Reviewerly/exHarmony huggingface dataset:
Combining uniform manifold approximation with localized affine shadowsampling improves classification of imbalanced datasets

Oversampling approaches are a popular choice to improve classification on imbalanced datasets. The SMOTE algorithm is the pioneer for many algorithms, built as extensions of SMOTE, to solve its problem of over-generalization of the minority class. Some extensions adopt the approach of learning the minority class data distribution through clustering and manifold learning techniques. The Localised Random Affine Shadowsampling (LoRAS) algorithm, models the convex space, controlling the local variance of a synthetic sample by constructing them from convex combinations of multiple shadow samples generated by adding Gaussian noise to the original minority samples. LoRAS also uses t-SNE for a manifold learning step to identify minority class data neighbourhoods. The algorithm is known to outperform some early SMOTE extensions, improving F1-Score and Balanced accuracy for highly imbalanced classification problems. However, the state-of-the-art manifold learning algorithm UMAP is known to preserve the local and global structure of the latent data manifold better than t-SNE and is considerably faster. We have integrated the UMAP for manifold learning with localized affine shadowsampling, to build the LoRAS-UMAP algorithm. We have benchmarked the new algorithm LoRAS-UMAP against some state-of-the-art oversampling algorithms on 14 publicly available datasets characterized by high imbalance, high dimensionality, and high absolute imbalance. In summary, we incorporated UMAP for the manifold learning step yielding better F1-Score, Balanced accuracy and runtime for the LoRAS algorithm in comparison to t-SNE for manifold learning, particularly in the case of high-dimensional datasets.
A Proposed Requirements Prioritization Model Based on Cost-Value Approach with Collaboration Perspective

Criteria analysis in requirement prioritization should always be transparent and holistic to increase the stakeholder’s satisfaction. Different stakeholders have their views on the same criteria, making it challenging to agree on a specific set of criterion weights. In software development, collaboration between client and developer is needed for requirements to be achieved. The cost-value approach to determining priority requirements requires a client perspective to determine the value and developer’s concept to ascertain costs, then form a cost-value diagram for analysis. Therefore, the research conducted has not been detailed using a cost-value approach involving a client perspective and a developer concept. Usually, the criteria and alternatives weighted by the decision-maker are vague, uncertain, and subjective. Furthermore, using fuzzy numbers help solve different types of uncertainty and conflicting requirements to produce reliable work. This study proposes increasing the weighting of criteria in the requirements prioritization technique, based on cost-value, by doing a collaboration perspective through Fuzzy AHP and TOPSIS using linguistic terms by presenting a proposed model.
Is It a Filler or a Pause? A Quantitative Analysis of Filled Pauses in Hebrew

In this study, we investigate quantitatively the use of Filled Pauses in Hebrew and compare them to the use of silence-based attributes and fluency-based attributes in task-oriented dialogues. Our aim is to explore whether the use of Filled Pauses in Hebrew supports either the filler-as-word hypothesis or the filler-as-pause hypothesis. We computed seven measures as independent variables, among them: Normalized rate of words, Filled Pauses rate, silent pauses rate, and articulation rate. Findings show that Filled Pauses occur twice as many as the most frequent words and on the other hand, Filled Pauses are far less frequent than the overall number of silent pauses. We did find that Filled Pauses are correlated with a sub-category of silent pauses. Our analysis supports that Filled Pauses used by Hebrew speakers are closer to the filler-as-pause hypothesis. Further, speakers would use FPs differently depending on their role in the Map Task dialogues.
AdvCGAN: An Elastic and Covert Adversarial Examples Generating Framework

Recently, a new methodology using generative adversarial network (GAN) has been proposed to produce adversarial examples, which breaks the limitations of the previous methods dependent on different norm-levels. It can efficiently generate perturbations for any instance once the generator is trained, arising from the learning to approximate the distribution of real instances. However, there are still two shortcomings for this category of GAN-based method: i) the predicted label in attacking stage totally depend on a fixed or randomly-chosen label in training stage, which cannot tackle the elasticity problem on how to elastically produce adversarial example with any arbitrarily-assigned label in targeted attack scene when the generator has finished training; and ii) it only considering the produced adversarial example is as close as the real instances, which cannot guarantee the generated adversarial example is visually indistinguishable from its corresponding original instance perceptually. The aboved two disadvantages make this kind of method lack of flexibility and covertness. To circumvent these two predicaments, we in this paper propose a simple and easy-to-use adversarial example generating framework AdvCGAN through training a conditional generative adversarial network under the co-consideration on the similarities in data distributions and the image labels between the adversarial examples and the original instances to be imperceptible to humans. Concretely, our proposed AdvCGAN trains the conditional GAN with both image data and label (normal and attack) information, by which the generator can utilizing the guidance of label information to appropriately produce the adversarial example with any specific label in attacking stage. Extensive experiments using the commonly used MNIST and CIFAR-10 datasets show that our proposed AdvCGAN significantly outperforms other methods in terms of multi-facet evaluation. The results exhibit that our AdvCGAN can elastically produce more realistic adversarial examples with any arbitrarily-assigned attack label and achieve higher attack accuracy, especially in targeted attack.
UAV Inspection Technology Based on Lightweight Edge Computing Framework

Abstract At present, unmanned aerial vehicle (UAV) is usually used to capture the ground images for power inspections, then transmit the images to the fixed ground station for analysis. That method is not conducive to the rapid positioning of key parts or timely treatment. At the same time, the automatic sensing of power inspections mainly adopts the targets detection and classification algorithms based on deep learning, which has a large amount of calculation, and the processor installed in the UAV terminal is difficult to achieve the effect of real-time detection. In order to improve the accuracy and real-time of key targets detection and classification of power facilities in the inspection process, a lightweight edge computing framework AirNet is proposed. In AirNet, simple linear iterative clustering (SLIC) algorithm and direct convolution method are used to optimize the UAV input image to simplify multiple granularity feature information and improve the accuracy of the algorithm. Real time intelligent analysis of the algorithm model is carried out in UAV terminal, and the key parts of transmission towers, houses and vehicles are selected for test. The results show that the algorithm can achieve 64 ms detection speed and 85% accuracy on Huawei Atlas 200 chip equipment.
Thai/English Cross-Language Transliterated Word Retrieval using Transformer

Cross-language transliterated word retrieval is a challenging task for Thai Natural Language Processing due to the difference between languages such as pronunciation and transliteration rules. In this paper, we present Thai/English cross-language transliterated word retrieval using Transformer. The proposed method enables transliterated word retrieval without using a dictionary. The phonetic code is used for cross-language retrieval. The phonetic code of a word represents the sound of a word. The results from our semi-supervised model using K-Fold cross validation showed that the model yielded precision, recall and F1 at 85.08%, 88.25% and 86.63% respectively for Thai-based datasets, and 80.44%, 87.15% and 83.66% respectively for English-based datasets.
Deep-Attention Model to Analyze Reliable Customers via Federated Learning

In this research, we propose a collaborative clustering method where the exchange of raw data is not required. The attention-based model is used with a federated learning framework. The edge devices compute the model updates using local data and send them to the server for aggregation. Repetition is performed in multiple rounds until a convergence point is reached. The transaction data is used to train the attention model that gives a low dimensional embedding. Afterward, we share the convergence model among the client/stores. Then, efficient clustering-based dynamic method is then utilized. For experimentation, we used retail store data to cluster the customer based on purchase behavior. The proposed clustering method used semantic embedding to extract centroid and then cluster them by discovering relevant patterns. The method achieved the 0.75 ROC values for the random distribution and 0.70 for the fixed distribution. The clustering method can help to reduce communication costs while ensuring privacy.
Mixed Real-Life and Anime Harmful Images Classification Using Deep Residual Neural Networks and Migration Learning

Harmful image detection and classification is a significant work for network security. Earlier studies based on skin color detection using manually extracted features are inaccurate and vulnerable to environmental change. Recently deep learning-based methods, especially the convolutional neural network (CNN) based models, have received widespread attention and show promising performance. However, as the neural network depth tends to deepen, popular CNN models for harmful image classification face two problems: the degradation problem, causing a decrease in accuracy with the increase in network layers; and the overfitting problem due to the relatively small samples of harmful images, resulting in lower test accuracy and unstable performance. In this paper, we propose a classification based on deep residual neural network (ResNet) and migration learning. ResNet copes with the degradation problem by a well-designed residual block structure, preventing performance loss in deeper networks. By introducing Batch Normalization (BN) and migration learning method to ResNet, the overfitting problem during model training and initialization phases can be mitigated. Besides, a fine-grained classification is proposed for both real-life images and 2D anime images as the latter is rapidly developing, especially in online communities for young people. Experiments show that the proposed method has better fine-grained classification accuracy. The degradation and overfitting problems are well solved. The optimized network depth for online mixed harmful image classification is also discussed.
Practical Considerations and Challenges When Conducting an Individual Participant Data (IPD) Meta-Analysis

This chapter provides a broad overview of the use of individual participant (sometimes referred to as patient) data (IPD ) within meta-analyses, the associated advantages of using IPD in meta-analysis compared to aggregate data, and when IPD should be used in meta-analysis.This chapter also outlines the steps of conducting an IPD meta-analysis, with practical guidance relating to requesting and obtaining IPD for meta-analysis. Challenges that can be associated with conducting an IPD meta-analysis are also discussed, including consideration of availability bias, when a subset of the relevant IPD is not available for meta-analysis.
Recognizing the Type of Mask or Respirator Worn Through a CNN Trained with a Novel Database

Since the onset of the coronavirus pandemic, researchers from all over the world have been working on projects aimed at countering its advance. The authors of this paper want to go in this direction through the study of a system capable of recognizing the type of mask or respirator worn by a person. It can be used to implement automatic entry controls in high protection areas, where people can feel comfortable and safe. It can also be used to make sure that people who work daily in contact with particles, chemicals, or other impurities wear appropriate respiratory protection. In this paper, a proof-of-concept of this system will be presented. It has been realized by using a state-of-the-art Convolutional Neural Network (CNN), EfficientNet, which was trained on a novel database, called the Facial Masks and Respirators Database (FMR-DB). Unlike other databases released so far, it has an accurate classification of the most important types of facial masks and respirators and their degree of protection. It is also at the complete disposal of the scientific community.
Asynchronous Decentralized Learning of Randomization-Based Neural Networks

In a communication network, decentralized learning refers to the knowledge collaboration between the different local agents (processing nodes) to improve the local estimation performance without sharing private data. The ideal case is that the decentralized solution approximates the centralized solution, as if all the data are available at a single node, and requires low computational power and communication overhead. In this work, we propose a decentralized learning of randomization-based neural networks with asynchronous communication and achieve centralized equivalent performance. We propose an ARock-based alternating-direction-method-of-multipliers (ADMM) algorithm that enables individual node activation and one-sided communication in an undirected connected network, characterized by a doubly-stochastic network policy matrix. Besides, the proposed algorithm reduces the computational cost and communication overhead due to its asynchronous nature. We study the proposed algorithm on different randomization-based neural networks, including ELM, SSFN, RVFL, and its variants, to achieve the centralized equivalent performance under efficient computation and communication costs. We also show that the proposed asynchronous decentralized learning algorithm can outperform a synchronous learning algorithm regarding computational complexity” especially when the network connections are sparse.
Particle filter-based perception method for obstacles in dynamic environment of a mobile robot

The state perception problem for obstacles occurring in the workspace of a mobile robot is a challenging task. The main goal of this work is to introduce a novel concept of the Particle filter algorithm where not only the positions but also the velocities of static and moving obstacles can be perceived using measurement data of an onboard LiDAR sensor. Next to the well-known resampling methods, a novel resampling algorithm was also introduced where the effective number of the particles can be considered. The introduced algorithm can generate an appropriate solution for both the position and velocity perception task.
Laplacian state transfer on graphs with an edge perturbation between twin vertices

We consider quantum state transfer relative to the Laplacian matrix of a graph. Let N(u) denote the set of all neighbors of a vertex u in a graph G. A pair of vertices u and v are called twin vertices of G provided N(u)∖{v}=N(v)∖{u}. We investigate the existence of quantum state transfer between a pair of twin vertices in a graph when the edge between the vertices is perturbed. We find that removal of any set of pairwise non-adjacent edges from a complete graph with a number of vertices divisible by 4 results Laplacian perfect state transfer (or LPST) at π2 between the end vertices of every edge removed. Further, we show that all Laplacian integral graphs with a pair of twin vertices exhibit LPST when the edge between the vertices is perturbed. In contrast, we conclude that LPST can be achieved in every complete graph between the end vertices of any number of suitably perturbed non-adjacent edges. The results are further generalized to obtain a family of edge perturbed circulant graphs exhibiting Laplacian pretty good state transfer (or LPGST) between twin vertices. A subfamily of which is also identified to admit LPST at π2.
Two-Dimensional Learning Rate Decay: Towards Accurate Federated Learning with Non-IID Data

In federated learning a global model is trained with training data geographically distributed over a number of clients. To reduce the communication cost over the expensive wide area network, clients complete multiple local iterations before synchronization. However, since the training data are non-iid, such infrequent synchronization would compromise the accuracy after model convergence. In order to tackle this problem, we propose Two-Dimensional Learning Rate Decay (2D-LRD) in this paper, which aims to improve the model performance by adaptively tuning the learning rate on two dimensions: round-dimension and iteration-dimension during the model training. That is, we gradually decrease the learning rate and decrease the learning rates of local iterations in a synchronization round with different speeds. Based on our experiments and analysis, we find that the sum of the inner product of round updates is a valuable signal for learning rate tuning. We perform evaluation and demonstrate that 2D-LRD can make great progress compared to the baseline scheme.
Sino-Japanese Relations from the Perspective of Chinese Mainstream Media During the Pandemic: Based on Word2vec and DTM Model

We conducted sentiment classification for news reports during the COVID-19 pandemic on Sino-Japanese relations from Chinese and Japanese media. Based on the Word2vec and Dynamic Topic Models, we analyzed the macroscopic dimension of theme distribution and the semantic change process of “Sino-Japanese” related words. The results indicate that Chinese media reports are mainly neutral, but Japanese ones are mainly negative. The meaning of the terms related to “Sino-Japanese” has not changed significantly, but there are minor differences in the content of reports on the same subject in different periods. During Suga’s administration, the overall stability of Sino-Japanese relations and the continued advancement of pragmatic cooperation are expected to continue.
Teaching Congruences in Connection with Diophantine Equations

The presented paper is devoted to the new teaching model of congruences of computer science students within the subject of discrete mathematics at universities. The main goal was to create a new model of teaching congruences on the basis of their connection with Diophantine equations and subsequently to verify the effectiveness and efficiency of the proposed model experimentally. The teaching of congruences was realized in two phases: acquisition of procedural knowledge and use of Diophantine equations to obtain conceptual knowledge of congruences. Experiments confirmed that conceptual understanding of congruences is positively related to increasing the procedural fluidity of congruence resolution. Research also demonstrated the suitability of using Diophantine equations to link congruences and equations. Among other things, the presented research has confirmed the justification of teaching mathematics in computer-oriented study programs.
Measurements of corneal topography using a compact null-screen corneal topographer with a mobile device

In this work, we use a null-screen corneal topographer with a semiradial spot pattern by using a mobile device’s camera to obtain images of the reflected pattern with the main aim of obtaining the corneal topography. First, we discuss how to integrate the system to calibrate it by testing a reference surface where we obtain optical parameters such as the radius of curvature and the conic constant, as well as elevation, sagittal curves, and meridional curvature maps with this method. Finally, we show some prelaminar measurements of the topography of some human corneas.
A Framework for Cypher-Physical Human-robot Collaborative Immersive MR Interaction – Beaux Arts Ball 4.0

Abstract In this paper, we presented a human-robot collaborative mixed reality application – Beaux Arts Ball 4.0, in which a real-time interactive hybrid and physical architectural environment were designed and experienced through the tools and techniques of mixed reality, cypher-physical, teleoperation, telepresence, and automation. The application engaged the user and observer in a continuous loop of architectural transformation during the experience, where every type of sensory was blurred between physical and digital perception.
When Different Language Groups Meet Online: Covert and Overt Focus on Form in Text-Based Chats

Focus on form has been extensively studied in text-based online dyadic chats but much less has been explored in group chats with interlocutors from different language backgrounds. Additionally, there are very few studies investigating covert focus on form. This study investigated the effects of interlocutor types on errors and focus on form episodes, both covert and overt, in text-based online group chats. We collected chat logs from two collaborative online international learning projects. One project was developed for the collaboration between an English course at a Chinese university and an art history course at a U.S. university; the other between another cohort of the same English course and a cultural studies course at a Mexican university. We compared errors, feedback, and other characteristics of focus on form episodes between the two projects. Analyses revealed significant differences in characteristics such as overtness (overt, covert), linguistic focus (mechanical, lexical, and grammatical), and source (code, message). However, no significant differences were found for the type of focus on form (preemptive, reactive), presence of uptake, uptake quality (successful, unsuccessful), and repair provider (self, other). Students showed a preference for self-repair over other-repair and for lexical focus over mechanical and grammatical foci in both projects. Overall, only a small proportion of errors were followed by feedback. Therefore, a small amount of uptake and successful uptake occurred in both projects. The results can shed light on how instructors could provide effective scaffolding and tasks to make virtual exchange projects more rewarding.
An Efficient Approach for Multiple Moving Objects Tracking with Occlusion

Some of the main issues for the governments and businesses nowadays are security and monitoring. Several critical locations need high security such as military bases, airports, malls, and checkpoints. The ambition and objective of this paper are to detect and track multiple objects while considering the challenges of partial and full occlusion. This paper presents an efficient algorithm for object tracking which can track objects in the presence of occlusion. The results are compared with the original Kalman filter-based approach. The proposed algorithm has an average of 0.51% improvement comparing to the original algorithm according to the following three test scenarios. It has 0.43% improvement for test 1, which is object tracking having full occlusion, 0.57% improvement for the second scenario, which is object tracking having full occlusion having a different dataset and 0.52% improvement for the third scenario in the tracked objects have been partially occluded. The accuracy, efficiency, and analogy of the proposed algorithm with the original algorithm show promising results.