Reviewerly/exHarmony huggingface dataset:
Fusing CNNs and statistical indicators to improve image classification

Convolutional Neural Networks have dominated the field of computer vision for the last ten years, exhibiting extremely powerful feature extraction capabilities and outstanding classification performance. The main strategy to prolong this trend in the state-of-the-art literature relies on further upscaling networks in size. However, costs increase rapidly while performance improvements may be marginal. Our main hypothesis is that adding additional sources of information can help to increase performance and that this approach is more cost-effective than building bigger networks, which involve higher training time, larger parametrisation space and higher computational resources requirements. In this paper, an ensemble method for accurate image classification is proposed, fusing automatically detected features through a Convolutional Neural Network and a set of manually defined statistical indicators. Through a combination of the predictions of a CNN and a secondary classifier trained on statistical features, a better classification performance can be achieved cheaply. We test five different CNN architectures and multiple learning algorithms in a diverse number of datasets to validate our proposal. According to the results, the inclusion of additional indicators and an ensemble classification approach help to increase the performance in all datasets. Both code and datasets are publicly available via GitHub at: https://github.com/jahuerta92/cnn-prob-ensemble.
Realistic Virtual Humans for Cultural Heritage Applications

Virtual Humans are becoming a commodity in computing technology and lately have been utilized in the context of interactive presentations in Virtual Cultural Heritage environments and exhibitions. To this end, this research work underlines the importance of aligning and fine-tuning Virtual Humans’ appearance to their roles and highlights the importance of affective components. Building realistic Virtual Humans was traditionally a great challenge requiring a professional motion capturing studio and heavy resources in 3D animation and design. In this paper, a workflow for their implementation is presented, based on current technological trends in wearable mocap systems and advancements in software technology for their implementation, animation, and visualization. The workflow starts from motion recording and segmentation to avatar implementation, retargeting, animation, lip synchronization, face morphing, and integration to a virtual or physical environment. The testing of the workflow occurs in a use case for the Mastic Museum of Chios and the implementation is validated both in a 3D virtual environment accessed through Virtual Reality and on-site at the museum through an Augmented Reality application. The findings, support the initial hypothesis through a formative evaluation, and lessons learned are transformed into a set of guidelines to support the replication of this work.
Global uncertainty, economic governance institutions and foreign direct investment inflow in Africa

Based on the fact that Africa has not fared well in attracting foreign direct investments in the last decade compared to other regions of the world, especially during periods of high uncertainty occasioned by one crisis or the other, this study investigated: the impacts of global uncertainty and economic governance institutions on FDI inflow to Africa; the moderating effect of economic governance institutions on global uncertainty-FDI relationship in Africa; and other significant drivers of FDI inflow to Africa. The study used the system GMM modeling framework and a panel of 46 African economies over the period 2010–2019. The results indicate that global uncertainty has a significant dampening effect on FDI inflow to Africa, and economic governance institutions on the continent amplify this effect rather than mitigate it. The results further indicate that natural resource endowment, market size, and initial FDI inflows are robust drivers of FDI inflows to Africa, while the roles of financial development and trade openness remained muted. Overall, the study concludes that policymakers in Africa should take urgent steps to strengthen the quality of economic governance institutions as a means of mitigating the excruciating effect of global uncertainty on FDI inflows to Africa.
OntoSenticNet 2: Enhancing Reasoning Within Sentiment Analysis

Sentiment analysis is a trending topic that has not yet exhausted its attractiveness, despite the huge research effort carried out in the last 15 years. One of the most promising directions to investigate is the integration of knowledge-based representations within sentiment analysis systems in order to enhance their expressiveness and, at the same time, to enable reasoning over the relevant information detected within opinion-based sources. In this article, we present an improved version of OntoSenticNet providing: i) an updated definition of concepts, properties, and individuals together with an improved hierarchical organization of such entities; ii) the modeling of the sentic algebra elements for supporting the execution of semantic sentiment operations at reasoning time; and iii) the conceptual model of sentiment dependencies and discovery paths. The process of building OntoSenticNet 2 is discussed and some examples are proposed in order to illustrate the conceptual model.
Parallel Corpus Research and Target Language Representativeness: The Contrastive, Typological, and Translation Mining Traditions

This paper surveys the strategies that the Contrastive, Typological, and Translation Mining parallel corpus traditions rely on to deal with the issue of target language representativeness of translations. On the basis of a comparison of the corpus architectures and research designs of the three traditions, we argue that they have each developed their own representativeness strategies: (i) monolingual control corpora (Contrastive tradition), (ii) limits on the scope of research questions (Typological tradition), and (iii) parallel control corpora (Translation Mining tradition). We introduce normalized pointwise mutual information (NPMI) as a bi-directional measure of cross-linguistic association, allowing for an easy comparison of the outcomes of different traditions and the impact of the monolingual and parallel control corpus representativeness strategies. We further argue that corpus size has a major impact on the reliability of the monolingual control corpus strategy and that a sequential parallel control corpus strategy is preferable for smaller corpora.
Toward Human-in-the-Loop AI: Enhancing Deep Reinforcement Learning via Real-Time Human Guidance for Autonomous Driving

Due to its limited intelligence and abilities, machine learning is currently unable to handle various situations thus cannot completely replace humans in real-world applications. Because humans exhibit robustness and adaptability in complex scenarios, it is crucial to introduce humans into the training loop of artificial intelligence (AI), leveraging human intelligence to further advance machine learning algorithms. In this study, a real-time human-guidance-based (Hug)-deep reinforcement learning (DRL) method is developed for policy training in an end-to-end autonomous driving case. With our newly designed mechanism for control transfer between humans and automation, humans are able to intervene and correct the agent’s unreasonable actions in real time when necessary during the model training process. Based on this human-in-the-loop guidance mechanism, an improved actor-critic architecture with modified policy and value networks is developed. The fast convergence of the proposed Hug-DRL allows real-time human guidance actions to be fused into the agent’s training loop, further improving the efficiency and performance of DRL. The developed method is validated by human-in-the-loop experiments with 40 subjects and compared with other state-of-the-art learning approaches. The results suggest that the proposed method can effectively enhance the training efficiency and performance of the DRL algorithm under human guidance without imposing specific requirements on participants’ expertise or experience.
Autoregressive Image Generation using Residual Quantization

For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for an AR model to reduce its computational costs to consider long-range interactions of codes. However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off. In this study, we propose the two-stage framework, which consists of Residual-Quantized VAE (RQ-VAE) and RQ-Transformer, to effectively generate high-resolution images. Given a fixed codebook size, RQ-VAE can precisely approximate a feature map of an image and represent the image as a stacked map of discrete codes. Then, RQ-Transformer learns to predict the quantized feature vector at the next position by predicting the next stack of codes. Thanks to the precise approximation of RQ-VAE, we can represent a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$256\times 256$</tex> image as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8\times 8$</tex> resolution of the feature map, and RQ-Transformer can efficiently reduce the computational costs. Consequently, our framework out-performs the existing AR models on various benchmarks of unconditional and conditional image generation. Our approach also has a significantly faster sampling speed than previous AR models to generate high-quality images.
10 Best practices in Immersive Learning Design and 10 points of connection with the Metaverse

Immersive learning design is the process of creating immersive learning experiences using technologies such as augmented reality virtual reality, and mixed reality. The goal of immersive learning design is to create engaging and interactive learning experiences that immerse learners in a virtual environment where they can explore and interact with the subject matter in meaningful ways. Today, immersive learning design is used in a wide range of settings, including education and corporate training. It is increasingly being recognized as an effective way to engage learners and enhance the learning experience. In this article, we systematize 10 best practices for immersive learning design. These technologies allow learners to experience and interact with the virtual environment in a way that is more realistic and immersive than traditional methods, such as lectures or reading materials. On the other hand, 10 points of connection between immersive learning design and the metaverse. The metaverse is transforming education by promoting communication and supporting immersive learning. The metaverse is also projected to dramatically boost e-learning by making virtual learning environments more lifelike and learning more engaging and experiential. Based on this reflection, it could be stated that metaverse could be a promising tool to provide an immersive learning experience for today's users.
A Hybrid Cuckoo Search and Simulated Annealing Algorithm

Abstract Simulated annealing (SA) proved its success as a single-state optimization search algorithm for both discrete and continuous problems. On the contrary, cuckoo search (CS) is one of the well-known population-based search algorithms that could be used for optimizing some problems with continuous domains. This paper provides a hybrid algorithm using the CS and SA algorithms. The main goal behind our hybridization is to improve the solutions generated by CS using SA to explore the search space in an efficient manner. More precisely, we introduce four variations of the proposed hybrid algorithm. The proposed variations together with the original CS and SA algorithms were evaluated and compared using 10 well-known benchmark functions. The experimental results show that three variations of the proposed algorithm provide a major performance enhancement in terms of best solutions and running time when compared to CS and SA as stand-alone algorithms, whereas the other variation provides a minor enhancement. Moreover, the experimental results show that the proposed hybrid algorithms also outperform some well-known optimization algorithms.
Automatic Code Review by Learning the Revision of Source Code

Code review is the process of manual inspection on the revision of the source code in order to find out whether the revised source code eventually meets the revision requirements. However, manual code review is time-consuming, and automating such the code review process will alleviate the burden of code reviewers and speed up the software maintenance process. To construct the model for automatic code review, the characteristics of the revisions of source code (i.e., the difference between the two pieces of source code) should be properly captured and modeled. Unfortunately, most of the existing techniques can easily model the overall correlation between two pieces of source code, but not for the “difference” between two pieces of source code. In this paper, we propose a novel deep model named DACE for automatic code review. Such a model is able to learn revision features by contrasting the revised hunks from the original and revised source code with respect to the code context containing the hunks. Experimental results on six open source software projects indicate by learning the revision features, DACE can outperform the competing approaches in automatic code review.
Differential Networks for Visual Question Answering

The task of Visual Question Answering (VQA) has emerged in recent years for its potential applications. To address the VQA task, the model should fuse feature elements from both images and questions efficiently. Existing models fuse image feature element vi and question feature element qi directly, such as an element product viqi. Those solutions largely ignore the following two key points: 1) Whether vi and qi are in the same space. 2) How to reduce the observation noises in vi and qi. We argue that two differences between those two feature elements themselves, like (vi − vj) and (qi −qj), are more probably in the same space. And the difference operation would be beneficial to reduce observation noise. To achieve this, we first propose Differential Networks (DN), a novel plug-and-play module which enables differences between pair-wise feature elements. With the tool of DN, we then propose DN based Fusion (DF), a novel model for VQA task. We achieve state-of-the-art results on four publicly available datasets. Ablation studies also show the effectiveness of difference operations in DF model.
Density-sensitive fuzzy kernel maximum entropy clustering algorithm

Maximum entropy clustering algorithm (ME) has lately received great attention for its high performance in large-scale data clustering and simplicity in implementation. However, previous studies have demonstrated that different clusters obtained by traditional ME tend to converge to the same one during its process of iteration affected by regularization coefficient and these cluster centers are subject to bias due to its sensitivity to different distributions of objects. These drawbacks of traditional ME can result in its failure of revealing the natural groupings in most datasets, especially in non-Gaussian distributed datasets. In order to address those limitations, we present a novel density-sensitive fuzzy kernel maximum entropy clustering algorithm in this paper. In the proposed approach, to accommodate non-Gaussian distributed cases, the dataset to be clustered in the original space is firstly implicitly mapped into high-dimensional feature space through the kernel function. By introducing the kernel function-based similarity terms in the update formula of the cluster centers, the effect of the objects not belonging to the current cluster on the update of its corresponding center can be counteracted, and simultaneously the influence of regularization coefficient on the clustering result is restricted as well, which can effectively overcome the convergence of the different clusters encountered by traditional ME. In addition, in order to prevent cluster centers from biases caused by the different distribution of the objects in the feature space, the relative density-based weights are also incorporated into the cost function, which can help the proposed approach produce more reasonable and accurate clustering results. In the experiments, the influence of the different parameters on the clustering performance is discussed in detail and some suggestions are also provided. Theoretical analysis and experimental results on several synthetic datasets, UCI benchmark datasets and generated large MNIST handwritten digits datasets demonstrate that the proposed approach is superior to other existing clustering techniques with good robustness.
Blind quality index for tone-mapped images based on luminance partition

Abstract Tone-mapping operators (TMOs), which are designed to convert high dynamic range (HDR) images to standard low dynamic range (LDR) images for displaying on conventional devices, have gained extensive attention recently. The quality of tone-mapped images generated by different TMOs varies significantly, which depend upon the image contents and the parameter settings. A quality index that can accurately evaluate the performances of TMOs is thus highly needed. With this motivation, this paper presents a blind quality index based on luminance partition for tone-mapped images. It is based on the fact that the Human Visual System (HVS) has different sensitivities to image regions with different luminance levels. Specifically, two adaptive thresholds are first employed to segment an image into the dark, bright and normal areas. Then, we calculate the quality-aware features from different luminance areas: 1) local entropy feature is extracted from the dark and bright areas to measure the information loss due to the overexposure or underexposure during the tone mapping process; 2) local colorfulness feature is extracted from the normal area to evaluate the reproduction of colors. With the consideration that the perception of image quality depends on the combined effects of the salient local distortion and global quality degradation, the global contrast feature is also calculated and integrated for better evaluation performance. Moreover, to take advantage of the hierarchical characteristic of the HVS, all features are calculated under a multi-resolution framework. Eventually, the extracted features are mapped into an objective quality score based on the random forest regression. The proposed metric is shown to outperform those state-of-the-art metrics according to extensive experiments conducted on two publicly available databases.
VirtualComponent

Prototyping electronic circuits is an increasingly popular activity, supported by researchers, who develop toolkits to improve the design, debugging, and fabrication of electronics. Although past work mainly dealt with circuit topology, in this paper we propose a system for determining or tuning the values of the circuit components. Based on the results of a formative study with seventeen makers, we designed VirtualComponent, a mixed-reality tool that allows users to digitally place electronic components on a real breadboard, tune their values in software, and see these changes applied to the physical circuit in real-time. VirtualComponent is composed of a set of plug-and-play modules containing banks of components, and a custom breadboard managing the connections and components' values. Through demonstrations and the results of an informal study with twelve makers, we show that VirtualComponent is easy to use and allows users to test components' value configurations with little effort.
Daily Motion Recognition System by a Triaxial Accelerometer Usable in Different Positions

Human-computer interaction has been a more popular research field in recent years with the escalating interest in wearable systems. In this context, evaluating the details of the physical movement of the human body has become a prominent subject for researchers. The systems, which are yielded from this subject, are utilized in such areas as health, public safety, access control, and smart home systems. This kind of system is expected to give fast and reliable results and can be used comfortably for the user. In this paper, a fast and accurate system design about recognition of daily human activities, including falls in the elderly, is proposed. In addition, another main objective of the study is to examine whether an accelerometer can be set in different positions during person's daily activities. Motion data have been acquired from the waist, wrist, and knee positions of 5 female and 15 male volunteers using a three-axis accelerometer. In this way, walking, sitting, standing up, jumping, and falling motions were collected from volunteers. The principal component analysis method was used as a feature reduction method to reduce the process time and get quick results. Furthermore, any feature extraction method was not used. The support vector machine, multi-layer perceptron, radial basis function, and Naive Bayes classifiers were employed to perform classification success. The proposed technique achieved a maximum of 100%, an average of 96.54% accuracy on the three defined positions. As a result, it has been determined that a single accelerometer can be set in different positions. The findings also suggest that this method can be used in home care and follow-up procedures and fall detection for elderly people.
Unsupervised Stereo Matching Using Confidential Correspondence Consistency

Stereo matching aims to perceive the 3D geometric configuration of scenes and facilitates a variety of computer vision in advanced driver assistance systems (ADAS) applications. Recently, deep convolutional neural networks (CNNs) have shown dramatic performance improvements for computing the matching cost in the stereo matching. However, the performance of CNN-based approaches relies heavily on datasets, requiring a large number of ground truth data which needs tremendous works. To overcome this limitation, we present a novel framework to learn CNNs for matching cost computation in an unsupervised manner. Our method leverages an image domain learning combined with stereo epipolar constraints. By exploiting the correspondence consistency between stereo images, our method selects putative positive samples in each training iteration and utilizes them to train the networks. We further propose a positive sample propagation scheme to leverage additional training samples. Our unsupervised learning method is evaluated with two kinds of network architectures, simple and precise CNNs, and shows comparable performance to that of the state-of-the-art methods including both supervised and unsupervised learning approaches on KITTI, Middlebury, HCI, and Yonsei datasets. This extensive evaluation demonstrates that the proposed learning framework can be applied to deal with various real driving conditions.
VIFIDEL: Evaluating the Visual Fidelity of Image Descriptions

We address the task of evaluating image description generation systems.We propose a novel image-aware metric for this task: VIFIDEL.It estimates the faithfulness of a generated caption with respect to the content of the actual image, based on the semantic similarity between labels of objects depicted in images and words in the description.The metric is also able to take into account the relative importance of objects mentioned in human reference descriptions during evaluation.Even if these human reference descriptions are not available, VIFIDEL can still reliably evaluate system descriptions.The metric achieves high correlation with human judgments on two well-known datasets and is competitive with metrics that depend on and rely exclusively on human references.