Reviewerly/exHarmony huggingface dataset:
Automatic Evaluation of Descriptive Answers Using NLP and Machine Learning.

The answer script analysis is a crucial a part of assessing student’s performance. Typically, an answer script analysis is finished manually that typically are often biased. The analysis depends on varied factors like mood swing of the authority, the inter-relation between the student and authority. To boot, analysis could be a very tedious and long task. During this paper, a linguistic communication processing-based methodology is shown for automatic answer script analysis. Our experiment consists of text extraction from answer script, measuring various similarities between summarized extracted text and hold on correct answers, so assign a weight value to every calculated parameters to attain the solution script. For outline generation from the extracted text, we've got used keyword-based summarisation techniques. Here four similarity measures (Cosine, Jaccard, Bigram, and Synonym) square measure used as parameters for generating the ultimate mark. Automatic analysis of answer scripts has been found terribly helpful from our experiments, and infrequently the assigned marks is that the same as manually scored marks.
A Literature-Based Thematic Network to Provide a Comprehensive Understanding of Agile Teamwork (106)

Agile Software Development (ASD) has become the mainstream software development method of choice. Its core fundamentals are based on Teamwork factors and the higher value of individuals and their interactions over processes and tools. However, there is no common understanding regarding the factors that should be considered for defining an ASD Teamwork construct. Driven by this problem, we present a thematic network that synthesizes the information presented in the literature, and eases knowledge sharing by defining a terminology. The thematic network is the result of the following process: (i) studies definition to be used as data source through a literature review; (ii) data extraction from these studies; (iii) data translation into codes; (iv) codes translation into themes; (v) creation of higher-order themes model; and (vi) assessment of synthesis trustworthiness. The resulting thematic network comprises four higher-order themes: Cohesion, Orientation, Shared Leadership, and Autonomy. We also evaluate the applicability of the identified themes in ASD Teamwork constructs in the literature. We concluded that the constructed thematic network can be generalized to ASD, and used as basis by researchers who intend to explore ASD Teamwork. Further, practitioners can use our results to understand agile teams’ dynamics better and improve their efficiency.
A deep grouping fusion neural network for multimedia content understanding

How Deep Neural Networks (DNNs) best cope with the understanding of multimedia contents still remains an open problem, mainly due to two factors. First, conventional DNNs cannot effectively learn the representations of the images with sparse visual information. For example, the images describing knowledge concepts in textbooks. Second, existing DNNs cannot effectively capture the fine-grained interactions between the images and text descriptions. To address these issues, we propose a deep Cross-Media Grouping Fusion Network (CMGFN), which mainly has two distinctive properties: 1) CMGFN can effectively learn visual features from the images with sparse visual information. This is achieved by first progressively adjusting the attention of convolution filters to valuable visual regions, and then enhancing the use of key visual information in feature construction. 2) By a cross-media grouping co-attention mechanism, CMGFN can effectively use the interactions between visual features of different semantics and textual descriptions, to learn cross-media features representing different fine-grained semantics in different groups. Empirical studies demonstrate that CMGFN not only achieves state-of-the-art performance on the multimedia documents containing sparse visual information, but also shows superior general applicability on other multimedia data, e.g., the multimedia fake news.
Multi-object tracking based on attention networks for Smart City system

The Smart City is a hot topic at present. Pedestrian tracking and behavior analysis play an important role in Smart City system. Therefore, it is urgent to design an universal and robust multi-object tracking(MOT) algorithm. MOT is a challenging problem with numerous significant applications, including robot navigation, autonomous driving and pedestrian behavior analysis. Among the existing MOT approaches, MOT models which joint learning detection and Re-ID are attracting increasing attention. Subsequently, there is competition between these two tasks jointly in a network inevitably leading to performance degradation. To address these difficulties, we propose an adaptive joint learning approach, called UnionTrack, for MOT in this paper. It handles with the problem of uniform learning between tasks in online MOT system. Firstly, UnionTrack introduces a scale-aware attention network that aggregates features from different scales for object localization and learning of the corresponding discriminative embeddings. Secondly, a cross-correlation module is designed to construct task-relevant feature maps and interact adaptively during training detection and Re-ID. Then, we perform inter-frame correlation at different stages by using high and low confidence detection to avoid occlusions and backgrounds. UnionTrack is trained in an end-to-end manner to better accommodate the training between detection and Re-ID tasks. Finally, our proposed UnionTrack is compared with state-of-the-art methods in extensively experiments and detailed performance analysis is performed in MOT15, MOT16, MOT17 and MOT20 which are all urban scene to demonstrate the effectiveness of our proposed method. Looking forward to playing a role in the Smart City systems.
Trusted-Data-Guided Label Enhancement on Noisy Labels

Label distribution covers a certain number of labels, representing the degree to which each label describes the instance. Label enhancement (LE) is a procedure of recovering the label distribution from the logical labels in the training data, the purpose of which is to better depict the label ambiguity through label distribution. However, data annotation inevitably introduces label noise, and it is extremely challenging to implement LE on corrupted labels. To deal with this problem, one way to recover the label distribution from the corrupted labels is to be guided by a small batch of trusted data. In this article, a novel LE method named TALEN is proposed via recovering and progressively refining label distribution guided by trusted data. Specifically, an LE process is applied to the untrusted data to select samples with a clean label. In addition, a combined loss function is designed to train the predictive model for classification. Experiments on datasets with synthetic label noise validate the feasibility of identifying clean labels via the recovered label distribution. Furthermore, experimental results on both synthetic label noise and real-world label noise on image datasets and additional experiments on text datasets show a clear advantage of TALEN over several existing noise-robust learning methods.
Rock Classification in a Vanadiferous Titanomagnetite Deposit Based on Supervised Machine Learning

As the potential locations of undiscovered ore deposits become deeper, a technique for predicting promising areas in the subsurface media has become necessary. Geoscience data on a wide range of underground media can be obtained through geophysical field exploration, but integration and interpretation of multi-geophysical data are difficult because of differences in spatial resolution. We developed a rock classifier that can predict promising vanadiferous titanomagnetite deposits from multi-geophysical data using supervised machine learning. Vanadiferous titanomagnetite ores are the main source of vanadium, which can be used as a large-scale energy storage system. Model training was conducted using rock samples from drilling cores, and the density of rock samples was used as a criterion for data labeling. We employed the support vector machine, random forest, extreme gradient boosting, LightGBM, and deep neural network for supervised learning, and the accuracy of all methods was 0.95 or greater. We applied trained models to three-dimensional geophysical field data to predict ore body locations. These candidate regions were distributed in the northeast of the geophysical survey area, and some classified areas were verified using a geological map.
Locust Mayfly Optimization-Tuned Neural Network for AI-Based Pruning in Chess Game

The art of mimicking a human’s responses and behavior in a programming machine is called Artificial intelligence (AI). AI has been incorporated in games in such a way to make them interesting, especially in chess games. This paper proposes a hybrid optimization tuned neural network (NN) to establish a winning strategy in the chess game by generating the possible next moves in the game. Initially, the images from Portable Game Notation (PGN) file are used to train the NN classifier. The proposed Locust Mayfly algorithm is utilized to optimally tune the weights of the NN classifier. The proposed Locust Mayfly algorithm inherits the characteristic features of hybrid survival and social interacting search agents. The NN classifier involves in finding all the possible moves in the board, among which the best move is obtained using the mini-max algorithm. At last, the performance of the proposed Locust mayfly-based NN method is evaluated with help of the performance metrics, such as specificity, accuracy, and sensitivity. The proposed Locust mayfly-based NN method attained a specificity of 98%, accuracy of 98%, and a sensitivity of 98%, which demonstrates the productiveness of the proposed mayfly-based NN method in pruning.
BaNeL: an encoder-decoder based Bangla neural lemmatizer

Abstract This study presents an efficient framework of deriving lemma from an inflected Bangla word considering its parts-of-speech as context. Bangla is a morphologically rich Indo-Aryan language where around 70% words are inflected, and some words have around 90 different inflected forms making it one of the most challenging languages for lemmatization. The unavailability of a sufficiently large appropriate dataset in Bangla makes the task even more strenuous. A reliable robust Bangla lemmatizer will create new possibilities for other dependent fields like automatic language translation and grammatical correction to flourish in Bangla. In this paper, we have described a new larger Bangla dataset for lemmatization and an encoder-decoder-based sequence_to_sequence framework for it. After tuning the hyper-parameters, the proposed framework yielded 95.75% character accuracy and 91.81% exact match on the testing split of the prepared dataset which is significantly higher than existing other approaches in Bangla for lemmatization. Article Highlights This article: Discusses lemmatization task in Bangla and demonstrates difference with stemming Presents an artificial neural network based efficient model for lemmatization that yields comparatively better performance than existing ones Describes a new large dataset for lemmatization in Bangla language
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search

The key challenge in neural architecture search (NAS) is designing how to explore wisely in the huge search space. We propose a new NAS method called TNAS (NAS with trees), which improves search efficiency by exploring only a small number of architectures while also achieving a higher search accuracy. TNAS introduces an architecture tree and a binary operation tree, to factorize the search space and substantially reduce the exploration size. TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture. Impressively, TNAS finds the global optimal architecture on CIFAR-10 with test accuracy of 94.37% in four GPU hours in NAS-Bench-201. The average test accuracy is 94.35%, which outperforms the state-of-the-art. Code is available at: https://github.com/guochengqian/TNAS.
Personalized Recommendation Method of Entrepreneurial Service Information Based on Blockchain

In order to improve the effect of personalized recommendation of entrepreneurial service information, this paper combines blockchain technology to improve personalized recommendation algorithm. Moreover, for these four types of incentive mechanisms, this paper establishes corresponding income models, and further derives the optimal weights for each type of incentive mechanism. Finally, this paper designs a personalized recommendation system of entrepreneurial service information based on blockchain, and verifies the performance of the personalized recommendation system of entrepreneurial service information based on blockchain, and evaluates its data mining and recommendation effects based on simulation research. Through experimental research, we know that the personalized recommendation system of entrepreneurial service information based on blockchain can provide entrepreneurs with more reliable information services.
Survey of Machine Reading Comprehension Models and its Evaluation Metrics

Machine Reading Comprehension (MRC) is a challenging task in Natural Language Processing (NLP) field. It is a fast evolving field in the recent years because of the development of large-scale datasets and the deep learning technology. Even though, numerous MRC models have been developed for Question Answer (QA) system, the existing MRC models are not on par with reading comprehension. In order to assess and improve the performance of the projected datasets, an advanced level of evaluation metrics have been introduced. Therefore, this work has conducted a survey over the existing MRC models and evaluation metrics utilized in them to address the lack of MRC with human reading.
A video painterly stylization using semantic segmentation

Most deep-learning-based style transfers methods for video extraction use features from only a single style image to perform texture synthesis. However, this does not allow users to be creative or selective. Moreover, styles are applied to both foreground objects and backgrounds. This paper presents a painterly style transfer algorithm for video based on semantic segmentation that can segment the foreground and background, enabling different stylizations. First, a fully convolutional neural network was constructed for semantic segmentation, and a GrabCut method with a dynamic bounding box was used to correct segments and refine contours and edges. Second, an enhanced motion estimation method was applied between the foreground and background objects. Third, style transfer was used to extract textures from a style image and perform texture synthesis on a content image while preserving the architecture of the content image. The proposed method not only improves the motion boundaries of optical flow but also rectifies discontinuous and irregular segmentation due to occlusion and shape deformation. Finally, the proposed method was evaluated on various videos.
Best-in-class imitation: Non-negative positive-unlabeled imitation learning from imperfect demonstrations

Although imitation learning can learn an optimal policy from expert demonstrations, it may fail to be transferred to practical environments because it is difficult to collect high-quality demonstrations for which the ultimate policy is not accurate enough and converges slowly. To solve the problem, an algorithm that utilizes Non-negative Positive-unlabeled learning (nnPU) as the probabilistic classifier to evaluate the quality of demonstrations, referred to as Non-negative Positive-unlabeled Importance Weighting Imitation Learning (PUIWIL), is proposed to increase the utilization of imperfect demonstrations and improve the performance of imitation learning. PUIWIL introduces confidence scores calculated by the nnPU classifier for expert demonstrations, which indicates the probability that the demonstration is generated by an optimal policy, and reweights all expert demonstrations according to their confidence scores. In addition, PUIWIL reconstructs the standard GAIL framework to make high-quality demonstrations have a more significant impact on imitation learning, which is called Best-in-class Imitation. The experiments demonstrate that PUIWIL improves both the performance and robustness of imitation learning from imperfect demonstrations.
Assessing Students’ Mathematical Knowledge with Fuzzy Logic

Assessing student mathematical knowledge is an important factor in the mathematics learning process because students obtain important feedback to improve their knowledge and learning. Despite the importance of student assessment, several researchers have shown that student grades comprise noncognitive and metacognitive factors and teachers’ prejudices and beliefs. One method to obtain a more objective view of student mathematical knowledge is through standardized assessments. In this paper, we analyze two methods of assessing student mathematical knowledge by considering their written and oral grades and achievements on the Italian National Assessment of Knowledge (INVALSI). The final grade was produced using the fuzzy logic inference system. It was tested on a sample of 2279 Grade 13 Italian high school students, who had both an oral and written grade in mathematics and who took the INVALSI assessment in the school year 2020–2021. Both tested fuzzy-logic-based assessment methods lowered the mean grades.
Who Is Using the Phone? Representation-Learning-Based Continuous Authentication on Smartphones

Recently, mobile technology has become closely linked with our daily activities. Smartphones are used for multiple personal tasks involving private information, such as communication, healthcare, and banking. Therefore, there is a high demand for user-friendly authentication methods that prevent unauthorized access to sensitive information. This paper proposes a novel feature representation tactic for continuous authentication named Multiple Channels Biological Graph (MCBG). Unlike conventional techniques, MCBG divides the smartphone usage scenarios into more fine-grained cases, including the operation interval features. To this end, we extract the screen touch and handheld features from multiple built-in sensors without extra user interaction. We conduct experiments on 180 participants (130 adults and 50 minors) and investigate the sufficiency of different sensor combinations required to authenticate identity accurately. Results show that our MCBG-based model achieves 99.38% authentication accuracy within 1.9 seconds. Furthermore, MCBG also represents the intrinsic differences between grown-ups and minors, achieving 96% identification accuracy.
Linear Prediction-based Parallel WaveGAN Speech Synthesis

This paper proposes a linear prediction (LP)-based neural speech synthesis method for a Parallel WaveGAN (PWG) framework. A recently proposed PWG vocoder successfully generates waveform sequences using a fast non-autoregressive Wave Net model. However, it often suffers from noisy outputs because of difficulties in capturing the complicated time-varying nature of speech signals. To improve synthesis quality, we introduce a back-propagable LP synthesis method for a PWG framework. Based on a source-filter theory of speech production model, the proposed PWG model learns the behavior of a source excitation signal, which is decoupled from a speech signal using the LP synthesis filter. In this way, it is possible to separately train the characteristics of only excitation signal while considering the interaction between the vocal source and vocal tract filter. Thus, the quality of the synthesized speech signal can be further improved. Objective and subjective evaluation results verified that the proposed methods reconstruct significantly better quality of synthetic speech than conventional methods.
A Novel 2D Clustering Algorithm Based on Recursive Topological Data Structure

In the field of data science and data mining, the problem associated with clustering features and determining its optimum number is still under research consideration. This paper presents a new 2D clustering algorithm based on a mathematical topological theory that uses a pseudometric space and takes into account the local and global topological properties of the data to be clustered. Taking into account cluster symmetry property, from a metric and mathematical-topological point of view, the analysis was carried out only in the positive region, reducing the number of calculations in the clustering process. The new clustering theory is inspired by the thermodynamics principle of energy. Thus, both topologies are recursively taken into account. The proposed model is based on the interaction of particles defined through measuring homogeneous-energy criterion. Based on the energy concept, both general and local topologies are taken into account for clustering. The effect of the integration of a new element into the cluster on homogeneous-energy criterion is analyzed. If the new element does not alter the homogeneous-energy of a group, then it is added; otherwise, a new cluster is created. The mathematical-topological theory and the results of its application on public benchmark datasets are presented.
Online paths planning method for unmanned surface vehicles based on rapidly exploring random tree and a cooperative potential field

The unstructured, dynamic marine environmental information and the cooperative obstacle avoidance problem greatly challenge the online path planner for unmanned surface vehicles. Efficiency and optimization are crucial for online path planning schemes. Thus, we proposed an algorithmic combination of the optimal rapidly exploring random tree and artificial potential field methods. First, we built a repulsive potential field by considering the relative velocity and position of the unmanned surface vehicle to obstacles and the international regulations for preventing collisions at sea, wherein we designed a repulsive force calculation method using radar readings to avoid irregular obstacles. Then, we guided the sampling process of rapidly exploring random tree using the potential field to accelerate the convergence rate of rapidly exploring random tree to low-cost obstacle avoidance paths. Finally, we planned for multiple paths based on the leader–follower architecture with the guidance of a cooperative potential field. In the experiments, the proposed method consistently outperformed the benchmark methods. We also verified the effectiveness of the algorithmic modifications by conducting ablation experiments.
Mathematical Criteria for a Priori Performance Estimation of Activities of Daily Living Recognition

Monitoring Activities of Daily Living (ADL) has become a major occupation to respond to the aging population and prevent frailty. To do this, the scientific community is using Machine Learning (ML) techniques to learn the lifestyle habits of people at home. The most-used formalism to represent the behaviour of the inhabitant is the Hidden Markov Model (HMM) or Probabilistic Finite Automata (PFA), where events streams are considered. A common decomposition to design ADL using a mathematical model is Activities-Actions-Events (AAE). In this paper, we propose mathematical criteria to evaluate a priori the performance of these instrumentations for the goals of ADL recognition. We also present a case study to illustrate the use of these criteria.