Reviewerly/exHarmony huggingface dataset:
Adversarial Robustness in Graph-Based Neural Architecture Search for Edge AI Transportation Systems

Edge AI technologies have been used for many Intelligent Transportation Systems, such as road traffic monitor systems. Neural Architecture Search (NAS) is a typcial way to search high-performance models for edge devices with limited computing resources. However, NAS is also vulnerable to adversarial attacks. In this paper, A One-Shot NAS is employed to realize derivative models with different scales. In order to study the relation between adversarial robustness and model scales, a graph-based method is designed to select best sub models generated from One-Shot NAS. Besides, an evaluation method is proposed to assess robustness of deep learning models under various scales of models. Experimental results shows an interesting phenomenon about the correlations between network sizes and model robustness, reducing model parameters will increase model robustness under maximum adversarial attacks, while, increasing model paremters will increase model robustness under minimum adversarial attacks. The phenomenon is analyzed, that is able to help understand the adversarial robustness of models with different scales for edge AI transportation systems.
The Russian prepositional TIPA and VRODE in online student discourse: evidence of attraction?

Abstract This article scrutinizes the use of two competing Russian prepositions, tipa and vrode ‘like, such as’, in online student discourse. Both are associated with the crosslinguistically attested grammaticalization of taxonomic nouns, with tipa having derived from the noun tip ‘type’ and vrode from the noun rod ‘kind’. A prior study contemporary to the fairly recent grammaticalization of tipa forecasted a substitution scenario for the two competing forms. Forty years later, the present article shows that in the current discourse of younger speakers (i) there is indeed a selectional bias in favor of the prepositional tipa (which does not extend to the items’ particle uses) and (ii) the two prepositions demonstrate a high degree of attraction. However, this article also provides quantitative evidence that suggests the presence of a counterforce – subjectification. The selection is modeled on the basis of the items’ functions, realizations of the right- and left-side slots, the speaker’s gender, their evaluative attitude to the referent of the construction, and the perceived casualness of the discourse situation proxied by neutral or markedly informal expression styles.
Modeling how different user groups perceive webpage aesthetics

Aesthetics is a central consideration in user interface design. It is known to affect end-user behavior and perception, in particular the first impression of graphical user interfaces. However, what users perceive as pleasant or good design is highly subjective. We contribute a computational model that estimates the visual appeal of a given webpage for several common cohorts, or user groups, including gender, age, and education level. Our model, a convolutional neural network trained on 418 webpage screenshots having 771k aesthetic scores (in a 1–9 Likert scale) from 32k users, achieves high accuracy and is always less than 1 point off from ground-truth ratings. Designers can use our model to anticipate how people would rate their webpage, offer personalized designs according to the visual preferences of their users, and support rapid evaluations of webpage design prototypes for specific cohorts.
How to generate Muong speech directly from Vietnamese text: Cross-lingual speech synthesis for close language pair

The paper introduces a method for automatic translation of Vietnamese text into Muong speech in two dialects, Muong Bi - Hoa Binh and Muong Tan Son - Phu Tho, which are all unwritten dialects of the Muong language. Due to the very close relationship between the Vietnamese and Muong languages, the translation system was built to look like a cross-lingual speech synthesis system, in which the input is the text of one language (i.e., the Vietnamese) and the output is the speech of another language (i.e., the two Muong dialects). The system used the modern sequence-to-sequence TTS neural models Tacotron2 and WaveGlow. The evaluation results showed a high quality of translation (with a fluency score of 4.61/5.0 and an adequacy score of 4.79/5.0) and also synthesized speech quality (with naturalness on the MOS scale of 4.68/5.0 and intelligibility of 94.60%). The received results show that the applicability of the proposed system to other minority languages is promising, especially in the case of unwritten languages.
Survey on the Improvement and Application of HHL Algorithm

Abstract Quantum computing is a new computing mode that follows the laws of quantum mechanics to control quantum information units for computation. In terms of computational efficiency, due to the existence of quantum mechanical superposition, some known quantum algorithms can process problems faster than traditional general-purpose computers. HHL algorithm is an algorithm for solving linear system problems. Compared with classical algorithms in solving linear equations, it has an exponential acceleration effect in certain cases and as a sub-module, it is widely used in some machine learning algorithms to form quantum machines learning algorithms. However, there are some limiting factors in the use of this algorithm, which affect the overall effect of the algorithm. How to improve it to make the algorithm perform better has become an important issue in the field of quantum computing. This paper summarizes the optimization and improvement of HHL algorithm since it was proposed, and the application of HHL algorithm in machine learning, and discusses some possible future improvements of some subroutines in HHL algorithm.
Synonym Prediction for Vietnamese Occupational Skills

To date, online job portals are the main media to connect job seekers and employers. These platforms offer job catalogues and search functionality where one can search for jobs that alphabetically match specific keywords. This setup has limitations in terms of retrieval and accuracy since keyword matching suffers from inconsistent representations of meaning such as typo, slang, abbreviations, and synonyms. Thus, to enable advanced search, it is needed to have a machine learning model that can automatically detect occupational skill synonyms. In this work, we propose a rational process to construct a practical labeled Vietnamese Skill Synonym (ViSki) dataset. We experiment with 2 approaches for the synonym prediction task: cosine similarity-based and classification-based. Our best model, XGBoost with LaBSE embedding, achieved 96.15% accuracy and an F1-score of 0.9285.
Non Immersive Virtual Laboratory Applied to Robotics Arms

This article presents a non immersive virtual laboratory to emulate the behavior of Mitsubishi Melfa RV 2SDB robotic arm, allowing students and users to acquire skills and experience related to real robot, augmenting the access and learning of robotics in Universidad de las Fuerzas Armadas (ESPE). It was developed using the mathematical model of the robotic arm, thus defining the parameters for the virtual recreation. The environment, interaction and behavior of robotic arm was developed in a graphic engine (Unity3D) to emulate learning tasks such in a robotics's laboratory. In the virtual system, 4 inputs were development for the movement of the robot arm, further to program the robot a user interface was created where the user selects the trajectory such as point to point, line, arc or circle. Finally the hypothesis of the industrial robotic learning process is validated through the level of knowledge acquired after using the system.
A Hybrid Approach Based on Genetic Algorithm with Ranking Aggregation for Feature Selection

Recently, feature selection has become challenging for many machine learning disciplines. The success of most existing approaches depends on the effectiveness of searching strategies to select the most salient features from the original feature space. Unfortunately, these approaches may become impractical when dealing with high-dimensional datasets. In order to overcome this problem, recent studies rely on a boundary scheme (i.e., fixing a number of selected features) to reduce the searching space or a ranking scheme (e.g., features with less correlated scores) to guide the selection phase. However, choosing the best-fitted size for the feature subset is also a hard problem, and relying on one feature comparison criteria may ignore important features. In this paper, we propose a genetic algorithm that aims to optimize the feature subset and the appropriate number of selected features to maximize the performance of an Artificial Neural Network (ANN) classifier. To improve the efficiency of the selection phase, we combine the proposed GA with a local search algorithm based on a ranking aggregation approach. Our objective is to speed up the searching algorithm by taking advantage of different feature scoring criteria. We have assessed the performance of our approach over three categories of datasets: small, medium and high in terms of feature dimensionality (e.g., the smallest and the largest datasets include 8 and 7129 features, respectively). The empirical results have shown that our proposed approach outperforms the other state-of-the-art works when dealing with medium- and high-dimensional datasets and is comparable to them in the case of small-dimensional datasets.
Machine Reading Comprehension Model for Low-Resource Languages and Experimenting on Vietnamese

Machine Reading Comprehension (MRC) is a challenging task in natural language processing. In recent times, many large datasets and good models are public for this task, but most of them are for English only. Building a good MRC dataset always takes much effort, this paper proposes a method, called UtlTran, to improve the MRC quality for low-resource languages. In this method, all available MRC English datasets are collected and translated into the target language with some context-reducing strategies for better results. Tokens of question and context are initialized word representations using a word embedding model. They are then pre-trained with the MRC model with the translated dataset for the specific low-resource language. Finally, a small manual MRC dataset is used to continue fine-tuning the model to get the best results. The experimental results on the Vietnamese language show that the best word embedding model for this task is a multilingual one - XLM-R. Whereas, the best translation strategy is to reduce context by answer positions. The proposed model gives the best quality, i.e. F1 = 88.2% and Exact Match (EM) = 71.8%, on the UIT-ViQuAD dataset, compared to the state-of-the-art models.
Deepfake Detection Using CNN Trained on Eye Region

In this work, we will develop a simple convolutional neural network to detect deepfakes in videos on a frame-by-frame level, focusing on the region around the eyes. Since deepfakes are increasingly being created using forms of CNN, it should be possible to also detect deepfakes using CNN. OpenCV allows for frame extraction from videos, while also allowing image cropping. The well-developed Multitask Cascade Neural Network (MTCNN) is a stacked neural network for face detection and alignment. MTCNN is used for high accuracy face detection to greatly reduce false positive images in the dataset. Finally, a region around both eyes are cropped, with extra padding, to be used as input to train a CNN, using returned coordinates from MTCNN for the eyes. This research will focus on measuring if the eye region can be a useful area of interest for comparing original videos to deepfake videos.
Question Difficulty Estimation with Directional Modality Association in Video Question Answering

The questions in question-answering (QA) tasks have a different level of difficulty. Thus, a number of methods have been proposed to estimate a difficulty level of the questions. However, the existing methods estimate the difficulty based only on text information, and thus loose other modalities if a QA task is intrinsically multi-modal. To solve this problem, this paper proposes a novel question difficulty estimator for multi-modal QAs. The proposed estimator is designed to solve the question difficulty estimation for video QA but is not limited to. That is, it is capable of managing both a text and a video as a sequence of images. In addition, it models the directional influence of one modality to the other modality with Directional Modality Association Transformer (DiMAT). Inspired by the transformer, the directional influence in DiMAT is expressed through a directional attention layer and a feed-forward network layer. Then, the representations for the directional influence are used together with the representations of each modality to determine the difficulty of the questions. The experiments on two benchmark video QA data sets show that the proposed question estimator outperforms the SOTA modality interaction models, which proves the effectiveness of the proposed model.
Fast Weighted Sequential Pattern Mining

In the real world, ordered sequence data is commonly seen, and sequence analysis plays an important role in a wide range of real applications, such as market basket analysis. The weight concept helps to find more interesting sequences, whereas they may be treated as meaningless patterns in sequential pattern mining. Therefore, how to effectively discover these high weighted sequences from a quantitative sequential database is an urgent task. Based on the remaining weight concept, we propose a novel algorithm called Fast Weighted Sequential Pattern Mining (FWSPM) by utilizing an upper-bound called the remaining sequence maximum weight. Based on this upper-bound, an effective pruning strategy is designed to reduce the search space and save memory cost. Experimental results on both real and synthetic datasets show that the designed FWSPM algorithm is more efficient than the existing algorithms, and also has good scalability on large-scale datasets.
MDNCaching: A Strategy to Generate Quality Negatives for Knowledge Graph Embedding

Knowledge graph embedding (KGE) has become an integral part of AI as it enables knowledge construction and exploring missing information. KGE encodes the entities and relations (elements) in a knowledge graph into a low-dimensional vector space. The conventional KGE models are trained using positive and negative examples by discriminating the positives from the negatives. However, the existing knowledge graphs contain only the positives. Hence, it is required to generate negatives to train KGE models. This remains a key challenge in KGE due to various reasons. Among them, the quality of the negatives is a critical factor for KGE models to produce accurate embeddings of the observed facts. Therefore, researchers have introduced various strategies such as Bernoulli negative sampling to generate quality negatives that are hard to distinguish from positives. However, fixed negative sampling strategies are suffering from vanishing gradients and false negatives. Later, the dynamic negative sampling techniques were introduced to overcome the vanishing gradient, but the false negatives still remain as a challenge to the research community. The present research introduces a new strategy called MDNCaching (Matrix Decomposed Negative Caching), which generates negatives considering the dynamics of the embedding space while exploring the quality negatives with large similarity scores. Matrix decomposition is used to eliminate false negatives, and hence, the MDNCaching ensures the quality of the generated negatives. The performance of MDNCaching was compared with the existing state-of-art negative sampling strategies, and the results reflect that the proposed negative sampling strategy can produce a notable improvement in existing KGE models.
A Preliminary Approach for using Metric Learning in Monotonic Classification

The purpose of this paper is to introduce, for the first time, a distance metric learning algorithm for monotonic classification. Monotonic classification is performed on labeled data in which both input and output data have an order relation. It addresses the problem of predicting labels in such a way that, if the data to be predicted is greater or lower than any training sample, its prediction should be greater or lower as well. On a different note, the use of distance metric learning algorithms enables significant performance improvements in distance-based classifiers, such as the nearest neighbors classifier. Several distance-based classifiers that are able to respect the monotonicity constraints of the datasets have been proposed. However, the development of distance metric learning algorithms remains a challenge, since, when these algorithms transform the space they can negatively modify the monotonic constraints. In our work, we propose a new methodology for learning distances that does not corrupt these constraints and allows for the reduction of the non-monotonicity of the dataset as well. The conducted experimental analysis also shows that the learned distances allow to improve the performance of the analyzed classifiers.
Dragonfly Algorithm for Multi-target Search Problem in Swarm Robotic with Dynamic Environment Size

Target search elements are very important in real-world applications such as post-disaster search and rescue missions, and pollution detection. In such situations, there will be time limitations, especially under a dynamic environment size which makes multi-target search problems are more demanding and need a special approach and intention. To answer this need, a proposed multi-target search strategy, based on Dragonfly Algorithm (DA) has been presented in this paper for a Swarm Robotic application. The proposed strategy utilized the DA static swarm (food hunting process) and dynamic swarm (migration process) to achieve the optimized balance between the exploration and exploitation phases during the multi-target search process. For performance evaluation, numerical simulations have been done and the initial results of the proposed strategy show more stability and efficiency than the previous works.
A hybrid‐driven continuous‐time filter for manoeuvering target tracking

This paper considers the problem of target tracking in complex manoeuvering scenarios with a lack of relevant prior knowledge. This is a challenge for classical model-based manoeuvering target tracking algorithms because they rely heavily on accurate domain and prior knowledge of target motion. To address this problem, we propose a hybrid-driven continuous-time filter algorithm in this paper, which combines the advantages of the model-driven and data-driven. We use the stochastic differential equation (SDE) with the acceleration model as the basic framework of the proposed algorithm. In order to deal with unpredictable manoeuvres and unknown perturbations, we adopt neural networks as data-driven to estimate target accelerations and compensations of composite perturbations in real time using historical and current measurements. And the direction of gradient descent of the neural network is constrained by the motion model, thus the learning efficiency of the network and the interpretability of the proposed algorithm is improved. The proposed algorithm can simultaneously utilise historical trajectory information and domain knowledge as hybrid-driven to achieve complex manoeuvering target tracking with little prior information. Experimental results demonstrate the superiority of our algorithm in tracking accuracy, robustness and environmental adaptability.
Incorporating exceptions and limitations to copyright law to incentivize the development of Artificial Intelligence in Latin America

In this paper, I describe the advantages and disadvantages of incorporating new Artificial Intelligence (AI)-enabling exceptions and limitations to the copyright laws of Latin American countries. Section 1 describes the threats and opportunities AI may pose to the region. Section 2 refers to the effects copyright legislation may have on investment decisions of AI firms, the potential benefits for countries incorporating them, Law & Economics and Comparative Law perspectives, and the current regulatory asymmetry in this matter across the region. Section 3 concludes with policy recommendations.
Organizational training in agile settings under the lens of CMMI V2.0

Abstract The software industry is a dynamic and ever‐changing environment where knowledge dissemination and ongoing learning is an open challenge, especially in agile organizations where continuous learning is essential. Organizational training covers the set of activities that drive this learning process and is one of the process areas of process improvement models like CMMI. In this paper, we discuss how the organizational training activity has been carried out at agile organizations under the lens of CMMI V2.0. In other words, we use the CMMI‐DEV V2.0 Organizational Training practices to provide an overview of how agile organizations plan, perform, and assess their training activities as part of their continuous learning process. Existing literature primarily discusses the techniques used to train and retrain employees. However, there is little evidence on how to plan training, how to assure alignment with the organization's strategic and tactical goals, and how to evaluate the effectiveness of training, which are very important challenges in the organizational training process. The paper concludes by presenting eight valuable insights identified by this research and matching them up with the CMMI‐DEV V2.0 Organizational Training practices.
Systematic Analysis of Risk Associated with Supply Chain Operations Using Blockchain Technology

Advancements in information and communication technologies (ICT), big data analytics, and artificial intelligence- (AI-) based techniques brought a dramatic revolution in diverse research domains, including healthcare, IoT, and networking. Blockchain technologies are gaining traction from both private and government organizations at an incredible rate. Emerging technologies have different levels of technological complexities and commercial ramifications. This technology is playing an essential role in the financial revolution of banking and regulatory sectors. Blockchain has piqued the interest of many academics, organizations, and businesses, particularly in using bitcoin. To grasp the significance of this revolution, a comprehensive assessment is performed to bridge the gaps in the targeted blockchain-driven domain with different perspectives. For this systematic review process, a set of four distinct research questions were formulated to accumulate the most relevant research trends. In private and public organizations, it is a securing technology to deliver trustworthy and protected services to users because of its decentralized, controlled aspect. Financial services, real estate, supply chain management, healthcare, academics, and other industries benefit significantly from this evolutionary technology. These application cases are diverse and far-reaching, ranging from smart contracts to blockchain-encrypted educational certificates. This systematic analysis has investigated a total of 113 relevant articles and concluded with the features and functions in an economic setting and briefed how these variables can balance players’ incentives, define core blockchain-related features, and present new research ideas to solve the proposed risk.