Reviewerly/exHarmony huggingface dataset:
DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation

We propose Depth-to-Space Net (DTS-Net), an effective technique for semantic segmentation using the efficient sub-pixel convolutional neural network. This technique is inspired by depth-to-space (DTS) image reconstruction, which was originally used for image and video super-resolution tasks, combined with a mask enhancement filtration technique based on multi-label classification, namely, Nearest Label Filtration. In the proposed technique, we employ depth-wise separable convolution-based architectures. We propose both a deep network, that is, DTS-Net, and a lightweight network, DTS-Net-Lite, for real-time semantic segmentation; these networks employ Xception and MobileNetV2 architectures as the feature extractors, respectively. In addition, we explore the joint semantic segmentation and depth estimation task and demonstrate that the proposed technique can efficiently perform both tasks simultaneously, outperforming state-of-art (SOTA) methods. We train and evaluate the performance of the proposed method on the PASCAL VOC2012, NYUV2, and CITYSCAPES benchmarks. Hence, we obtain high mean intersection over union (mIOU) and mean pixel accuracy (Pix.acc.) values using simple and lightweight convolutional neural network architectures of the developed networks. Notably, the proposed method outperforms SOTA methods that depend on encoder–decoder architectures, although our implementation and computations are far simpler.
A Comparative Study on Population-Based Evolutionary Algorithms for Multiple Traveling Salesmen Problem with Visiting Constraints

The multiple traveling salesmen problem with visiting constraints (VCMTSP) is an extension of the multiple traveling salesmen problem (MTSP). In this problem, some cities are restricted to be only accessed by certain salesmen, which is very common in real-world applications. In the literature, evolutionary algorithms (EAs) have been demonstrated to effectively solve MTSP. In this paper, we aim to adapt three widely used EAs in solving MTSP, namely the genetic algorithm (GA), the ant colony optimization algorithm (ACO), and the artificial bee colony algorithm (ABC), to solve VCMTSP. Then, we conduct extensive experiments to investigate the optimization performance of the three EAs in solving VCMTSP. Experimental results on various VCMTSP instances demonstrate that by means of its strong local exploitation ability, ABC shows much better performance than the other two algorithms, especially on large-scale VCMTSP. Though GA and ACO are effective to solve small-scale VCMTSP, their effectiveness degrades drastically on large-scale instances. Particularly, it is found that local exploitation is very vital for EAs to effectively solve VCMTSP. With the above observations, it is expected that this paper could afford a basic guideline for new researchers who want to take attempts in this area.
HRANet: Hierarchical region-aware network for crowd counting

Aiming to tackle the most intractable problems of scale variation and complex backgrounds in crowd counting, we present an innovative framework called Hierarchical Region-Aware Network (HRANet) for crowd counting in this paper, which can better focus on crowd regions to accurately predict crowd density. In our implementation, first, we design a Region-Aware Module (RAM) to capture the internal differences within different regions of the feature map, thus adaptively extracting contextual features within different regions. Furthermore, we propose a Region Recalibration Module (RRM) which adopts a novel region-aware attention mechanism (RAAM) to further recalibrate the feature weights of different regions. By the integration of the above two modules, the influence of background regions can be effectively suppressed. Besides, considering the local correlations within different regions of the crowd density map, a Region Awareness Loss (RAL) is designed to reduce false identification while producing the locally consistent density map. Extensive experiments on five challenging datasets demonstrate that the proposed method significantly outperforms existing methods in terms of counting accuracy and quality of the generated density map. In addition, a series of specific experiments in crowd gathering scenes indicate that our method can be practically applied to crowd localization.
An Analysis of Mobile Learning Tools in Terms of Pedagogical Affordances and Support to the Learning Activity Life Cycle

Mobile learning (m-learning) tools foster seamless learning environments that create new possibilities for practitioners to design innovative learning activities, as well as for students to learn in contextualised settings. Nevertheless, m-learning also entails difficulties as for instance, the aforementioned stakeholders should consider learning that happens across physical and digital spaces, blending elements from formal and informal learning. Thus, it is crucial to understand how existing m-learning technologies contribute to make learning effective and efficient. This chapter aims to present an overview of the pedagogical affordances of m-learning tools and the support that they provide to design and analytics practices of different stakeholders (during the learning activity life cycle). Concretely, we analyse nine tools resulting from a systematic review on LD and LA, in m-learning. We propose an analytical framework of orchestration affordances and use it together with the iPAC framework, to look respectively at the orchestration affordances of the tools throughout the life cycle of the learning activities and their pedagogical affordances. The results show the support that the tools offer to the phases of the learning activity life cycle, as well as the role that aligning LD and LA practices can play in this respect for different stakeholders’ practices. Moreover, we identify a number of gaps needing further investigation that can lead to future research avenues in m-learning.
The moderating effect of the need for interaction with a service employee on purchase intention in chatbots

Due to the advances of AI, the chatbot has replaced several functions once performed by humans. However, despite the growing adoption of chatbots by companies, consumers have held some reservations about using the tool – especially when finalizing purchases. Our study aims to verify the role of brand attachment in the purchase intention, and the moderating effect of the need to interact with service employees (NFI-SE) in these relationships. Using PLS-SEM, the results reveal that TAM and ISS influence satisfaction and purchase intention. In addition, NFI-SE moderates the relationship between satisfaction and brand attachment, and the relationship between satisfaction and purchase intention. Offering the possibility of interaction with human service during the conversation can increase satisfaction and brand attachment, as well as increasing purchase intentions. The absence of an escape mechanism from a robotic interaction to a human interaction can cause frustration in users when not having their needs met.
Low-Rank Linear Embedding for Robust Clustering

The performance of k-means clustering is often degenerate when dealing with high-dimensional and noisy scenarios. In this study, an end-to-end robust clustering method with low-rank linear embedding techniques (RCLR) is presented in conjunction with k-means. Sparse coefficients and a space projection matrix can be simultaneously learned. The global structures and local neighborhood properties are well captured in the learning procedures. Both the processes of clustering and dimensionality reduction are realized at the same time. The notions of clustering, dimensionality reduction, low-rank representation, and local property preservation are seamlessly integrated into a unified model. The limitation of error accumulation encountered in the previous two-stage clustering framework involving low-rank representation can be alleviated. This is the first attempt to introduce both the global and local geometrical structures into k-means directly, as well L2,1-norm is used as a basic metric instead of the conventional F-norm to further improve the robustness and interpretation of the model. The superiority of the proposed RCLR method is demonstrated by extensive experiments completed on various well-known benchmark datasets.
Efektivitas Penggunaan Aplikasi CANVA sebagai Media Pembelajaran IPA MI/SD

This article intends to describe the effectiveness of using Canva as an MI/SD science learning medium. Globalization brings major changes in the way people live. These changes are driven by the growing development of science and technology, which has an impact on various sectors of life, including in the field of education. The design of learning media at this time not only utilizes objects that can be found in everyday life but also utilizes the digital world. Canva is an application that can be developed in the process of making science learning media that really needs the media as an introduction to information from the content of abstract learning materials. The Canva application provides a variety of interesting features that can make it easier for teachers to create learning media, one of which is the availability of various templates that can be used in the process of designing learning media, one of which is science subjects in MI/SD. In carrying out this research, the literature review method was used, namely the process of placing, obtaining, reading, and evaluating various research literature related to or related to the issue to be studied. The data described are the results of research on the effectiveness of using the Canva application that has been carried out by previous researchers.
Generative Adversarial Network-Based Short Sequence Machine Translation from Chinese to English

With the acceleration of economic globalization, the economic contact, information exchange, and financial integration between countries become more and more frequent. In this context, the communication between different languages is also closer, so accurate translation between languages is of great significance. However, existing methods give little thought to short sequence machine translation from Chinese to English. This paper designs a generative adversarial network to solve the above problem. First, a conditional sequence generating adversarial net is constructed, which includes two adversarial submodels: a generator and a discriminator. The generator is designed to generate sentences that are difficult to distinguish from human-translated sentences, and the discriminator is designed to distinguish the sentences generated by the generator from human-translated sentences. In addition, static sentence-level BLEU values will be used as reinforcement targets for the generator. During training, both dynamic discriminators and static BLEU targets are used to evaluate the generated sentences, and the evaluation results are fed back to the generator to guide the generator's learning. Finally, experimental results on English-Chinese translation dataset show that the translation effect is improved by more than 8% compared with the traditional neural machine translation model based on recurrent neural network (RNN) after the introduction of generative adversative network.
Autonomous Navigation of Robots Based on the Improved Informed-RRT<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mrow> <mi>∗</mi> </mrow> </msup> </math> Algorithm and DWA

An improved method is proposed in this investigation to solve the problems of poor path quality and low navigation efficiency of the Informed-RRT <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow /> <mrow> <mi>∗</mi> </mrow> </msup> </math> algorithm in robot autonomous navigation. First, the greedy algorithm is introduced in the path planning procedure. When a new node is obtained, it will be judged whether it can directly reach the target point. Second, the search scope of the potential optimal parent node becomes the constructed path, instead of the node tree, which reduces the number of nodes to be searched and improves the navigation efficiency. Combined with the dynamic window approach (DWA), the improved algorithm is utilized to simulate the autonomous navigation process of the robot based on the Robot Operating System (ROS) platform. The simulation results show that compared with the original algorithm, the length of the global path is reduced by 5.15%, and the time of planning path and autonomous navigation is shortened by 78.34% and 21.67%, respectively.
CodeProcess Charts: Visualizing the Process of Writing Code

Instructors of computer programming courses evaluate student progress on code submissions, exams, and other activities. The evaluation of code submissions is typically a summative assessment that gives very little insight into the process the student used when designing and writing the code. Thus, a tool that offers instructors a view into how students actually write their code could have broad impacts on assessment, intervention, instructional design, and plagiarism detection. In this article we propose an interactive software tool with a novel visualization that includes both static and dynamic views of the process that students take to complete computer programming assignments. We report results of an exploratory think-aloud study in which instructors offer thoughts as to the utility and potential of the tool. In the think-aloud study, we observed that the instructors easily identified multiple coding strategies (or the lack of thereof), were able to recognize plagiarism, and noticed a clear need for wider dissemination of tools for visualizing the programming process.
Hyperconnected Openings Codified in a Max Tree Structure: An Application for Skull-Stripping in Brain MRI T1

This article presents two procedures involving a maximal hyperconnected function and a hyperconnected lower leveling to segment the brain in a magnetic resonance imaging T1 weighted using new openings on a max-tree structure. The openings are hyperconnected and are viscous transformations. The first procedure considers finding the higher hyperconnected maximum by using an increasing criterion that plays a central role during segmentation. The second procedure utilizes hyperconnected lower leveling, which acts as a marker, controlling the reconstruction process into the mask. As a result, the proposal allows an efficient segmentation of the brain to be obtained. In total, 38 magnetic resonance T1-weighted images obtained from the Internet Brain Segmentation Repository are segmented. The Jaccard and Dice indices are computed, compared, and validated with the efficiency of the Brain Extraction Tool software and other algorithms provided in the literature.
Scientists’ Research Data Management Questions: Lessons Learned at a Data Help Desk

This study investigates scientists’ data needs to provide a basis for professionals in research data management (RDM) to tailor services to meet those needs. Eighty-one participants completed a survey after they had asked a question at a Data Help Desk staffed by data management professionals at one of two science conferences. The qualitative responses were coded for common themes, and the quantitative questions were tabulated to compare results between the two conferences. The combined results provide an overview of scientists’ questions and training experiences on RDM. The study found that 70 percent of scientists in the survey had no prior RDM training, and the most common RDM need for both ecologists and geologists was storage. This study provides evidence that scientists need additional information about RDM. Traditional service desk models offer one way of assisting them with data needs, but the results indicate a need for more training to meet the expectations of data sharing. Academic and other libraries should consider expanding hours and services for RDM to provide help where and when scientists need it.
BAT: Small and Fast KEM over NTRU Lattices

We present BAT – an IND-CCA secure key encapsulation mechanism (KEM) that is based on NTRU but follows an encryption/decryption paradigm distinct from classical NTRU KEMs. It demonstrates a new approach of decrypting NTRU ciphertext since its introduction 25 years ago. Instead of introducing an artificial masking parameter p to decrypt the ciphertext, we use 2 linear equations in 2 unknowns to recover the message and the error. The encryption process is therefore close to the GGH scheme. However, since the secret key is now a short basis (not a vector), we need to modify the decryption algorithm and we present a new NTRU decoder. Thanks to the improved decoder, our scheme works with a smaller modulus and yields shorter ciphertexts, smaller than RSA-4096 for 128-bit classical security with comparable public-key size and much faster than RSA or even ECC. Meanwhile, the encryption and decryption are still simple and fast in spite of the complicated key generation. Overall, our KEM has more compact parameters than all current lattice-based schemes and a practical efficiency. Moreover, due to the similar key pair structure, BAT can be of special interest in some applications using Falcon signature that is also the most compact signature in the round 3 of the NIST post-quantum cryptography standardization. However, different from Falcon, our KEM does not rely on floating-point arithmetic and can be fully implemented over the integers.
Hybrid Autoregressive and Non-Autoregressive Transformer Models for Speech Recognition

The autoregressive (AR) models, such as attention-based encoder-decoder models and RNN-Transducer, have achieved great success in speech recognition. They predict the output sequence conditioned on the previous tokens and acoustic encoded states, which is inefficient on GPUs. The non-autoregressive (NAR) models can get rid of the temporal dependency between the output tokens and predict the entire output tokens in at least one step. However, the NAR model still faces two major problems. On the one hand, there is still a great gap in performance between the NAR models and the advanced AR models. On the other hand, it's difficult for most of the NAR models to train and converge. To address these two problems, we propose a new model named the two-step non-autoregressive transformer(TSNAT), which improves the performance and accelerating the convergence of the NAR model by learning prior knowledge from a parameters-sharing AR model. Furthermore, we introduce the two-stage method into the inference process, which improves the model performance greatly. All the experiments are conducted on a public Chinese mandarin dataset ASIEHLL-1. The results show that the TSNAT can achieve a competitive performance with the AR model and outperform many complicated NAR models.
Implications of the imposed and extensive use of online education in an early childhood education program

The COVID-19 pandemic forced universities to engage in a transformation regarding how education is provided. The pandemic led to education being pushed in a new direction regarding digital practices. This paper discusses the implications related to this transformation by asking the following research question: After being subjected to the imposed and extensive use of online education due to the pandemic, what implications can be found when we examine pre-service teachers’ digital competence and attitudes towards and use of digital technology? This study is based on data from Norwegian pre- service teachers in their third year of an early childhood education program. Data was collected during the spring semester of 2020 and 2021. The data were subjected to regression and correlation analyses to investigate how the pre-service teachers’ use of technology, attitudes and digital competence may have been affected 10 months into this educational transformation. The main difference found between the two groups of preservice teachers is related to their attitudes and how these attitudes relate to the use of digital technology. Our findings indicate the importance of not neglecting attitude as an important component of professional digital competence. Attitude seems to be the component that is most affected by pandemic- related consequences for education, when looking at the dynamics between use of technology, professional digital competence and attitudes.
Cancer Diagnosis With the Aid of Artificial Intelligence Modeling Tools

Cancer is one of the deadliest diseases in the present days.Its survivability is mostly corelated to early detection and treatment, which means that it is of utmost importance to successfully diagnose the patients.Unfortunately, even with years of experience human errors can happen which leads to the death of many individuals being misdiagnosed.Throughout the years there have been several applications created which could possibly aid doctors in the diagnosis.Neural Networks have always been a powerful tool which can be used in different applications that require an accurate model and the complexity of these models exceeds a human's computational capabilities.In image processing for example, a convolutional neural network can analyze each particular pixel and determine through the convolution function the common properties of different pictures.The objective of this study is to analyze different types of cancer diagnosing methods that have been developed and tested using image processing methods.The analyzed factors are training parameters, image processing technique and the obtained performances.This survey/review can be of significant value to researchers and professionals in medicine and computer science, highlighting areas where there are opportunities to make significant new contributions.
Mixedness, Coherence and Entanglement in a Family of Three-Qubit States

We consider a family of states describing three-qubit systems. We derived formulas showing the relations between linear entropy and measures of coherence such as degree of coherence, first- and second-order correlation functions. We show that qubit-qubit states are strongly entangled when linear entropy reaches some range of values. For such states, we derived the conditions determining boundary values of linear entropy parametrized by measures of coherence.
Simultaneous film thickness and refractive index measurement using a constrained fitting method in a white light spectral interferometer

Film is widely used in optoelectronic and semiconductor industries. The accurate measurement of the film thickness and refractive index, as well as the surface topography of the top and bottom surfaces are necessary to ensure its processing quality. Multiple measurement methods were developed; however, they are limited by the requirements of a known dispersion model and initial values of thickness and refractive index. Further, their systems are rarely compatible with surface topography measurement methods. We propose a constrained nonlinear fitting method to simultaneously measure the thickness and refractive index of film in a simple white-light spectral interferometer. The nonlinear phase extracted by the spectral phase-shifting is fitted with the theoretical nonlinear phase obtained by multiple reflection model. The constraints of nonlinear fitting are obtained by the interferometric signal of vertical scanning, reconstructed by the integration of the white-light spectral signal to avoid local minima. The proposed method does not require a priori knowledge of the dispersion model and initial values of thickness and refractive index, and its system is compatible with the vertical scanning interferometry (VSI) method to reconstruct the surface topography of the top and bottom surfaces of film. Three SiO2 films with different thicknesses are measured, and the results show that the measured refractive index is within the theoretical value range of wavelength bandwidth and the measured thicknesses are closely aligned with the values provided by the commercial instrument. The measurement repeatability of refractive index reaches 10-3. Measurements on a polymer film demonstrate that this method is feasible for measuring the film without a priori information.