Reviewerly/exHarmony huggingface dataset:
Digging through Databases—A Case Study of Iron Age Sites in Finland by Generating and Analysing Keywords

The primary purpose of this study is to open the discussion of FAIR principles in Finnish archaeology. This topic has not yet been actively discussed in Finnish archaeology. In this study, we were testing whether adding keywords could increase the reusability of the Register of Ancient Sites, as the reusability level of the current register is low. The case study focuses on creating an overview of the Iron Age sites in the Itä-Häme area. Keywords were added for all of the 585 archaeological sites in the study area, based on field reports and find catalogues. After giving keywords to all of the sites, it was possible to make queries from the register and form a new, more detailed overview of the Iron Age sites in the Itä-Häme area. Our study revealed mistakes and ambiguities in the register, but adding keywords increased its reusability. However, it cannot solve the main issue: basic research has not been carried out extensively enough in Finland. Based on the overview, it can be seen that Itä-Häme was an active and important area in the Iron Age, and an especially meaningful possible new site type was identified; however, more research in this area is needed.
A heterogeneous two-stream network for human action recognition

The most widely used two-stream architectures and building blocks for human action recognition in videos generally consist of 2D or 3D convolution neural networks. 3D convolution can abstract motion messages between video frames, which is essential for video classification. 3D convolution neural networks usually obtain good performance compared with 2D cases, however it also increases computational cost. In this paper, we propose a heterogeneous two-stream architecture which incorporates two convolutional networks. One uses a mixed convolution network (MCN), which combines some 3D convolutions in the middle of 2D convolutions to train RGB frames, another one adopts BN-Inception network to train Optical Flow frames. Considering the redundancy of neighborhood video frames, we adopt a sparse sampling strategy to decrease the computational cost. Our architecture is trained and evaluated on the standard video actions benchmarks of HMDB51 and UCF101. Experimental results show our approach obtains the state-of-the-art performance on the datasets of HMDB51 (73.04%) and UCF101 (95.27%).
Pool-based active learning framework for concept prerequisite learning

Concept prerequisite relation refers to the learning order of concepts, which is useful in education. Concept prerequisite learning refers to using machine learning methods to infer prerequisite relation of a concept pair. The process of concept prerequisite learning requires large amounts of labeled data to train classifier. Usually, the labels of prerequisite relation are assigned by specialists. The specialist labelling method is costly. Thus, it is necessary to reduce labeling expense. An effective strategy is using active learning methods. In this paper, we propose a pool-based active learning framework for concept prerequisite learning named PACOL. It is a fact that concept u and concept v cannot be prerequisite of each other simultaneously. The idea of PACOL is to select the concept pair with the greatest deviation between the classifier’s prediction and the fact. Besides, PACOL can be used in two situations: when specialists assign three kinds of labels or two kinds of labels. In experiments, we constructed data sets for three subjects. Experimental results on both our constructed data sets and public data sets demonstrate that PACOL outperforms than existing active learning methods in all situations.
Virtual Urban Field Studies: Evaluating Urban Interaction Design Using Context-Based Interface Prototypes

In this study, we propose the use of virtual urban field studies (VUFS) through context-based interface prototypes for evaluating the interaction design of auditory interfaces. Virtual field tests use mixed-reality technologies to combine the fidelity of real-world testing with the affordability and speed of testing in the lab. In this paper, we apply this concept to rapidly test sound designs for autonomous vehicle (AV)–pedestrian interaction with a high degree of realism and fidelity. We also propose the use of psychometrically validated measures of presence in validating the verisimilitude of VUFS. Using mixed qualitative and quantitative methods, we analysed users’ perceptions of presence in our VUFS prototype and the relationship to our prototype’s effectiveness. We also examined the use of higher-order ambisonic spatialised audio and its impact on presence. Our results provide insights into how VUFS can be designed to facilitate presence as well as design guidelines for how this can be leveraged.
TTS System for Deafened and Vocally impaired persons in Native Language

This is contrary for Voice impaired people since their speech is tough for others to recognize even by their parents and teachers. Provided if their parents are illiterate. So our TTS system can be used for converting their written text to speech for their illiterate parents and friends around them. Though many methods have been adopted for the concatenation of the basic sound units, the HMM-based approach in modeling the sound is utilized by many researchers in many languages. In this paper, we have tried to implement, text to speech systems of synthesis for a Tamil text uses a phonemic concatenation approach in MATLAB. Instead of utilizing Tamil letters as it is, due to its difficulty in production, Tamil text is transliterated into English then it is converted into intelligible speech. The performance of the output is verified for various examples by changing its parameters, in which the quality of the sound is comparable to that of English text. So the proposed system is utilized for all languages other than Tamil also if it is properly transliterated for limited vocabulary.
Enhancing Elementary Classroom Learning Experiences With Mobile Learning

This chapter discusses the importance of mobile learning in teaching elementary classroom digital natives. The author grounds mobile learning into the individual characteristics of young learners, the theory of mobile learning, multiliteracy pedagogy, and the SAMR framework to emphasize a dynamic interplay between learners, teachers, technology, and learning environment created by mobile learning. The chapter presents a range of activities that leverage the affordances of mobile devices by using images, audio, and QR codes in the instructional design in elementary classrooms. These activities foster student autonomy, collaboration, and critical thinking while promoting visual and digital literacies. The chapter emphasizes the importance of scaffolding and instructional design in leveraging mobile learning effectively. Activities suggested by the author are accompanied by step-by-step instructions and examples, which makes them relevant both for pre-service and in-service elementary school teachers, teacher trainers, and professional development providers.
Application of "Production-Oriented Approach" in College English Teaching: Adaptation of Teaching Materials

According to the theory of " Production-Oriented Approach " (POA) , the team of the author has carried out POA processing on the college English comprehensive course materials, and carried out an experiment in the college English comprehensive class for one semester. This paper focuses on the adaptation of teaching materials, discusses the process of adaptation from the aspects of preliminary analysis, goal setting, selection and conversion of input materials, and points out the implications of this study for the preparation and use of college English textbooks.
Research on Digitally-empowered Street Dance Creation

As a new dance form, digitally-empowered street dance is deeply loved by young people for its unique expression and style. The form of street dance is usually rooted in cultural traditions and historical background. The integration of digital technologies in street dance can provide a way to preserve these traditional art forms and allow to achieve innovative development. Researchers can explore how digital tools, such as motion-capture systems, virtual reality, or augmented reality, can be used to document, analyze, and revitalize street dance styles. However, the development of digitally-empowered street dance is limited due to the lack of professional theoretical guidance and practical experience. Therefore, the purpose of this study is to provide more scientific and reasonable creative methods and technical support through an in-depth exploration of digitally-empowered street dance. It is to promote the development and progress of digitally-empowered street dance.
Semanticized Visual Representation of Harmonic Auspicious Patterns

Harmonic auspicious motifs are an important part of the auspicious culture of praying for good fortune, which has been widely circulated since ancient times. With the continuation and development of the culture, the traditional harmonic auspicious patterns are combined with modern and novel ways to show a richer visual expression. In this paper, we try to explore a more innovative visual expression of harmonic auspicious motifs in modernized semantics from three aspects, such as the way of thinking, cultural connotation and expression of harmonic auspicious motifs.
OPM2L: An optimal instance partition-based multi-metric learning method for heterogeneous dataset classification

Multi-metric learning -a method to learn multiple local metrics to reveal the feature's correlations of samples from different local regions-has become an essential tool to measure the similarities between instances from heterogeneous datasets. However, most existing cluster-based MML methods first partition the training data with a predefined metric and then learn multiple metrics via the local instances, leading to these two independent procedures fail to cooperate with each other. In this paper, we propose an Optimal instance Partition-based Multi-Metric Learning (OPM2L) method for heterogeneous dataset classification by unifying the instance partition and multiple local metrics learning into a single objective. In particular, multiple anchor centers together with a global metric are employed to assist the instance partition process. During the training, the shared information contained in local metrics is aggregated into the global metric by a dedicated regularizer, which improves the instance partition process and offers the subsequent multiple local metrics learning with more informative instances. Moreover, an efficient alternating direction technology is employed to seek a feasible solution to the proposed method. We further confirmed that the sub-problems can be settled with closed-form solutions, while the superiority of the proposed method is also proved by experimental results on extensive datasets.
Modelling geochemical indices from hyperspectral drill core data from the Eucla Basin basement

Whole rock geochemical analysis of drill core is crucial for rock classification and understanding the evolution of a geological province. This becomes even more important when access to fresh rock samples is hindered by considerable overburden, such as in the case of the basement to the Eucla Basin sediments in Australia. A partial least squares (PLS) regression method was trialled to model geochemical indices, commonly applied for classification of igneous rocks, from hyperspectral visible-near infrared (VNIR; 350 to 1200 nm), shortwave infrared (SWIR; 1200 to 2500 nm) and thermal infrared (TIR; 6000 to 14,500 nm) reflectance spectra of three drill cores intersecting the basement of the Eucla Basin. Modelling of the Mg# (Mg/Mg + Fe) achieved a high correlation between the calculated and predicted values (i.e. R2 > 0.9) at an RMSE of about 10 %. The 2200 to 2400 nm wavelength region of the SWIR was key in modelling the Mg#, with amphibole and chlorite-related absorption features overlapping with the most important input bands in the PLS model. Modelling of the silicon, calcium, iron, magnesium index (SCFM; SiO2/(SiO2 + CaO + FeO + MgO)) required the TIR wavelength range, where diagnostic spectral signatures of silicates are located. PLS-based modelling of the SCFM achieved even a high correlation (i.e. R2 > 0.8) when the model was developed for one drill core but applied to another drill core that intersects the same lithologies. The third geochemical index trialled was the aluminium saturation index (ASI; Al2O3/(Na2O + K2O + CaO), producing high correlation between calculated and predicted ASI when using either, the VNIR-SWIR and the TIR wavelength ranges as input. Our study demonstrates how PLS regression can utilise hyperspectral data to determine geochemical indices, without the cost of full geochemical analysis of every sample in a wide array of rock types. The higher spatial resolution of the modelled geochemistry and the extrapolation of the modelling results to intervals that haven't been sampled for geochemistry supports stratigraphic correlation between drill cores. Furthermore, the combined analysis of geochemical and hyperspectrally-derived mineralogy helps mapping the intensity of weathering of basement as well as mineralogical and physicochemical gradients potentially related to hydrothermal systems.
Class semantic enhancement network for semantic segmentation

Existing semantic segmentation methods favor class semantic consistency by extracting long-range contextual features through multi-scale and attention strategies. These methods ignore the relations between feature channels and classes, which are essential to represent consistent class semantics. To this end, we propose the Class Semantic Enhancement Network (CSENet) which boosts the segmentation performance of a backbone network in a coarse-to-fine manner. CSENet consists of two basic modules – (1) the Class Semantic Channel Graph Module (CSCG) module, which captures inter-dependencies among channels and strengthens the channel-class relation, and (2) the Class Prior Fully Convolution (CP-FC) module, which utilizes the channel-class relation as class priors to refine the segmentation results. Extensive experiments have demonstrated that the proposed CSENet is able to learn discriminative feature representations and achieves state-of-the-art performance on three benchmark datasets, including PASCAL Context, ADE20K and COCO Stuff.
Crossing language identification: Multilingual ASR framework based on semantic dataset creation &amp; Wav2Vec 2.0

This study proposes an innovative methodology to enhance the performance of multilingual Automatic Speech Recognition (ASR) systems by capitalizing on the high semantic similarity between sentences across different languages and eliminating the requirement for Language Identification (LID). To achieve this, special bilingual datasets were created from the Mozzila Common Voices datasets in Spanish, Russian, and Portuguese. The process involves computing sentence embeddings using Language-agnostic BERT and selecting sentence pairs based on high and low cosine similarity. Subsequently, we train the Wav2vec 2.0 XLSR53 model on these datasets and assess its performance utilizing Character Error Rate (CER) and Word Error Rate (WER) metrics. The experimental results indicate that models trained on high-similarity samples consistently surpass their low-similarity counterparts, emphasizing the significance of high semantic similarity data selection for precise and dependable ASR performance. Furthermore, the elimination of LID contributes to a simplified system with reduced computational costs and the capacity for real-time text output. The findings of this research offer valuable insights for the development of more efficient and accurate multilingual ASR systems, particularly in real-time and on-device applications.
A novel watermarking framework for intellectual property protection of NLG APIs

Natural language generation (NLG) models have attracted extensive attention and applications due to their combination with powerful deep learning techniques. Many NLG models are encapsulated in cloud APIs serving commercial organizations, which have become very important profitable services for these institutions. However, cloud platforms may suffer from model extraction attacks that aim to imitate the functionality of these NLG models in practical applications, thus infringing the intellectual property (IP) of the NLG APIs. Unfortunately, most current watermarking methods for protecting deep model IP are not directly applicable to IP protection of NLG APIs. In addition, the semantic similarity between the watermarked texts generated by the baseline method and the original texts is not high enough, which can be easily detected by attackers. To make up these gaps, we propose a novel watermarking framework which embeds watermarks by conducting lexical modification to the outputs of the NLG models, and uses the corresponding watermark identification method can identify the attackers and protect the IP of NLG APIs. Experiment result shows that our proposed watermarking method not only generates watermarked texts with higher semantically similar to the original texts but also achieves better identifiable performance compared with the baseline method. In addition, our watermarking method also exhibits outstanding performance in other aspects such as transferability, watermark undetectability and robustness.
Towards real-world haze removal with uncorrelated graph model

The scene restoration effect of the dehazing algorithms usually suffers from double quality interference. First, the dehazing methods based on the atmospheric scattering model (ASM) lead to light loss. Second, the synthetic data used for supervised learning may produce apparent data deviation from the real hazy scene, which seriously weakens the generalization ability of the model. To address the above problems, we propose an uncorrelated graph dehazing model for real-world scenes. The process firstly eliminates the correlation in the representation space by establishing a directed acyclic graph with isolated points, thus enhancing the generalization performance of the model. Secondly, to suppress over-exposure that may occur on real-world data, the proposed model training is performed with a combination of light control. This work improves the ASM and constructs a new dataset applicable to natural haze scenes. Finally, the effectiveness of the proposed method can be verified through multiple experimental comparisons.
Dynamic plugging regulating strategy of pipeline robot based on reinforcement learning

Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process.
A novel application of XAI in squinting models: A position paper

Artificial Intelligence, and Machine Learning especially, are becoming increasingly foundational to our collective future. Recent developments around generative models such as ChatGPT, and DALL-E represent just the tip of the iceberg in new gadgets that will change the way we live our lives. Convolutional Neural Networks (CNNs) and Transformer models are at the heart of advancements in the autonomous vehicles and health care industries as well. Yet these models, as impressive as they are, still make plenty of mistakes without justifying or explaining what aspects of the input or internal state, was responsible for the error. Often, the goal of automation is to increase throughput, processing as many tasks as possible in a short a period of time. For some use cases the cost of mistakes might be acceptable as long as production is increased above some set margin. However, in health care, autonomous vehicles, and financial applications, the cost of a mistake might have catastrophic consequences. For this reason, industries where single mistakes can be costly are less enthusiastic about early AI adoption. The field of eXplainable AI (XAI) has attracted significant attention in recent years with the goal of producing algorithms that shed light into the decision-making process of neural networks. In this paper we show how robust vision pipelines can be built using XAI algorithms with the goal of producing automated watchdogs that actively monitor the decision-making process of neural networks for signs of mistakes or ambiguous data. We call these robust vision pipelines, squinting pipelines.
Design of Intelligent Robots Based on Body Sense Control

Abstract Recognizing human actions to control the robot has become a challenging task. This paper provides a manipulator control system based on a mobile platform. The bending Angle of the specific connecting rod mechanism is collected to obtain the bending degree of the finger. The nine-axis gyroscope is used to detect the attitudes of the hand and the arm, and the data is transmitted to the HT32F5352 control board. Experimental results show that intelligent robots based on motion sensing can be equipped with automatic and manual control, environmental detection, and other functions.
Units of Translation and the Limited Capacity of Working Memory

A unit of translation is a source text fragment of any length or nature that piques a translator’s interest during translation. Alves and Vale proposed the concept of macro and micro translation units based on pauses and times identified in Translog software while translating a document. The objective of this exploratory study was to describe the characteristics of translation units in relation to the limited capacity of working memory (WM) and the information storage-processing capacity. Four trained translators participated in this study, translating a short text about underground mining. The data collection tools were Translog II and Camtasia software. Findings showed that participants used an analytic and sequential processing method and their limited capacity of WM exhibited a lower processing speed, which had an impact on the length of the macro units of translation (UTs).