Reviewerly/exHarmony huggingface dataset:
Think outside the search box: A comparative study of visual and form-based query builders

Knowledge workers such as healthcare information professionals, legal researchers and librarians need to create and execute search strategies that are comprehensive, transparent and reproducible. The traditional solution is to use proprietary query-building tools provided by literature database vendors. In the majority of cases, these query builders are designed using a form-based paradigm that requires the user to enter keywords and ontology terms on a line-by-line basis and then combine them using Boolean operators. However, recent years have witnessed significant changes in human–computer interaction technologies, and users can now engage with online information systems using a variety of novel data visualisation techniques. In this article, we evaluate a new approach to query building in which users express concepts as objects on a visual canvas and compare this with a traditional form-based query builder in a laboratory-based user study. The results demonstrate the potential of visual interfaces to mitigate some of the shortcomings associated with form-based interfaces and encourage more exploratory search behaviour. They also demonstrate the value of having a temporary ‘scratch’ space in query formulation. In addition, the findings highlight an ongoing need for transparency and reproducibility in professional search and raise further questions about how these properties may best be supported.
Enhancing the Power of CNN Using Data Augmentation Techniques for Odia Handwritten Character Recognition

The performance of any machine learning model largely depends on the type of input data provided. The higher the volume and variety of the data, the better the machine learning models get trained, thereby producing more accurate results. However, it is a challenging task to get high volume of data in some cases containing enough variety. Handwritten character recognition for Odia language is one of them. NITROHCS v1.0 for handwritten Odia characters and the ISI image database for handwritten Odia numerals are the standard Odia language datasets available for the research community. This paper shows the performance of five different machine learning models that uses a convolutional neural network to identify handwritten characters in response to handwritten datasets that are manipulated and expanded using several augmentation techniques to create variation and increase the volume of the data in the given dataset. These models, with the augmentation techniques discussed in the paper, even lead to a further increase in accuracy by approximately 1% across the models. The claims are supported by the results from the experiments done on the proposed convolutional neural network models on standard available Odia character and numeral data set.
Intelligent Recommendation Algorithm Combining RNN and Knowledge Graph

With the continuous application and development of big data and algorithm technology, intelligent recommendation algorithms are gradually affecting all aspects of people’s daily life. The impact of smart recommendation algorithm has both advantages and disadvantages; it can facilitate people’s life, but also exists at the same time the invasion of privacy, information cocoon, and other problems. How to optimize intelligent recommendation algorithms to serve the society more safely and efficiently becomes a problem that needs to be solved nowadays. We propose an intelligent recommendation algorithm combining recurrent neural network (RNN) and knowledge graph (KG) and analyze and demonstrate its performance by building models and experiments. The results show that among the five different recommendation models, the intelligent recommendation algorithm model combining RNN and knowledge graph has the highest AUC and ACC values in the Book-Crossing and MovieLens-1M. At the same time, the algorithm’s rating prediction error values are small (less than 2%) in extracting different users’ ratings for different books. In addition, the intelligent recommendation algorithm combining RNN and knowledge graph has the lowest RMSE and MAE values in the comparison of three different recommendation algorithms, indicating that it has better performance and stability, which is important for the improvement of user recommendation effect.
Path Planning Algorithms for Self-Driving vehicle based on improved RRT-Connect

Abstract This study aims to solve path planning of intelligent vehicles in self-driving. In this study, an improved path-planning method combining constraints of the environment and vehicle is proposed. The algorithm designs a reasonable path cost function, then uses a heuristic guided search strategy to improve the speed and quality of path planning, and finally generates smooth and continuous curvature paths based on the path post-processing method focusing on the requirements of path smoothness. A simulation test shows that compared with the basic rapidly-exploring random tree (RRT), RRT-Connect and RRT* algorithms, the path length of the proposed algorithm can be reduced by 19.7%, 29.3% and 1% respectively, and the maximum planned path curvature of the proposed algorithm is 0.0796 m-1 and 0.1512 m-1 respectively, under the condition of a small amount of planning time. The algorithm can plan the more suitable driving path for intelligent vehicles in a complex environment.
Analysis of Clusters Number Effect Based on K-Means Method for Tourist Attractions Segmentation

Abstract The current development of tourism potential can have an impact on the implementation of regional autonomy. Madura is an island located at the northern tip of East Java Province, which has tourist attractions spread over four regencies, namely Bangkalan, Sampang, Pamekasan, and Sumenep. However, there are still many attractions that tourists have not touched. Therefore, the Madurese government tries to develop the existing potential. However, the development of this potential has not been carried out evenly by the government. This is due to the lack of grouping of data on the number of tourists, making it difficult for the tourism office to analyze the dominant visitor effect based on the variables that become characteristics in mapping the entrance ticket to increase the potential of tourism objects in Madura. There are 21 tourist objects in Bangkalan. There are several influencing factors to mapping, such as: Gender, age, occupation, education, and marital status. In this study, the admission ticket mapping system applies the K-Means method by dividing tourist objects into three groups including, (C1) high cluster, (C2) medium set, and (C3) low cluster. K-Means functions to map objects by mapping the analysis of data mining applications that can increase the number of tourist visits that impact the introduction of tourist objects and improve the country’s foreign exchange through the development of facilities and infrastructure.
Vortex Artificial Potential Field for Mobile Robot Path Planning

Abstract Artificial Potential Field (APF) is one of path planning strategies which offers a relatively low cost on computational. It has been implemented in many real-time applications. Unfortunately, it is quite susceptible to local minima problems. In this paper, we offer a scheme for escaping local minima by using vortex field. Based on the results and simulation, it can be verified that the vortex field prevails to navigate the path while escaping local minima.
Invariance encoding in sliced-Wasserstein space for image classification with limited training data

Deep convolutional neural networks (CNNs) are broadly considered to be state-of-the-art generic end-to-end image classification systems. However, they are known to underperform when training data are limited and thus require data augmentation strategies that render the method computationally expensive and not always effective. Rather than using a data augmentation strategy to encode invariances as typically done in machine learning, here we propose to mathematically augment a nearest subspace classification model in sliced-Wasserstein space by exploiting certain mathematical properties of the Radon Cumulative Distribution Transform (R-CDT), a recently introduced image transform. We demonstrate that for a particular type of learning problem, our mathematical solution has advantages over data augmentation with deep CNNs in terms of classification accuracy and computational complexity, and is particularly effective under a limited training data setting. The method is simple, effective, computationally efficient, non-iterative, and requires no parameters to be tuned. Python code implementing our method is available at https://github.com/rohdelab/mathematical augmentation. Our method is integrated as a part of the software package PyTransKit, which is available at https://github.com/rohdelab/PyTransKit.
HiBERT: Detecting the illogical patterns with hierarchical BERT for multi-turn dialogue reasoning

Dialogue reasoning is a new task beyond the traditional dialogue system, because it requires recognizing not only semantic relevance but also logical consistency between the candidate response and the dialogue history. Like “all happy families are happy alike, all unhappy families are unhappy in their own way”, various illogical patterns exist in the data. For example, some candidate responses use many similar words but with contradicted meanings with history; while some candidates may employ totally different words but convey consistent meanings. Therefore, an ideal dialogue reasoning model should gather clues from both coarse-grained utterance-level and fine-grained word-level to determine the logical relation between candidates and the dialogue history. However, traditional models mainly rely on the widely used BERT to read all the history and candidates word by word but ignore the utterance-level signals, which cannot well capture various illogical patterns in this task. To tackle this problem, we propose a novel Hierarchical BERT (HiBERT) to recognize both utterance-level and word-level illogical patterns in this paper. Specifically, BERT is firstly utilized to encode the dialogue history and each candidate response as the contextualized representation. Secondly, hierarchical reasoning architecture is conducted with this contextualized representation to obtain the word-level and the utterance-level attention distributions, respectively. In detail, we utilize the word-grained attention mechanism to obtain the word-level representation, and propose two different types of attention function, i.e, hard attention and soft attention, to obtain the utterance-grained representation. Finally, we fuse both the word-grained representation and the utterance-grained representation to calculate the logical ranking scores for the given candidate. Experimental results on two public dialogue datasets show that our model obtains higher ranking measures than the widely used BERT model, validating the effectiveness of hierarchical reading of HiBERT. Further analysis on the impact of context length and attention weights shows that the HiBERT actually has the ability to recognize different illogical patterns.
Robust discriminant analysis with feature selective projection and between-classes structural incoherence

Our paper proposes a new feature extraction method, named as robust discriminant analysis (RDA), for data classification tasks. Based on linear discriminant analysis (LDA), RDA integrates the feature selection and feature extraction into a unified framework. The transformation matrix with l2,1-norm constraint is introduced to map original data feature into a discriminative low-dimensional subspace, in which the l2,1 sparsity regularizer can endow the feature selection with better interpretability. And, we use two different matrices (i.e., transformation matrix P and reconstruction matrix Q) for better data reconstruction, which can provide more freedom to ensure that the learned data representation holds the main variance and hence improve robustness to noises. To ensure that the learned features are optimal for classification, the structurally incoherent learning is introduced to add additional discriminant ability by minimizing the correlation of different classes. In other hand, the between-classes structural incoherence term is also equivalent to cosine distance metric, which is robust to noises and outliers. An efficient optimization algorithm is designed to solve the proposed optimization model. Extensive experiments conducted on all kinds of benchmark databases confirm the superiority of the proposed method.
Methodology for Designing an Optimal Test Stand for Camera Thermal Drift Measurements and Its Stability Verification

The effects of temperature changes on cameras are realized by observing the drifts of characteristic points in the image plane. Compensation for these effects is crucial to maintain the precision of cameras applied in machine vision systems and those expected to work in environments with varying factors, including temperature changes. Generally, mathematical compensation models are built by measuring the changes in the intrinsic and extrinsic parameters under the temperature effect; however, due to the assumptions of certain factors based on the conditions of the test stand used for the measurements, errors can become apparent. In this paper, test stands for thermal image drift measurements used in other works are assessed, and a methodology to design a test stand, which can measure thermal image drifts while eliminating other external influences on the camera, is proposed. A test stand was built accordingly, and thermal image drift measurements were performed along with a measurement to verify that the test stand did eliminate external influences on the camera. The experiment was performed for various temperatures from 5 °C to 45 5 °C, and as a result, the thermal image drift measured with the designed test stand showed its maximum error of 16% during its most rapid temperature change from 25 °C to 5 °C.
Representation learning for continuous action spaces is beneficial for efficient policy learning

Deep reinforcement learning (DRL) breaks through the bottlenecks of traditional reinforcement learning (RL) with the help of the perception capability of deep learning and has been widely applied in real-world problems. While model-free RL, as a class of efficient DRL methods, performs the learning of state representations simultaneously with policy learning in an end-to-end manner when facing large-scale continuous state and action spaces. However, training such a large policy model requires a large number of trajectory samples and training time. On the other hand, the learned policy often fails to generalize to large-scale action spaces, especially for the continuous action spaces. To address this issue, in this paper we propose an efficient policy learning method in latent state and action spaces. More specifically, we extend the idea of state representations to action representations for better policy generalization capability. Meanwhile, we divide the whole learning task into learning with the large-scale representation models in an unsupervised manner and learning with the small-scale policy model in the RL manner. The small policy model facilitates policy learning, while not sacrificing generalization and expressiveness via the large representation model. Finally, the effectiveness of the proposed method is demonstrated by MountainCar, CarRacing and Cheetah experiments.
Age Differences in Gaze Following: Older Adults Follow Gaze More than Younger Adults When free-viewing Scenes

Previous research investigated age differences in gaze following with an attentional cueing paradigm where participants view a face with averted gaze, and then respond to a target appearing in a location congruent or incongruent with the gaze cue. However, this paradigm is far removed from the way we use gaze cues in everyday settings. Here we recorded the eye movements of younger and older adults while they freely viewed naturalistic scenes where a person looked at an object or location. Older adults were more likely to fixate and made more fixations to the gazed-at location, compared to younger adults. Our findings suggest that, contrary to what was observed in the traditional gaze-cueing paradigm, in a non-constrained task that uses contextualized stimuli older adults follow gaze as much as or even more than younger adults.
Decision Support System for Selecting Robot Systems for Pick-and-place Operation of Robot Manipulator

In recent years, robots have been introduced to production sites due to the shortage of manpower. As a result, there is a need to reduce the costs of robot systems. Conventional research has focused on the optimization of robot configuration and motion planning, In this study, we propose a decision support system for the selection of equipment for robots, hands, and workpieces in a transfer system. Given information on the length and payload of each element, the problem of selecting equipment that minimizes the sum of the costs of these three elements is formulated as an integer programming problem. The system considers various conditions necessary for equipment selection, such as physical constraints and reachability of the robot end-effector to the workpiece, and expresses the grasp availability of the workpiece, payload, and reachability of the robot as inequality constraints. Using the proposed system, the optimal solution was obtained in the computational experiments.
Data-driven Industrial Machine Failure Detection in Imbalanced Environments

Machine failure often leads to unplanned downtime in industrial manufacturing, which could result in a significant loss in the manufacturer’s revenue. Several machine learning-based approaches have been proposed to alleviate the problem by instantly detecting occurring failures or predicting any potential breakdowns. However, there still exist limitations and issues that require attention. These include the difficulty of collecting real-world industrial data, especially big data, the challenge of feature selections and the under-representation of machine failure events in the data. In this paper, we present the use of a small predictive maintenance dataset with basic supervised learning algorithms for industrial machine failure detection. Moreover, we show the need of handling the imbalanced class distribution in such data for more accurate detection. Several non-deep learning algorithms were used for the classification task, and data resampling methods were applied to improve the model performance. Results show that decision tree could provide promising classification results, and with an undersampling method, the detection accuracy of 91% could be achieved.
Gender and Age Estimation From Facial Images using Deep Learning

Automated gender and age estimation from facial images are important for many real-world applications. Although, several studies have been proposed in the past, most of them are proposed as individual models and a considerable performance gap is noticed. Moreover, deep learning based approaches treated their model as a black box classifier and hence their model’s knowledge representation is not understandable and difficult to further improve. In this manuscript, we have proposed a simple and efficient CNN model architecture by considering gender and age estimation as a multi-label classification problem. The proposed model is trained and then evaluated on the publicly available Adience benchmark dataset. Experimental results demonstrated that the proposed model showed better performance than the similar approaches with an accuracy of 84.20% on gender estimation and an accuracy of 57.60% on age estimation. In addition, we have proposed a visualization technique to explain the classification results and then the gender-specific and age group-specific landmark facial regions are identified.
A Visually Interpretable Forensic Deepfake Detection Tool Using Anchors

“Deepfakes” have seen a dramatic rise in recent times and are becoming quite realistic and indistinguishable with the advancement of deepfake generation techniques. Promising strides have been made in the deepfake detection area even though it is a relatively new research domain. Majority of current deepfake detection solutions only classify a video as a deepfake without providing any explanations behind the prediction. However, these works fail in situations where transparency behind a tool’s decision is crucial, especially in a court of law, where digital forensic investigators maybe called to testify if a video is a deepfake with evidence; or where justifications behind tool decisions plays a key role in the jury’s verdict. Explainable AI (XAI) has the power to make deepfake detection more meaningful, as it can effectively help explain why the detection tool classified the video as a deepfake by highlighting forged super-pixels of the video frames. This paper proposes the use of “Anchors” XAI method, a model-agnostic high precision explainer to build the prediction explainer model, that can visually explain the predictions of a deepfake detector model built on top of the EfficientNet architecture. Evaluation results show that Anchors fair better than LIME in terms of producing visually explainable and easily interpretable explanations and produces an anchor affinity score of 70.23%. The deepfake detector model yields an accuracy of 91.92%.
Board Game as Financial Literacy Education Media for Indonesian High School Students

Research results from the Program for International Student Assessment (PISA) in 2019 showed that Indonesian high school students were ranked 62nd out of 70 member countries of the Organization for Economic Co-operation and Development (OECD). One of the problems is that financial literacy education is not optimal. Effective financial literacy education must involve students doing practical work so that financial literacy knowledge and experience will become part of the habits of high school students. Therefore, a board game was chosen as a supporting medium to complement traditional learning. This study uses the Delphi Method to determine the appropriate financial-themed board game as a medium to support financial literacy education for high school students in Indonesia based on expert judgment. This research identifies the board game with the highest relevance score in the financial literacy domain.
A New Deep Reinforcement Learning Algorithm for the Online Stochastic Profitable Tour Problem

This work presents an end-to-end framework for solving online stochastic optimization problems based on deep reinforcement learning. In particular, we focus on an online stochastic version of the profitable tour problem (PTP), which is a variant of the TSP with profits. The goal is to pick a subset of customers and maximize the total profits made from these customers, from which the total travel costs have to be subtracted. Profits are modeled through time-dependent random variables, whose realizations become available online. Most classical heuristic solution methods for combinatorial optimization problems require in-depth knowledge and expertise about the respective problem. In contrast to this, a deep reinforcement learning algorithm, called AlphaZero, has recently achieved state-of-the-art performance in combinatorial games, such as chess or Go, solely through self-play. We adapt this methodology to apply it to problems of online stochastic optimization, in particular a version of the PTP. Training is performed on a set of scenarios on a per-instance basis. First computational studies have shown promising results, improving the solution quality significantly through training.