Reviewerly/exHarmony huggingface dataset:
Using Gaze-based Interaction to Alleviate Situational Mobility Impairment in Extended Reality

Significant advancements of eye-tracking technology in extended reality (XR) head-mounted displays have increased the interest in gaze-based interactions. The benefits of gaze interaction proved that it could be a suitable alternative for hand-based interactions when users face situations where they must maintain their position due to mobility impairment. This study aims to assess the user experience of the gaze-based interaction, compared to hand-based interaction, in two movement conditions of static and dynamic. Twenty-four participants took part in this study, and their experience was evaluated in terms of perceived workload, usability, and performance. The results show that gaze-based interactions significantly outperform the hand-based interaction in terms of perceived workload and usability in case of limited mobility. Also, the user performance is significantly higher in gaze-based modes under situational impairment. The findings of this study can be used for designing XR interfaces considering the situation in which the task is performed.
Neural Collaborative Filtering for Chinese Movies Based on Aspect-Aware Implicit Interactions

Aspect information mining from user comments has become an important means to improve the performance of recommendation systems (RSs). This is because aspect information in comments is fine-grained and tends to reflect the interactions and preferences of users over items in multiple dimensions. These interactions are different from ratings, which are often explicit and linear. Most current RSs based on aspect information learn the contribution of explicit interactions of aspects in a linear manner, while ignoring the implicit features and non-linear interactions of aspects. Since Chinese grammar is greatly different with English grammar, there are few recommendation models based on Chinese movie comment aspects. In this work, we propose an architecture, named aspect-based neural collaborative filtering (ANCF), to extract comment aspect terms based on rules formulated in Chinese dependency parsing. The proposed ANCF integrates a generalized tensor factorization and a tensorized multi-layer perceptrons into the neural network to capture user-item-aspect interactions in a mixed linear and nonlinear way. The aspect potential interaction vector and the actual interaction vector are layered and fused into tensor processing, which can reduce the tensor sparsity and solve the cold start problem of collaborative filtering to a certain extent. Performance results show that the proposed model outperforms some of the traditional ones in terms of recommendation accuracy and effectiveness.
Divide-and-Conquer Completion Network for Video Inpainting

Video inpainting aims to utilize plausible contents to complete missing regions in the video. For different components, the reconstruction targets of missing regions are different,e.g.,smoothness preserving for flat regions, sharpening for edges and textures. Typically, existing methods treat the missing regions as a whole and holistically train the model by optimizing homogenous pixel-wise losses (e.g.,MSE). In this way, the trained models will be easily dominated and determined by flat regions, failing to infer realistic details (edges and textures) that are difficult to reconstruct but necessary for practical applications. In this paper, we propose a divide-and-conquer completion network for video inpainting. In particular, our network first uses discrete wavelet transform to decompose the deep features into low-frequency components containing structural information (flat regions) and high-frequency components involving detailed texture information. Thereafter, we feed these components into different branches and adopt the temporal attention feature aggregation module to generate missing contents, separately. It hence can realize flexible supervision utilizing the intermediate supervision learning strategy for each component, which has not been noticed and explored by current state-of-the-art video inpainting methods. Furthermore, we adopt a gradient-weighted reconstruction loss to supervise the completed frame reconstruction process, which can use the gradients in all directions of the video frame to emphasize the difficultly reconstructed textures regions, making the model pay more attention to the complex detailed textures. Extensive experiments validate the superior performance of our divide-and-conquer model over state-of-the-art baselines in both quantitative and qualitative evaluations.
RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition

The visual relationship recognition (VRR) task aims at understanding the pairwise visual relationships between interacting objects in an image. These relationships typically have a long-tail distribution due to their compositional nature. This problem gets more severe when the vocabulary becomes large, rendering this task very challenging. This paper shows that modeling an effective message-passing flow through an attention mechanism can be critical to tackling the compositionality and long-tail challenges in VRR. The method, called RelTransformer, represents each image as a fully-connected scene graph and restructures the whole scene into the relation-triplet and global-scene contexts. It directly passes the message from each element in the relation-triplet and global-scene contexts to the target relation via self-attention. We also design a learnable memory to augment the long-tail relation representation learning. Through extensive experiments, we find that our model generalizes well on many VRR benchmarks. Our model outperforms the best-performing models on two large-scale long-tail VRR benchmarks, VG8K-LT (+2.0% overall acc) and GQA-LT (+26.0% overall acc), both having a highly skewed distribution towards the tail. It also achieves strong results on the VG200 relation detection task. Our code is available at https://github.com/Vision-CAIR/ReITransformer.
Marine Data Sharing: Challenges, Technology Drivers and Quality Attributes

Context: Many companies have been adopting data-driven applications in which products and services are centered around data analysis to approach new segments of the marketplace. Data ecosystems rise from data sharing among organizations premeditatedly. However, this migration to this new data sharing paradigm has not come that far in the marine domain. Nevertheless, better utilizing the ocean data might be crucial for humankind in the future, for food production, and minerals, to ensure the ocean’s health. Research goal: We investigate the state-of-the-art regarding data sharing in the marine domain with a focus on aspects that impact the speed of establishing a data ecosystem for the ocean. Methodology: We conducted an exploratory case study based on focus groups and workshops to understand the sharing of data in this context. Results: We identified main challenges of current systems that need to be addressed with respect to data sharing. Additionally, aspects related to the establishment of a data ecosystem were elicited and analyzed in terms of benefits, conflicts, and solutions.
A Systematic Approach for Interfacing Component-Based Software with an Active Automata Learning Tool

Applying Model-Driven Engineering can improve development efficiency. But gaining such benefits for legacy software requires models, and creating them manually is both laborious and error prone. Active automata learning has the potential to make it cost-effective, but practitioners face practical challenges applying it to software components of industrial cyber-physical systems. To overcome these challenges, we present a framework to learn the behavior of component-based software with a client/server architecture, focusing on interfacing isolated component code with an active learning tool. An essential part of the framework is an interfacing protocol that provides a structured way of handling the (a)synchronous communications between the component and learning tool. Our main contribution is the systematic derivation of such interfacing protocols for component-based software, which we demonstrate on the software architecture of ASML, a leading company in developing lithography machines. Through several practical case studies we show that our semi-automatic approach enables setting up a learning environment to learn component behaviors within hours. The protocol’s responsibilities and the way it handles different communication types apply to component-based software in general. Our framework could thus be adapted for companies with similar software architectures.
Class-Agnostic Object Counting Robust to Intraclass Diversity

Most previous works on object counting are limited to pre-defined categories. In this paper, we focus on class-agnostic counting, i.e., counting object instances in an image by simply specifying a few exemplar boxes of interest. We start with an analysis on intraclass diversity and point out three factors: color, shape and scale diversity seriously hurts counting performance. Motivated by this analysis, we propose a new counter robust to high intraclass diversity, for which we propose two effective modules: Exemplar Feature Augmentation (EFA) and Edge Matching (EM). Aiming to handle diversity from all aspects, EFA generates a large variety of exemplars in the feature space based on the provided exemplars. Additionally, the edge matching branch focuses on the more reliable cue of shape, making our counter more robust to color variations. Experimental results on standard benchmarks show that our Robust Class-Agnostic Counter (RCAC) achieves state-of-the-art performance. The code is publicly available at https://github.com/Yankeegsj/RCAC .
FoggyStereo: Stereo Matching with Fog Volume Representation

Stereo matching in foggy scenes is challenging as the scattering effect of fog blurs the image and makes the matching ambiguous. Prior methods deem the fog as noise and discard it before matching. Different from them, we propose to explore depth hints from fog and improve stereo matching via these hints. The exploration of depth hints is designed from the perspective of rendering. The rendering is conducted by reversing the atmospheric scattering process and removing the fog within a selected depth range. The quality of the rendered image reflects the correctness of the selected depth, as the closer it is to the real depth, the clearer the rendered image is. We introduce a fog volume representation to collect these depth hints from the fog. We construct the fog volume by stacking images rendered with depths computed from disparity candidates that are also used to build the cost volume. We fuse the fog volume with cost volume to rectify the ambiguous matching caused by fog. Experiments show that our fog volume representation significantly promotes the SOTA result on foggy scenes by 10% ~ 30% while maintaining a comparable performance in clear scenes.
Boosting Monocular 3D Object Detection With Object-Centric Auxiliary Depth Supervision

Recent advances in monocular 3D detection leverage a depth estimation network explicitly as an intermediate stage of the 3D detection network. Depth map approaches yield more accurate depth to objects than other methods thanks to the depth estimation network trained on a large-scale dataset. However, depth map approaches can be limited by the accuracy of the depth map, and sequentially using two separated networks for depth estimation and 3D detection significantly increases computation cost and inference time. In this work, we propose a method to boost the RGB image-based 3D detector by jointly training the detection network with a depth prediction loss analogous to the depth estimation task. In this way, our 3D detection network can be supervised by more depth supervision from raw LiDAR points, which does not require any human annotation cost, to estimate accurate depth without explicitly predicting the depth map. Our novel object-centric depth prediction loss focuses on depth around foreground objects, which is important for 3D object detection, to leverage pixel-wise depth supervision in an object-centric manner. Our depth regression model is further trained to predict the uncertainty of depth to represent the 3D confidence of objects. To effectively train the 3D detector with raw LiDAR points and to enable end-to-end training, we revisit the regression target of 3D objects and design a network architecture. Extensive experiments on KITTI and nuScenes benchmarks show that our method can significantly boost the monocular image-based 3D detector to outperform depth map approaches while maintaining the real-time inference speed.
A Novel Integrated Eye-Tracking System with Stereo Stimuli for 3D Gaze Estimation

Eye-tracking technology is increasingly used in applications such as 3D displays and human-computer interaction (HCI). However, most current eye trackers focus on 2D point-of-gaze (PoG) estimation and cannot provide accurate gaze depth. Concerning future applications such as HCI with 3D displays, we propose an integrated binocular eye-tracking device with stereo stimuli to provide highly accurate 3D PoG estimation. In our device, the 3D stereo imaging system can provide users with a friendly and immersive 3D visual experience without wearing any accessories. The eye-capturing system can directly record the users’ eye movements under 3D stimuli without disturbance, which can realize front views of eye images while ensuring the user’s wide field of view (FOV). A regression-based 3D eye-tracking model is built based on collected eye movement data under stereo stimuli. Our model estimates users’ 2D gaze with features defined by eye region landmarks and further estimates 3D gaze with a multi-source feature set constructed by comprehensive eye movement features and disparity features from stereo stimuli. Two test stereo scenes with different depths of field are designed to verify the model’s effectiveness. Experimental results of four-fold cross-validation show that the average error for 2D gaze estimation was 0.65°. For 3D gaze estimation, the average errors are 1.69 cm over the workspace volume 50 × 30 × 75 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and 0.14 m over the workspace volume 2.4 × 4.0 × 7.9 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Two preliminary validation experiments also demonstrate that our system has broad application prospects, such as 3D vision technologies and medical applications related to neurological diseases.
Balancing Between Forgetting and Acquisition in Incremental Subpopulation Learning

The subpopulation shifting challenge, known as some subpopulations of a category that are not seen during training, severely limits the classification performance of the state-of-the-art convolutional neural networks. Thus, to mitigate this practical issue, we explore incremental subpopulation learning (ISL) to adapt the original model via incrementally learning the unseen subpopulations without retaining the seen population data. However, striking a great balance between subpopulation learning and seen population forgetting is the main challenge in ISL but is not well studied by existing approaches. These incremental learners simply use a pre-defined and fixed hyperparameter to balance the learning objective and forgetting regularization, but their learning is usually biased towards either side in the long run. In this paper, we propose a novel two-stage learning scheme to explicitly disentangle the acquisition and forgetting for achieving a better balance between subpopulation learning and seen population forgetting: in the first “gain-acquisition” stage, we progressively learn a new classifier based on the margin-enforce loss, which enforces the hard samples and population to have a larger weight for classifier updating and avoid uniformly updating all the population; in the second “counter-forgetting” stage, we search for the proper combination of the new and old classifiers by optimizing a novel objective based on proxies of forgetting and acquisition. We benchmark the representative and state-of-the-art non-exemplar-based incremental learning methods on a large-scale subpopulation shifting dataset for the first time. Under almost all the challenging ISL protocols, we significantly outperform other methods by a large margin, demonstrating our superiority to alleviate the subpopulation shifting problem (Code is released in https://github.com/wuyujack/ISL ).
Application of Intelligent Fuzzy Decision Tree Algorithm in English Machine Translation

With the development of artificial intelligence technology and the launch of translation engine, fuzzy machine translation has attracted more and more attention because of its high construction efficiency and translation quality. Using the intelligent fuzzy decision tree algorithm, this paper puts forward that in the network translation teaching, fuzzy machine translation can not only help students carry out basic data retrieval, such as vocabulary and professional terms, but also provide many auxiliary functions such as reference translation and translation skills learning. It has a broad application prospect in the construction of professional translation teaching.
Describing Digital IT Consulting Services: The DITCOS Ontology Proposal and its Evaluation

The digital transformation of the consulting sector has recently gained momentum due to the Covid-19 pandemic. In particular, the areas of financial and insurance services are receiving strong attention from digitization researchers. However, the field of IT consulting itself evaded the attention of scientists. Moreover, despite the heavy use of digital technologies such as on-line conferencing and digital collaboration, the actual consulting process itself has hardly changed. This indicates the weaknesses of IT consulting as a field in establishing true digital business models and consulting service delivery processes. The present paper makes a twofold contribution to the domain of digitization of the IT consulting domain. First, it introduces the DITCOS-O ontology for semantic description of digital IT consulting services. Second, the DITCOS-DN description notation is derived from DITCOS-O, as a new approach to ontology-based definition of domain specific languages. Then, DITCOS-DN is used to describe different real-world services. The result is the analysis of the coverage of real-world service and the comprehensibility of their digitally described service model representations with the help of IT consulting practitioners.
Fraud Detection in Credit Card Data using Unsupervised &amp; Supervised Machine Learning-Based Algorithms

Online voting methods safeguard your vote's integrity by stopping people from voting more than once. This digital technology eliminates the necessity for physical gatherings and the use of paper ballots for voting For example, by email or an unprotected kind of online survey software. When referring to an electronic voting system, you could hear the terms "online election system" or "online e voting system" used interchangeably. Your organization's ability to collect and monitor real-time input from its members is the common denominator in this case. Keep reading to find out all you need to know about voting online.
Research on clustering algorithms in data mining

Clustering is a necessary data pre-processing method in data mining research, which aims to obtain the intrinsic distribution structure of valuable datasets from unlabeled datasets and thus simplify the description of the datasets. Data mining technology can discover potential and valuable knowledge from a large amount of data, giving a new meaning to the massive amount of data accumulated by people in the information age—new meaning. With the rapid development of data mining technology, as an essential part of it, grid clustering technology has been widely used in data analysis images. As a mainstream data mining method, cluster analysis facilitates data processing in machine learning by implementing clustering algorithms to analyze data and the proper operation of machines. Through the continuous experiments and proofs of previous generations, the algorithms on cluster analysis are becoming more and more mature. The text lists an overview of the evolution of high-quality clustering algorithms to understand the applications better and explore multiple clustering approaches. This study reviews papers that refer to 16 more well-known and research-discussed developmental academic articles to understand their applications on clustering and explore the development of clustering in various fields. This systematic review aims to summarize the cluster analysis and grouping techniques used to date and make recommendations for future developments.
Privacy-Preserving Multi-Source Image Retrieval in Edge Computing

Users outsource images to edge servers physically closer to their location for real time applications because of the low latency and low transmission overhead. Outsourcing to these edge servers however, increases the risks to data privacy. Almost all existing privacy preserving image retrieval schemes utilize a single cloud server to execute retrieval tasks and provide centralized image retrieval but at high computational costs, thus are not suitable for the distributed edge environments with limited computing resources. We propose a lightweight privacy-preserving multi-source image retrieval scheme adapted specifically for the distributed edge environment. We apply high efficiency orthogonal decomposition and learning with errors (LWE) strategy to encrypt image features and construct cipher indexes and trapdoors, guaranteeing the security of the data, while reducing computational costs. The orthogonality of data ensures that the accuracy of retrieval results is not compromised by the random numbers used in the scheme. In addition, the proxy re-encryption technology is adopted to support the retrieval of multi-source images encrypted by unique data owners with different keys. A detailed performance analysis and comprehensive experiments demonstrate that our scheme guarantees data security with very high retrieval accuracy and a low computational burden, consistent with the demands of edge environments.
What is There About DevOps Assessment? A Systematic Mapping

DevOps has been established as a framework used by software development companies seeking to set mechanisms to automate their development processes. Consequently, over the last decade, many companies have adopted DevOps to support their project’s development process and perform continuous improvement tasks to ensure that it is applied correctly. To achieve this, companies are looking for solutions that allow them to evaluate the degree of implementation of DevOps in their internal processes. In this sense, the objective of this study focuses on identifying, through a systematic mapping of the literature, the mechanisms used to assess DevOps in software development companies. According to the above, the current state of knowledge related to the proposal of processes, models, techniques, tools, and methodological guides is presented to conduct the DevOps assessment. As a result, it is noted that there are multiple methodological solutions that seek to assess DevOps; however, a high degree of heterogeneity was evidenced in the identified solutions, resulting in the need to establish a clear framework that serves as the basis for proposing a generic, structured, and unambiguous DevOps assessment model applicable to software companies.