Reviewerly/exHarmony huggingface dataset:
Tourism development and poverty alleviation in sub-Saharan African countries: an empirical investigation

In this study, the impact of tourism development on poverty alleviation is examined using panel data from 32 sub-Saharan African (SSA) countries during the period 2005–2014. Two indicators of tourism development are used, namely tourist arrivals and tourism revenue. In addition, four control variables have been used, namely economic growth, trade, the rule of law, and income inequality (measured by the Gini coefficient, the Atkinson index and the Palma ratio), thereby leading to three separate specifications for each tourism development proxy. Using the generalized method of moments (GMM) regression analysis, the study found that the impact of tourism development on poverty alleviation is not unanimous. When the number of tourist arrivals is used as a proxy, the results show that an increase in tourism development consistently leads to an increase in household welfare; hence, a decrease in poverty, irrespective of the specification used. However, when tourism revenue is used as a proxy, no significant impact of tourism development on household welfare is found to exist, irrespective of the model specification used. The results also show that income inequality has a clear negative impact on household welfare in SSA countries when it is measured by the Gini coefficient, while economic growth and the rule of law have a distinct positive effect.
Impact of Hyperparameter Tuning in Classifying Highly Imbalanced Big Data

Working with Machine Learning algorithms and Big Data, one may be tempted to skip the process of hyperparameter tuning, since algorithms generally take longer to train on larger datasets. Hyperparameter tuning is not something we get for free; one must allocate either more computing time or resources to run more iterations of experiments with different hyperparameter settings to find their optimal values. For small datasets the extra resources needed may be negligible, but for larger datasets resources necessary for tuning may be considerable. In this study, due to the size of the data we use, we find experiments where we do hyperparameter tuning take many times longer to complete than experiments where we do not do any tuning. Here, our contribution is to provide evidence that the investment in computing resources for hyperparameter tuning pays off, since we show that, for experiments involving highly imbalanced Big Data, we obtain better results when we incorporate hyperparameter tuning. With regard to the performance of CatBoost and LightGBM classifiers, we compare both default and tuned hyperparameter values. Furthermore our experiments encompass different techniques for encoding high-cardinality categorical features. We find in all cases, regardless of the classifier or encoding technique for categorical features, classifiers with tuned values for hyperparameters yield better results than those with default values. To the best of our knowledge, we are the first to do such a study on hyperparameter tuning to analyze the performance of LightGBM and CatBoost in classifying highly imbalanced Big Data.
Savalan: Multi objective and homogeneous method for software modules clustering

Abstract Reverse engineering is used for extracting and understanding software architecture models from source code when the source code is the only available software product. Software module clustering is a reverse engineering method which decomposes software modules into several clusters (subsystems) by using module dependency graph. Finding the best clusters for the modules of software is a multi‐objective and NP‐hard problem; maximizing the cohesion among the modules, minimizing the coupling among different clusters, and maximizing the software modularization quality are considered as the main objectives of software module clustering. Some of these objectives, such as cohesion and coupling, are in contradiction with each other. Simultaneous improvement of all clustering objectives (cohesion, coupling, modularization quality, size, and number of clusters) is the main goal of this study. In this paper, by capitalizing on multi objective genetic algorithm and a new combination of objective functions, we proposed a homogeneous method, namely, Savalan, for clustering software modules. The proposed method generates high‐quality clusters with strong cohesion within clusters and weak connections between clusters for the input source code. The results of conducted experiments on the 14 benchmark programs indicate that simultaneous improvement of all clustering objectives is the main merit of this method. According to the experimental results, the proposed algorithm was able to outperform the previous multi objective methods.
How have smallholder farmers used digital extension tools? Developer and user voices from Sub-Saharan Africa, South Asia and Southeast Asia

Digital extension tools (DETs) include phone calls, WhatsApp groups and specialised smartphone applications used for agricultural knowledge brokering. We researched processes through which DETs have (and have not) been used by farmers and other extension actors in low- and middle-income countries. We interviewed 40 DET developers across 21 countries and 101 DET users in Bihar, India. We found DET use is commonly constrained by fifteen pitfalls (unawareness of DET, inaccessible device, inaccessible electricity, inaccessible mobile network, insensitive to digital illiteracy, insensitive to illiteracy, unfamiliar language, slow to access, hard to interpret, unengaging, insensitive to user's knowledge, insensitive to priorities, insensitive to socio-economic constraints, irrelevant to farm, distrust). These pitfalls partially explain why women, less educated and less wealthy farmers often use DETs less, as well as why user-driven DETs (e.g. phone calls and chat apps) are often used more than externally-driven DETs (e.g. specialised smartphone apps). Our second key finding was that users often made - not just found - DETs useful for themselves and others. This suggests the word 'appropriation' conceptualises DET use more accurately and helpfully than the word 'adoption'. Our final key finding was that developers and users advocated almost ubiquitously for involving desired users in DET provision. We synthesise these findings in a one-page framework to help funders and developers facilitate more useable, useful and positively impactful DETs. Overall, we conclude developers increase DET use by recognizing users as fellow developers - either through collaborative design or by designing adaptable DETs that create room for user innovation.
VLSI Design of Advanced-Features AES Cryptoprocessor in the Framework of the European Processor Initiative

This article presents a cryptographic hardware (HW) accelerator supporting multiple advanced encryption standard (AES)-based block cipher modes, including the more advanced cipher-based MAC (CMAC), counter with CBC-MAC (CCM), Galois counter mode (GCM), and XOR-encrypt-XOR-based tweaked-codebook mode with ciphertext stealing (XTS) modes. The proposed design implements advanced and innovative features in HW, such as AES key secure management, on-chip clock randomization, and access privilege mechanisms. The system has been tested in a RISC-V-based system-on-chip (SoC), specifically designed for this purpose, on an Ultrascale + Xilinx FPGA, analyzing resource and power consumption, together with system performances. The cryptoprocessor has been then synthesized on a 7-nm CMOS standard-cells technology; performances, complexity, and power consumption information are analyzed and compared with the state of the art. The proposed cryptoprocessor is ready to be embedded within the innovative European Processor Initiative (EPI) chip.
Multi-class Sports News Categorization using Machine Learning Techniques: Resource Creation and Evaluation

The proliferation of the Internet and social media usage creates enormous textual data (specifically, news content) on the web. The most proportion of contents primarily are unstructured. Extracting meaningful insights from unstructured content is nearly impossible or extremely hard, and time-consuming by human labor. Thus, automatic text classification has gained much attention from NLP experts in recent years. Several techniques have been developed to classify news text in high resource languages (e.g., English, Chinese, French). However, the automatic classification of Bengali news text is in a primitive stage to date. This paper investigates the six most popular machine learning techniques (such as Logistic Regression (LR), Support Vector Classifier (SVC), Decision Tree (DT), Multinomial Naive Bayes (MNB), Random Forest (RF), etc.) with Term Frequency-Inverse Document Frequency (TF-IDF) features for automatic sports news classification in Bengali. Due to the unavailability of benchmark corpus, this work also developed a Bengali news corpus (called BNeC) consisting of 43306 news documents with 202830 unique words in multiple classes: Cricket, Football, Tennis, and Athletics. Experimental results on the test dataset show that the Support Vector Classifier (SVC) with unigram+bigram+trigram feature space obtained the highest weighted f1-score of 97.60% than the other classifiers and feature combinations.
Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset

Labelling of human behavior analysis data is a complex and time consuming task. In this paper, a fully automatic technique for labelling an image based gaze behavior dataset for driver gaze zone estimation is proposed. Domain knowledge is added to the data recording paradigm and later labels are generated in an automatic manner using Speech To Text conversion (STT). In order to remove the noise in the STT process due to different illumination and ethnicity of subjects in our data, the speech frequency and energy are analysed. The resultant Driver Gaze in the Wild (DGW) dataset contains 586 recordings, captured during different times of the day including evenings. The large scale dataset contains 338 subjects with an age range of 18-63 years. As the data is recorded in different lighting conditions, an illumination robust layer is proposed in the Convolutional Neural Network (CNN). The extensive experiments show the variance in the dataset resembling real-world conditions and the effectiveness of the proposed CNN pipeline. The proposed network is also fine-tuned for the eye gaze prediction task, which shows the discriminativeness of the representation learnt by our network on the proposed DGW dataset. Project Page: https://sites.google.com/view/drivergazeprediction/home
Using meta‐learning to predict performance metrics in machine learning problems

Abstract Machine learning has been facing significant challenges over the last years, much of which stem from the new characteristics of machine learning problems, such as learning from streaming data or incorporating human feedback into existing datasets and models. In these dynamic scenarios, data change over time and models must adapt. However, new data do not necessarily mean new patterns. The main goal of this paper is to devise a method to predict a model's performance metrics before it is trained, in order to decide whether it is worth it to train it or not. That is, will the model hold significantly better results than the current one? To address this issue, we propose the use of meta‐learning. Specifically, we evaluate two different meta‐models, one built for a specific machine learning problem, and another built based on many different problems, meant to be a generic meta‐model, applicable to virtually any problem. In this paper, we focus only on the prediction of the root mean square error (RMSE). Results show that it is possible to accurately predict the RMSE of future models, event in streaming scenarios. Moreover, results also show that it is possible to reduce the need for re‐training models between 60% and 98%, depending on the problem and on the threshold used.
An adaptive bi-level task planning strategy for multi-USVs target visitation

This paper considers a task planning problem, which dispatches multiple unmanned surface vehicles (USVs) to visit a set of targets located in ocean environments. The problem is modeled as a bi-level optimization to reduce the total and the individual navigation costs simultaneously. The upper-level allocates targets and schedules target visitation sequences, while the lower-level plans safe and economical paths between two targets under the current influence. Subsequently, a novel nested strategy is proposed to solve the bi-level problem, which modifies each level initialization process and can adaptively give the lower-level function evaluation number according to the problem complexity. Besides, the proposed strategy can adopt general metaheuristics as optimizers. Thus, two upper-level and five lower-level algorithms are employed in combination, which covers most kinds of metaheuristics. Finally, the ten combinations of algorithms are tested on three large-scale and complex cases, and the results verify the effectiveness of the proposed model and strategy.
Research and Application of Artificial Intelligence Based Integrated Teaching-Learning Modular Approach in Colleges and Universities

Nowadays, in various educational institutions, artificial intelligence technology is applied effectively and successfully. This artificial intelligence improves learning and student development in academic performance. Challenges of the conventional education approach, students’ dependence on teachers in all resources for study, unavailability of professional instructors, and a greater focus on conditioning learning than practical usefulness lead to lower learning performance. In this paper integrated teaching-learning model approach has been proposed using artificial intelligence in student education. It involves speeding up fulfilling education targets by reducing barriers to entry, automating management processes, and maximizing learning performance. The proposed ITLMA method used the naive Bayes algorithm to evaluate the student ranking using a class score, task, project score, and final exam. The result of artificial intelligence-based ITLMA and naive Bayes algorithm hasa high accuracy ratio of 80.1% with less error ratio of 15.7%, high prediction 88.2%, precision 98.2%, and improves student and teacher interaction compared to other existing methods.
Combining Graph Convolutional Neural Networks and Label Propagation

Label Propagation Algorithm (LPA) and Graph Convolutional Neural Networks (GCN) are both message passing algorithms on graphs. Both solve the task of node classification, but LPA propagates node label information across the edges of the graph, while GCN propagates and transforms node feature information. However, while conceptually similar, theoretical relationship between LPA and GCN has not yet been systematically investigated. Moreover, it is unclear how LPA and GCN can be combined under a unified framework to improve the performance. Here we study the relationship between LPA and GCN in terms of feature/label influence , in which we characterize how much the initial feature/label of one node influences the final feature/label of another node in GCN/LPA. Based on our theoretical analysis, we propose an end-to-end model that combines GCN and LPA. In our unified model, edge weights are learnable, and the LPA serves as regularization to assist the GCN in learning proper edge weights that lead to improved performance. Our model can also be seen as learning the weights of edges based on node labels, which is more direct and efficient than existing feature-based attention models or topology-based diffusion models. In a number of experiments for semi-supervised node classification and knowledge-graph-aware recommendation, our model shows superiority over state-of-the-art baselines.
Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Fall detection using body geometry and human pose estimation in video sequences

According to the World Health Organization, falling is a significant health problem that causes thousands of deaths every year. Fall detection and fall prediction tasks enable accurate medical assistance to vulnerable populations whenever required, allowing local authorities to predict daily health care resources and to reduce fall damages accordingly. We present in this paper, a fall detection approach that explores human body geometry available at different frames of the video sequence. Especially, pose estimation, the angle and the distance between the vector formed by the head-centroid of the identified facial image and the center hip of the body, and the vector aligned with the horizontal axis of the center hip, are employed to construct new distinctive image features. A two-class Support Vector Machine (SVM) classifier and a Temporal Convolution Network (TCN) are trained on the newly constructed feature images. At the same time, a Long-Short-Term Memory (LSTM) network is trained on the calculated angle and distance sequences to classify fall and non-fall activities. We perform experiments on the Le2i FD dataset and the UR FD dataset, where we also propose a cross-dataset evaluation. The results demonstrate the effectiveness and efficiency of the developed approach.
Cross-dataset emotion recognition from facial expressions through convolutional neural networks

The face is the window to the soul. This is what the 19th-century French doctor Duchenne de Boulogne thought. Using electric shocks to stimulate muscular contractions and induce bizarre-looking expressions, he wanted to understand how muscles produce facial expressions and reveal the most hidden human emotions. Two centuries later, this research field remains very active. We see automatic systems for recognizing emotion and facial expression being applied in medicine, security and surveillance systems, advertising and marketing, among others. However, there are still fundamental questions that scientists are trying to answer when analyzing a person’s emotional state from their facial expressions. Is it possible to reliably infer someone’s internal state based only on their facial muscles’ movements? Is there a universal facial setting to express basic emotions such as anger, disgust, fear, happiness, sadness, and surprise? In this research, we seek to address some of these questions through convolutional neural networks. Unlike most studies in the prior art, we are particularly interested in examining whether characteristics learned from one group of people can be generalized to predict another’s emotions successfully. In this sense, we adopt a cross-dataset evaluation protocol to assess the performance of the proposed methods. Our baseline is a custom-tailored model initially used in face recognition to categorize emotion. By applying data visualization techniques, we improve our baseline model, deriving two other methods. The first method aims to direct the network’s attention to regions of the face considered important in the literature but ignored by the baseline model, using patches to hide random parts of the facial image so that the network can learn discriminative characteristics in different regions. The second method explores a loss function that generates data representations in high-dimensional spaces so that examples of the same emotion class are close and examples of different classes are distant. Finally, we investigate the complementarity between these two methods, proposing a late-fusion technique that combines their outputs through the multiplication of probabilities. We compare our results to an extensive list of works evaluated in the same adopted datasets. In all of them, when compared to works that followed an intra-dataset protocol, our methods present competitive numbers. Under a cross-dataset protocol, we achieve state-of-the-art results, outperforming even commercial off-the-shelf solutions from well-known tech companies.
Fault information mining with causal network for railway transportation system

Various sensors implemented in the railway transportation system brings opportunities in improving its safety and challenges in fault information mining. Extracting effective and synthetic fault-specific information from the over-rich data is one of the key challenges. The classical feature dimension reduction methods are mostly based on the statistical correlation among variables. Considering the cause-effect relationship may reflect the true influence of one variable on the other, this paper proposes three unsupervised feature extraction methods based on causal network. Precisely, after discovering the causal network among the monitoring variables in a rail transportation system, principle components related to the specific fault are extracted from the causal strength matrix or the full causal strength matrix constructed from the causal network. In comparison with the state-of-art correlation-based feature reduction methods, the effectiveness of the proposed methods is verified on two public datasets and a real dataset considering high-speed train braking system. In addition, the intrinsic working mechanism of the proposed methods is analyzed with respect to the constructed causal network, which improves the interpretability of the fault detection and diagnosis.
Robust image watermarking algorithm based on QWT and QSVD using 2D Chebyshev-Logistic map

In this paper, a novel watermarking algorithm based on quaternion wavelet transform(QWT) and quaternion singular value decomposition(QSVD) is proposed for copyright protection. Firstly, color images are converted from RGB space to YCbCr space. Then one-level QWT transform is applied to the luminance component Y. The LL component is split to 4×4 non-overlapping blocks. Every block is decomposed by QSVD, and the bits of watermark are embedded in singular vectors or singular values. Moreover, 2D Chebyshev-Logistic map is proposed to encrypt the watermarks and scramble the embedding positions. In the watermarking extraction process, both blind and semi-blind watermarking extraction methods are introduced. Experiments show that proposed watermarking algorithm has good robust against common attacks and has good invisibility.
Object Detection of Road Assets Using Transformer-Based YOLOX with Feature Pyramid Decoder on Thai Highway Panorama

Due to the various sizes of each object, such as kilometer stones, detection is still a challenge, and it directly impacts the accuracy of these object counts. Transformers have demonstrated impressive results in various natural language processing (NLP) and image processing tasks due to long-range modeling dependencies. This paper aims to propose an exceeding you only look once (YOLO) series with two contributions: (i) We propose to employ a pre-training objective to gain the original visual tokens based on the image patches on road asset images. By utilizing pre-training Vision Transformer (ViT) as a backbone, we immediately fine-tune the model weights on downstream tasks by joining task layers upon the pre-trained encoder. (ii) We apply Feature Pyramid Network (FPN) decoder designs to our deep learning network to learn the importance of different input features instead of simply summing up or concatenating, which may cause feature mismatch and performance degradation. Conclusively, our proposed method (Transformer-Based YOLOX with FPN) learns very general representations of objects. It significantly outperforms other state-of-the-art (SOTA) detectors, including YOLOv5S, YOLOv5M, and YOLOv5L. We boosted it to 61.5% AP on the Thailand highway corpus, surpassing the current best practice (YOLOv5L) by 2.56% AP for the test-dev data set.