Reviewerly/exHarmony huggingface dataset:
Feature Distillation Interaction Weighting Network for Lightweight Image Super-resolution

Convolutional neural networks based single-image superresolution (SISR) has made great progress in recent years. However, it is difficult to apply these methods to real-world scenarios due to the computational and memory cost. Meanwhile, how to take full advantage of the intermediate features under the constraints of limited parameters and calculations is also a huge challenge. To alleviate these issues, we propose a lightweight yet efficient Feature Distillation Interaction Weighted Network (FDIWN). Specifically, FDIWN utilizes a series of specially designed Feature Shuffle Weighted Groups (FSWG) as the backbone, and several novel mutual Wide-residual Distillation Interaction Blocks (WDIB) form an FSWG. In addition, Wide Identical Residual Weighting (WIRW) units and Wide Convolutional Residual Weighting (WCRW) units are introduced into WDIB for better feature distillation. Moreover, a Wide-Residual Distillation Connection (WRDC) framework and a Self-Calibration Fusion (SCF) unit are proposed to interact features with different scales more flexibly and efficiently. Extensive experiments show that our FDIWN is superior to other models to strike a good balance between model performance and efficiency. The code is available at https://github.com/IVIPLab/FDIWN.
Social Virtual Reality: Neurodivergence and Inclusivity in the Metaverse

Whereas traditional teaching environments encourage lively and engaged interaction and reward extrovert qualities, introverts, and others with symptoms that make social engagement difficult, such as autism spectrum disorder (ASD), are often disadvantaged. This population is often more engaged in quieter, low-key learning environments and often does not speak up and answer questions in traditional lecture-style classes. These individuals are often passed over in school and later in their careers for not speaking up and are assumed to not be as competent as their gregarious and outgoing colleagues. With the rise of the metaverse and democratization of virtual reality (VR) technology, post-secondary education is especially poised to capitalize on the immersive learning environments social VR provides and prepare students for the future of work, where virtual collaboration will be key. This study seeks to reconsider the role of VR and the metaverse for introverts and those with ASD. The metaverse has the potential to continue the social and workplace changes already accelerated by the pandemic and open new avenues for communication and collaboration for a more inclusive audience and tomorrow.
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation

Supervised deep learning-based methods yield accurate results for medical image segmentation. However, they require large labeled datasets for this, and obtaining them is a laborious task that requires clinical expertise. Semi/self-supervised learning-based approaches address this limitation by exploiting unlabeled data along with limited annotated data. Recent self-supervised learning methods use contrastive loss to learn good global level representations from unlabeled images and achieve high performance in classification tasks on popular natural image datasets like ImageNet. In pixel-level prediction tasks such as segmentation, it is crucial to also learn good local level representations along with global representations to achieve better accuracy. However, the impact of the existing local contrastive loss-based methods remains limited for learning good local representations because similar and dissimilar local regions are defined based on random augmentations and spatial proximity; not based on the semantic label of local regions due to lack of large-scale expert annotations in the semi/self-supervised setting. In this paper, we propose a local contrastive loss to learn good pixel level features useful for segmentation by exploiting semantic label information obtained from pseudo-labels of unlabeled images alongside limited annotated images with ground truth (GT) labels. In particular, we define the proposed contrastive loss to encourage similar representations for the pixels that have the same pseudo-label/GT label while being dissimilar to the representation of pixels with different pseudo-label/GT label in the dataset. We perform pseudo-label based self-training and train the network by jointly optimizing the proposed contrastive loss on both labeled and unlabeled sets and segmentation loss on only the limited labeled set. We evaluated the proposed approach on three public medical datasets of cardiac and prostate anatomies, and obtain high segmentation performance with a limited labeled set of one or two 3D volumes. Extensive comparisons with the state-of-the-art semi-supervised and data augmentation methods and concurrent contrastive learning methods demonstrate the substantial improvement achieved by the proposed method. The code is made publicly available at https://github.com/krishnabits001/pseudo_label_contrastive_training.
Long Dialogue Emotion Detection Based on Commonsense Knowledge Graph Guidance

Dialogue emotion detection is always challenging due to human subjectivity and the randomness of dialogue content. In a conversation, the emotion of each person often develops via a cumulative process, which can be influenced by many elements of uncertainty. Much commonsense knowledge influences people's emotions imperceptibly, such as experiential or habitual knowledge. In the process of conversation, this commonsense knowledge information can be used to enrich the semantic information of each utterance and improve the accuracy of emotion recognition. In this paper, we propose a growing graph model for dialogues emotion detection based on retrieval of external knowledge atlas ATOMIC from local and global respectively, which can effectively represent the dialogues as a process variable in a sequence and the correlation among utterances also can be represented by the graph model. In particular, 1) we introduce a common sense knowledge graph for linking the commonsense knowledge retrieved from external knowledge atlas ATOMIC, which can effectively add auxiliary information to improve the performance of each utterance's representation. 2) We propose a novel self-supervised learning method for extracting the latent topic of each dialogue. Based on this design, we also propose an effective optimization mechanism to make the representation (embedding) of latent topic has a better distinction for the next operation. 3) Finally, the cross-attention module is utilized to combine the utterances' features and the latent conversation topic information. The attention mechanism can effectively use topic information to supplement the representation of utterances and improve recognition performance. The model is tested on three popular datasets in dialogue emotion detection and is empirically demonstrated to outperform the state-of-the-art approaches. Meanwhile, to demonstrate the performance of our approach, we also build a long dialogue dataset. The average length of each conversation is over 50 utterances. The final experimental results also demonstrate the superior performance of our approach.
A Study on Smart Energy Management Framework Using Cloud Computing

This study focuses on investigating energy-aware resources and designing an energy-efficient cloud architecture. It aims to optimize the decentralization model and establish migration behavior to improve IT business operations using cloud computing. Several models were evaluated for performance metrics such as physical host use, power utilization, and service level agreement (SLA) misuse. These models include smart cloud optimization for resource configuration handling (SCORCH), V-MAN, decentralized energy-aware collaborative model (DEACM), efficient energy-aware resource management model (EEARMM), and energy and power-aware dynamic migration (EPADM). To handle resource configuration efficiently, the study employed V-MAN, DEACM, EEARMM, and EPADM within the framework of smart cloud optimization. Among these models, EPADM proved to be the most effective for resource utilization through dynamic VM migration. By leveraging EPADM, the cloud system can dynamically migrate virtual machines (VMs) to optimize resource allocation and energy consumption.
The Use of Asynchronous Learning Networks in Nutrition Education: Student Attitude, Experiences and Performance

In this study a change in teaching strategy to involve a greater emphasis on asynchronous learning networks (ALNs) was implemented and the views of students (n=51) to this change were evaluated through responses to an online questionnaire. In response to Likert-type questions the majority of students demonstrated a positive view of this new model. Sixty-one percent of students felt that other types of online material would benefit the learning process and 80 % would recommend this module to a friend. Students acknowledged that the use of ALN-supported learning made the material easierto understand (52%), the lecturer more accessible (66%) and enabled them to take a more active role in the learning process (55%). Though only 10% of students utilized the asynchronous newsgroup more than 5 times, 77% found reading the contributions of others useful. Contrary to this 76% preferred the more familiar lecturebased environment for subject delivery. In response to open-ended questions students’ views were more reserved and highlighted a range of problems such as inadequate infrastructure, unreliable computers, and poor access to the online material as well as resistance to a new teaching paradigm. Student performance was influenced by age and contribution to the newsgroup. Those who were younger had a lower grade (47.8 ? 15.8) than those who were older (52.0 ? 11.4). Students with higher grades (56.2 ? 10.3) contributed to the newsgroup while students with lower grades (45.7 ?12.5) did not. Based on these observations, it is apparent that students do appreciate the advantages of ALNsupported learning though for a shift toward this model to be effective problems of access and system failure must be resolved. Implications for future ALN-based modules are discussed.
Differentiating 3D textile composites: A novel field of application for Digital Volume Correlation

Digital Volume Correlation is an appealing technique for establishing a novel comparison (differentiating) tool based on full field measurements of structured materials such as 3D woven composites. This approach provides a quantifiable description of the weaving distortions on woven composites. It also offers applications ranging from the quantification of the influence of different weaving conditions, up to nondestructive testing of composite parts. The method is validated on real 3D woven composite samples revealing both “metric differences” (deformations of a textile with respect to reference one) and “topological differences” (such as the occurrence of missing yarns).
Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding

Heterogeneous Transfer Learning (HTL) aims to solve transfer learning problems where a source domain and a target domain are of heterogeneous types of features. Most existing HTL approaches either explicitly learn feature mappings between the heterogeneous domains or implicitly reconstruct heterogeneous cross-domain features based on matrix completion techniques. In this paper, we propose a new HTL method based on a deep matrix completion framework, where kernel embedding of distributions is trained in an adversarial manner for learning heterogeneous features across domains. We conduct extensive experiments on two different vision tasks to demonstrate the effectiveness of our proposed method compared with a number of baseline methods.
Adapting Translation Models for Transcript Disfluency Detection

Transcript disfluency detection (TDD) is an important component of the real-time speech translation system, which arouses more and more interests in recent years. This paper presents our study on adapting neural machine translation (NMT) models for TDD. We propose a general training framework for adapting NMT models to TDD task rapidly. In this framework, the main structure of the model is implemented similar to the NMT model. Additionally, several extended modules and training techniques which are independent of the NMT model are proposed to improve the performance, such as the constrained decoding, denoising autoencoder initialization and a TDD-specific training object. With the proposed training framework, we achieve significant improvement. However, it is too slow in decoding to be practical. To build a feasible and production-ready solution for TDD, we propose a fast non-autoregressive TDD model following the non-autoregressive NMT model emerged recently. Even we do not assume the specific architecture of the NMT model, we build our TDD model on the basis of Transformer, which is the state-of-the-art NMT model. We conduct extensive experiments on the publicly available set, Switchboard, and in-house Chinese set. Experimental results show that the proposed model significantly outperforms previous state-ofthe-art models.
Multi-Label Learning With Label Specific Features Using Correlation Information

To deal with the problem where each instance is associated with multiple labels, a lot of multi-label learning algorithms have been developed in recent years.Some approaches have been proposed to select label-specific features to utilize discriminate features for multi-label classification.Although label correlation has been considered in learning label-specific features, the critical correlation among instances was less taken into account.In this paper, we proposed a new approach called multi-label learning with label-specific features using correlation information (LSF-CI) to learn label-specific features for each label with the consideration of both correlation information in label space and correlation information in feature space.In the LSF-CI, the instance correlation in feature space is computed by a probabilistic neighborhood graph model, and label correlation in label space is computed by cosine similarity.For multi-label data, the LSF-CI has the capability to select Label-specific features for each label as well as classify an unseen instance into a set of relevant labels.To validate the effectiveness of LSF-CI, we conducted comprehensive experiments on eight multi-label datasets.The experimental results demonstrate that the LSF-CI is capable of selecting compact label-specific features, and achieving a competitive performance in comparison with the performances of the existing multi-label learning approaches.
A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection

Selecting the right features for further data analysis is important in the process of equipment anomaly detection, especially when the origin data source involves high dimensional data with a low value density. However, existing researches failed to capture the fact that the sensor data are usually correlated (e.g., duplicated deployed sensors), and the correlations would be broken when anomalies occur with happen to the monitored equipment. In this paper, we propose to capture such sensor data correlation changes to improve the performance of IoT (Internet of Things) equipment anomaly detection. In our feature selection method, we first cluster correlated sensors together to recognize the duplicated deployed sensors according to sensor data correlations, and we monitor the data correlation changes in real time to select the sensors with correlation changes as the representative features for anomaly detection. To that end, (1) we conducted curve alignment for the sensor clustering; (2) we discuss the appropriate window size for data correlation calculation; (3) and adopted MCFS (Multi-Cluster Feature Selection) into our method to adapt to the online feature selection scenario. According to the experiment evaluation derived from real IoT equipment, we prove that our method manages to reduce the false negative of IoT equipment anomaly detection of 30% with almost the same level of false positive.
Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data collected unobtrusively in home environments can be challenging, especially for vulnerable ageing population. Under that premise, we propose an activity recognition scheme for older people exploiting feature extraction and machine learning, along with heuristic computational solutions to address the challenges due to inconsistent measurements in non-standardized environments. In addition, we compare the customized pipeline with deep learning architectures, such as convolutional neural networks, applied to raw sensor data without any pre- or post-processing adjustments. The results demonstrate that the generalizable deep architectures can compensate for inconsistencies during data acquisition providing a valuable alternative.
The Relationship between Organizational Culture and Organizational Commitment

The main purpose of this research is to investigate the relationship between organizational culture and organizational commitment, in Information Technology (IT) companies in Jordan. A convenience sample was selected from employees working in 24 IT companies in Jordan. A total of 711 questionnaires were distributed among them; 371 were returned, and 342 were valid for statistical analysis, resulting in response rate of 52 percent. Linear regression analysis was also used to test the hypothesis. The results of the analysis indicated that there is a positive and significant relationship between organizational culture and organizational commitment. Based on the results, the research provides several recommendations. IT companies in Jordan should place emphasis on building better culture, in order to achieve higher levels of organizational commitment. The research also suggests that future research should take job satisfaction as a mediator variable of the relationship between organizational culture and organizational commitment.
Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent

Minhao Cheng, Wei Wei, Cho-Jui Hsieh. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Pattern Memory Surface (PMS) with Dynamic Wrinkles for Unclonable Anticounterfeiting

To avoid the losses caused by counterfeit commodity, documents and currency, various anticounterfeiting strategies have aroused an intensive interest and have been developed rapidly; however, obtaining unclonable anticounterfeiting technology that is sufficiently adaptable for industry-suitable authentication is still a challenge. Here, we report a facile yet effective anticounterfeiting strategy by utilizing pattern memory surface with NIR-driven dynamic wrinkles, which can be used as dynamic biomimetic fingerprints. The randomness and unpredictability of the wrinkles’ morphology enable them to be analogous to the minutiae of fingerprints. The dynamic nature of the fingerprint-like wrinkles endows them with hidden information to further improve security levels, and their NIR-responsiveness can help maintain a nearly identical topography during the cycles of erasure and regeneration, thus truly realizing unclonable anticounterfeiting. On the basis of these characteristics of NIR-driven dynamic wrinkles, such as simple preparation and easy readout, unique and unclonable identity tags were fabricated for antique authentication, which can be further combined with fluorescence patterns to achieve multiple anticounterfeiting technologies.
Using Retweets When Shaping Our Online Persona: Topic Modeling Approach

Theories of personal branding are built on the idea that each individual should be aware of the persona they present to the world. Nowadays, as social interactions are increasingly shifting to the online arena, users of social platforms are presented wit
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation

Deep neural networks are vulnerable to adversarial attacks. The literature is rich with algorithms that can easily craft successful adversarial examples. In contrast, the performance of defense techniques still lags behind. This paper proposes ME-Net, a defense method that leverages matrix estimation (ME). In ME-Net, images are preprocessed using two steps: first pixels are randomly dropped from the image; then, the image is reconstructed using ME. We show that this process destroys the adversarial structure of the noise, while re-enforcing the global structure in the original image. Since humans typically rely on such global structures in classifying images, the process makes the network mode compatible with human perception. We conduct comprehensive experiments on prevailing benchmarks such as MNIST, CIFAR-10, SVHN, and Tiny-ImageNet. Comparing ME-Net with state-of-the-art defense mechanisms shows that ME-Net consistently outperforms prior techniques, improving robustness against both black-box and white-box attacks.
ConvAI at SemEval-2019 Task 6: Offensive Language Identification and Categorization with Perspective and BERT

This paper presents the application of two strong baseline systems for toxicity detection and evaluates their performance in identifying and categorizing offensive language in social media.Perspective is an API, that serves multiple machine learning models for the improvement of conversations online, as well as a toxicity detection system, trained on a wide variety of comments from platforms across the Internet.BERT is a recently popular language representation model, fine tuned per task and achieving state of the art performance in multiple NLP tasks.Perspective performed better than BERT in detecting toxicity, but BERT was much better in categorizing the offensive type.Both baselines were ranked surprisingly high in the SEMEVAL-2019 OFFENSE-VAL competition, Perspective in detecting an offensive post (12th) and BERT in categorizing it (11th).The main contribution of this paper is the assessment of two strong baselines for the identification (Perspective) and the categorization (BERT) of offensive language with little or no additional training data.
What's inside the black-box?

Interpreting state-of-the-art machine learning algorithms can be difficult. For example, why does a complex ensemble predict a particular class? Existing approaches to interpretable machine learning tend to be either local in their explanations, apply only to a particular algorithm, or overly complex in their global explanations. In this work, we propose a global model extraction method which uses multi-objective genetic programming to construct accurate, simplistic and model-agnostic representations of complex black-box estimators. We found the resulting representations are far simpler than existing approaches while providing comparable reconstructive performance. This is demonstrated on a range of datasets, by approximating the knowledge of complex black-box models such as 200 layer neural networks and ensembles of 500 trees, with a single tree.