Reviewerly/exHarmony huggingface dataset:
A Study on the Occupational Name of Korean Textbooks using NER - Expanded exploration of Korean language education and Artificial Intelligence Humanities -

This study extracted occupational names and classified them through a classification system by applying the Named entity recognition (NER) analysis technique, a quantitative tool, to 1,788 listening texts from Korean textbooks from 9 universities. A multi- faceted review was conducted by dividing the characteristics of occupational names in a textbook by institution and level. As a result, 151 types of occupational names were extracted, and [experts and related workers] items accounted for 68% of the total. In addition, for the detailed items within the larger classification, an imbalance appeared biased toward a specific occupational group, and each large classification item showed a difference in frequency trend based on its classification. Important considerations include: The phenomenon of occupational name bias can be seen as a result of the job needs that society admires and lower social perception of a specific job. Even though there are many occupational names that are necessary for a Korean learner's primary life, these were not sufficiently reflected in the textbooks. The occupational names of some proportionally small items showed a lack of specific job expertise or appeared in a negative context, resulting in negative learning results. Notable occupational names appeared in Korean textbooks, following developments in culture, science technology. Certain occupational names, which contained gender discrimination factors, continued to be fixedly assigned gender roles, but they also confirmed signs of positive change against them. This study reveals the need to make conscious efforts to exclude social discrimination elements from Korean textbooks in the future.
Lärares iscensättning av stöttning vid nyanlända elevers digitala textproduktion

Previous studies describe scaffolding as fundamental for newly arrived students to understand and create texts. In this article, scaffolding students’ multilingual palette (the full range of multilingual resources) is vital for their meaning-making. This study aims to make linguistic diversity visible when scaffolding newly arrived students during digital text activities in narrative and retelling text in Swedish as a second language. The following research question guides the study: How do scaffolding processes during digital text activities contribute to opportunities for newly arrived students with varying backgrounds and experiences of the Swedish language to use their multilingual palettes? The data consists of ethnographic fieldwork in three primary schools (year 3), focusing on scaffolding during nine newly arrived students’ text activities. Through an iterative analysis process, scaffolding emerged to have the following functions: 1) to promote multilingualism, 2) to develop textual competence, 3) to support student space. Scaffolding of newly arrived students who did not yet speak Swedish contributed to all students being able to create text within the text types. However, the scaffolding did not support their use of multilingual resources during individual text activities. For students to develop multilingual resources, scaffolding must focus on translanguaging and non-verbal means of expression, even during individual activities. For this, using digital technology is advantageous.
Basketball Motion Posture Recognition Based on Recurrent Deep Learning Model

In order to improve the training effect of athletes and effectively identify the movement posture of basketball players, we propose a basketball motion posture recognition method based on recurrent deep learning. A one-dimensional convolution layer is added to the neural network structure of the deep recurrent Q network (DRQN) to extract the athlete pose feature data before the long short-term memory (LSTM) layer. The acceleration and angular velocity data of athletes are collected by inertial sensors, and the multi-dimensional motion posture features are extracted from the time domain and frequency domain, respectively, and the posture recognition of basketball is realized by DRQN. Finally, the new reinforcement learning algorithm is trained and tested in a time-series-related environment. The experimental results show that the method can effectively recognize the basketball motion posture, and the average accuracy of posture recognition reaches 99.3%.
PROOF SYSTEMS FOR EXACT ENTAILMENT

Abstract We present a series of proof systems for exact entailment (i.e., relevant truthmaker preservation from premises to conclusion) and prove soundness and completeness. Using the proof systems, we observe that exact entailment is hyperintensional not only in the sense of Cresswell, but also in the sense recently proposed by Odintsov and Wansing.
Real-Time Occlusion-Robust Deformable Linear Object Tracking With Model-Based Gaussian Mixture Model

Tracking and manipulating deformable linear objects (DLOs) has great potential in the industrial world. However, estimating the object's state is crucial and challenging, especially when dealing with heavy occlusion situations and physical properties of different objects. To address these problems, we introduce a novel tracking algorithm to observe and estimate the states of DLO. The proposed tracking algorithm is based on the Coherent Point Drift (CPD), which registers the observed point cloud, and the finite element method (FEM) model encodes physical properties. The Gaussian mixture model with CPD regularization generates constraints to deform a given FEM model into desired shapes. The FEM model encodes the local structure, the global topology, and the material property to better approximate the deformation process in the real world without using simulation software. A series of simulations and real data tracking experiments have been conducted on deformable objects, such as rope and iron wire, to demonstrate the robustness and accuracy of our method in the presence of occlusion.
Aerial gamma-ray spectrometric survey data as a guide for probable hydrocarbon accumulations at Abu-Zeneima/Al-Tur area, Southwestern Sinai, Egypt

A method using surface and aerial gamma-ray spectral measurements to explore the hydrocarbon accumulations in Abu-Zeneima/Al-Tur area of Southwestern Sinai, Egypt, was selected due to both its structural setting and its location in close proximity to the Gulf of Suez, which represents one of the important sites of oil production in Egypt. Examination of the normalized potassium map (KD %) shows low concentrations above Abu-Alaqah, Thebes, and Kariem formations, while normalized equivalent uranium map (eUD %) shows a high concentration above these formations. The Deviation of Radio-Active Derivative (DRAD) map shows some of the delineated high positive DRAD anomalies, which are elongated in the NW-SE direction and related to Abu-Alaqah, Thebes, and Kariem formations. Data from every formation and rock unit as well as each flight line were extracted in XYZ format and evaluated with simple statistical applications. The DRAD arithmetic means plus three standard deviations (x + 3s) and plus two standard deviations (x + 2s) for the data set all over the study area were computed and reached the values of 2.5 and 2.1, respectively. Any single point that possesses a value greater than these two values should have a probability of 99.7% and 95%, respectively, of possible petroleum accumulations and represents a sure anomaly. The use of these criteria helped identify Abu-Alaqah, Thebes, and Kariem formations. Moreover, five zones along four flight lines correlating geologically with Abu-Alaqah, Thebes, and Kariem formations have sure anomalies. This might indicate a prospective possibility of feasible subsurface petroleum accumulations in Abu-Zeneima/Al-Tur area, Southwestern Sinai, Egypt.
Analysis of rotation and shift based diffusion functions

‘Rotation and shift’ based diffusion functions are used in modern ciphers especially in ARX (Addition, Rotation, and XOR) based designs. They are preferred over other alternatives as they are efficient and lightweight. Usually, such diffusion functions map an n-bit block to another n-bit block. One of the requirements for an n-bit to n-bit diffusion function is that, it should be invertible. In this paper, we present mathematical models for different classes of ‘rotation and shift’ based diffusion functions and explore their invertibility. We derive sufficient conditions under which diffusion functions of these classes are invertible.
Inter-continental Data Centre Power Load Balancing for Renewable Energy Maximisation

The ever increasing popularity of Cloud and similar services pushes the demand for data centres, which have a high power consumption. In an attempt to increase the sustainability of the power generation, data centres have been fed by microgrids which include renewable generation—so-called ‘green data centres’. However, the peak load of data centres often does not coincide with solar generation, because demand mostly peaks in the evening. Shifting power to data centres incurs transmission losses; shifting the data transmission has no such drawback. We demonstrate the effectivity of computational load shifting between data centres located in different time zones using a case study that balances demands between three data centres on three continents. This study contributes a method that exploits the opportunities provided by the varied timing of peak solar generation across the globe, transferring computation load to data centres that have sufficient renewable energy whenever possible. Our study shows that balancing computation loads between three green data centres on three continents can improve the use of renewables by up to 22%. Assuming the grid energy does not include renewables, this amounts to a 13% reduction in CO2 emissions.
Evolution Characteristics of Seismic Detection Probability in Underground Mines and Its Application for Assessing Seismic Risks—A Case Study

Seismic hazards are typical mining hazards causing dynamic failure of coal and rock mass, which greatly threatens the safety of personnel and equipment. At present, various seismic analysis methods are used to assess seismic risks but their accuracy is significantly limited by the incompleteness of seismic data. The probability of detecting earthquakes (PDE) method has been proven as a powerful means for retrieving missed seismic events and enhancing the seismic data integrity in mines. However, to date, the reliability of the results of the PDE method has not been assessed and the highly integrated seismic data have not been linked with the actual hazard potential. To fill these gaps, this paper investigated the impacts of the seismic data volume used for calculation and the modification of the layout of sensors on the reliability and robustness of the PDE method. The event counts and seismic energy were compensated using the PDE method, correlated with strong seismic events. The results indicated that the compensated seismic data presented higher accuracy in locating future hazardous events than before. This research provides references on enhancing the performance of seismic analysing methods for seismic risk assessments.
A multilevel thresholding method based on HPSO for the segmentation of various objective functions

Image segmentation is very challenging due to its complex working. Researchers created a number of bio-inspired algorithms to calculate the optimal threshold values for segmenting such pictures. Extending them to multilevel thresholding, their exhaustive nature makes them computationally costly. Using the Particle Swarm Optimization (PSO) technique, the author presents computationally efficient image segmentation approach, termed hybrid PSO (HPSO). Improved segmentation quality was achieved by using the HPSO method with less computing cost & time. MSE, ME, PSNR, Entropy, CPU time, FSIM and SSIM were measured for all instances studied. For image segmentation, the suggested HPSO method has shown to be the most promising and computationally efficient approach to date. In addition, the suggested method surpasses others in achieving stable global optimal thresholds, according to a study of its convergence rate. Image processing, remote sensing and Computer vision applications may benefit from the findings of this investigation.
High fidelity quantum blind dual-signature protocols

This work proposes two kinds of fault tolerant quantum blind dual-signature (QBDS) protocols with photon quartets, which are robust against the collective-dephasing noise and collective-rotation noise, respectively. In the protocols, the initial information needs to be signed by the message owner in addition to the signer, which will be well suitable for application in some E-payment and E-government systems that require the message owner to provide authentication information. Both QBDS protocols are constructed with logical qubits which are immune to the collective noise, thereby the protocols can provide higher communication fidelity with respect to the existing quantum signature protocols. Furthermore, the protocols had been proved to be secure against some individual eavesdropping attacks. Meanwhile, the protocols satisfy the characteristics of quantum blind signature (QBS) protocols such as unforgeability, undeniability and blindness.
Energy Efficient Design of Fast Fourier Transform using Reversible Logic

Digital computation using reversible logic has gained significant research interest in recent years. It promises energy-efficient digital systems due to its information lossless characteristics. A thorough physical design of computationally intensive circuits is essential to analyze the true potential of this developing paradigm. This paper presents a reversible logic implementation of the Fast Fourier Transform (FFT) algorithm as a relevant design case. The proposed architecture’s hierarchical design is based on a reversible standard cell approach. CMOS 0.35μm technology is used for the pass transistor design as well as the full custom layout. Post layout simulations and technology design rule checks are used to validate all design units. The reversible design of an 8-point FFT with 8-bit quantization occupies 0.768 mm2 area and consumes 6.5 mW power at 10 MHz frequency and 3.3 V supply voltage. The major functional units of proposed FFT architecture demonstrate a considerable power saving as compared to their various existing implementations.
Career Counseling Chatbot using Microsoft Bot Frameworks<sup>1</sup>

Career counseling assists students in the decision-making process for a future career path. The current work augments the counseling activity conducted by a professional, with a proposal of an assistive NLP agent (chatbot) that provides a support for identifying and mapping out action tracks for a future career under the student&#x2019;s profile. The resulting prototype has been deployed as a conversational service, though an institutional eCampus infrastructure and has been validated by both a focus group of students and experts in the field.
PIMCloud: QoS-Aware Resource Management of Latency-Critical Applications in Clouds with Processing-in-Memory

The slowdown of Moore&#x2019;s Law, combined with advances in 3D stacking of logic and memory, have pushed architects to revisit the concept of processing-in-memory (PIM) to overcome the memory wall bottleneck. This PIM renaissance finds itself in a very different computing landscape from the one twenty years ago, as more and more computation shifts to the cloud. Most PIM architecture papers still focus on best-effort applications, while PIM&#x2019;s impact on latency-critical cloud applications is not well understood.This paper explores how datacenters can exploit PIM architectures in the context of latency-critical applications. We adopt a general-purpose cloud server with HBM-based, 3D-stacked logic+memory modules, and study the impact of PIM on six diverse interactive cloud applications. We reveal the previously neglected opportunity that PIM presents to these services, and show the importance of properly managing PIM-related resources to meet the QoS targets of interactive services and maximize resource efficiency. Then, we present PIMCloud, a QoS-aware resource manager designed for cloud systems with PIM allowing colocation of multiple latency-critical and best-effort applications. We show that PIMCloud efficiently manages PIM resources: it (1) improves effective machine utilization by up to 70% and 85% (average 24% and 33%) under 2-app and 3-app mixes, compared to the best state-of-the-art manager; (2) helps latency-critical applications meet QoS; and (3) adapts to varying load patterns.
Hyperspectral and Geochemical Analysis of Chlorites at the Gongchangling High-Grade Magnetite Deposit, NE China: Implications for Alteration Processes

The Gongchangling deposit is a representative banded iron formation (BIF) in China, in which developed several high-grade magnetite ores. The surrounding alteration rocks recorded the genesis information of the high-grade ores. However, the study related to alteration processes remains poor. In this study, we investigate the sub-types and formation temperature of chlorite using hyperspectral imaging and electronic probe microanalysis (EPMA), and deciphered the elemental migration trend during alteration processes by whole-rock geochemistry. The chlorites in the alteration rocks were divided into three sub-types according to the spectral features of the Fe-OH band near 2250 nm. The range of wavelength position is approximately 2250–2255 nm for chlorite-I, 2255–2260 nm for chlorite-II, and 2260–2265 nm for chlorite-III. The variation in Mg# is 0.32–0.44 in chlorite-I, 0.20–0.34 in chlorite-II, and 0.15–0.23 in chlorite-III, which is consistent with the range of wavelength position. The hydrothermal alteration resulted in the enrichment of iron and the depletion of silicon. The results shed new light on the recognition of chlorite sub-types and deciphered the hydrothermal alteration processes of high-grade magnetite ores, which proposed an effective method for mineralogical mapping.
RASCL: a randomised approach to subspace clusters

Abstract Subspace clustering aims to discover clusters in projections of highly dimensional numerical data. In this paper, we focus on discovering small collections of highly interesting subspace clusters that do not try to cluster all data points, leaving noisy data points unclustered. To this end, we propose a randomised method that first converts the highly dimensional database to a binarised one using projected samples of the original database. Subsequently, this database is mined for frequent itemsets, which we show can be translated back to subspace clusters. In this way, we are able to explore multiple subspaces of different sizes at the same time. In our extensive experimental analysis, we show on synthetic as well as real-world data that our method is capable of discovering highly interesting subspace clusters efficiently.
MACHINE LEARNING FOR TEXT CLASSIFICATION IN BUILDING MANAGEMENT SYSTEMS

In building management systems (BMS), a medium building may have between 200 and 1000 sensor points. Their labels need to be translated into a naming standard so they can be automatically recognised by the BMS platform. The current industrial practices often manually translate these points into labels (this is known as the tagging process), which takes around 8 hours for every 100 points. We introduce an AI-based multi-stage text classification that translates BMS points into formatted BMS labels. After comparing five different techniques for text classification (logistic regression, random forests, XGBoost, multinomial Naive Bayes and linear support vector classification), we demonstrate that XGBoost is the top performer with 90.29% of true positives, and use the prediction confidence to filter out false positives. This approach can be applied in sensors networks in various applications, where manual free-text data pre-processing remains cumbersome.
Accurate detection technology of super long bored cast-in-place pile concrete pouring thickness based on ultrasonic inclined plane reflection measurement

Aiming at the accurate detection problem of concrete pouring thickness, a solution idea of synchronous data acquisition and concrete thickness detection technology based on ultrasonic inclined plane reflection and vertical reflection of ultrasonic full pulse signal is proposed. Taking full advantage of the acoustic response characteristics of different reflection models, three-dimensional modeling and data visualization optimization scheme are formed. By combining the different depths data and local measurement data, the characteristic parameter extraction technology of different media in the borehole is formed, which lays a foundation for solving the key problem of accurate detection of laitance layer thickness. Finally, a comprehensive technical system is formed, and the technical system is applied to practical engineering. The measurement results are basically consistent with the actual situation. The practical application results show that this technology can realize the three-dimensional imaging and quantitative characterization of the medium state in the borehole during concrete pouring.
Enhancing Natural Language Inference of Cross-lingual N-shot Transfer with Multilingual Data

Cross-lingual N-shot transfers are often used to solve low-resource language problems. However, the result of transfer in different languages can be inconsistent due to large fluctuations in accuracy. To reduce the inconsistency caused by the fluctuation of accuracy and improve the overall accuracy of Cross-lingual N-shot transfer, we propose the use of Multitasking Cross-lingual N-shot transfer. To the best of our knowledge, our propose Multitasking Cross-lingual N-shot transfer is the first method to combine Multitasking Learning and Cross-lingual N-shot transfer. By training with multiple languages simultaneously, the model can learn multilingual common semantic features from different languages, decreasing the fluctuations in accuracy, and also having a higher overall accuracy compared to traditional method with single high-resource language. The method presented in this paper can effectively reduce 93.2% of the accuracy fluctuations and improve the overall accuracy by 10.4% in NLI.
Two Low-Level Feature Distributions Based No Reference Image Quality Assessment

No reference image quality assessment (NR IQA) aims to develop quantitative measures to automatically and accurately estimate perceptual image quality without any prior information about the reference image. In this paper, we introduce two low-level feature distributions (TLLFD) based method for NR IQA. Different from the deep learning method, the proposed method characterizes image quality with the distributions of low-level features, thus it has few parameters, simple model, high efficiency, and strong robustness. First, the texture change of distorted image is extracted by the weighted histogram of generalized local binary pattern. Second, the Weibull distribution of gradient is extracted to represent the structural change of the distorted image. Furthermore, support vector regression is adopted to model the complex nonlinear relationship between feature space and quality measure. Finally, numerical tests are performed on LIVE, CISQ, MICT, and TID2008 standard databases for five different distortion categories JPEG2000 (JP2K), JPEG, White Noise (WN), Gaussian Blur (GB), and Fast Fading (FF). The experimental results indicate that TLLFD method achieves superior performance and strong generalization for image quality prediction as compared to state-of-the-art full-reference, no reference, and even deep learning IQA methods.