Reviewerly/exHarmony huggingface dataset:
Detection of non-designated shelters by extracting population concentrated areas after a disaster (industrial paper)

In a disaster situation, local and municipal governments need to distribute relief supplies and provide administrative support to evacuee. Although people are supposed to evacuate to evacuation shelters designated by local governments, some people take refuge at non-designated facilities, called non-designated evacuation shelters, due to unavoidable circumstances such as damages on the access route to designated evacuation shelters. Upon occurrence of a disaster, therefore, it is necessary for the local governments to quickly find the locations of non-designated evacuation shelters. In this paper, we propose a method to detect non-designated evacuation shelters based on autoencoder (AE)-based anomaly detection using real-time population dynamics generated from operation data of cellular phone networks. We assume that reconstruction errors of an AE model include both the errors due to characteristic differences between locations and the errors due to anomalies in population dynamics. Thus, we propose to use the ratio of the reconstruction error before and after the earthquake to determine the threshold of anomaly detection. We evaluate the performance of the proposed method on data from three actual earthquakes in Japan. The evaluation results show that our reconstruction-error-based approach can achieve better accuracy for the actual disaster data compared to a baseline method that exploits statistical anomaly detection.
Active learning for transformer models in direction query tagging

Correct understanding of direction queries is essential in map search for providing accurate direction related results, including routing, travel distance, travel time estimation, etc. Slot tagging is the process of recognizing and annotating query terms as entities such as source, destination, travel mode, travel distance, or travel time, so that downstream map search components can surface the expected result. Transformer-based models have achieved state-of-the-art performance on various language understanding tasks, including slot tagging. However, such models require either good quality labeled data for fine-tuning or large amount of labeled data for full training. Active learning provides a solution for improving training efficiency by selecting only a small amount of very informative queries for labelling. It is not yet clear, though, how to properly apply active learning for transformer-based language models. In this paper, we propose a novel active learning method designed specifically for transformer models and demonstrate its effectiveness for slot tagging of direction queries.
An Evolutionary Algorithm: An Enhancement of Binary Tournament Selection for Fish Feed Formulation

Binary tournament (BT) selection is known as an established selection operator that has been employed in various problems. However, in the development of evolutionary algorithms (EA), this selection operator has a drawback in providing an efficient implementation of the union procedure, which cannot guarantee a parsimonious knowledge base with reduced number of rules. Therefore, this paper introduces binary-standard deviation (SD) tournament selection into EA as an enhancement of BT that can lead to focus on more exploration in terms of searching for the best solutions. The proposed selection operator has been experimented within fish feed formulation in grouper fish farming as a case study on finding the minimum cost and fulfilling constraints. This approach is better than experimental design in terms of costs and time. The motivation for doing so is to search for alternative ingredients for the grouper fish, as the price of trash fish is too luxurious. It is because grouper fish are carnivorous and need many trash fish for better growth. The novelty of the proposed SD tournament selection is compared with BT selection in terms of searching for an efficient but not myopic algorithm. Hence, based on the comparative study, the findings of the enhanced selection operator towards the EA have been convinced and accepted in terms of better cost and fulfilling constraints requirements.
Qubit and bit-based quantum hybrid secret key generation

For the generation of a hybrid secret key, hardly any quantum algorithms integrating computed qubits and decrypted bits have yet been developed. Integrating computed qubits and decrypted bits in a predetermined order is difficult for a combiner component. The underlying problems of the study are the design of a quantum circuit, an algorithm, base state polarizer setup, and the concatenation of decrypted bits and computed qubits. By combining either rectilinear, orthogonal (superposition), or both states with decrypted bits, we have investigated three different possibilities for the quantum hybrid protocol. We investigated errors in each case and compared them with regard to decoherence and other quantum mechanics properties by taking into consideration the effectiveness of base states during transmission time across an untrusted channel. Furthermore, we addressed the key size, base state errors, design complexity, and security in a reasonable manner for identifying solutions and compared our results to earlier proposed quantum protocols. Finally, our suggested key protocol is more effective than that of earlier proposed protocols.
Emoji meanings (pleasure–arousal–dominance dimensions) in consumer research: Between‐country and interpersonal differences

In line with the increasing popularity of emoji, the need for methodological research into these pictorial representations of emotion remains. The present research contributes to this goal by continuing to establish the meaning of emoji and exploring these according to between-country and interpersonal differences. The emoji (n = 12) were selected to span the valence × arousal emotion space, and the PAD model (Pleasure-Arousal-Dominance) was used to establish emoji meaning for the three dimensions, operationalized as measurement on 6 × 3 semantic differentials. Participants in the main study came from three countries-Germany, Singapore, and Malaysia (n = 2465), and a supplementary study included the United Kingdom and New Zealand (n = 600) (subset of four emoji). The results confirmed that emoji meanings according to the PAD model were largely similar between countries (albeit not identical). There were multiple minor significant differences for individual emoji, and where these existed, they often related to the dimension of Arousal, prompting a need for further investigation. Interpersonal differences were examined for gender (men and women), age group (18-45 and 46-69 years old), and frequency of emoji use. Again, significant differences were smaller rather than larger and supported the notion that emoji are generally applicable for multicountry research. However, caution regarding the participants who use emoji infrequently may be warranted. PRACTICAL APPLICATION: The findings from this research will help academics and practitioners who are interested in using emoji for sensory and consumer research (or are already doing so) with more robust interpretations of their findings. For a set of 12 emoji that provide broad coverage of the valence × arousal emotional space, meanings are provided on the three dimensions of the PAD model. The data is collected in five countries and contributes to increased confidence that emoji meanings are by and large similar in these countries.
Human-like tele-health robotics for older adults – A preliminary feasibility trial and vision

The global increase of the aging population presents major challenges to healthcare service delivery. Further, the COVID-19 pandemic exposed older adults' vulnerability to rapid deterioration of health when deprived of access to care due to the need for social distancing. Robotic technology advancements show promise to improve provision of quality care, support independence for patients and augment the capabilities of clinicians to perform tasks remotely.This study explored the feasibility and end-user acceptance of using a novel human-like tele-robotic system with touch feedback to conduct a remote medical examination and deliver safe care.Testing of a remotely controlled robot was conducted with in-person clinician support to gather ECG readings of 11 healthy participants through a digital medical device. Post-study feedback about the system and the remote examinations conducted was obtained from study participants and study clinicians.The findings demonstrated the system's capability to support remote examination of participants, and validated the system's perceived acceptability by clinicians and end-users who all reported feeling safe interacting with the robot and 72% preferred remote robotic exam over in-person examination.This paper discusses potential implications of robot-assisted telehealth for patients including older adults who are precluded from having in-person medical visits due to geographic distance or mobility, and proposes next steps for advancing robot-assisted telehealth delivery.
Unsupervised Global and Local Homography Estimation With Motion Basis Learning

In this paper, we introduce a new framework for unsupervised deep homography estimation. Our contributions are 3 folds. First, unlike previous methods that regress 4 offsets for a homography, we propose a homography flow representation, which can be estimated by a weighted sum of 8 pre-defined homography flow bases. Second, considering a homography contains 8 Degree-of-Freedoms (DOFs) that is much less than the rank of the network features, we propose a Low Rank Representation (LRR) block that reduces the feature rank, so that features corresponding to the dominant motions are retained while others are rejected. Last, we propose a Feature Identity Loss (FIL) to enforce the learned image feature warp-equivariant, meaning that the result should be identical if the order of warp operation and feature extraction is swapped. With this constraint, the unsupervised optimization can be more effective and the learned features are more stable. With global-to-local homography flow refinement, we also naturally generalize the proposed method to local mesh-grid homography estimation, which can go beyond the constraint of a single homography. Extensive experiments are conducted to demonstrate the effectiveness of all the newly proposed components, and results show that our approach outperforms the state-of-the-art on the homography benchmark dataset both qualitatively and quantitatively. Code is available at https://github.com/megvii-research/BasesHomo.
Compact Representation and Reliable Classification Learning for Point-Level Weakly-Supervised Action Localization

Point-level weakly-supervised temporal action localization (P-WSTAL) aims to localize temporal extents of action instances and identify the corresponding categories with only a single point label for each action instance for training. Due to the sparse frame-level annotations, most existing models are in the localization-by-classification pipeline. However, there exist two major issues in this pipeline: large intra-action variation due to task gap between classification and localization and noisy classification learning caused by unreliable pseudo training samples. In this paper, we propose a novel framework CRRC-Net, which introduces a co-supervised feature learning module and a probabilistic pseudo label mining module, to simultaneously address the above two issues. Specifically, the co-supervised feature learning module is applied to exploit the complementary information in different modalities for learning more compact feature representations. Furthermore, the probabilistic pseudo label mining module utilizes the feature distances from action prototypes to estimate the likelihood of pseudo samples and rectify their corresponding labels for more reliable classification learning. Comprehensive experiments are conducted on different benchmarks and the experimental results show that our method achieves favorable performance with the state-of-the-art.
The context problem in artificial intelligence

The artificial intelligence design challenge of teaming humans and machines is difficult because machines cannot read the context of use.
Data Type Bugs Taxonomy: Integer Overflow, Juggling, and Pointer Arithmetics in Spotlight

In this work, we present an orthogonal classification of data type bugs, allowing precise structured descriptions of related software vulnerabilities. We utilize the Bugs Framework (BF) approach to define four language-independent classes that cover all possible kinds of data type bugs. In BF each class is a taxonomic category of a weakness type defined by sets of operations, cause$\rightarrow$consequence relations, and attributes. A BF description of a bug or a weakness is an instance of a taxonomic BF class with one operation, one cause, one consequence, and their attributes. Any vulnerability then can be described as a chain of such instances and their consequence-cause transitions. With our newly developed classes Declaration Bugs, Name Resolution Bugs, Type Conversion Bugs, and Type Computation Bugs, we confirm that BF is a classification system that extends the Common Weakness Enumeration (CWE). The proposed classes allow clear communication about software bugs that relate to misuse of data types, and provide a structured way to precisely describe data type related vulnerabilities.
Evolutionary Feature Selection Method via A Chaotic Binary Dragonfly Algorithm

Feature selection aims at reducing the number of attributes while achieving a high classification accuracy in machine learning. In this paper, we design a fitness function to jointly reduce the number of the selected features and enhance the accuracy. Then, we propose a chaotic binary dragonfly algorithm (CBDA) with several improved factors on the conventional dragonfly algorithm (DA) for developing a wrapper-based feature selection method to solve the fitness function. Specifically, the CBDA introduces three improved factors that are the chaotic map, evolutionary population dynamics mechanism and binarization strategy to make the algorithm more suitable for the problem. Experiments are conducted to evaluate the performance of the proposed CBDA on 24 well-known data sets from the UCI repository, and the results demonstrate that the proposed CBDA outperforms other comparative algorithms on the majority of the tested data sets.
Exploring Embedding Spaces for more Coherent Topic Modeling in Electronic Health Records

The written notes in the Electronic Health Records contain a vast amount of information about patients. Implementing automated approaches for text classification tasks requires the automated methods to be well-interpretable, and topic models can be used for this goal as they can indicate what topics in a text are relevant to making a decision. We propose a new topic modeling algorithm, FLSA-E, and compare it with another state-of-the-art algorithm FLSA-W. In FLSA-E, topics are found by fuzzy clustering in a word embedding space. Since we use word embeddings as the basis for our clustering, we extend our evaluation with word-embeddings-based evaluation metrics. We find that different evaluation metrics favour different algorithms. Based on the results, there is evidence that FLSA-E has fewer outliers in its topics, a desirable property, given that within-topic words need to be semantically related.
Double Feature Pyramid Networks for Classification and Localization on Object Detection

The decoupled head for classification and localization have been proven powerful in the most of one-stage and two-stage detectors. However, most object detection algorithm share a Feature Pyramid Networks. We perform a thorough analysis about the effectiveness of Feature Pyramid Networks for these two tasks. The decoupled feature pyramid network performs better than the shared network. Going a step further, we found that the two tasks have different preferences for feature pyramid networks. For higher accuracy, we propose a Scene Parsing Pyramid Network for Classification and a Feature Pyramid Transformer Network for Localization. Scene Parsing Pyramid Network exploit the capability of global context information by different region based context aggregation through pyramid pooling module and pyramid attention feature extraction module. Feature Pyramid Transformer Network can capture the suitable contexts of objects residing in different scales. We evaluate our double Feature Pyramid Networks feature pyramid network in the object detection task by integrating it into the FCOS algorithm. The modified algorithm outperforms previous state-of-the-art feature pyramid based methods with a clear margin on both MS-COCO 2017 validation and test datasets.
On Transformations among Opacity Notions

Opacity is a property asking whether a system may reveal its secret to a passive observer who knows the structure of the system but has only limited observations of its behavior. Several notions of opacity have been studied. Similarities among the opacity notions have been investigated via transformations, which have many potential applications. We investigate K-step opacity (K-SO), a notion that generalizes both current-state opacity and infinite-step opacity, and asks whether the intruder cannot decide, at any instant, whether or when the system was in a secret state during the last K observable steps. We provide new polynomial-time transformations among K-SO and other opacity notions. Our results lead, among others, to the general solution of an open problem concerning the computational complexity of the verification of K-SO.
Towards a Human-like Chatbot using Deep Adversarial Learning

Conversational agents are getting more popular and applied in a wide range of practical application areas. The main task of these agents is not only to generate context-appropriate responses to a given query but also to make the conversation human-like. Thanks to the ability of deep learning based models in natural language modeling, recent studies have made progress in designing conversational agents that can provide more semantically accurate responses. However, the naturalness in such conversation setting has not been given adequate attention in these studies. This paper aims to incorporate both important criteria of accuracy and naturalness of conversation in developing a new model for conversational agents. To this end, inspired by the idea of Turing test and the idea of adversarial learning strategy, we propose to design a model based on generative deep neural networks that interestingly allow to generate accurate responses optimized by the mechanics of imitating human-generated conversations. Experimental results demonstrate that the proposed models produce more natural and accurate responses, yielding significant gains in BLEU scores.
Online pseudo labeling for polyp segmentation with momentum networks

emantic segmentation is an essential task in developing medical image diagnosis systems. However, building an annotated medical dataset is expensive. Thus, semi-supervised methods are significant in this circumstance. In semi-supervised learning, the quality of labels plays a crucial role in modelperformance. In this work, we present a new pseudo labeling strategy that enhances the quality of pseudo labels used for training student networks. We follow the multi-stage semi-supervised training approach, which trains a teacher model on a labeled dataset and then uses the trained teacher to render pseudo labels for student training. By doing so, the pseudo labels will be updated and more precise as training progress. The key difference between previous and our methods is that we update the teacher model during the student training process. So the quality of pseudo labels is improved during the student training process. We also propose a simple but effective strategy to enhance the quality of pseudo labels using a momentum model - a slow copy version of the original model during training. By applying the momentum model combined with re-rendering pseudo labels during student training, we achieved an average of 84.1% Dice Score on five datasets (i.e., Kvarsir, CVC-ClinicDB, ETIS-LaribPolypDB, CVC-ColonDB, and CVC-300) with only 20% of the dataset used as labeled data. Our results surpass common practice by 3% and even approach fully-supervised results on some datasets. Oursource code and pre-trained models are available at https://github.com/sun-asterisk-research/online_learning_ssl
Knowledge-Enhanced Graph Transformer Network for Multi-Behavior and Item-Knowledge Session-based Recommendation

Session-based recommendations already play an important role in platforms such as e-commerce and streaming media, which are designed to predict the next interaction item based on a given session. Most of the current recommendation models only use the interaction sequence of the session to capture the potential conversion patterns between items, often ignore the user’s multi-type interaction behavior that reflects the user’s fine-grained preferences. At present, most models of multi-type interaction behaviors only learn user-item multi-type interaction behaviors and item-item dependencies relatively independently, and ignore the problems of item cold start and data sparsity. These issues motivate us to propose a new model MKGTN in this paper, we apply multi-type user-item interaction behaviors and item-item dependencies to session recommendation via MLP. Simultaneously, using a multi-task learning MLT paradigm involving learning knowledge embeddings as an auxiliary task to facilitate the main task of SR. Evaluations on three datasets show that MKGTN outperforms state-of-the-art multi-action interaction models, demonstrating the superiority of our model’s performance.
A New Approach for Vietnamese Aspect-Based Sentiment Analysis

Intelligent systems, especially smartphones, have become crucial parts of the world. These devices can solve various human tasks, from long-distance communication to healthcare assistants. For this tremendous success, customer feedback on a smartphone plays an integral role during the development process. This paper presents an improved approach for the Vietnamese Smartphone Feedback Dataset (UIT-ViSFD), collected and annotated carefully in 2021 (including 11,122 comments and their labels) by employing the pretrained PhoBERT model with a proper pre-processing method. In the experiments, we compare the approach with other transformer-based models such as XLM-R, DistilBERT, RoBERTa, and BERT. The experimental results show that the proposed method can bypass the state-of-the-art methods related to the UIT-ViSFD corpus. As a result, our model can achieve better macro-F1 scores for the Aspect and Sentiment Detection task, which are 86.03% and 78.76%, respectively. In addition, our approach could improve the results of Aspect-Based Sentiment Analysis datasets in the Vietnamese language.
Effect of Cluster-based Sampling on the Over-smoothing Issue in Graph Neural Network

Graph neural networks (GNNs) are among the dominated approaches for learning graph structured data and are used in various applications such as social network or product recommendation. The GNN operates mainly on the message passing mechanism which a node receives related nodes information to improve its internal representation. However, when the depth of the GNN increases, the message passing mechanism cut-offs the high-frequency component of the nodes’ representation, thus leads to the over-smoothing issue. In this paper, we propose the usage of cluster-based sampling to reduce the smoothing effect of the high number of layers in GNN. Given each nodes is assigned to a specific region of the embedding space, the cluster-based sampling is expected to propagate this information to the node’s neighbour, thus improve the nodes’ expressivity. Our approach is tested with several popular GNN architecture and the experiments show that our approach could reduce the smoothing effect in comparison with the standard approaches using the Mean Average Distance metric.