Reviewerly/exHarmony huggingface dataset:
Development and Implementation of Augmented Reality-Based Card Game Learning Media with Environmental Literacy in Improving Students' Understanding of Carbon Compounds

The lack of knowledge integration about the learning environment is one of the causes of the low environmental awareness. The purpose of this study was to develop learning media for carbon compounds based on augmented reality and environmental literacy and to find out student learning outcomes after using media, students' perceptions of the media, and their environmental insight. This research is a research and development (R&amp;D) research by following the ADDIE model. One group pre-test post-test research design was used to examine the effect of using the media. The research instruments were multiple choice test, questionnaires, and interviews which were developed independently and tested for validity &amp; reliability. The analysis technique was the paired sample t-test and content analysis. The results of this study showed that the learning media was very eligible with a validation score reaching 88.1%. The result of the implementation of the final product of ChemiCa shows that there is an impact of using media on student learning outcomes (Sig. 0.000&lt;0.05). Students' perception of the media are very good, learning media can increase their understanding of carbon compounds concepts, as well as their motivation to learn chemistry. This study implies that teachers can use augmented reality-based card game as a supplementary learning media to improve students’ understanding of others relevant chemistry concepts and their environment literacy.
Safe screening rules for multi-view support vector machines

Multi-view learning aims to make use of the advantages of different views to complement each other and fully mines the potential information in the data. However, the complexity of multi-view learning algorithm is much higher than that of single view learning algorithm. Based on the optimality conditions of two classical multi-view models: SVM-2K and multi-view twin support vector machine (MvTwSVM), this paper analyzes the corresponding relationship between dual variables and samples, and derives their safe screening rules for the first time, termed as SSR-SVM-2K and SSR-MvTwSVM. It can assign or delete four groups of different dual variables in advance before solving the optimization problem, so as to greatly reduce the scale of the optimization problem and improve the solution speed. More importantly, the safe screening criterion is “safe”, that is, the solution of the reduced optimization problem is the same as that of the original problem before screening. In addition, we further give a sequence screening rule to speed up the parameter optimization process, and analyze its properties, including the similarities and differences of safe screening rules between multi-view SVMs and single-view SVMs, the computational complexity, and the relationship between the parameter interval and screening rate. Numerical experiments verify the effectiveness of the proposed methods.
ESAMask: Real-Time Instance Segmentation Fused with Efficient Sparse Attention

Instance segmentation is a challenging task in computer vision, as it requires distinguishing objects and predicting dense areas. Currently, segmentation models based on complex designs and large parameters have achieved remarkable accuracy. However, from a practical standpoint, achieving a balance between accuracy and speed is even more desirable. To address this need, this paper presents ESAMask, a real-time segmentation model fused with efficient sparse attention, which adheres to the principles of lightweight design and efficiency. In this work, we propose several key contributions. Firstly, we introduce a dynamic and sparse Related Semantic Perceived Attention mechanism (RSPA) for adaptive perception of different semantic information of various targets during feature extraction. RSPA uses the adjacency matrix to search for regions with high semantic correlation of the same target, which reduces computational cost. Additionally, we design the GSInvSAM structure to reduce redundant calculations of spliced features while enhancing interaction between channels when merging feature layers of different scales. Lastly, we introduce the Mixed Receptive Field Context Perception Module (MRFCPM) in the prototype branch to enable targets of different scales to capture the feature representation of the corresponding area during mask generation. MRFCPM fuses information from three branches of global content awareness, large kernel region awareness, and convolutional channel attention to explicitly model features at different scales. Through extensive experimental evaluation, ESAMask achieves a mask AP of 45.4 at a frame rate of 45.2 FPS on the COCO dataset, surpassing current instance segmentation methods in terms of the accuracy–speed trade-off, as demonstrated by our comprehensive experimental results. In addition, the high-quality segmentation results of our proposed method for objects of various classes and scales can be intuitively observed from the visualized segmentation outputs.
A path planning method using modified harris hawks optimization algorithm for mobile robots

Path planning is a critical technology that could help mobile robots accomplish their tasks quickly. However, some path planning algorithms tend to fall into local optimum in complex environments. A path planning method using a modified Harris hawks optimization (MHHO) algorithm is proposed to address the problem and improve the path quality. The proposed method improves the performance of the algorithm through multiple strategies. A linear path strategy is employed in path planning, which could straighten the corner segments of the path, making the obtained path smooth and the path distance short. Then, to avoid getting into the local optimum, a local search update strategy is applied to the HHO algorithm. In addition, a nonlinear control strategy is also used to improve the convergence accuracy and convergence speed. The performance of the MHHO method was evaluated through multiple experiments in different environments. Experimental results show that the proposed algorithm is more efficient in path length and speed of convergence than the ant colony optimization (ACO) algorithm, improved sparrow search algorithm (ISSA), and HHO algorithms.
Diagnosis of creative potential of future teachers as a mental basis for professional self-improvement

The need for innovative approaches to the training of future teachers is determined by the renewal and creation of the content of the educational process in higher education institutions, taking into account the creative development of the individual.Unfortunately, the traditional methods and forms of teaching used in higher education do not provide full and continuous effectiveness of professional training of future teachers.While the specialists of the new formation are required to acquire thorough professional knowledge and skills, a high level of intellectual development and civic position, the ability to constant personal and professional self-improvement.The purpose of the study is to reveal the essence of the creative potential of future teachers as a mental basis for professional self-improvement of high school teachers and experimental analysis of the components of the creative potential of future teachers, the study of teachers' attitudes to professional development.Based on the results of the research, the criteria and indicators of the formation of the creative potential of future teachers were determined; developed diagnostic and methodological tools for certain criteria and indicators.Psychological and pedagogical diagnostics were carried out to differentiate the levels of formation of the creative potential of students of pedagogical specialties.
Designing and Training of Lightweight Neural Networks on Edge Devices using Early Halting in Knowledge Distillation

Automated feature extraction capability and significant performance of Deep Neural Networks (DNN) make them suitable for Internet of Things (IoT) applications. However, deploying DNN on edge devices becomes prohibitive due to the colossal computation, energy, and storage requirements. This paper presents a novel approach, EarlyLight, for designing and training lightweight DNN using large-size DNN. The approach considers the available storage, processing speed, and maximum allowable processing time to execute the task on edge devices. We present a knowledge distillation based training procedure to train the lightweight DNN to achieve adequate accuracy. During the training of lightweight DNN, we introduce a novel early halting technique, which preserves network resources; thus, speedups the training procedure. Finally, we present the empirically and real-world evaluations to verify the effectiveness of the proposed approach under different constraints using various edge devices.
Precision Analysis on Automatic Writing Machine Using Arduino (Case Study: Printer Plotter)

In this era of increasingly widespread innovation developments in the field of Information Technology (IT), technology is something that is implemented as a tool to facilitate human activities. In daily human activities, it is always related to the use of writing instruments for administrative needs or other needs. In the process of printing images or writing, a high-quality printer is needed to get good printouts, of course this will cause a lot of costs to be incurred. Apart from needing a high-quality printer machine, we also have to prepare the best version of ink so that the resulting prints will be better. With the advancement of technology in the Internet of Things (IoT) which is fast enough, a tool can be created to make writing or pictures automatically and can produce good and precise prints. This machine is based on the X and Y axes, to operate the machine requires a computer unit, arduino, and also Computer Numerical Control (CNC), later this machine operates according to instructions from the computer.
Self-improving Q-learning based controller for a class of dynamical processes

This paper presents how Q-learning algorithm can be applied as a general-purpose selfimproving controller for use in industrial automation as a substitute for conventional PI controller implemented without proper tuning.Traditional Q-learning approach is redefined to better fit the applications in practical control loops, including new definition of the goal state by the closed loop reference trajectory and discretization of state space and accessible actions (manipulating variables).Properties of Q-learning algorithm are investigated in terms of practical applicability with a special emphasis on initializing of Q-matrix based only on preliminary PI tunings to ensure bumpless switching between existing controller and replacing Q-learning algorithm.A general approach for design of Q-matrix and learning policy is suggested and the concept is systematically validated by simulation in the application to control two examples of processes exhibiting first order dynamics and oscillatory second order dynamics.Results show that online learning using interaction with controlled process is possible and it ensures significant improvement in control performance compared to arbitrarily tuned PI controller.
Speeding-Up Backpropagation of Gradients Through the Kalman Filter via Closed-Form Expressions

In this article, we provide novel closed-form expressions enabling differentiation of any scalar function of the Kalman filter's outputs with respect to all its tuning parameters and to the measurements. The approach differs from the previous well-known sensitivity equations in that it is based on a backward (matrix) gradient calculation, which leads to drastic reductions in the overall computational cost. It is our hope that practitioners seeking numerical efficiency and reliability will benefit from the concise and exact equations derived in this article and the methods that build upon them. They may notably lead to speed-ups when interfacing a neural network with a Kalman filter.
A Survey on Deep Learning for Named Entity Recognition : Extended Abstract

Named entity recognition (NER) is the task to identify text spans that mention named entities, and to classify them into predefined categories such as person, location, organization, etc. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In our TKDE paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing solutions based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area.
High-fidelity trapped-ion qubit operations with scalable photonic modulators

Abstract Experiments with trapped ions and neutral atoms typically employ optical modulators in order to control the phase, frequency, and amplitude of light directed to individual atoms. These elements are expensive, bulky, consume substantial power, and often rely on free-space I/O channels, all of which pose scaling challenges. To support many-ion systems like trapped-ion quantum computers or miniaturized deployable devices like clocks and sensors, these elements must ultimately be microfabricated, ideally monolithically with the trap to avoid losses associated with optical coupling between physically separate components. In this work we design, fabricate, and test an optical modulator capable of monolithic integration with a surface-electrode ion trap. These devices consist of piezo-optomechanical photonic integrated circuits configured as multi-stage Mach-Zehnder modulators that are used to control the intensity of light delivered to a single trapped ion on a separate chip. We use quantum tomography employing hundreds of multi-gate sequences to enhance the sensitivity of the fidelity to the types and magnitudes of gate errors relevant to quantum computing and better characterize the performance of the modulators, ultimately measuring single qubit gate fidelities that exceed 99.7%.
SNR-Prior Guided Trajectory-Aware Transformer for Low-Light Video Enhancement

Recently, deep learning has been widely employed to improve the quality of low-light videos. However, most existing low-light video enhancement methods fail to effectively explore temporal dependence, and the enhanced videos may suffer from severe noise, loss of detailed texture, and temporal inconsistency. In this paper, we propose a novel SNR-prior Guided Trajectory-aware Transformer (SGTT) to enable effective video representation learning for low-light video enhancement. Specifically, signal-to-noise ratio prior and cosine similarity are introduced to build the trajectory-aware dual-attention for exploiting the dependence of long-range spatio-temporal information, which searches for sharper and highly correlated patches within the same trajectory to assist in enhancing the target frames. Moreover, to adaptively fuse spatio-temporal information of support frames propagated bidirectionally, an attention-guided spatio-temporal feature aggregation module is proposed to perceive and enhance the specific high-quality features. The evaluation of both dynamic and static videos shows the effectiveness of our network, which significantly outperforms the state-of-the-art methods.
A Gaussian Boson Sampling for Graph Computation

A gaussian boson sampling microprocessor for graph applications, such as solving task assignment, Boolean satisfiability, graph clique, max cut, vertex cover problems, is demonstrated and achieved. It manifests the ability of photonic quantum computing to realize practical applications for conventionally intractable computations.
Under-reported time-varying MINAR(1) process for modeling multivariate count series

A time-varying multivariate integer-valued autoregressive of order one (tvMINAR(1)) model is introduced for the non-stationary time series of correlated counts when under-reporting is likely present. A non-diagonal autoregression probability network is structured to preserve the cross-correlation of multivariate series, provide a necessary condition to ease model-fittings computations, and derive the full likelihood using the Viterbi algorithm. The motivating construction applies to fully under-reported counts that rely on a mixture presentation of the random thinning operator. Simulation studies are conducted to examine the proposed model, and the analysis of COVID-19 daily cases is accomplished to highlight its usefulness in applications. Finally, the comparison of models is presented using the posterior predictive checking method.
Model-based explanations of concept drift

Concept drift refers to the phenomenon that the distribution generating the observed data changes over time. If drift is present, machine learning models can become inaccurate and need adjustment. While there do exist methods to detect concept drift or to adjust models in the presence of observed drift, the question of explaining drift, i.e., describing the potentially complex and high dimensional change of distribution in a human-understandable fashion, has hardly been considered so far. This problem is of importance since it enables an inspection of the most prominent characteristics of how and where drift manifests itself. Hence, it enables human understanding of the change and it increases acceptance of life-long learning models. In this paper, we present a novel technology characterizing concept drift in terms of the characteristic change of spatial features based on various explanation techniques. To do so, we propose a methodology to reduce the explanation of concept drift to an explanation of models that are trained in a suitable way to extract relevant information regarding the drift. This way, a large variety of explanation schemes is available. Thus, a suitable method can be selected for the problem of drift explanation at hand. We outline the potential of this approach and demonstrate its usefulness in several examples.
Utilization of the Weighted Product-Based CIPP Evaluation Model in Determining the Best Online Platform

Since the COVID-19 pandemic, there have been many free online platforms that can be used to support the online learning process at health colleges in Bali. However, it is difficult to determine the best online platform from the various choices of free online platforms that are scattered on the internet. Therefore, it needs innovations that contribute to helping solve these problems. One model as an innovation that can be used and contributes to solving problems is the Weighted Product-based CIPP evaluation model. The model needs to be measured for the quality of its calculations to ensure success in determining the best online platform. Therefore, this research aimed to show the quality of the Weighted Product method calculation integrated with the CIPP (Context-Input-Process-Product) model in determining the best platform used in health colleges during the COVID-19 pandemic. The instrument used to assess the quality of that calculation was a questionnaire consisting of eight questions. The subjects involved in the assessment were 20 experts. The research was carried out at several health colleges in Bali. The analytical technique used in analyzing the research data was descriptive-quantitative. The analysis was carried out by comparing the quality percentage of the calculation simulation with the quality standard based on an eleven-point scale. The study results showed the quality percentage of calculation simulation was 87.250%, so it was included in the very good category. This research has a significant impact on the progress of the educational evaluation field through research findings in the form of the appearance of the combination of the Weighted Product method with the CIPP evaluation model. The novelty of this research is the combination of the Weighted Product method and the CIPPmodel, which makes it easier for educational evaluators to determine the best online platform that supports online learning during the COVID-19 pandemic and even post-COVID-19. Doi: 10.28991/HIJ-2023-04-01-015 Full Text: PDF
Contract-Oriented Programming with Timed Session Types

Contract-oriented programming is a software engineering paradigm which proposes the use of behavioural contracts to discipline the interaction among software components.In a distributed setting, the various components of an application may be developed and run by untrustworthy parties, which could opportunistically diverge from the expected behaviour when they find it convenient.The use of contracts in this setting is essential: by binding the behaviour of each component to a contract, and by sanctioning contract violations, components are incentivized to behave in a correct and cooperative manner.This chapter is a step-by-step tutorial on programming contract-oriented distributed applications.The glue between components is a middleware which establishes sessions between services with compliant contracts, and monitors sessions to detect and punish violations.Contracts are formalised as timed session types, which describe timed communication protocols between two components at the endpoints of a session.We illustrate some basic primitives of contract-oriented programming: advertising contracts, performing contractual actions, and dealing with violations.We then show how to exploit these primitives to develop some small distributed applications.
W-Sec: A Model-Based Formal Method for Assessing the Impacts of Security Countermeasures

The chapter provides a detailed description of W-Sec, a formal model-based countermeasures’ impact assessment method. It also introduces a new formal definition of the two SysML profiles used in SysML-Sec and W-Sec, enabling (i) for the future automation of several W-Sec stages and (ii) for the definition of consistency rules ensuring the consistency of the models written in these two distinct modeling languages. In addition, the chapter evaluates W-Sec with a new industry 4.0 case-study and discusses the strengths and the current limitations of the approach in this new application field.
Dating Greek Papyri with Text Regression

Dating Greek papyri accurately is crucial not only to edit their texts but also to understand numerous other aspects of ancient writing, document and book production and circulation, as well as various other aspects of administration, everyday life and intellectual history of antiquity.Although a substantial number of Greek papyri documents bear a date or other conclusive data as to their chronological placement, an even larger number can only be dated tentatively or in approximation, due to the lack of decisive evidence.By creating a dataset of 389 transcriptions of documentary Greek papyri, we train 389 regression models and we predict a date for the papyri with an average MAE of 54 years and an MSE of 1.17, outperforming image classifiers and other baselines.Last, we release date estimations for 159 manuscripts, for which only the upper limit is known.
Annotating Research Infrastructure in Scientific Papers: An NLP-driven Approach

Seyed Amin Tabatabaei, Georgios Cheirmpos, Marius Doornenbal, Alberto Zigoni, Veronique Moore, Georgios Tsatsaronis. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track). 2023.