Reviewerly/exHarmony huggingface dataset:
Unsupervised domain adaptation with Joint Adversarial Variational AutoEncoder

Unsupervised domain adaptation techniques increase the classification performance of tasks from the target domain by utilizing the information in a related source domain. Since the target labeled samples are unavailable, matching similar samples across different domains effectively becomes increasingly hard, which retards the progress in this field. In this paper, we propose a Joint Adversarial Variational AutoEncoder (JVA2E) for unsupervised domain adaptation tasks. JVA2E chooses variational autoencoder as the basic framework to improve the generative ability. Both the marginal and conditional distributions are considered for joint distribution adaptation. The Wasserstein distance is chosen for improving the final performance. Multiple unique classifiers are carefully designed for generating pseudo labels which are utilized to increase intra-class similarity as well as narrow conditional distribution. Experiments are conducted on three publicly available datasets and the final results are compared with some state-of-the-art techniques. It illustrates that our proposed method yields better performances for most tasks against previous domain adaptation methods.
A multi-embedding neural model for incident video retrieval

Many internet search engines have been developed, however, the retrieval of video clips remains a challenge. This paper considers the retrieval of incident videos, which may contain more spatial and temporal semantics. We propose an encoder-decoder ConvLSTM model that explores multiple embeddings of a video to facilitate comparison of similarity between a pair of videos. The model is able to encode a video into an embedding that integrates both its spatial information and temporal semantics. Multiple video embeddings are then generated from coarse- and fine-grained features of a video to capture high- and low-level meanings. Subsequently, a learning-based comparative model is proposed to compare the similarity of two videos based on their embeddings. Extensive evaluations are presented and show that our model outperforms state-of-the-art methods for several video retrieval tasks on the FIVR-200K, CC_WEB_VIDEO, and EVVE datasets.
Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation

The task of Grammatical Error Correction (GEC) has received remarkable attention with wide applications in Natural Language Processing (NLP) in recent years. While one of the key principles of GEC is to keep the correct parts unchanged and avoid over-correction, previous sequence-to-sequence (seq2seq) models generate results from scratch, which are not guaranteed to follow the original sentence structure and may suffer from the over-correction problem. In the meantime, the recently proposed sequence tagging models can overcome the over-correction problem by only generating edit operations, but are conditioned on human designed language-specific tagging labels. In this paper, we combine the pros and alleviate the cons of both models by proposing a novel Sequence-to-Action (S2A) module. The S2A module jointly takes the source and target sentences as input, and is able to automatically generate a token-level action sequence before predicting each token, where each action is generated from three choices named SKIP, COPY and GENerate. Then the actions are fused with the basic seq2seq framework to provide final predictions. We conduct experiments on the benchmark datasets of both English and Chinese GEC tasks. Our model consistently outperforms the seq2seq baselines, while being able to significantly alleviate the over-correction problem as well as holding better generality and diversity in the generation results compared to the sequence tagging models.
Application of Theory of Planned Behavior (TPB) in Cryptocurrency Investment Prediction: A Literature Review

This study aims to identify the application of Theory of Planned Behavior to predict people's intentions to invest in cryptocurrencies. Theory of Planned Behavior remains the most prominent theory in behavioral research to predict one’s intention or behavior. This research selected and reviewed articles related to the application of Theory of Planned Behavior. Data was collected from journals, papers, books, and websites related to the theory of planned behavior (TPB) and cryptocurrency. This study revealed that Theory of Planned Behavior is valid and reliable to measure cryptocurrency investment intention. Self-efficacy was used to as a proxy for perceived of behavioral control. This study can be used to develop responsive strategies.
XAI-Fall: Explainable AI for Fall Detection on Wearable Devices Using Sequence Models and XAI Techniques

A fall detection system is vital for the safety of older people, as it contacts emergency services when it detects a person has fallen. There have been various approaches to detect falls, such as using a single tri-axial accelerometer to detect falls or fixing sensors on the walls of a room to detect falls in a particular area. These approaches have two major drawbacks: either (i) they use a single sensor, which is insufficient to detect falls, or (ii) they are attached to a wall that does not detect a person falling outside its region. Hence, to provide a robust method for detecting falls, the proposed approach uses three different sensors for fall detection, which are placed at five different locations on the subject’s body to gather the data used for training purposes. The UMAFall dataset is used to attain sensor readings to train the models for fall detection. Five models are trained corresponding to the five sensors models, and a majority voting classifier is used to determine the output. Accuracy of 93.5%, 93.5%, 97.2%, 94.6%, and 93.1% is achieved on each of the five sensors models, and 92.54% is the overall accuracy achieved by the majority voting classifier. The XAI technique called LIME is incorporated into the system in order to explain the model’s outputs and improve the model’s interpretability.
Towards Building a Visual Behaviour Analysis Pipeline for Suicide Detection and Prevention

Understanding human behaviours through video analysis has seen significant research progress in recent years with the advancement of deep learning. This topic is of great importance to the next generation of intelligent visual surveillance systems which are capable of real-time detection and analysis of human behaviours. One important application is to automatically monitor and detect individuals who are in crisis at suicide hotspots to facilitate early intervention and prevention. However, there is still a significant gap between research in human action recognition and visual video processing in general, and their application to monitor hotspots for suicide prevention. While complex backgrounds, non-rigid movements of pedestrians and limitations of surveillance cameras and multi-task requirements for a surveillance system all pose challenges to the development of such systems, a further challenge is the detection of crisis behaviours before a suicide attempt is made, and there is a paucity of datasets in this area due to privacy and confidentiality issues. Most relevant research only applies to detecting suicides such as hangings or jumps from bridges, providing no potential for early prevention. In this research, these problems are addressed by proposing a new modular design for an intelligent visual processing pipeline that is capable of pedestrian detection, tracking, pose estimation and recognition of both normal actions and high risk behavioural cues that are important indicators of a suicide attempt. Specifically, based on the key finding that human body gestures can be used for the detection of social signals that potentially precede a suicide attempt, a new 2D skeleton-based action recognition algorithm is proposed. By using a two-branch network that takes advantage of three types of skeleton-based features extracted from a sequence of frames and a stacked LSTM structure, the model predicts the action label at each time step. It achieved good performance on both the public dataset JHMDB and a smaller private CCTV footage collection on action recognition. Moreover, a logical layer, which uses knowledge from a human coding study to recognise pre-suicide behaviour indicators, has been built on top of the action recognition module to compensate for the small dataset size. It enables complex behaviour patterns to be recognised even from smaller datasets. The whole pipeline has been tested in a real-world application of suicide prevention using simulated footage from a surveillance system installed at a suicide hotspot, and preliminary results confirm its effectiveness at capturing crisis behaviour indicators for early detection and prevention of suicide.
Minimum Quantum Run-Time Characterization and Calibration via Restless Measurements with Dynamic Repetition Rates

Qubit calibration and characterization are important for attaining high-fidelity gates in quantum computing, but require a significant amount of time, which limits a device's ability to run useful quantum circuits. This study uses restless measurement, in which the qubits are never reset, to speed up characterization and calibration tasks. The authors also show how quantum process tomography may be performed in a restless setting. This approach reduces the footprint of characterization and calibration on quantum processors by quickly and faithfully executing the corresponding circuits.
Dual Adversarial Attention Mechanism for Unsupervised Domain Adaptive Medical Image Segmentation

Domain adaptation techniques have been demonstrated to be effective in addressing label deficiency challenges in medical image segmentation. However, conventional domain adaptation based approaches often concentrate on matching global marginal distributions between different domains in a class-agnostic fashion. In this paper, we present a dual-attention domain-adaptative segmentation network (DADASeg-Net) for cross-modality medical image segmentation. The key contribution of DADASeg-Net is a novel dual adversarial attention mechanism, which regularizes the domain adaptation module with two attention maps respectively from the space and class perspectives. Specifically, the spatial attention map guides the domain adaptation module to focus on regions that are challenging to align in adaptation. The class attention map encourages the domain adaptation module to capture class-specific instead of class-agnostic knowledge for distribution alignment. DADASeg-Net shows superior performance in two challenging medical image segmentation tasks.
Delegated Proof of Accessibility (DPoAC): A Novel Consensus Protocol for Blockchain Systems

As the backbone of every blockchain application, the consensus protocol is impacted by numerous risks, namely resource requirements and energy consumption, which limit the usage of blockchain. Applications such as IoT/IIoT cannot use these high-cost consensus methods due to limited resources. Therefore, we introduce Delegated Proof of Accessibility (DPoAC), a new consensus technique that employs secret sharing, PoS with random selection, and an interplanetary file system (IPFS).DPoAC is decomposed into two stages. During the initial stage, a secret is generated by a randomly chosen super node and divided into n shares. These shares are encrypted and stored in different n nodes on the IPFS network. The nodes will compete to access these shareholders to reconstruct the secret. The winning node will be awarded block generation rights. PoS with random selection is used in the second stage to compute the appropriate hash value and construct a block with valid transactions. In this novel approach, a node with few computational resources and small stakes can still obtain block generation rights by providing access to secret shares and reconstructing the secret, making the system reasonably fair. We qualitatively analyze and compare our scheme based on performance parameters against existing mainstream consensus protocols in the context of IoT/IIoT networks.
Exploring Safer Behaviors for Deep Reinforcement Learning

We consider Reinforcement Learning (RL) problems where an agent attempts to maximize a reward signal while minimizing a cost function that models unsafe behaviors. Such formalization is addressed in the literature using constrained optimization on the cost, limiting the exploration and leading to a significant trade-off between cost and reward. In contrast, we propose a Safety-Oriented Search that complements Deep RL algorithms to bias the policy toward safety within an evolutionary cost optimization. We leverage evolutionary exploration benefits to design a novel concept of safe mutations that use visited unsafe states to explore safer actions. We further characterize the behaviors of the policies over desired specifics with a sample-based bound estimation, which makes prior verification analysis tractable in the training loop. Hence, driving the learning process towards safer regions of the policy space. Empirical evidence on the Safety Gym benchmark shows that we successfully avoid drawbacks on the return while improving the safety of the policy.
Expert-Informed, User-Centric Explanations for Machine Learning

We argue that the dominant approach to explainable AI for explaining image classification, annotating images with heatmaps, provides little value for users unfamiliar with deep learning. We argue that explainable AI for images should produce output like experts produce when communicating with one another, with apprentices, and with novices. We provide an expanded set of goals of explainable AI systems and propose a Turing Test for explainable AI.
L-CoDe:Language-Based Colorization Using Color-Object Decoupled Conditions

Colorizing a grayscale image is inherently an ill-posed problem with multi-modal uncertainty. Language-based colorization offers a natural way of interaction to reduce such uncertainty via a user-provided caption. However, the color-object coupling and mismatch issues make the mapping from word to color difficult. In this paper, we propose L-CoDe, a Language-based Colorization network using color-object Decoupled conditions. A predictor for object-color corresponding matrix (OCCM) and a novel attention transfer module (ATM) are introduced to solve the color-object coupling problem. To deal with color-object mismatch that results in incorrect color-object correspondence, we adopt a soft-gated injection module (SIM). We further present a new dataset containing annotated color-object pairs to provide supervisory signals for resolving the coupling problem. Experimental results show that our approach outperforms state-of-the-art methods conditioned on captions.
Learning 3D Skeletal Representation From Transformer for Action Recognition

Skeleton-based human action recognition has attracted significant interest due to its simplicity and good accuracy. Diverse end-to-end trainable frameworks based on skeletal representation have been proposed so far to map the representation to human action classes better. Most skeleton-based human action recognition approaches are based on the skeletons, which are heuristically pre-defined by the commercial sensors. Nevertheless, it is not confirmed whether the sensor-captured skeletons is the best representation of human bodies for the action recognition task, while in general, the dedicated representation is required for achieving the successful performance on subsequent tasks such as action recognition. In this paper, we try to deal with the issue by explicitly learning the skeletal representation in the context of the human action recognition task. We start our investigation by reconstructing 3D meshes of the human bodies from RGB videos. Then we involve the transformer architecture to sample the most informative skeletal representation from reconstructed 3D meshes, considering the inner and inter structural relationship of 3D meshes and sensor-captured skeletons. Experimental results on challenging human action recognition benchmarks (i.e., SYSU and UTD-MHAD datasets) have shown the superiority of our skeletal representation compared to the sensor-captured skeletons for the action recognition task.
BLMDP: A new bi-level Markov decision process approach to joint bidding andtask-scheduling in cloud spot market

In the cloud computing market (CCM), computing services are traded between cloud providers and consumers in the form of the computing capacity of virtual machines (VMs).The Amazon spot market is one of the most well-known markets in which the surplus capacity of data centers is auctioned off in the form of VMs at relatively low prices.For each submitted task, the user can offer a price that is higher than the current price.However, uncertainty in the market environment confronts the user with challenges such as the variable price of VMs and the variable number of users.An appropriate strategy should both maximize the user's utility and determine the best bid for him/her.In this paper, we aim to solve the problem of joint minimizing the cost of processing tasks and maximizing user satisfaction.Our proposed method, which we call bi-level Markov decision-making process (BLMDP), works on two levels.At the top level, it selects the most appropriate user bids and adjusts the spot price to minimize the cost of VMs on the cloud provider side.At a low level, it decides to admit tasks in such a way as to maximize the user side satisfaction.Performance evaluation based on real data collected from Amazon web services shows that BLMDP manages to minimize cloud provider's costs and maximize user gain more effectively compared to heuristic methods.
Enhancing Cross-lingual Natural Language Inference by Prompt-learning from Cross-lingual Templates

Cross-lingual natural language inference (XNLI) is a fundamental task in cross-lingual natural language understanding.Recently this task is commonly addressed by pre-trained cross-lingual language models.Existing methods usually enhance pre-trained language models with additional data, such as annotated parallel corpora.These additional data, however, are rare in practice, especially for low-resource languages.Inspired by recent promising results achieved by prompt-learning, this paper proposes a novel prompt-learning based framework for enhancing XNLI.It reformulates the XNLI problem to a masked language modeling problem by constructing cloze-style questions through cross-lingual templates.To enforce correspondence between different languages, the framework augments a new question for every question using a sampled template in another language and then introduces a consistency loss to make the answer probability distribution obtained from the new question as similar as possible with the corresponding distribution obtained from the original question.Experimental results on two benchmark datasets demonstrate that XNLI models enhanced by our proposed framework significantly outperform original ones under both the full-shot and few-shot cross-lingual transfer settings.
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation

In this paper, we present a substantial step in better understanding the SOTA sequence-tosequence (Seq2Seq) pretraining for neural machine translation (NMT).We focus on studying the impact of the jointly pretrained decoder, which is the main difference between Seq2Seq pretraining and previous encoderbased pretraining approaches for NMT.By carefully designing experiments on three language pairs, we find that Seq2Seq pretraining is a double-edged sword: On one hand, it helps NMT models to produce more diverse translations and reduce adequacy-related translation errors.On the other hand, the discrepancies between Seq2Seq pretraining and NMT finetuning limit the translation quality (i.e., domain discrepancy) and induce the over-estimation issue (i.e., objective discrepancy).Based on these observations, we further propose simple and effective strategies, named in-domain pretraining and input adaptation to remedy the domain and objective discrepancies, respectively.Experimental results on several language pairs show that our approach can consistently improve both translation performance and model robustness upon Seq2Seq pretraining.
Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis

<i>Introduction</i>: The precise estimation of software effort is a significant difficulty that project managers encounter during software development. Inaccurate forecasting leads to either overestimating or underestimating software effort, which can be detrimental for stakeholders. The International Function Point Users Group&#x2019;s Function Point Analysis (FPA) method is one of the most critical methods for software effort estimation. However, the practice of using the FPA method in the same fashion across all software areas needs to be reexamined. <i>Aim</i>: We propose a model for evaluating the influence of data clustering on software development effort estimation and then finding the best clustering method. We call this model the effort estimation using machine learning applied to the clusters (EEAC) model. <i>Method</i>: We cluster the dataset according to the clustering method and then apply the FPA and EEAC methods to these clusters for effort estimation. The clustering methods we use in this study include five categorical variable criteria (Development Platform, Industrial Sector, Language Type, Organization Type, and Relative Size) and the k-means clustering algorithm. <i>Results</i>: The experimental results show that the estimation accuracy obtaining with clustering consistently outperforms the accuracy without clustering for both the FPA and EEAC methods. Significantly, using the FPA method, the average improvement rate from using clustering as opposed to non-clustered was highest at 58.06%, according to the RMSE. With the EEAC method, this number reached 65.53%. The Industry Sector categorical variable achieves the best accuracy estimation compared to the other clustering criteria and k-means clustering. The improvement in accuracy in terms of the RMSE when applying this criterion is 63.68% for the FPA method and 72.02% for the EEAC method. <i>Conclusion</i>: Better results are obtained through dataset clustering compared to no clustering for both the FPA and EEAC methods. The Industry Sector is the most suitable clustering method among the tested clustering methods.
Structural Characterization for Dialogue Disentanglement

Tangled multi-party dialogue contexts lead to challenges for dialogue reading comprehension, where multiple dialogue threads flow simultaneously within a common dialogue record, increasing difficulties in understanding the dialogue history for both human and machine.Previous studies mainly focus on utterance encoding methods with carefully designed features but pay inadequate attention to characteristic features of the structure of dialogues.We specially take structure factors into account and design a novel model for dialogue disentangling.Based on the fact that dialogues are constructed on successive participation and interactions between speakers, we model structural information of dialogues in two aspects: 1)speaker property that indicates whom a message is from, and 2) reference dependency that shows whom a message may refer to.The proposed method achieves new state-of-the-art on the Ubuntu IRC benchmark dataset and contributes to dialogue-related comprehension.