Reviewerly/exHarmony huggingface dataset:
A Study of Physical Fitness and Enjoyment on Virtual Running for Exergames

Virtual Reality (VR) technology has advanced forward in everyday life where virtual fitness is possible through physically moving around in the real world. Exergame is a video game for exercise aimed at making exercise more fun. VR exergame applies these trends together for virtual fitness with immersive game play. The VR locomotion is traveling in VR, which is commonly used in adventure role-playing games (RPG). Virtual running can be applied as a locomotion technique for VR exergames. The design of virtual running in VR exergames should be considered as an exercise for fitness and also for enjoyment. This paper proposed two motion-based locomotion techniques: ArmSwing and Squat for virtual running, which are considered as aerobic and strength exercise. These two postures were used to study how physical exertion affected players while interacting in the test scene. Usability, motion sickness, and enjoyment were assessed to analyze the differences of each posture. The results showed that motion sickness and enjoyment of ArmSwing and Squat were not different, while usability was different where ArmSwing was rated higher than Squat. The results from the interviews suggest that most players preferred aerobic exercise (ArmSwing) more than strength exercise (Squat) for a long period of exercise. However, for a short period of exercise, players preferred strength exercise more than aerobic exercise. The adventure-based RPG for exercise needs a solution design appropriate for virtual running in VR, and our results can be a guideline for developers in order to handle motion-based locomotion for VR exergames.
An Improved Robust Kalman Filter for SINS/DVL Tightly Integrated Navigation System

Aiming at the problem of outliers in Doppler velocity log (DVL) beam measurements, for strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) tightly coupled system, an improved robust Kalman filter (KF) algorithm is proposed, which can handle the measurement noise variance of each DVL beam individually. First, the multidimensional measurement equation is decomposed into several 1-D measurement equations, i.e., different DVL beam measurements are assumed to be obtained by different sensors. Second, the statistical similarity measure (SSM) is introduced to quantify the similarity between two random vectors. And a new cost function based on SSM is constructed for the dynamic system containing one state equation and multiple measurement equations. To optimize this cost function, the iterative procedures to update the posterior state is derived based on the Gauss-Newton iteration algorithm. Both the simulation and lake trial illustrate the superiority and effectiveness of the proposed algorithm. It is shown that in the case of partial DVL beams with outliers, the proposed algorithm can avoid the loss of normal measurement information and thus improve estimation accuracy.
Picturing the Meaning of Scandinavian Rock Art – Graphic Representations, Archaeological Interpretations and Material Alterations

The paper presents a reflective overview of the recursive relation between the archaeological practice of picturing Scandinavian rock art in printed works since the mid-19th century, and how archaeologists have constructed its meaning. There seem to be an intimate connection between graphic representations of rock art and an interpretative bias towards the mimetic qualities of images. When picturing rock art, the identification of motifs is prioritized at the expense of the materiality of rock art. Ultimately, the production of graphic representations has influenced the antiquarian alteration of the archaeological remains. Today, major Scandinavian rock art sites are frequently painted red, with the purpose of highlighting the engraved imagery for visitor legibility. This practice transforms the materiality of stone into a visual language of graphic representations.
A Joint Energy and Latency Framework for Transfer Learning Over 5G Industrial Edge Networks

In this article, we propose a transfer learning (TL) enabled edge convolutional neural network (CNN) framework for 5G industrial edge networks with privacy-preserving characteristic. In particular, the edge server can use the existing image dataset to train the CNN in advance, which is further fine-tuned based on the limited datasets uploaded from the devices. With the aid of TL, the devices that are not participating in the training only need to fine-tune the trained edge-CNN model without training from scratch. Due to the energy budget of the devices and the limited communication bandwidth, a joint energy and latency problem is formulated, which is solved by decomposing the original problem into an uploading decision subproblem and a wireless bandwidth allocation subproblem. Experiments using ImageNet demonstrate that the proposed TL-enabled edge-CNN framework can achieve almost 85% prediction accuracy of the baseline by uploading only about 1% model parameters, for a compression ratio of 32 of the autoencoder.
Modeling Fine-Grained Entity Types with Box Embeddings

Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling these types' complex interdependencies.We study the ability of box embeddings, which embed concepts as d-dimensional hyperrectangles, to capture hierarchies of types even when these relationships are not defined explicitly in the ontology.Our model represents both types and entity mentions as boxes.Each mention and its context are fed into a BERT-based model to embed that mention in our box space; essentially, this model leverages typological clues present in the surface text to hypothesize a type representation for the mention.Box containment can then be used to derive both the posterior probability of a mention exhibiting a given type and the conditional probability relations between types themselves.We compare our approach with a vector-based typing model and observe state-of-the-art performance on several entity typing benchmarks.In addition to competitive typing performance, our box-based model shows better performance in prediction consistency (predicting a supertype and a subtype together) and confidence (i.e., calibration), demonstrating that the box-based model captures the latent type hierarchies better than the vector-based model does. 1
Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge

Past research has demonstrated that large neural language models (LMs) encode surprising amounts of factual information: however, augmenting or modifying this information requires modifying a corpus and retraining, which is computationally expensive.To address this problem, we develop a neural LM that includes an interpretable neuro-symbolic KB in the form of a "fact memory".Each element of the fact memory is formed from a triple of vectors, where each vector corresponds to a KB entity or relation.Our LM improves performance on knowledge-intensive question-answering tasks, sometimes dramatically, including a 27 point increase in one setting of WebQuestionsSP over a state-of-the-art open-book model, despite using 5% of the parameters.Most interestingly, we demonstrate that the model can be modified, without any re-training, by updating the fact memory.
How transfer learning impacts linguistic knowledge in deep NLP models?

Transfer learning from pre-trained neural language models towards downstream tasks has been a predominant theme in NLP recently.Several researchers have shown that deep NLP models learn non-trivial amount of linguistic knowledge, captured at different layers of the model.We investigate how fine-tuning towards downstream NLP tasks impacts the learned linguistic knowledge.We carry out a study across popular pre-trained models BERT, RoBERTa and XLNet using layer and neuronlevel diagnostic classifiers.We found that for some GLUE tasks, the network relies on the core linguistic information and preserve it deeper in the network, while for others it forgets.Linguistic information is distributed in the pre-trained language models but becomes localized to the lower layers post-fine-tuning, reserving higher layers for the task specific knowledge.The pattern varies across architectures, with BERT retaining linguistic information relatively deeper in the network compared to RoBERTa and XLNet, where it is predominantly delegated to the lower layers.
AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses

Facial landmark localization aims to detect the predefined points of human faces, and the topic has been rapidly improved with the recent development of neural network based methods. However, it remains a challenging task when dealing with faces in unconstrained scenarios, especially with large pose variations. In this paper, we target the problem of facial landmark localization across large poses and address this task based on a split-and-aggregate strategy. To split the search space, we propose a set of anchor templates as references for regression, which well addresses the large variations of face poses. Based on the prediction of each anchor template, we propose to aggregate the results, which can reduce the landmark uncertainty due to the large poses. Overall, our proposed approach, named AnchorFace, obtains state-of-the-art results with extremely efficient inference speed on four challenging benchmarks, i.e. AFLW, 300W, Menpo, and WFLW dataset. Code will be available soon.
Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation

A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional textonly translation models with multimodal information.Many recent studies report improvements when equipping their models with the multimodal module, despite the controversy of whether such improvements indeed come from the multimodal part.We revisit the contribution of multimodal information in MMT by devising two interpretable MMT models.To our surprise, although our models replicate similar gains as recently developed multimodalintegrated systems achieved, our models learn to ignore the multimodal information.Upon further investigation, we discover that the improvements achieved by the multimodal models over text-only counterparts are in fact results of the regularization effect.We report empirical findings that highlight the importance of MMT models' interpretability, and discuss how our findings will benefit future research.
Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer

Scarcity of parallel data causes formality style transfer models to have scarce success in preserving content.We show that fine-tuning pre-trained language (GPT-2) and sequenceto-sequence (BART) models boosts content preservation, and that this is possible even with limited amounts of parallel data.Augmenting these models with rewards that target style and content -the two core aspects of the task-we achieve a new state-of-the-art.
Humor Knowledge Enriched Transformer for Understanding Multimodal Humor

Recognizing humor from a video utterance requires understanding the verbal and non-verbal components as well as incorporating the appropriate context and external knowledge. In this paper, we propose Humor Knowledge enriched Transformer (HKT) that can capture the gist of a multimodal humorous expression by integrating the preceding context and external knowledge. We incorporate humor centric external knowledge into the model by capturing the ambiguity and sentiment present in the language. We encode all the language, acoustic, vision, and humor centric features separately using Transformer based encoders, followed by a cross attention layer to exchange information among them. Our model achieves 77.36% and 79.41% accuracy in humorous punchline detection on UR-FUNNY and MUStaRD datasets -- achieving a new state-of-the-art on both datasets with the margin of 4.93% and 2.94% respectively. Furthermore, we demonstrate that our model can capture interpretable, humor-inducing patterns from all modalities.
Type-augmented Relation Prediction in Knowledge Graphs

Knowledge graphs (KGs) are of great importance to many real world applications, but they generally suffer from incomplete information in the form of missing relations between entities. Knowledge graph completion (also known as relation prediction) is the task of inferring missing facts given existing ones. Most of the existing work is proposed by maximizing the likelihood of observed instance-level triples. Not much attention, however, is paid to the ontological information, such as type information of entities and relations. In this work, we propose a type-augmented relation prediction (TaRP) method, where we apply both the type information and instance-level information for the relation prediction. In particular, type information and instance-level information are encoded as prior probabilities and likelihoods of relations respectively, and are combined by following the Bayes' rule. Our proposed TaRP method achieves significantly better performance than state-of-the-art methods on four benchmark datasets: FB15K, FB15K-237, YAGO26K-906, and DB111K-174. In addition, we show that the TaRP achieves the significantly improved data efficiency. More importantly, the type information extracted from a specific dataset can generalize well to different datasets through the proposed TaRP model.
Computer-aided diagnosis tool for cervical cancer screening with weakly supervised localization and detection of abnormalities using adaptable and explainable classifier

While pap test is the most common diagnosis methods for cervical cancer, their results are highly dependent on the ability of the cytotechnicians to detect abnormal cells on the smears using brightfield microscopy. In this paper, we propose an explainable region classifier in whole slide images that could be used by cyto-pathologists to handle efficiently these big images (100,000x100,000 pixels). We create a dataset that simulates pap smears regions and uses a loss, we call classification under regression constraint, to train an efficient region classifier (about 66.8% accuracy on severity classification, 95.2% accuracy on normal/abnormal classification and 0.870 KAPPA score). We explain how we benefit from this loss to obtain a model focused on sensitivity and, then, we show that it can be used to perform weakly supervised localization (accuracy of 80.4%) of the cell that is mostly responsible for the malignancy of regions of whole slide images. We extend our method to perform a more general detection of abnormal cells (66.1% accuracy) and ensure that at least one abnormal cell will be detected if malignancy is present. Finally, we experiment our solution on a small real clinical slide dataset, highlighting the relevance of our proposed solution, adapting it to be as easily integrated in a pathology laboratory workflow as possible, and extending it to make a slide-level prediction.
High-Order Pair-Wise Aspect and Opinion Terms Extraction With Edge-Enhanced Syntactic Graph Convolution

Pair-wise aspect and opinion terms extraction (PAOTE), aiming at detecting the pair of the correlated aspect and opinion terms jointly, recently has drawn increasing research attention in the community of sentiment analysis and opinion mining. Recent works largely employ joint methods for the task, while they do not sufficiently incorporate the external syntactic knowledge, such as the dependency edges and labels. Besides, these methods fail to capture the underlying shared interactions among the overlapping aspect-opinion pair structures. In this paper we address the above issues for better PAOTE. Specifically, we present a span graph-based model for joint PAOTE, based on which we propose a edge-enhanced syntactic graph convolutional network (ESGCN) to simultaneously encode the syntactic dependency edges and the corresponding labels, for enhancing the extraction and pairing of aspect and opinion terms. During relational pairing, we employ a Biaffine and a Triaffine scorer respectively for high-order scoring, fully exploring the mutual interactions among different aspect-opinion pairs. Experimental results on four benchmark datasets show that our proposed method significantly outperforms all the baseline systems, giving the state-of-the-art performances. Further analysis proves the effectiveness of our framework on modeling the syntactic edge and label knowledge, as well as the advance of the high-order pairing mechanism for the task.
Cryptanalysis of Internet of Health Things Encryption Scheme Based on Chaotic Maps

Many encryption algorithms are designed to decrease the probability of cyberattacks by assuring data security as well as system and participant authentication.However, in the development of computer resources cryptanalytical techniques have been developed and performed competitively in information security with good results.In this paper, we reported security flaws in the recently offered encryption technique based on a chaotic map for Internet of Health Things (IoHT) security.The scheme was based on a new chaotic map, modified Mandelbrot set, and conditional shift algorithm asserting the encryption algorithm is secure.We have performed some cryptographic attacks to retrieve the key from the understudy cryptosystem.The key was retrieved in little computation by using a chosen-plaintext attack and one known plaintext ciphertext pair.The minimum execution time of performed attacks indicates the vulnerability of the diffusion-based encryption algorithm.To enhance the security of the understudy cryptographic algorithm, we have also suggested certain improvements.
Survey on depth and RGB image-based 3D hand shape and pose estimation

The field of vision-based human hand three-dimensional (3D) shape and pose estimation has attracted significant attention recently owing to its key role in various applications, such as natural humancomputer interactions. With the availability of large-scale annotated hand datasets and the rapid developments of deep neural networks (DNNs), numerous DNN-based data-driven methods have been proposed for accurate and rapid hand shape and pose estimation. Nonetheless, the existence of complicated hand articulation, depth and scale ambiguities, occlusions, and finger similarity remain challenging. In this study, we present a comprehensive survey of state-of-the-art 3D hand shape and pose estimation approaches using RGB-D cameras. Related RGB-D cameras, hand datasets, and a performance analysis are also discussed to provide a holistic view of recent achievements. We also discuss the research potential of this rapidly growing field.
Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation

Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborative signals to be propagated from correlated POIs belonging to other sequences. We then devise a novel Sequence-to-Graph POI Recommender (SGRec), which jointly learns POI embeddings and infers a user's temporal preferences from the graph-augmented POI sequence. To overcome the sparsity of POI-level interactions, we further infuse category-awareness into SGRec with a multi-task learning scheme that captures the denser category-wise transitions. As such, SGRec makes full use of the collaborative signals for learning expressive POI representations, and also comprehensively uncovers multi-level sequential patterns for user preference modelling. Extensive experiments on two real-world datasets demonstrate the superiority of SGRec against state-of-the-art methods in next POI recommendation.
Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

Photovoltaic (PV) power forecasting urges in economic and secure operations of power systems. To avoid an inaccurate individual forecasting model, we propose an approach for a one-day to three-day ahead PV power hourly forecasting based on the stacking ensemble model with a recurrent neural network (RNN) as a meta-learner. The proposed approach is built by using real weather data and forecasted weather data in the training and testing stages, respectively. To accommodate uncertain weather, a daily clustering method based on statistical features, e.g., daily average, maximum, and standard deviation of PV power is applied in the data sets. Historical PV power output and weather data are used to train and test the model. The single learner considered in this research are artificial neural network, deep neural network, support vector regressions, long short-term memory, and convolutional neural network. Then, RNN is used to combine the forecasting results of each single learner. It is also important to observe the best combination of the single learners in this paper. Furthermore, to compare the performance of the proposed method, a random forest ensemble instead of RNN is used as a benchmark for comparison. Mean relative error (MRE) and mean absolute error (MAE) are used as criteria to validate the accuracy of different forecasting models. The MRE of the proposed RNN ensemble learner model is 4.29%, which has significant improvements by about 7–40%, 7–30%, and 8% compared to the single models, the combinations of fewer single learners, and the benchmark method, respectively. The results show that the proposed method is promising for use in real PV power forecasting systems.
Combining Pose Invariant and Discriminative Features for Vehicle Reidentification

Vehicle reidentification, aiming at identifying vehicles across images, has drawn a lot of attention and has made significant achievements in recent years. However, vehicle reidentification remains a challenging task caused by severe appearance changes due to different orientations. In practice, the result of reidentification is greatly influenced by the pose of vehicles, and we call this influence as a pose barrier problem. One way to address the pose barrier problem is to train a feature representation that is invariant for various vehicle poses. To this end, we present pose robust features (PRFs) that contains two components: 1) pose-invariant features (PIFs) and 2) pose discriminative features (PDFs). On the one hand, PIF is the expert in exploring the overall characteristic of vehicles. When training PIF, we adopt an identity classifier as well as an orientation classifier. In addition, an adversarial loss is deployed in the PIF network. On the other hand, we design a PDF network, which has a similar architecture to the PIF network but can distinguish the difference between local details. The difference between PDF and PIF is that the network of training PDF does not apply the adversarial loss. Finally, by combining PIF and PDF, PRF has the advantages of the two features and can alleviate the influence of the pose barrier problem. Experiments are conducted on the VeRi-776 and VehicleID data sets. We show that PIF and PDF are complementary and that PRF produces competitive performance compared with state-of-the-art approaches.