Reviewerly/exHarmony huggingface dataset:
Predicting aspect-based sentiment using deep learning and information visualization: The impact of COVID-19 on the airline industry

• The main purpose of this study is to analyze airline reviews and understand consumer satisfaction before and during COVID-19 pandemic. • This study applies deep learning-based natural language processing, word embeddings such as BERT, and visual analytics to learn linguistic features and aspect-level ratings. • Our model outperforms traditional machine learning methods and thus can be used to predict unrated aspect ratings. • Our data and visual analyses reveal that both review and aspect ratings drop significantly during the pandemic. • This study complements the existing research methods and extends the use of data-driven and visual analytics approaches to better understand customer satisfaction within the context of the COVID-19 pandemic. This study investigates customer satisfaction through aspect-level sentiment analysis and visual analytics. We collected and examined the flight reviews on TripAdvisor from January 2016 to August 2020 to gauge the impact of COVID-19 on passenger travel sentiment in several aspects. Till now, information systems, management, and tourism research have paid little attention to the use of deep learning and word embedding techniques, such as bidirectional encoder representations from transformers, especially for aspect-level sentiment analysis. This paper aims to identify perceived aspect-based sentiments and predict unrated sentiments for various categories to address this research gap. Ultimately, this study complements existing sentiment analysis methods and extends the use of data-driven and visual analytics approaches to better understand customer satisfaction in the airline industry and within the context of the COVID-19. Our proposed method outperforms baseline comparisons and therefore contributes to the theoretical and managerial literature.
Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming

Certifying the safety or robustness of neural networks against input uncertainties and adversarial attacks is an emerging challenge in the area of safe machine learning and control. To provide such a guarantee, one must be able to bound the output of neural networks when their input changes within a bounded set. In this paper, we propose a semidefinite programming (SDP) framework to address this problem for feed-forward neural networks with general activation functions and input uncertainty sets. Our main idea is to abstract various properties of activation functions (e.g., monotonicity, bounded slope, bounded values, and repetition across layers) with the formalism of quadratic constraints. We then analyze the safety properties of the abstracted network via the S-procedure and semidefinite programming. Our framework spans the trade-off between conservatism and computational efficiency and applies to problems beyond safety verification. We evaluate the performance of our approach via numerical problem instances of various sizes.
Comparing the Gear VR, Oculus Go, and Oculus Quest

In this chapter, we look at the key differences between the Gear VR, the Oculus Go, and the Oculus Quest (based on prerelease information and subject to change when it ships). Even though each one of these headsets runs on an Android operating system, hardware differences play an important role when targeting the individual headset and its use. We will compare hardware specifications and different input events, and learn how to assign them in Unreal.
A Phenomenographic Research Study of Students’ Conceptions of Mobile Learning: An Example From Higher Education

This article aims to identify different ways of using mobile devices in students’ learning in higher education. This qualitative research presents the findings from a phenomenographic research of students’ conceptions of mobile learning (m-learning) in higher education. A cohort of 16 students from four universities of Bangladesh took part in semi-structured interviews to explore their in-depth understandings and experiences of m-learning. The findings indicate that university students perceived five qualitatively different ways of using mobile devices in their learning: a medium for communication; a medium for management of learning materials; a tool for effective learning; a means for collaborative learning; and a means for development of new ideas. The findings of this research demonstrate students’ pedagogical understanding of using mobile devices in higher education. The outcomes of this research could play a crucial role in informing students on how they can use their mobile devices for learning purposes and providing educators with empirical evidence on students’ pedagogical practices of using mobile devices in other developing and more developed countries in the world.
Appearance and Pose-Conditioned Human Image Generation Using Deformable GANs

In this paper, we address the problem of generating person images conditioned on both pose and appearance information. Specifically, given an image xa of a person and a target pose P(xb), extracted from an image xb, we synthesize a new image of that person in pose P(xb), while preserving the visual details in xa. In order to deal with pixel-to-pixel misalignments caused by the pose differences between P(xa) and P(xb), we introduce deformable skip connections in the generator of our Generative Adversarial Network. Moreover, a nearest-neighbour loss is proposed instead of the common L1 and L2 losses in order to match the details of the generated image with the target image. Quantitative and qualitative results, using common datasets and protocols recently proposed for this task, show that our approach is competitive with respect to the state of the art. Moreover, we conduct an extensive evaluation using off-the-shell person re-identification (Re-ID) systems trained with person-generation based augmented data, which is one of the main important applications for this task. Our experiments show that our Deformable GANs can significantly boost the Re-ID accuracy and are even better than data-augmentation methods specifically trained using Re-ID losses.
How we trust, perceive, and learn from virtual humans: The influence of voice quality

Research has shown that creating environments in which social cues are present (social agency) benefits learning. One way to create these environments is to incorporate a virtual human as a pedagogical agent in computer-based learning environments. However, essential questions remain about virtual human design, such as what voice should the virtual human use to communicate. Furthermore, to date research in the education literature around virtual humans has largely ignored one potentially salient construct – trust. This study examines how the quality of a virtual human's voice influences learning, perceptions, and trust in the virtual human. Results of on an online study show that the voice quality did not significantly influence learning, but it did influence trust and learners' other perceptions of the virtual human. This study, consistent with recent work around the voice effect, questions the efficacy of the voice effect and highlights areas of research around trust to further extend social agency theory in virtual human based learning environments.
Language-Agnostic Visual-Semantic Embeddings

This paper proposes a framework for training language-invariant cross-modal retrieval models. We also introduce a novel character-based word-embedding approach, allowing the model to project similar words across languages into the same word-embedding space. In addition, by performing cross-modal retrieval at the character level, the storage requirements for a text encoder decrease substantially, allowing for lighter and more scalable retrieval architectures. The proposed language-invariant textual encoder based on characters is virtually unaffected in terms of storage requirements when novel languages are added to the system. Our contributions include new methods for building character-level-based word-embeddings, an improved loss function, and a novel cross-language alignment module that not only makes the architecture language-invariant, but also presents better predictive performance. We show that our models outperform the current state-of-the-art in both single and multi-language scenarios. This work can be seen as the basis of a new path on retrieval research, now allowing for the effective use of captions in multiple-language scenarios. Code is available at https://github.com/jwehrmann/lavse.
A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing

Cloud computing has become the main source for executing scientific experiments. It is an effective technique for distributing and processing tasks on virtual machines. Scientific workflows are complex and demand efficient utilization of cloud resources. Scheduling of scientific workflows is considered as NP-complete. The problem is constrained by some parameters such as Quality of Service (QoS), dependencies between tasks and users’ deadlines, etc. There exists a strong literature on scheduling scientific workflows in cloud environments. Solutions include standard schedulers, evolutionary optimization techniques, etc. This article presents a hybrid algorithm for scheduling scientific workflows in cloud environments. In the first phase, the algorithm prepares tasks lists for PSO algorithm. Bottleneck tasks are processed on high priority to reduce execution time. In the next phase, tasks are scheduled with the PSO algorithm to reduce both execution time and monetary cost. The algorithm also monitors the load balance to efficiently utilize cloud resources. Benchmark scientific workflows are used to evaluate the proposed algorithm. The proposed algorithm is compared with standard PSO and specialized schedulers to validate the performance. The results show improvement in execution time, monetary cost without affecting the load balance as compared to other techniques.
Uranium, thorium and potassium insights on Campania region (Italy) soils: Sources patterns based on compositional data analysis and fractal model

Uranium, thorium and potassium are distinguished in different compartments of the environmental media according to the inputs, the fate and the distribution patterns that occur in the ecosystems. Thus, an understanding of the sources patterns and behaviours of these elements in soils, their rock-soil relationships and potential toxicity or deficiency problems associated with them is nowadays a relevant concern for environmental protection and human health. A robust compositional computation analysis coupled with frequency spatial-method (Fractal model) is applied on 3 selected elements (U, Th and K), based on a large dataset of 7134 topsoil samples over the whole Campania region, and investigated elements concentrations were analysed (after aqua regia extraction) by inductively coupled plasma mass spectrometry (ICP-MS). In this survey, U concentrations ranged from 0.05 to 43.2 mg kg−1 with a mean value of 3.35 mg kg−1, Th concentrations ranged from 0.3 to 64.30 mg kg−1 with a mean values of 11.95 mg kg−1 and K concentrations ranged from 0.03 to 5.97% with a mean value of 0.68%. The mean values for U and Th are slightly above the mean crustal abundances, while K is slightly below them. Fractal mapping of Centred-log transformed (clr) data of investigated elements show that the U, Th and K geochemical anomalies can be ascribed to geology-related volcanic sources. Indeed, the highest values in soils were assessed in the surveyed alkaline magmatic areas in the central-western part of the region characterized by volcanic rocks, whereas lowest values are found in areas characterized by silico-clastic and carbonate deposits, occurring mostly in the southern and eastern part of the region. Evidence from this study showed that compositional data transformations such as clr transformation could help to avoid artefacts, prior to statistical computations.
Traversing large graphs on GPUs with unified memory

Due to the limited capacity of GPU memory, the majority of prior work on graph applications on GPUs has been restricted to graphs of modest sizes that fit in memory. Recent hardware and software advances make it possible to address much larger host memory transparently as a part of a feature known as unified virtual memory. While accessing host memory over an interconnect is understandably slower, the problem space has not been sufficiently explored in the context of a challenging workload with low computational intensity and an irregular data access pattern such as graph traversal. We analyse the performance of breadth first search (BFS) for several large graphs in the context of unified memory and identify the key factors that contribute to slowdowns. Next, we propose a lightweight offline graph reordering algorithm, HALO (Harmonic Locality Ordering), that can be used as a pre-processing step for static graphs. HALO yields speedups of 1.5x-1.9x over baseline in subsequent traversals. Our method specifically aims to cover large directed real world graphs in addition to undirected graphs whereas prior methods only account for the latter. Additionally, we demonstrate ties between the locality ordering problem and graph compression and show that prior methods from graph compression such as recursive graph bisection can be suitably adapted to this problem.
EGroupNet

Although age estimation is easily affected by smiling, race, gender, and other age-related attributes, most of the researchers did not pay attention to the correlations among these attributes. Moreover, many researchers perform age estimation from a wide range of age; however, conducting an age prediction over a narrow age range may achieve better results. This article proposes a hierarchic approach referred to as EGroupNet for age prediction. The method includes two main stages, i.e., feature enhancement via excavating the correlations among age-related attributes and age estimation based on different age group schemes. First, we apply the multi-task learning model to learn multiple face attributes simultaneously to obtain discriminative features of different attributes. Second, we project the outputs of fully connected layers of several subnetworks into a highly correlated matrix space via the correlation learning process. Third, we classify these enhanced features into narrow age groups using two Extreme Learning Machine models. Finally, we make predictions based on the results of the age groups mergence. We conduct a large number of experiments on MORPH-II, LAP-2016 dataset, and Adience benchmark. The mean absolute errors of the two different settings on MORPH-II are 2.48 and 2.13 years, respectively; the normal score (ε) on the LAP-2016 dataset is 0.3578; and the accuracy of age prediction on Adience benchmark is 0.6978.
Bayesian cumulative shrinkage for infinite factorizations

Summary The dimension of the parameter space is typically unknown in a variety of models that rely on factorizations. For example, in factor analysis the number of latent factors is not known and has to be inferred from the data. Although classical shrinkage priors are useful in such contexts, increasing shrinkage priors can provide a more effective approach that progressively penalizes expansions with growing complexity. In this article we propose a novel increasing shrinkage prior, called the cumulative shrinkage process, for the parameters that control the dimension in overcomplete formulations. Our construction has broad applicability and is based on an interpretable sequence of spike-and-slab distributions which assign increasing mass to the spike as the model complexity grows. Using factor analysis as an illustrative example, we show that this formulation has theoretical and practical advantages relative to current competitors, including an improved ability to recover the model dimension. An adaptive Markov chain Monte Carlo algorithm is proposed, and the performance gains are outlined in simulations and in an application to personality data.
Eco-Environment Construction of English Teaching Using Artificial Intelligence Under Big Data Environment

Application of big data and artificial intelligence has become one influence factor of English teaching, which have broken the balance of the teaching Eco-environment for English. In this article, the artificial intelligence and big data are introduced into English teaching to propose a new teaching Eco-environment construction method to meet the needs of the social development and international communication in English. In the proposed method, the characteristics of English teaching under big data environment are analyzed in detail. Then the big data technology is used to construct a new Eco-environment of English teaching to improve the teaching and learning quality. The data mining method is one of artificial intelligence methods, which is used to analyze the relationship of interdependence and mutual restriction among various factors in English teaching in order to build and implement a new Eco-environment with the information sharing, quality teaching and personalized learning of English. Finally, through the practical application of the constructed Eco-environment, the experiment results show that the proposed method can help students update their learning concepts, methods and contents of English, inspire their interest and initiative by comparing with some existed teaching methods, so as to improve their learning effects and application ability of English. Therefore, the constructed Eco-environment provides a new idea and direction for English teaching reform by application of big data and artificial intelligence.
Object Goal Navigation using Goal-Oriented Semantic Exploration

This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments. End-to-end learning-based navigation methods struggle at this task as they are ineffective at exploration and long-term planning. We propose a modular system called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently based on the goal object category. Empirical results in visually realistic simulation environments show that the proposed model outperforms a wide range of baselines including end-to-end learning-based methods as well as modular map-based methods and led to the winning entry of the CVPR-2020 Habitat ObjectNav Challenge. Ablation analysis indicates that the proposed model learns semantic priors of the relative arrangement of objects in a scene, and uses them to explore efficiently. Domain-agnostic module design allow us to transfer our model to a mobile robot platform and achieve similar performance for object goal navigation in the real-world.
Conservative Q-Learning for Offline Reinforcement Learning

Effectively leveraging large, previously collected datasets in reinforcement learning (RL) is a key challenge for large-scale real-world applications. Offline RL algorithms promise to learn effective policies from previously-collected, static datasets without further interaction. However, in practice, offline RL presents a major challenge, and standard off-policy RL methods can fail due to overestimation of values induced by the distributional shift between the dataset and the learned policy, especially when training on complex and multi-modal data distributions. In this paper, we propose conservative Q-learning (CQL), which aims to address these limitations by learning a conservative Q-function such that the expected value of a policy under this Q-function lower-bounds its true value. We theoretically show that CQL produces a lower bound on the value of the current policy and that it can be incorporated into a policy learning procedure with theoretical improvement guarantees. In practice, CQL augments the standard Bellman error objective with a simple Q-value regularizer which is straightforward to implement on top of existing deep Q-learning and actor-critic implementations. On both discrete and continuous control domains, we show that CQL substantially outperforms existing offline RL methods, often learning policies that attain 2-5 times higher final return, especially when learning from complex and multi-modal data distributions.
Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement

A novel approach for paired and unpaired image enhancement is proposed in this work. First, we develop global enhancement network (GEN) and local enhancement network (LEN), which can faithfully enhance images. The proposed GEN performs the channel-wise intensity transforms that can be trained easier than the pixel-wise prediction. The proposed LEN refines GEN results based on spatial filtering. Second, we propose different training schemes for paired learning and unpaired learning to train GEN and LEN. Especially, we propose a two-stage training scheme based on generative adversarial networks for unpaired learning. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-arts in paired and unpaired image enhancement. Notably, the proposed unpaired image enhancement algorithm provides better results than recent state-of-the-art paired image enhancement algorithms. The source codes and trained models are available at https://github.com/hukim1124/GleNet .
Learning Region-Based Attention Network for Traffic Sign Recognition

Traffic sign recognition in poor environments has always been a challenge in self-driving. Although a few works have achieved good results in the field of traffic sign recognition, there is currently a lack of traffic sign benchmarks containing many complex factors and a robust network. In this paper, we propose an ice environment traffic sign recognition benchmark (ITSRB) and detection benchmark (ITSDB), marked in the COCO2017 format. The benchmarks include 5806 images with 43,290 traffic sign instances with different climate, light, time, and occlusion conditions. Second, we tested the robustness of the Libra-RCNN and HRNetv2p on the ITSDB compared with Faster-RCNN. The Libra-RCNN performed well and proved that our ITSDB dataset did increase the challenge in this task. Third, we propose an attention network based on high-resolution traffic sign classification (PFANet), and conduct ablation research on the design parallel fusion attention module. Experiments show that our representation reached 93.57% accuracy in ITSRB, and performed as well as the newest and most effective networks in the German traffic sign recognition dataset (GTSRB).
PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer

The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting tumor progression and its treatment response. The value of the Ki-67 index and TILs in approach to heterogeneous tumors such as Breast cancer (BC), known as the most common cancer in women worldwide, has been highlighted in the literature. Due to the indeterminable and subjective nature of Ki-67 as well as TILs scoring, automated methods using machine learning, specifically approaches based on deep learning, have attracted attention. Yet, deep learning methods need considerable annotated data. In the absence of publicly available benchmarks for BC Ki-67 stained cell detection and further annotated classification of cells, we propose SHIDC-BC-Ki-67 as a dataset for the aforementioned purpose. We also introduce a novel pipeline and a backend, namely PathoNet for Ki-67 immunostained cell detection and classification and simultaneous determination of intratumoral TILs score. Further, we show that despite facing challenges, our proposed backend, PathoNet, outperforms the state of the art methods proposed to date in the harmonic mean measure.