Reviewerly/exHarmony huggingface dataset:
Unsupervised Anomaly Detection in Medical Images with a Memory-Augmented Multi-level Cross-Attentional Masked Autoencoder

Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a detector using a training set that contains only normal images. UAD approaches can be based on reconstruction methods, self-supervised approaches, and Imagenet pre-trained models. Reconstruction methods, which detect anomalies from image reconstruction errors, are advantageous because they do not rely on the design of problem-specific pretext tasks needed by self-supervised approaches, and on the unreliable translation of models pre-trained from non-medical datasets. However, reconstruction methods may fail because they can have low reconstruction errors even for anomalous images. In this paper, we introduce a new reconstruction-based UAD approach that addresses this low-reconstruction error issue for anomalous images. Our UAD approach, the memory-augmented multi-level cross-attentional masked autoencoder (MemMC-MAE), is a transformer-based approach, consisting of a novel memory-augmented self-attention operator for the encoder and a new multi-level cross-attention operator for the decoder. MemMC-MAE masks large parts of the input image during its reconstruction, reducing the risk that it will produce low reconstruction errors because anomalies are likely to be masked and cannot be reconstructed. However, when the anomaly is not masked, then the normal patterns stored in the encoder's memory combined with the decoder's multi-level cross-attention will constrain the accurate reconstruction of the anomaly. We show that our method achieves SOTA anomaly detection and localisation on colonoscopy, pneumonia, and covid-19 chest x-ray datasets.
AI-generated feedback on writing: insights into efficacy and ENL student preference

Abstract The question of how generative AI tools, such as large language models and chatbots, can be leveraged ethically and effectively in education is ongoing. Given the critical role that writing plays in learning and assessment within educational institutions, it is of growing importance for educators to make thoughtful and informed decisions as to how and in what capacity generative AI tools should be leveraged to assist in the development of students’ writing skills. This paper reports on two longitudinal studies. Study 1 examined learning outcomes of 48 university English as a new language (ENL) learners in a six-week long repeated measures quasi experimental design where the experimental group received writing feedback generated from ChatGPT (GPT-4) and the control group received feedback from their human tutor. Study 2 analyzed the perceptions of a different group of 43 ENLs who received feedback from both ChatGPT and their tutor. Results of study 1 showed no difference in learning outcomes between the two groups. Study 2 results revealed a near even split in preference for AI-generated or human-generated feedback, with clear advantages to both forms of feedback apparent from the data. The main implication of these studies is that the use of AI-generated feedback can likely be incorporated into ENL essay evaluation without affecting learning outcomes, although we recommend a blended approach that utilizes the strengths of both forms of feedback. The main contribution of this paper is in addressing generative AI as an automatic essay evaluator while incorporating learner perspectives.
Understanding of Digital Ethics for Information Trust: A Critical Insight into Gender Violence Anticipation

This chapter aims to examine the demanding needs of digital ethics elaborated as a strategic foundation to expand safety concern amongst digital community and partnership for gender violence anticipation. The literature review was applied from the referred articles from peer-reviewed journals, proceedings, chapters and books related to the topic. The finding reveals that the significant alignment of digital ethics in expanding the digital partnership skills has a core value to expand the digital information trust in the order for gender violence anticipation. Those are continuing digital ethics’ information quality for gender violence anticipation, rebuilding digital ethics’ information trust to prevent gender violence potentials, and promoting digital ethics’ grant on digital safety environment assurance for gender violence anticipation. This chapter may contribute to give insights into developing the outstanding interplay of digital ethics as a safety strategy to achieve digital information trust for gender violence anticipation.
Path planning for mobile robots in unstructured orchard environments: An improved kinematically constrained bi-directional RRT approach

In complex orchard environments, efficient path-planning algorithms for mobile robots are crucial for achieving accurate autonomous navigation and mobile operations. The existence of kinematic constraints for mobile robots in orchards and the unstructured layouts of orchard environments, which primarily feature irregular obstacles, pose considerable challenges for path-planning algorithms. Ideal path-planning algorithms must overcome the above problems and plan navigation paths that are safe, efficient, and consistent with the kinematic constraints of mobile robots in an obstacle-filled environment. In this study, to satisfy the above requirements, the Improved Kinematically Constrained Bi-directional Rapidly Exploring Random Tree (IKB-RRT) algorithm is proposed, which includes three innovations. First, an improved artificial potential field method is designed for guidance-node generation in complex orchard environments; hence, the nodes provide the path guidance for the random expansion trees that are subject to the kinematic constraints, avoiding obstacle regions and expanding towards the target point. Second, a motion-control parameter sampling method for the robot configuration is introduced to control the random expansion tree to generate path nodes and trajectories that satisfy the mobile robot constraints. Third, a path optimization strategy for dual-tree connected regions is proposed to avoid sudden changes in the heading angle at dual-tree connected waypoints and to improve the overall path smoothness. Path planning is performed in four orchard environments with different characteristics. The algorithm test results show that, in different scenarios, the proposed IKB-RRT algorithm reduces the path length by a maximum of 13.94% and 12.20% and the planning time by a maximum of 61.41% and 68.84%, significantly improving the overall smoothness of the path relative to the KB-RRT* and KB-RRT algorithms, respectively. The results fully verify that the IKB-RRT algorithm has obvious advantages in handling the path-planning problem that arises for mobile robots in complex orchard environments. The proposed algorithm can be implemented in future orchard mobile robots to enable effective path planning.
Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model

The Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images. SAM can be viewed as a general perception model for segmentation (partitioning images into semantically meaningful regions). Thus, how to utilize such a large foundation model for medical image segmentation is an emerging research target. This paper shows that although SAM does not immediately give high-quality segmentation for medical image data, its generated masks, features, and stability scores are useful for building and training better medical image segmentation models. In particular, we demonstrate how to use SAM to augment image input for commonly-used medical image segmentation models (e.g., U-Net). Experiments on three segmentation tasks show the effectiveness of our proposed SAMAug method.
Learning Qualitative Models by Demonstration

Creating software agents that learn interactively requires the ability to learn from a small number of trials, extracting general, flexible knowledge that can drive behavior from observation and interaction. We claim that qualitative models provide a useful intermediate level of causal representation for dynamic domains, including the formulation of strategies and tactics. We argue that qualitative models are quickly learnable, and enable model-based reasoning techniques to be used to recognize, operationalize, and construct more strategic knowledge. This paper describes an approach to incrementally learning qualitative influences by demonstration in the context of a strategy game. We show how the learned model can help a system play by enabling it to explain which actions could contribute to maximizing a quantitative goal. We also show how reasoning about the model allows it to reformulate a learning problem to address delayed effects and credit assignment, such that it can improve its performance on more strategic tasks such as city placement.
Terrain Following for Fixed-Wing Unmanned Aerial Vehicles Using Feedback Equivalence

We present a controller design methodology for unmanned aerial vehicles (UAVs) which is based on feedback equivalence of control affine nonlinear systems. We introduce an arbitrary coordinate transformation of the UAV's physical space, extend it to the entire state space and prove that there exists an input transformation such that the dynamical equations remain invariant. We obtain an explicit expression for this coordinate transformation by parameterizing a reference terrain by a flat surface therefore reducing the problem of terrain following to following a simple plane. Our theoretical contributions are accompanied by simulation results, by which the effectiveness of the method is demonstrated.
Implicit incremental natural actor critic algorithm

Natural policy gradient (NPG) methods are promising approaches to finding locally optimal policy parameters. The NPG approach works well in optimizing complex policies with high-dimensional parameters, and the effectiveness of NPG methods has been demonstrated in many fields. However, the incremental estimation of the NPG is computationally unstable owing to its high sensitivity to the step-sizes values, especially to the one used to update the estimate of NPG. In this study, we propose a new incremental and stable algorithm for the NPG estimation. We call the proposed algorithm the implicit incremental natural actor critic (I2NAC), and it is based on the idea of the implicit update. The convergence analysis for I2NAC is provided. Theoretical analysis results indicate the stability of I2NAC and the instability of conventional incremental NPG methods. Numerical experiments were performed, and the results show that I2NAC is less sensitive to the values of the meta-parameters, including the step-size for the NPG update, compared to the existing incremental NPG method.
A Pattern-Based Approach to Recognizing Time Expressions

Recognizing time expressions is a fundamental and important task in many applications of natural language understanding, such as reading comprehension and question answering. Several newest state-of-the-art approaches have achieved good performance on recognizing time expressions. These approaches are black-boxed or based on heuristic rules, which leads to the difficulty in understanding the temporal information. On the contrary, classic rule-based or semantic parsing approaches can capture rich structural information, but their performances on recognition are not so good. In this paper, we propose a pattern-based approach, called PTime, which automatically generates and selects patterns for recognizing time expressions. In this approach, time expressions in training text are abstracted into type sequences by using fine-grained token types, thus the problem is transformed to select an appropriate subset of the sequential patterns. We use the Extended Budgeted Maximum Coverage (EBMC) model to optimize the pattern selection. The main idea is to maximize the correct token sequences matched by the selected patterns while the number of the mistakes should be limited by an adjustable budget. The interpretability of patterns and the adjustability of permitted number of mistakes make PTime a very promising approach for many applications. Experimental results show that PTime achieves a very competitive performance as compared with existing state-of-the-art approaches.
APLIKASI GAME MATEMATIKA DALAM MENINGKATKAN KEMAMPUAN MENGHITUNG MATEMATIS

Today's users of gadgets are getting more and more from every year and continuously increasing along with the development of communication information technology. This development has triggered the emergence of various android applications specifically offered to support the sophistication of gadgets, including game applications, business applications, social applications, and educational applications. One android application that has more value is a math game, as if inviting users to play like a game in general but inside it can help develop numeracy skills. In this study, it is very interesting to examine how the application of mathematical games in improving mathematical calculation skills. Most of the gadgets currently used, offer more entertainment, whether it's games or shows that aren't good enough. Therefore the application of this mathematical game application can at least shift the use of the existence of gadgets commonly used for playing or entertainment can be used as a means for learning especially for learning mathematics. This research is an experimental study with a quasi-experimental method that tests a learning with the help of an android math game application through the scientific approach used in the 2013 curriculum. This learning provides a new breakthrough in learning, especially mathematics, because of the presuppositions of learning mathematics it is difficult and scary to switch to being fun and enjoyable with the use of gadgets during the learning process. Keywords: mathematical calculation skills, math game.
One Note About the Tu-Deng Conjecture in Case <inline-formula> <tex-math notation="LaTeX">$\mathop{\mathrm{w}}\nolimits(t)=5$ </tex-math> </inline-formula>

Let k ≥ 2 be an integer, and define S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> := {(a, b) ∈ Z <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> |0 ≤ a, b ≤ 2k - 2, a + b = t(mod 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> - 1), w(a) + w(b) ≤ k - 1}, where t ∈ Z, 1 ≤ t ≤ 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> - 2. This paper gives the upper bound of the cardinality of S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> in the case of w(t) = 5. With this one, we conclude that a conjecture proposed by Tu and Deng in 2011 is right when w(t) = 5.
Environmental Management Efficiency of GCC Countries: Linking Between Composite Index of Environmental Performance, Socio-Political and Economic Dimensions

This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of environmental performance.The investigation of the topic of the Composite Index of Environmental performance (CIEP) in the paper is carried out in the following logical sequence.The research is focused on the relationship between the composite index of environmental performances and the sociopolitical and economic elements of countries.Systematization literary sources and approaches for solving the problem of the influence of the integration of a composite Index of Environmental performance on the socio-political and economic dimensions.The paper presents the results of the empirical analysis with an econometric model which uses a composite index of environmental performance.The relevance of the decision of this scientific problem is that advanced and the methodological tools of the research methods try to measure the influence of Composite Index of Environmental performance model as a dependent variable on economic (technology, transportation, infrastructure, consumption of goods, international trade and tourism) and sociopolitical factors (democracy, freedom, transparency, and social policy) of countries.We used a generalized least squares estimation with a panel data from 6 countries observed during the period 2002-2016.Our results reveal that democracy, social policy, and transparency have a positive effect on environmental performance.Of the same, transport, infrastructure and tourism have a negative effect.The results of the research can be useful for countries, researchers and practitioners that try to improve the environmental performance with a good level of the social politico and economic dimensions.Finally, our results show that good environmental performance is related to the different characteristics of countries such as social, political and economics.And these results can be useful for countries to incorporate this valuable composite index in their objectives.
Impact of background and modulation on parameter estimation using fractional Fourier transform and its solutions

Analysis of closed-fringe patterns with quadratic phase that are often encountered plays an important role in optical interferometry. But because the frequency spectra of the two exponential signals that compose closed-fringe patterns overlap in the Fourier domain while one is clearly distinct from the other in the fractional Fourier domain, fractional Fourier transform (FRFT) is a useful method for analyzing the images to provide parameter estimation. However, when the fringe pattern has varying background and/or modulation due to non-uniform illumination, parameter estimation accuracy based on FRFT is affected, which lacks theoretical justification. Thus, the impact of varying background and/or modulation on the FRFT is studied with theoretic analysis and presented in this paper. Key factors that contribute to the optimal results are discussed when employing three kinds of fringe normalization methods to eliminate the impact. Here, the fringe pattern is first processed by the normalization technique. Then the cosine-only term is used to estimate parameter by use of the FRFT-based method. Physical quantities are then obtained by parameter estimation. In comparison with our previous method based on FRFT, more accurate results are achieved. The feasibility and applicability of the proposed approach are demonstrated using simulation and experimental results.
Sensitive Research, Practice and Design in HCI

New research areas in HCI examine complex and sensitive research areas, such as crisis, life transitions, and mental health. Further, research in complex topics such as harassment and graphic content can leave researchers vulnerable to emotional and physical harm. There is a need to bring researchers together to discuss challenges across sensitive research spaces and environments. We propose a workshop to explore the methodological, ethical, and emotional challenges of sensitive research in HCI. We will actively recruit from diverse research environments (industry, academia, government, etc.) and methods areas (qualitative, quantitative, design practices, etc.) and identify commonalities in and encourage relationship-building between these areas. This one-day workshop will be led by academic and industry researchers with diverse methods, topical, and employment experiences.
Efficient and Unconditionally Anonymous Certificateless Provable Data Possession Scheme With Trusted KGC for Cloud-Based EMRs

Electronic Medical Records (EMRs) are the collection of patient's health data systematically in a digital format.They eliminate the complicated paper-based medical records and ensure the accuracy and legibility of health data to improve quality care.As the amount of the EMRs explosion, the traditional medical system is unable to equip with sufficient storage and computing capacities.Therefore, it is crucial to outsourcing this EMRs to the cloud server, which is a flexible and cost-effective mode in data management.However, the loss of control over the patient's EMRs makes it a severe challenge in the integrity of outsourced data.In this paper, we propose an efficient certificateless provable data possession (CL-PDP) scheme without bilinear pairing for the EMRs stored in the cloud medical server.A formal security proof on the random oracle model demonstrates the security of our proposal.In addition, our CL-PDP protocol satisfies the following properties: unconditionally anonymous, privacy preservation, public verifiability, and reliability of the trusted third party.At last, an elaborate performance evaluation with other related schemes shows that this proposal is efficient and practical.
Information hiding based on Augmented Reality

Information hiding aims to achieve secret communication via certain carrier. However, these carrier-based methods often have different kinds of deficiencies. In order to solve the problems addressed by the traditional information hiding methods such as the difficult balance between secret embedding rate and detection rate, this paper proposes a novel approach which utilizes Augmented Reality (AR) to achieve secret communication. In this paper, we present an AR based information hiding architecture which combines information hiding, augmented reality, and deep learning methods altogether. The proposed architecture basically follows the idea of secret-key matching policy. The secret sender first maps the secret message to objects, images or coordinates, etc. The mapped objects, images or coordinates then serve as the secret key for further secret revealing. The secret key and concealing model are shared between two communication parties instead of direct transmitting the secret messages. Different secret keys can be combined in order to generate more mapping sequences. Also, deep learning based models are integrated in the architecture to extend the mapping varieties. By taking advantage of the augmented reality technique, the secret messages can be transmitted in various formats which results in higher secret embedding rate in potential. Furthermore, the proposed architecture can be seen as a useful application of coverless information hiding scheme. The experimental system realizes the proposed architecture by implementing convolutional neural network (CNN) based real-time object detection, image recognition, augmented reality and secret-key matching altogether which shows great promise in practice.
DAFL: Data-Free Learning of Student Networks

Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors. Most existing deep neural network compression and speed-up methods are very effective for training compact deep models, when we can directly access the training dataset. However, training data for the given deep network are often unavailable due to some practice problems (e.g. privacy, legal issue, and transmission), and the architecture of the given network are also unknown except some interfaces. To this end, we propose a novel framework for training efficient deep neural networks by exploiting generative adversarial networks (GANs). To be specific, the pre-trained teacher networks are regarded as a fixed discriminator and the generator is utilized for derivating training samples which can obtain the maximum response on the discriminator. Then, an efficient network with smaller model size and computational complexity is trained using the generated data and the teacher network, simultaneously. Efficient student networks learned using the proposed Data-Free Learning (DAFL) method achieve 92.22% and 74.47% accuracies using ResNet-18 without any training data on the CIFAR-10 and CIFAR-100 datasets, respectively. Meanwhile, our student network obtains an 80.56% accuracy on the CelebA benchmark.