However, when a given task does not need all items within one of its input datasets, sending that dataset entirely might lead to a severe time overhead. To address this concern, a data fragmentation strategy can be considered in order to partition the datasets and process them in that form. Such a strategy should be flexible enough to support any user-defined partitioning, and suitable enough to minimize the overhead of transferring the data in their fragmented form. To simulate and estimate the basic statistics of both fragmentation and migration mechanisms prior to an implementation in a real Cloud, we chose Cloudsim, with the goal of enhancing it with the corresponding extensions. Cloudsim is a popular simulator for Cloud Computing investigations. Our proposed extension is named DFMCloudsim, its goal is to provide an efficient module for implementing fragmentation and data migration strategies. We validate our extension using various simulated scenarios. The results indicate that our extension effectively achieves its main objectives and can reduce data transfer overhead by 74.75% compared to our previous work.
Crosslingual Content Scoring in Five Languages Using Machine-Translation and Multilingual Transformer Models

Abstract This paper investigates crosslingual content scoring, a scenario where scoring models trained on learner data in one language are applied to data in a different language. We analyze data in five different languages (Chinese, English, French, German and Spanish) collected for three prompts of the established English ASAP content scoring dataset. We cross the language barrier by means of both shallow and deep learning crosslingual classification models using both machine translation and multilingual transformer models. We find that a combination of machine translation and multilingual models outperforms each method individually - our best results are reached when combining the available data in different languages, i.e. first training a model on the large English ASAP dataset before fine-tuning on smaller amounts of training data in the target language.
A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations

The graph model enables a broad range of analyses; thus, graph processing (GP) is an invaluable tool in data analytics. At the heart of every GP system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, GP systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and fast, low-memory graph mutations. Existing graph structures offer a hard tradeoff among read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these tradeoffs and enables both fast read-only analytics, and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists (ALs) to achieve the best of both worlds. We compare CSR++ to CSR, ALs from the Boost Graph Library (BGL), and the following state-of-the-art update-friendly graph structures: LLAMA, STINGER, GraphOne, and Teseo. In our evaluation, which is based on popular GP algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average) while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates. We also show that both CSR++’s update throughput and analytics performance exceed those of several state-of-the-art graph structures while maintaining low memory consumption when the workload includes updates.
Neuromarketing as a Tool for Promoting Injection Medicine Services in Rus

Considering neuromarketing as a tool for promoting injection medicine services in Russia, the study provides an interpretation of the concept of "strategies for promoting injection medicine services using neuromarketing", since with the development of the Internet an independent direction of digital marketing has emerged, which provides significantly more opportunities for promoting goods and services than traditional marketing. The features of the use of neuromarketing technologies in strategies for promoting injection medicine (IM) services are determined. The study of customer preferences does not stand still; neuromarketing and new technologies for tracking consumer behavior by reading emotions and reactions to products or advertising help companies deeply study needs and create what people want. In retail outlets, tracking the customer’s journey with cameras and sensors helps identify those products that most attract the consumer’s attention. The trend that everything should be digitized is telling companies to change their mechanisms for collecting and processing consumer information. All information about the client must be digitalized in one way or another. End-to-end analytics consists of several levels: advertising, leads, sales, repeat sales. Tools are determined depending on the specifi cs and size of the company. PR and promotion is a technology in marketing, thanks to which public opinion about an organization, brand, and personality is formed over the long term. The goal of PR is to create a proper and mutually beneficial partnership between the company and society, providing reliable information about the company, products and services.