Reviewerly/exHarmony huggingface dataset:
G<scp>otta</scp>: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentation

Few-shot question answering (QA) aims at precisely discovering answers to a set of questions from context passages while only a few training samples are available. Although existing studies have made some progress and can usually achieve proper results, they suffer from understanding deep semantics for reasoning out the questions. In this paper, we develop Gotta, a Generative prOmpT-based da Ta Augmentation framework to mitigate the challenge above. Inspired by the human reasoning process, we propose to integrate the cloze task to enhance few-shot QA learning. Following the recent success of prompt-tuning, we present the cloze task in the same format as the main QA task, allowing the model to learn both tasks seamlessly together to fully take advantage of the power of prompt-tuning. Extensive experiments on widely used benchmarks demonstrate that Gotta consistently outperforms competitive baselines, validating the effectiveness of our proposed prompt-tuning-based cloze task, which not only fine-tunes language models but also learns to guide reasoning in QA tasks. Further analysis shows that the prompt-based loss incorporates the auxiliary task better than the multi-task loss, highlighting the strength of prompt-tuning on the few-shot QA task.Keywordsquestion answeringknowledge baseentitydata augmentation
Unsupervised Anomaly Segmentation for Brain Lesions Using Dual Semantic-Manifold Reconstruction

Unsupervised anomaly segmentation (UAS) is promising in many computer vision applications, e.g., the analysis of brain MRI, thanks to the advantage of detecting the anomalies (lesions) by only using the normal samples (healthy anatomies) in the training phase. Existing methods utilize the reconstruction process to model the normative distribution but inevitably lead to the impairment of localization information, which is critical for the pixel-level detection task. In this paper, we address this challenge by formulating a semantic layout of the healthy anatomy as the reconstruction manifold, which naturally forces the embedding to explicitly encode more semantic features as well as facilitates the preservation of spatial information during the reconstruction. Based on this special autoencoder framework of Semantic-Manifold Reconstruction (SMR), we further apply two consistency regularizations not only on the semantic layout but also the image appearance. In this way a Dual Semantic-Manifold Reconstruction (DSMR) is trained and then used to detect the anomalies accurately. Experiments reveal that the proposed DSMR approach exceeds the state-of-the-art performance on the benchmark datasets of BraTS and ISLES.
A Multi-scale Interaction Motion Network for Action Recognition Based on Capsule Network

Recently, action recognition has achieved impressive performance, mainly due to the aid of deep convolutional neural networks and large datasets. Traditionally, most efforts in action recognition have focused on capturing motion information by dense optical flow, but optical flow extraction is very time-consuming. Moreover, prior arts seek to improve accuracy but neglect the part-whole relationship between objects in videos, which may be self-defeating and even deteriorate the performance of methods. To circumvent the above challenges, we present a novel collaborative multipath capsule network (CMCN) for action recognition. In particular, we propose a plug-and-play collaborative multipath block containing spatiotemporal, channel, and motion units, which are complementary and crucial information for action recognition. We exploit the interaction of these three units and selectively emphasize informative spatial-temporal motion to reduce the expensive computational costs. Subsequently, we explore a new capsule voting procedure to reduce the computation used in the capsule dynamic routing mechanism. The critical insight is that the same type of capsules simulates the same entity in different positions, and their voting results should be consistent. This strategy lessens the number of learning parameters that backward pass in the training process, and thus strengthens part-whole relationships in a video. Extensive experiments on multiple real-world datasets for action recognition demonstrate that our model significantly outperforms state-of-the-art models.
Information recoverability of noisy quantum states

Extracting classical information from quantum systems is an essential step of many quantum algorithms. However, this information could be corrupted as the systems are prone to quantum noises, and its distortion under quantum dynamics has not been adequately investigated. In this work, we introduce a systematic framework to study how well we can retrieve information from noisy quantum states. Given a noisy quantum channel, we fully characterize the range of recoverable classical information. This condition allows a natural measure quantifying the information recoverability of a channel. Moreover, we resolve the minimum information retrieving cost, which, along with the corresponding optimal protocol, is efficiently computable by semidefinite programming. As applications, we establish the limits on the information retrieving cost for practical quantum noises and employ the corresponding protocols to mitigate errors in ground state energy estimation. Our work gives the first full characterization of information recoverability of noisy quantum states from the recoverable range to the recovering cost, revealing the ultimate limit of probabilistic error cancellation.
Semi-Supervised Lexicon-Aware Embedding for News Article Time Estimation

In the information retrieval community, Temporal Information Retrieval (TIR) has become increasingly popular. Documents focused on the time surrounding their publication are more likely to be accurate and contain information relevant to the reader. In this study, we explore the inverted pyramid paradigm by extracting temporal expressions from news documents, standardizing their values, and evaluating them based on their position within the text. We present a lexicon expansion method that employs WordNet as input. This approach enhances the lexicon by grouping words with similar meanings, potentially improving the accuracy of event detection algorithms. Additionally, this process can introduce new words and phrases to the lexicon, expanding the vocabulary. Using each tagged dataset, a classifier is trained with a pre-trained network. A pool of unlabeled data are processed, and high-confidence pseudo-labels are assigned. Pseudo-labels are generated by leveraging the partially trained model and the original labelled data. As the classifier predicts the correct label for a data sample, the pseudo-labels of other data samples are updated, and vice versa. At the end of this process, the predictions from different matching classifiers are combined. It takes several rounds to label the unlabeled inputs using this method. To evaluate the proposed solutions, we conducted experiments on 4,500 online news articles relevant to temporal retrieval. LSTM, BiLSTM, and BERT models with and without lexicon expansion were assessed based on log loss and relative divergence of entropy. A jointly trained semi-supervised learning model achieved a mean KL divergence of 0.89, an F1 score of 0.74 for temporal events, and 0.63 for non-temporal events. Besides alleviating data sparsity issues and enabling the training of more complex networks, this technique can also serve as an alternative to data augmentation methods.
The use of a virtual environment to improve students’ Listening skills: a learning analytics approach

This study analyzes the use of virtual environments to improve the listening skills of students of English as a foreign language (EFL) from a quantitative approach. To achieve this goal, one hundred participants enrolled in a free English course using a platform with H5p activities, videos, quizzes, images, infographics, audio, forum, and synchronous Zoom sessions for four weeks, in addition to a KET test for the assessment of listening comprehension. Quantitative data were analyzed by comparing the mean scores of the pretest, posttest as well as the logs of the platform. Analyses confirm the benefits of applying ICT tools to develop listening skills. All subskills of the listening sections were benefited from the interactivity displayed in the online activities. The current study reinforces the notion of interactive online activities as motivators to improve listening comprehension in EFL.
Overview of Internet of Medical Things Security Based on Blockchain Access Control

The Internet of Things provides convenience to health systems, especially for remote monitoring of patient physical indicators. While providing convenience, there may be more security vulnerabilities in protecting patient and doctor information and storing health data effectively. As an important research branch in the field of the Internet of Things, the Internet of Medical Things is important for the overall improvement of public health in terms of how to safely conduct technology development and application research and to effectively implement healthcare needs. Blockchain technology is decentralized and untrusted as well as prevents tampering with data and reduces the cost of trust. Its good performance has a strong developmental nature in the healthcare field. This paper analyses how to solve security problems through access control under the Internet of Medical Things, and optimizes three access control methods. The Internet of Medical Things accesses control approach that introduces blockchain technology enhances computational and storage capabilities and is a good solution to the problem of third-party trustworthiness. Even in the face of the rapid growth of end devices, blockchain technology can solve some of the problems arising from access control of massive devices through three directions: hierarchical management, compressed storage and performance optimization. Finally, it provides directions for future research on the security aspects of blockchain technology under the Internet of Medical Things.
Sensor Fusion Approach for Multiple Human Motion Detection for Indoor Surveillance Use-Case

Multi-human detection and tracking in indoor surveillance is a challenging task due to various factors such as occlusions, illumination changes, and complex human-human and human-object interactions. In this study, we address these challenges by exploring the benefits of a low-level sensor fusion approach that combines grayscale and neuromorphic vision sensor (NVS) data. We first generate a custom dataset using an NVS camera in an indoor environment. We then conduct a comprehensive study by experimenting with different image features and deep learning networks, followed by a multi-input fusion strategy to optimize our experiments with respect to overfitting. Our primary goal is to determine the best input feature types for multi-human motion detection using statistical analysis. We find that there is a significant difference between the input features of optimized backbones, with the best strategy depending on the amount of available data. Specifically, under a low-data regime, event-based frames seem to be the preferred input feature type, while higher data availability benefits the combined use of grayscale and optical flow features. Our results demonstrate the potential of sensor fusion and deep learning techniques for multi-human tracking in indoor surveillance, although it is acknowledged that further studies are needed to confirm our findings.
Counterfactual Causal Adversarial Networks for Domain Adaptation

To eliminate domain shift in domain adaptation, most methods do so by encouraging the model to learn common features. However, the interpretability of these domain adaptation methods lacks in-depth research, and we note that the domain adaptation process can be regarded as a causal intervention, which can further form theoretical explanations in the causal relationship. Our proposed counterfactual causal adversarial network CCAN performs better on the domain adaptation task. Supported by causal theory, CCAN completes the adversarial learning of the network through counterfactual intervention, and uses the first proposed domain-adaptive causal effect to supervise the entire network. CCAN successfully validates the goal of evaluating the quality of domain adaptation through counterfactual intervention effects in causality to supervise the better completion of the entire task. The causal theory endows the whole CCAN with good interpretability. Experimental results on two challenging UDA benchmarks validate the superiority and effectiveness of CCAN for domain adaptation with counterfactual interventions based on causal theory, and analyze the role that domain adaptation causal effects play in the overall supervision.
Impossibility of Indifferentiable Iterated Blockciphers from 3 or Less Primitive Calls

Virtually all modern blockciphers are iterated. In this paper, we ask: to construct a secure iterated blockcipher “non-trivially”, how many calls to random functions and permutations are necessary? When security means indistinguishability from a random permutation, optimality is achieved by the Even-Mansour scheme using 1 call to a public permutation. We seek for the arguably strongest security indifferentiability from an ideal cipher, a notion introduced by Maurer et al. (TCC 2004) and popularized by Coron et al. (JoC, 2014). We provide the first generic negative result/lower bounds: when the key is not too short, no iterated blockcipher making 3 calls is (statistically) indifferentiable. This proves optimality for a 4-call positive result of Guo et al. (Eprint 2016). Furthermore, using 1 or 2 calls, even indifferentiable iterated blockciphers with polynomial $$\text {keyspace}$$ are impossible. To prove this, we develop an abstraction of idealized iterated blockciphers and establish various basic properties, and apply Extremal Graph Theory results to prove the existence of certain (generalized) non-random properties such as the boomerang and yoyo.
An Improved Data Hiding Technique based on the Combination of Compression Steganography and RSA Cryptography

Image compression and encryption are crucial components of information security since it is safer to use compressed data than uncompressed data and can be handled more easily due to its smaller size.Therefore, efficient, safe, and connectable data is produced using effective data compression technology.Lossy and lossless compressions are the two main categories of compression algorithm approaches.Any type of data format, including text, audio, video, and picture files, may leverage these technologies.To further security, this data will be encrypted using the LSB algorithm, after which it will be hidden from prying eyes.
Application and functional simulation of data mining technology in Hadoop cloud platform based on improved algorithm

Data mining algorithms can process target data and extract useful hidden information, which is helpful for decision making. However, current mining algorithms have some shortcomings such as time-consuming processing of big data or inability to process massive data. Since data mining technology cannot be used in the traditional cloud platform environment, it is necessary to improve the algorithm to make it more suitable for the cloud platform environment. By analyzing the actual application process of BP classification algorithm, this paper expounds the practicability of BP classification algorithm, analyzes the data mining process based on Hadoop cloud platform, and expounds the development ideas of BP classification algorithm. The source of data mining algorithm supported by cloud computing is discussed. Finally, Hadoop cloud platform. This paper designed the corresponding system architecture and data interface, and established a suitable test environment for this system, and completed the simulation experiment test through the design. The calculation time of this algorithm is proportional to the amount of data, showing a linear relationship. In data mining, the optimized BP algorithm in this paper can significantly save resources in terms of spatial features. This paper designs an optimized operating system based on Hadoop platform through comprehensive analysis and improvement algorithm.
Evaluating the Impact of Human Explanation Strategies on Human-AI Visual Decision-Making

Artificial intelligence (AI) is increasingly being deployed in high-stakes domains, such as disaster relief and radiology, to aid practitioners during the decision-making process. Explainable AI techniques have been developed and deployed to provide users insights into why the AI made certain predictions. However, recent research suggests that these techniques may confuse or mislead users. We conducted a series of two studies to uncover strategies that humans use to explain decisions and then understand how those explanation strategies impact visual decision-making. In our first study, we elicit explanations from humans when assessing and localizing damaged buildings after natural disasters from satellite imagery and identify four core explanation strategies that humans employed. We then follow up by studying the impact of these explanation strategies by framing the explanations from Study 1 as if they were generated by AI and showing them to a different set of decision-makers performing the same task. We provide initial insights on how causal explanation strategies improve humans' accuracy and calibrate humans' reliance on AI when the AI is incorrect. However, we also find that causal explanation strategies may lead to incorrect rationalizations when AI presents a correct assessment with incorrect localization. We explore the implications of our findings for the design of human-centered explainable AI and address directions for future work.
Competencies for Code Review

Peer code review is a widely practiced software engineering process in which software developers collaboratively evaluate and improve source code quality. Whether developers can perform good reviews depends on whether they have sufficient competence and experience. However, the knowledge of what competencies developers need to execute code review is currently limited, thus hindering, for example, the creation of effective support tools and training strategies. To address this gap, we firstly identified 27 competencies relevant to performing code review through expert validation. Later, we conducted an online survey with 105 reviewers to rank these competencies along four dimensions: frequency of usage, importance, proficiency, and desire of reviewers to improve in that competency. The survey shows that technical competencies are considered essential to performing reviews and that respondents feel generally confident in their technical proficiency. Moreover, reviewers feel less confident in how to communicate clearly and give constructive feedback - competencies they consider like-wise an essential part of reviewing. Therefore, research and education should focus in more detail on how to support and develop reviewers' potential to communicate effectively during reviews. In the paper, we also discuss further implications for training, code review performance assessment, and reviewers of different experience level. Data and materials: https://doi.org/10.5281/zenodo.7401313
The Unsung Heroes of Facebook Groups Moderation: A Case Study of Moderation Practices and Tools

Volunteer moderators have the power to shape society through their influence on online discourse. However, the growing scale of online interactions increasingly presents significant hurdles for meaningful moderation. Furthermore, there are only limited tools available to assist volunteers with their work. Our work aims to meaningfully explore the potential of AI-driven, automated moderation tools for social media to assist volunteer moderators. One key aspect is to investigate the degree to which tools must become personalizable and context-sensitive in order to not just delete unsavory content and ban trolls, but to adapt to the millions of online communities on social media mega-platforms that rely on volunteer moderation. In this study, we conduct semi-structured interviews with 26 Facebook Group moderators in order to better understand moderation tasks and their associated challenges. Through qualitative analysis of the interview data, we identify and address the most pressing themes in the challenges they face daily. Using interview insights, we conceptualize three tools with automated features that assist them in their most challenging tasks and problems. We then evaluate the tools for usability and acceptance using a survey drawing on the technology acceptance literature with 22 of the same moderators. Qualitative and descriptive analyses of the survey data show that context-sensitive, agency-maintaining tools in addition to trial experience are key to mass adoption by volunteer moderators in order to build trust in the validity of the moderation technology.
Occlusion-robust scene flow-based tissue deformation recovery incorporating a mesh optimization model

Tissue deformation recovery is to reconstruct the change in shape and surface strain caused by tool-tissue interaction or respiration, which is essential for providing motion and shape information that benefits the improvement of the safety of minimally invasive surgery. The binocular vision-based approach is a practical candidate for deformation recovery as no extra devices are required. However, previous methods suffer from limitations such as the reliance on biomechanical priors and the vulnerability to the occlusion caused by surgical instruments. To address the issues, we propose a deformation recovery method incorporating mesh structures and scene flow.The method can be divided into three modules. The first one is the implementation of the two-step scene flow generation module to extract the 3D motion from the binocular sequence. Second, we propose a strain-based filtering method to denoise the original scene flow. Third, a mesh optimization model is proposed that strengthens the robustness to occlusion by employing contextual connectivity.In a phantom and an in vivo experiment, the feasibility of the method in recovering surface deformation in the presence of tool-induced occlusion was demonstrated. Surface reconstruction accuracy was quantitatively evaluated by comparing the recovered mesh surface with the 3D scanned model in the phantom experiment. Results show that the overall error is 0.70 ± 0.55 mm.The method has been demonstrated to be capable of continuously recovering surface deformation using mesh representation with robustness to the occlusion caused by surgical forceps and promises to be suitable for the application in actual surgery.
Supporting Complex Decision-Making: Evidence from an Eye Tracking Study on In-Person and Remote Collaboration

This article examines the attentional mechanism of in-person collaboration by means of System Dynamics-based simulations using an eye tracking experiment. Three experimental conditions were tested: in-person collaboration, remote collaboration, and single user. We hypothesized that collaboration focuses users’ attention on key information facilitating decision-making. Collaborating participants dwelt longer on key elements of the simulation than single users. Moreover, in-person collaboration and single users yielded a strategy of decision-making similar to an optimal strategy. Finally, in-person collaboration was less cognitively demanding and of higher quality. The contribution of this article is a deeper understanding of how in-person collaboration on a large display can help users focus their visual attention on the most important areas. With this novel understanding, we believe collaborative systems designers will be better equipped to design more effective attention-guiding mechanisms in remote collaboration systems. The present work has the potential to advance the study of collaborative, interactive technologies.