Reviewerly/exHarmony huggingface dataset:
A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication

Datasets are central to training machine learning (ML) models. The ML community has recently made significant improvements to data stewardship and documentation practices across the model development life cycle. However, the act of deprecating, or deleting, datasets has been largely overlooked, and there are currently no standardized approaches for structuring this stage of the dataset life cycle. In this paper, we study the practice of dataset deprecation in ML, identify several cases of datasets that continued to circulate despite having been deprecated, and describe the different technical, legal, ethical, and organizational issues raised by such continuations. We then propose a Dataset Deprecation Framework that includes considerations of risk, mitigation of impact, appeal mechanisms, timeline, post-deprecation protocols, and publication checks that can be adapted and implemented by the ML community. Finally, we propose creating a centralized, sustainable repository system for archiving datasets, tracking dataset modifications or deprecations, and facilitating practices of care and stewardship that can be integrated into research and publication processes.
Identifying Cognitive and Creative Support Needs for Remote Scientific Collaboration using VR: Practices, Affordances, and Design Implications

Remote scientific collaborations have been pivotal in generating scientific discoveries and breakthroughs that accelerate research in many fields. Emerging VR applications for remote work, which utilize commercially available head-mounted displays (HMDs), offer the promise to enhance collaboration, through spatial and embodied experiences. However, there is little evidence on how professionals in general, and scientists in particular, could use existing commercial VR applications to support their cognitive and creative collaborative processes while exploring real-world data as part of day-to-day collaborative work. In this paper, we present findings from an empirical study with 14 coral reef scientists, examining how they chose to utilize available resources in existing virtual environments for their ongoing data-driven collaborative research. We shed light on scientists’ data organization practices, identify affordances unique to VR for supporting cognition in a collaborative setting, and highlight design requirements for supporting cognitive and creative collaboration processes in future tools.
Information-based massive data retrieval method based on distributed decision tree algorithm

Based on the distributed decision tree algorithm, this paper first proposes a method of vertically partitioning datasets and synchronously updating the hash table to establish an information-based mass data retrieval method in a heterogeneous distributed environment, as well as using interval segmentation and interval filtering technologies for improved algorithm of distributed decision tree. The distributed decision tree algorithm uses the attribute histogram data structure to merge the category list into each attribute list, reducing the amount of data that needs to reside in the memory. Second, we adopt the strategy of vertically dividing the dataset and synchronously updating the hash table, select the hash table entries that can be used to update according to the minimum Gini value, modify the corresponding entries and use the hash table to record and control each sub-site. In the case of node splitting, it has a high accuracy rate. In addition, for classification problems that meet monotonic constraints in a distributed environment, this paper will extend the idea of building a monotonic decision tree in a distributed environment, supplementing the distributed decision tree algorithm, adding a modification rule and modifying the generated nonmonotonic decision tree to monotonicity. In order to solve the high load problem of the privacy-protected data stream classification mining algorithm under a single node, a Storm platform for the parallel algorithm PPFDT_P based on the distributed decision tree algorithm is designed and implemented. At the same time, considering that the word vector model improves the deep representation of features and solves the problem of feature high-dimensional sparseness, and the iterative decision tree algorithm GBDT model is more suitable for non-high-dimensional dense features, the iterative decision tree algorithm will be integrated into the word vector model (GBDT) in the data retrieval application, using the distributed representation of words, namely word vectors, to classify short messages on the GBDT model. Experimental results show that the distributed decision tree algorithm has high efficiency, good speed-up and good scalability, so that there is no need to increase the number of datasets at each sub-site at any time. Only a small number of data items are inserted. By splitting some leaf nodes, a small amount is added by branching to achieve a monotonic decision tree. The proposed system achieves a massive data ratio of 54.1% while compared with other networks of massive data ratio.
Modified grasshopper optimization algorithm-based genetic algorithm for global optimization problems: the system of nonlinear equations case study

Grasshopper optimization algorithm (GOA) is one of the promising optimization algorithms for optimization problems. However, it has the main drawback of trapping into a local minimum, which causes slow convergence or inability to detect a solution. Several modifications and combinations were suggested to overcome this problem. This paper presents a modified grasshopper optimization algorithm (MGOA)-based genetic algorithm to overcome this problem. Modifications rely on certain mathematical assumptions and varying the domain of the control parameter, Cmax, to escape from the local minimum and move the search process to an improved point. Parameter C is one of the essential parameters in GOA, where it balances the exploration and exploitation of the search space. These modifications aim to speed up the convergence rate by reducing the repeated solutions and the number of iterations. Both the original GOA and the proposed algorithms are tested with 19 main test functions to investigate the influence of the proposed modifications. In addition, the algorithm will be applied to solve five different cases of nonlinear systems with different types of dimensions and regularity to show the reliability and efficiency of the proposed algorithm. Promising results are achieved compared to the original GOA. The proposed approach shows an average percentage of improvement of 96.18 as illustrated in the detailed results.
Blockchain Technology

The objective of this chapter is to identify the applications of blockchain technology, to discuss the advantages of blockchain technology, and to identify the principles of blockchain technology. It was found from studies that in today's digital era, there are several applications of blockchain technology in the area of finance, voting, real estate, taxation, media, healthcare, food safety, data backup, data storage, and money transfer. Blockchain technology works on principles such as distribution database, transparency, peer-to-peer transmission, computational logic, etc. Moreover, blockchain technology provides several benefits over traditional technologies such as high speed, automation, reduced cost, innovation, enhancement of speed, and building trust, transparency, accuracy, traceability, etc.
Do Humans Dream of Digital Devices? Subconscious User Experiences and Narratives

Do humans dream of electronic and digital devices? Or are our dreams free of technology, its influences and disruptions. When technology does appear, what part does it play in our subconscious narratives? Dreams are a reflection and distillation of our daily cognitive processes and waking experiences, and ubiquitous items such as mobile phones have become part of our everyday existence. This pictorial explores subjective dream imagery and narrative concerning the appearance and user experience of technology within our subconscious.
Unsupervised depth estimation for ship target based on single view UAV image

The goal of depth estimation is to obtain the depth image which reflects the distance between the object and the camera point. Depth image can provide depth information for tasks, such as three-dimensional (3D) reconstruction and distance perception. With the development of the environment perception research of the unmanned ship and unmanned aerial vehicle (UAV), depth estimation has been widely used in the field of water transportation. However, the research of ship depth estimation based on monocular images has just started, and there is no unsupervised deep learning depth estimation method for ship target based on single view UAV image. This paper proposes an unsupervised deep learning ship depth estimation method based on single view UAV images. Firstly, the realistic rendering software is used to construct a new ship training dataset with simple backgrounds and regular lighting conditions. Secondly, based on the differentiable rendering framework, a knowledge distillation depth estimation network is designed to train a student network with much smaller number of model parameters. Finally, the ship depth estimation network is obtained through unsupervised training. The experiment results show that the designed knowledge distillation depth estimation network can generate better depth estimation results compared with the current state-of-the-art (SOTA) method, and the weight file size of our model is smaller.
A New Semiparametric Regression Framework for Analyzing Non-Linear Data

This work introduces a straightforward framework for semiparametric non-linear models as an alternative to existing non-linear parametric models, whose interpretation primarily depends on biological or physical aspects that are not always available in every practical situation. The proposed methodology does not require intensive numerical methods to obtain estimates in non-linear contexts, which is attractive as such algorithms’ convergence strongly depends on assigning good initial values. Moreover, the proposed structure can be compared with standard polynomial approximations often used for explaining non-linear data behaviors. Approximate posterior inferences for the semiparametric model parameters were obtained from a fully Bayesian approach based on the Metropolis-within-Gibbs algorithm. The proposed structures were considered to analyze artificial and real datasets. Our results indicated that the semiparametric models outperform linear polynomial regression approximations to predict the behavior of response variables in non-linear settings.
A Multi-Aspect Permutation Test for Goodness-of-Fit Problems

Parametric techniques commonly rely on specific distributional assumptions. It is therefore fundamental to preliminarily identify the eventual violations of such assumptions. Therefore, appropriate testing procedures are required for this purpose to deal with a the goodness-of-fit (GoF) problem. This task can be quite challenging, especially with small sample sizes and multivariate data. Previous studiesshowed how a GoF problem can be easily represented through a traditional two-sample system of hypotheses. Following this idea, in this paper, we propose a multi-aspect permutation-based test to deal with the multivariate goodness-of-fit, taking advantage of the nonparametric combination (NPC) methodology. A simulation study is then conducted to evaluate the performance of our proposal and to identify the eventual critical scenarios. Finally, a real data application is considered.
Adaptive Aggregate Stereo Matching Network with Depth Map Super-Resolution

In order to avoid the direct depth reconstruction of the original image pair and improve the accuracy of the results, we proposed a coarse-to-fine stereo matching network combining multi-level residual optimization and depth map super-resolution (ASR-Net). First, we used the u-net feature extractor to obtain the multi-scale feature pair. Second, we reconstructed global disparity in the lowest resolution. Then, we regressed the residual disparity using the higher-resolution feature pair. Finally, the lowest-resolution depth map was refined by using the disparity residual. In addition, we introduced deformable convolution and group-wise cost volume into the network to achieve adaptive cost aggregation. Further, the network uses ABPN instead of the traditional interpolation method. The network was evaluated on three datasets: scene flow, kitti2015, and kitti2012 and the experimental results showed that the speed and accuracy of our method were excellent. On the kitti2015 dataset, the three-pixel error converged to 2.86%, and the speed was about six times and two times that of GC-net and GWC-net.
Understanding and Being Understood: User Strategies for Identifying and Recovering From Mistranslations in Machine Translation-Mediated Chat

Machine translation (MT) is now widely and freely available, and has the potential to greatly improve cross-lingual communication. In order to use MT reliably and safely, end users must be able to assess the quality of system outputs and determine how much they can rely on them to guide their decisions and actions. However, it can be difficult for users to detect and recover from mistranslations due to limited language skills. In this work we collected 19 MT-mediated role-play conversations in housing and employment scenarios, and conducted in-depth interviews to understand how users identify and recover from translation errors. Participants communicated using four language pairs: English, and one of Spanish, Farsi, Igbo, or Tagalog. We conducted qualitative analysis to understand user challenges in light of limited system transparency, strategies for recovery, and the kinds of translation errors that proved more or less difficult for users to overcome. We found that users broadly lacked relevant and helpful information to guide their assessments of translation quality. Instances where a user erroneously thought they had understood a translation correctly were rare but held the potential for serious consequences in the real world. Finally, inaccurate and disfluent translations had social consequences for participants, because it was difficult to discern when a disfluent message was reflective of the other person's intentions, or an artifact of imperfect MT. We draw on theories of grounding and repair in communication to contextualize these findings, and propose design implications for explainable AI (XAI) researchers, MT researchers, as well as collaboration among them to support transparency and explainability in MT. These directions include handling typos and non-standard grammar common in interpersonal communication, making MT in interfaces more visible to help users evaluate errors, supporting collaborative repair of conversation breakdowns, and communicating model strengths and weaknesses to users.
LPG–PCFG: An Improved Probabilistic Context- Free Grammar to Hit Low-Probability Passwords

With the development of the Internet, information security has attracted more attention. Identity authentication based on password authentication is the first line of defense; however, the password-generation model is widely used in offline password attacks and password strength evaluation. In real attack scenarios, high-probability passwords are easy to enumerate; extremely low-probability passwords usually lack semantic structure and, so, are tough to crack by applying statistical laws in machine learning models, but these passwords with lower probability have a large search space and certain semantic information. Improving the low-probability password hit rate in this interval is of great significance for improving the efficiency of offline attacks. However, obtaining a low-probability password is difficult under the current password-generation model. To solve this problem, we propose a low-probability generator-probabilistic context-free grammar (LPG-PCFG) based on PCFG. LPG-PCFG directionally increases the probability of low-probability passwords in the models' distribution, which is designed to obtain a degeneration distribution that is friendly for generating low-probability passwords. By using the control variable method to fine-tune the degeneration of LPG-PCFG, we obtained the optimal combination of degeneration parameters. Compared with the non-degeneration PCFG model, LPG-PCFG generates a larger number of hits. When generating 107 and 108 times, the number of hits to low-probability passwords increases by 50.4% and 42.0%, respectively.
Creating Platforms to Support Craft and Creativity in Game Controller Design

Alternative Controllers (Alt Controls) enable game designers to creatively explore how humans interact with games and challenge the status-quo of game interfaces. Alt Controls, however, require technical skills and fabrication infrastructure that often make them inaccessible to the average designer. Tangible User Interface researchers stand to benefit from the unique approach that Alt Controls promote. My research aims to bridge the gap between game developers and Alt Controls through the use of everyday materials and crafting techniques. In this paper, I discuss a framework for physical computing that uses computer vision (Beholder) and an example introductory platform for Alt Controller design (TinyCade). Further research will refine this framework and incorporate the perspective of other game designers.
The Navigation of Mobile Robot in the Indoor Dynamic Unknown Environment Based on Decision Tree Algorithm

This study proposes an optimized algorithm for the navigation of the mobile robot in the indoor and dynamic unknown environment based on the decision tree algorithm. Firstly, the error of the yaw value outputted from IMU sensor fusion module is analyzed in the indoor environment; then, the adaptive FAST SLAM is proposed to optimize the yaw value from the odometer; in the next, a decision tree algorithm is applied which predicts the correct moving direction of the mobile robot through the outputted yaw value from the IMU sensor fusion module and adaptive FAST SLAM of the odometer data in the indoor and dynamic environment; the following is the navigation algorithm proposed for the mobile robot in the dynamic and unknown environment; finally, a real mobile robot is designed to verify the proposed algorithm.The final result shows the proposed algorithms are valid and effective.
Tapis Magique: Machine-knitted Electronic Textile Carpet for Interactive Choreomusical Performance and Immersive Environments

Tapis Magique is a pressure-sensitive, knitted large-scale electronic textile carpet that generates three-dimensional sensor data based on body location, postures, and gestures and drives an immersive sonic environment in real-time. Inspired by traditional textile arts and cultures, we applied an artistic approach into technological textile design and merged new materials, sensing technologies, and digital fabrication with contemporary dance and music into one united and harmonious piece of object and performance. In this pictorial, we present and discuss our textile design rationale, fabrication strategies, hardware systems, musical mappings, as well as our collaborative effort with a professional dancer to demonstrate the interactive electronic textile carpet. Our work challenges the conventional relationship between choreography and music and unveils dancer and/or sound artist's creative possibilities of agency, intimacy, and improvisation over the choreomusical performance through a novel textile interface.
A Novel Image Inpainting Method Used for Veneer Defects Based on Region Normalization

The quality of the veneer directly affects the quality and grade of a blockboard made of veneer. To improve the quality and utilization of a defective veneer, a novel deep generative model-based method is proposed, which can generate higher-quality inpainting results. A two-phase network is proposed to stabilize the network training process. Then, region normalization is introduced to solve the inconsistency problem between the mean and standard deviation, improve the convergence speed of the model, and prevent the model gradient from exploding. Finally, a hybrid dilated convolution module is proposed to reconstruct the missing areas of the panels, which alleviates the gridding problem by changing the dilation rate. Experiments on our dataset prove the effectiveness of the improved approach in image inpainting tasks. The results show that the PSNR of the improved method reaches 33.11 and the SSIM reaches 0.93, which are superior to other methods.
Continuous-time quantum hash function based on one-dimensional cycle lattice

The proposed quantum hash function (QHF) based on the discrete-time quantum walk (DTQW) structure requires enlarging the coin state space and the preparation of continuous quantum states is less difficult. Hence, a new construction method of QHF is proposed based on the continuous-time quantum walk (CTQW) of the one-dimensional (1D) lattice with boundary constraints and without additional coin space. In the scheme, the input of the QHF is an arbitrary binary string message which is used to control the selected Hamiltonian of CTQW at each time interval, and the output of the QHF is the final probability distribution of CTQW. Under the same computing environment, simulation and analysis indicate that our QHF can satisfy the same security requirements such as sensitivity, diffusion and confusion, collision, birthday attack, but the collision rate is reduced by 40% without reducing effectiveness.
Shedding Ageist Perceptions of Making: Creativity in Older Adult Maker Communities

Contrary to stereotypes, the capacity for creativity and innovation does not diminish post-retirement. The domain of older adult-led maker-based communities offers HCI researchers the opportunity to explore not only how older adults innovatively and collaboratively create in later life, but also how we might design with these communities rather than for them. We report findings from a design workshop study spanning over two weeks involving 13 members of an urban Australian men's shed. Our findings show that making in the men's shed was influenced by (1) socio-materiality, (2) interpersonal dynamics and (3) shared expertise. We provide HCI researchers with an understanding of how older adults worked collaboratively within their own creative community as found through the deployment of a novel card-based probe, a workshop and followup investigations. We further unpack recommendations for organising design workshops with older adult maker communities that work towards meaningful community-oriented participation and greater agency in co-design studies.
Intelligence artificielle : Place dans le dépistage du cancer du sein en France

This paper deals with the place in 2021 of artificial intelligence for screening of breast cancer in France et discusses of the different types of use and their performance in the literature (Standalone, Augmented radiologist, triage). Moreover, this paper will give an overview of the potential applications of AI in the future (Personalized screening, prediction of interval cancers).