Reviewerly/exHarmony huggingface dataset:
Self-Regulated Feature Learning via Teacher-free Feature Distillation

Knowledge distillation conditioned on intermediate feature representations always leads to significant performance improvements. Conventional feature distillation framework demands extra selecting/training budgets of teachers and complex transformations to align the features between teacher-student models. To address the problem, we analyze teacher roles in feature distillation and have an intriguing observation: additional teacher architectures are not always necessary. Then we propose Tf-FD, a simple yet effective Teacher- $$\boldsymbol{f}$$ ree Feature Distillation framework, reusing channel-wise and layer-wise meaningful features within the student to provide teacher-like knowledge without an additional model. In particular, our framework is subdivided into intra-layer and inter-layer distillation. The intra-layer Tf-FD performs feature salience ranking and transfers the knowledge from salient feature to redundant feature within the same layer. For inter-layer Tf-FD, we deal with distilling high-level semantic knowledge embedded in the deeper layer representations to guide the training of shallow layers. Benefiting from the small gap between these self-features, Tf-FD simply needs to optimize extra feature mimicking losses without complex transformations. Furthermore, we provide insightful discussions to shed light on Tf-FD from feature regularization perspectives. Our experiments conducted on classification and object detection tasks demonstrate that our technique achieves state-of-the-art results on different models with fast training speeds. Code is available at https://lilujunai.github.io/Teacher-free-Distillation/ .
Design of Oral English Training System Based on Big Data Content Recommendation Algorithm

Collaborative filtering is the most widely used and successful recommendation technology in recommendation systems. In view of the problem that the effect of collaborative filtering technology depends on an accurate similarity measurement method and its poor scalability can not deal with big data, in the information age of computer development, all undertakings are developing rapidly. English has long been an important tool for people to communicate with each other, but due to the limitation of learning mode and environment, the level of Chinese oral English is generally low. The cloud computation-based oral English learning system is designed. Users can learn oral English knowledge, simulate oral English testing and establish exclusive dictionaries through mobile terminals. According to the specific learning situation of Chinese English learners, a flexible and expandable virtual interactive platform is constructed through virtual technology and artificial intelligence, and multi-dimensional virtual scenes and intelligent AI roles are designed to realize the communication and learning of AI roles in different scenarios and improve learners' oral ability and language sense knowledge. However, compared with the traditional self-help learning method, there are huge changes. Our modern English self-help learning has basically got rid of the limitation of paper books, and most of it is carried out through the computer network.
A Conceptual Framework for Determining Metaverse Adoption in Vietnam IT Enterprises

Metaverse is an extension of synchronous communication that includes an efficient number of users to share different experiences. The study aims to investigate the acceptance of Metaverse-related projects by employees of technology companies in Vietnam, thereby finding out the characteristics that hinder the implementation of Metaverse. Moreover, the study also gives recommendations to develop Metaverse projects in Vietnam, especially in Technology companies, relying on 5 variables Perceived Compatibility, Perceived Trialability, Perceived Ease of Use, Perceived Usefulness, and Intention to Use. Authors already gained roughly 312 responses based on block simple random sampling. This study was conducted based on a survey of IT enterprises in Vietnam, thereby assessing the characteristics affecting the decision to participate in or implement Metaverse projects of enterprises.
BRec the Bank: Context-aware Self-attentive Encoder for Banking Products Recommendation

Credit cards, deposits, loans, pension funds, mutual funds-which of these products are relevant to a bank's clients, and at what time in their banking journey? We propose a modeling framework for item recommendation using a multi-head self-attentive encoder and a novel sequential input data representation accounting for the temporal context of both item ownership and user metadata. We evaluate our model on a large public dataset from Bank Santander, and achieve top-1 and top-5 precision of 98.9% and 40.2%, respectively, thereby improving upon a number of state-of-the-art recommendation models also based on self-attention. We find evidence that our model is effective at learning, from the temporal context of product acquisitions, user behavior that is more informative to recommendations than a bag of products or static user metadata. Further, we consider serendipity, novelty and coverage to exhibit a trade-off with recommendation relevance. We also analyze ethical considerations related to the banking products recommendation task. The continuous user representation learned by our model may inform decisions far more impactful than user-level product recommendations themselves.
Information Security Management Maturity Models

Several maturity models for organizations, their subsystems and processes, for example, information security (IS) incident management are known. Continuing our earlier analysis of the most common maturity models (MMs), models and approaches to assessing the maturity level (ML) of organization's IS management (ISM) maturity, namely The Bank of Russia's ISM MM, ISMS Maturity Capability Model, NIST's IS MM, Open Group's ISM MM, Gartner's Security and Risk Management ML assessment, IS Risk Management Process MM, Security Incident Management Models, and IS Monitoring MM, are selected for consideration within the framework of this research.
Analysis of Underwater Image Processing Methods for Annotation in Deep Learning Based Fish Detection

With the advent of deep-learning (DL) techniques, image annotation has become a fundamental part of the research process. In the case of underwater image annotation, the human in charge of the task is faced whith the inherent quality problems of this kind of images. A large number of underwater image enhancement (UIE) methods have been developed aimed at improving the colors and contrast of these images. However, no attention has been paid to the specific problem of image annotation. In this case the global image quality of the processed image is less important than the fact that the objects to be annotated stand out and that their contours are easy to delineate. In this paper we evaluate seven state-of-the-art UIE techniques and rank them, through a subjective approach, according to their utility for the annotation process. The conclusion of our study is that, in general, the model-free Multiscale Retinex algorithm is preferred over more complex techniques that try to model the formation of the underwater images.
White Blood Cells Image Classification Based on Radiomics and Deep Learning

White blood cell (WBC) identification performance is highly correlated with the quality of the extracted features, with radiomic features having greater resolution and more detailed information and deep features having more robust semantic information. This research integrates these two aspects to creatively suggest a WBC classification model based on radiomics and deep learning. This research suggests a brand-new method for extracting radiomic features from WBC images as well as a dual-branch feature fusion network RCTNet based on CNN and Transformer for extracting deep features. The radiomic feature extraction method not only has a simple segmentation algorithm but also solves the problem of cell adhesion, can obtain higher quality shape, color, and texture features without segmenting intact cells, and is more generalizable. RCTNet can extract more critical and recognizable deep features from WBC nuclei, avoiding the influence of too much redundant and invalid information on the results, and has better performance than several existing CNN models. We compared the classification outcomes based on radiomics with those based on deep learning to confirm the efficacy of the WBC classification model based on radiomics and deep learning suggested in this research. The experimental results demonstrated that combining radiomic features and deep features significantly improved the classification accuracy, with an AUC exceeding 0.9995, accuracy, sensitivity, specificity, precision, and F1-score reaching 0.9880, 0.9823, 0.9883, 0.9968, and 0.9881, respectively. The model has significant research significance in clinical applications and aids physicians in improving diagnosis and screening for diseases of WBCs.
Multi-task Active Learning for Pre-trained Transformer-based Models

Abstract Multi-task learning, in which several tasks are jointly learned by a single model, allows NLP models to share information from multiple annotations and may facilitate better predictions when the tasks are inter-related. This technique, however, requires annotating the same text with multiple annotation schemes, which may be costly and laborious. Active learning (AL) has been demonstrated to optimize annotation processes by iteratively selecting unlabeled examples whose annotation is most valuable for the NLP model. Yet, multi-task active learning (MT-AL) has not been applied to state-of-the-art pre-trained Transformer-based NLP models. This paper aims to close this gap. We explore various multi-task selection criteria in three realistic multi-task scenarios, reflecting different relations between the participating tasks, and demonstrate the effectiveness of multi-task compared to single-task selection. Our results suggest that MT-AL can be effectively used in order to minimize annotation efforts for multi-task NLP models.1
HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Tracking

Low-light environments have posed a formidable challenge for robust unmanned aerial vehicle (UAV) tracking even with state-of-the-art (SOTA) trackers since the poten-tial image features are hard to extract under adverse light conditions. Besides, due to the low visibility, accurate online selection of the object also becomes extremely difficult for human monitors to initialize UAV tracking in ground con-trol stations. To solve these problems, this work proposes a novel enhancer, i.e., HighlightNet, to light up potential objects for both human operators and UAV trackers. By employing Transformer, HighlightNet can adjust enhancement parameters according to global features and is thus adaptive for the illumination variation. Pixel-level range mask is introduced to make HighlightNet more focused on the enhancement of the tracking object and regions without light sources. Furthermore, a soft truncation mechanism is built to prevent background noise from being mistaken for crucial features. Evaluations on image enhancement benchmarks demonstrate HighlightNet has advantages in facilitating human perception. Experiments on the public UAVDark135 benchmark show that HightlightNet is more suitable for UAV tracking tasks than other state-of-the-art (SOTA) low-light enhancers. In addition, real-world tests on a typical UAV platform verify HightlightNet's practicability and efficiency in nighttime aerial tracking-related applications. The code and demo videos are available at https://github.com/vision4robotics/HighlightNet.
Studying Bias in GANs Through the Lens of Race

In this work, we study how the performance and evaluation of generative image models are impacted by the racial composition of their training datasets. By examining and controlling the racial distributions in various training datasets, we are able to observe the impacts of different training distributions on generated image quality and the racial distributions of the generated images. Our results show that the racial compositions of generated images successfully preserve that of the training data. However, we observe that truncation, a technique used to generate higher quality images during inference, exacerbates racial imbalances in the data. Lastly, when examining the relationship between image quality and race, we find that the highest perceived visual quality images of a given race come from a distribution where that race is well-represented, and that annotators consistently prefer generated images of white people over those of Black people.
Adding Context to Source Code Representations for Deep Learning

Deep learning models have been successfully applied to a variety of software engineering tasks, such as code classification, summarisation, and bug and vulnerability detection. In order to apply deep learning to these tasks, source code needs to be represented in a format that is suitable for input into the deep learning model. Most approaches to representing source code, such as tokens, abstract syntax trees (ASTs), data flow graphs (DFGs), and control flow graphs (CFGs) only focus on the code itself and do not take into account additional context that could be useful for deep learning models. In this paper, we argue that it is beneficial for deep learning models to have access to additional contextual information about the code being analysed. We present preliminary evidence that encoding context from the call hierarchy along with information from the code itself can improve the performance of a state-of-the-art deep learning model for two software engineering tasks. We outline our research agenda for adding further contextual information to source code representations for deep learning.
Event-guided Deblurring of Unknown Exposure Time Videos

Motion deblurring is a highly ill-posed problem due to the loss of motion information in the blur degradation process. Since event cameras can capture apparent motion with a high temporal resolution, several attempts have explored the potential of events for guiding deblurring. These methods generally assume that the exposure time is the same as the reciprocal of the video frame rate. However, this is not true in real situations, and the exposure time might be unknown and dynamically varies depending on the video shooting environment (e.g., illumination condition). In this paper, we address the event-guided motion deblurring assuming dynamically variable unknown exposure time of the frame-based camera. To this end, we first derive a new formulation for event-guided motion deblurring by considering the exposure and readout time in the video frame acquisition process. We then propose a novel end-to-end learning framework for event-guided motion deblurring. In particular, we design a novel Exposure Time-based Event Selection (ETES) module to selectively use event features by estimating the cross-modal correlation between the features from blurred frames and the events. Moreover, we propose a feature fusion module to fuse the selected features from events and blur frames effectively. We conduct extensive experiments on various datasets and demonstrate that our method achieves state-of-the-art performance. Our project code and dataset are available at: https://intelpro.github.io/UEVD/
Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems

When machine learning (ML) models are supplied with data outside their training distribution, they are more likely to make inaccurate predictions; in a cyber-physical system (CPS), this could lead to catastrophic system failure. To mitigate this risk, an out-of-distribution (OOD) detector can run in parallel with an ML model and flag inputs that could lead to undesirable outcomes. Although OOD detectors have been well studied in terms of accuracy, there has been less focus on deployment to resource constrained CPSs. In this study, a design methodology is proposed to tune deep OOD detectors to meet the accuracy and response time requirements of embedded applications. The methodology uses genetic algorithms to optimize the detector’s preprocessing pipeline and selects a quantization method that balances robustness and response time. It also identifies several candidate task graphs under the Robot Operating System (ROS) for deployment of the selected design. The methodology is demonstrated on two variational autoencoder based OOD detectors from the literature on two embedded platforms. Insights into the trade-offs that occur during the design process are provided, and it is shown that this design methodology can lead to a drastic reduction in response time in relation to an unoptimized OOD detector while maintaining comparable accuracy.
Improving generalizability of ML-enabled software through domain specification

While the conventional software components implement pre-defined specifications, Machine Learning (ML)-enabled Software Components (MLSC) learn the domain specifications from the training samples. Thus, the MLSC's data-driven and inductive reasoning becomes highly reliant on the quality of the training dataset, which are often arbitrarily collected in ad hoc manners. The random collection of samples leads to a significant gap between the actual specifications of a real-world concept, and the picture that a dataset represents of the concept, reducing MLSC generalizability, particularly in perceptual tasks where understanding the environment is an important factor of accurate prediction.
A Contract-Based, Decentralized and Privacy-Preserving Computation Scheme of Evaluation Indices of Shared Energy Storage Market for Renewable Energy Accommodation

With the promotion of shared energy storage (SES) business model, the evaluation indices of SES market can help the market entities understand the market states to adjust their market strategies in time. Considering that some basic data will be owned by the market entities, the computation of evaluation indices may require shared data from multiple parties. However, for the sake of privacy, the market entities may be reluctant to share their data. To address the above issues, a novel computation scheme of evaluation indices of SES market in the renewable energy accommodation scenario is proposed based on smart contracts with the key advantages of decentralization and data privacy protection. Firstly, a power blockchain-based information disclosure framework of SES market is presented to confirm the data ownership of the market entities. Moreover, a decentralized computation scheme combined with smart contracts and fully homomorphic encryption is proposed to compute the evaluation indices of SES market without exposing the original values of the shared data, preserving the privacy of data sharers, i.e., the market entities of SES market. Finally, case studies on the actual data from an SES trading pilot project in Qinghai, China, are performed to verify the effectiveness and performance of the proposed scheme based on a Hyperledger Fabric-based simulation system of power blockchain. The results show that the evaluation indices of SES market can be accurately computed on the power blockchain without privacy exposure risks.
CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations

Multimodal sentiment analysis has become an increasingly popular research area as the demand for multimodal online content is growing. For multimodal sentiment analysis, words can have different meanings depending on the linguistic context and non-verbal information, so it is crucial to understand the meaning of the words accordingly. In addition, the word meanings should be interpreted within the whole utterance context that includes nonverbal information. In this paper, we present a Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations (CMSBERT-CLR), which incorporates the whole context's non-verbal and verbal information and aligns modalities more effectively through contrastive learning. First, we introduce a Context-driven Modality Shifting (CMS) to incorporate the non-verbal and verbal information within the whole context of the sentence utterance. Then, for improving the alignment of different modalities within a common embedding space, we apply contrastive learning. Furthermore, we use an exponential moving average parameter and label smoothing as optimization strategies, which can make the convergence of the network more stable and increase the flexibility of the alignment. In our experiments, we demonstrate that our approach achieves state-of-the-art results.
Ready for Industrial Use? A User Study of Spatial Augmented Reality in Industrial Assembly

Spatial augmented reality (SAR) allows embedding instructions in the operator's field of view in manufacturing processes while hands-free working is possible. In this paper, we present the results of a field study in manufacturing to evaluate on the industrial readiness of SAR. We compared a worker assistance system via a tablet with a spatial augmented reality projection in a user study with skilled workers and students when assembling and wiring an industrial control panel. Human factors and process indicators are evaluated, qualitative feedback is summarized, and potential improvements are discussed.
Evaluating swift-to-kotlin and kotlin-to-swift transpilers

Unlike most popular mobile cross-platform development frameworks, transpilers promise maintainable code bases that are independent of the continued life of the development tools used. As more and more transpiler projects using the native programming languages Kotlin (Android) and Swift (iOS) were presented in recent years, this paper provides an overview of the language coverage of three representative transpilers, Gryphon (Swift-to-Kotlin), Kotlift (Kotlin-to-Swift), and SequalsK (both directions). For the test cases based on the overview chapters of the Swift and Kotlin documentation, good results were obtained in terms of functionality and readability of the output code for Gryphon and SequalsK. Although some shortcomings are visible in all transpilers, Kotlift is classified as a less mature project.
Inspect4py

This work presents inspect4py, a static code analysis framework designed to automatically extract the main features, metadata and documentation of Python code repositories. Given an input folder with code, inspect4py uses abstract syntax trees and state of the art tools to find all functions, classes, tests, documentation, call graphs, module dependencies and control flows within all code files in that repository. Using these findings, inspect4py infers different ways of invoking a software component. We have evaluated our framework on 95 annotated repositories, obtaining promising results for software type classification (over 95% F1-score). With inspect4py, we aim to ease the understandability and adoption of software repositories by other researchers and developers.