Reviewerly/exHarmony huggingface dataset:
On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance

Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS). However, existing explainable RS usually consider explanation as a side output of the recommendation model, which has two problems: (1) It is difficult to evaluate the produced explanations, because they are usually model-dependent, and (2) as a result, how the explanations impact the recommendation performance is less investigated. In this article, explaining recommendations is formulated as a ranking task and learned from data, similarly to item ranking for recommendation. This makes it possible for standard evaluation of explanations via ranking metrics (e.g., Normalized Discounted Cumulative Gain). Furthermore, this article extends traditional item ranking to an item–explanation joint-ranking formalization to study if purposely selecting explanations could reach certain learning goals, e.g., improving recommendation performance. A great challenge, however, is that the sparsity issue in the user-item-explanation data would be inevitably severer than that in traditional user–item interaction data, since not every user–item pair can be associated with all explanations. To mitigate this issue, this article proposes to perform two sets of matrix factorization by considering the ternary relationship as two groups of binary relationships. Experiments on three large datasets verify the solution’s effectiveness on both explanation ranking and item recommendation.
Texture transformer super-resolution for low-dose computed tomography

Abstract Computed tomography (CT) is widely used to diagnose many diseases. Low-dose CT has been actively pursued to lower the ionizing radiation risk. A relatively smoother kernel is typically used in low-dose CT to suppress image noise, which may sacrifice spatial resolution. In this work, we propose a texture transformer network to simultaneously reduce image noise and improve spatial resolution in CT images. This network, referred to as Texture Transformer for Super Resolution (TTSR), is a reference-based deep-learning image super-resolution method built upon a generative adversarial network (GAN). The noisy low-resolution CT (LRCT) image and the routine-dose high-resolution (HRCT) image are severed as the query and key in a transformer, respectively. Image translation is optimized through deep neural network (DNN) texture extraction, correlation embedding, and attention-based texture transfer and synthesis to achieve joint feature learning between LRCT and HRCT images for super-resolution CT (SRCT) images. To evaluate SRCT performance, we use the data from both simulations of the XCAT phantom program and the real patient data. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity (FSIM) index are used as quantitative metrics. For comparison of SRCT performance, the cubic spline interpolation, SRGAN (a GAN super-resolution with an additional content loss), and GAN-CIRCLE (a GAN super-resolution with cycle consistency) were used. Compared to the other two methods, TTSR can restore more details in SRCT images and achieve better PSNR, SSIM, and FSIM for both simulation and real-patient data. In addition, we show that TTSR can yield better image quality and demand much less computation time than high-resolution low-dose CT images denoised by block-matching and 3D filtering (BM3D) and GAN-CIRCLE. In summary, the proposed TTSR method based on texture transformer and attention mechanism provides an effective and efficient tool to improve spatial resolution and suppress noise of low-dose CT images.
Design of secured blockchain based decentralized authentication protocol for sensor networks with auditing and accountability

The traditional system designed by incorporating the Wireless Sensor Network (WSN) suffers from important issues such as centralization of information, single source of trust, and fails to provide a trusted data-auditing solution due to the involvement of Third Party Auditor (TPA). This article proposes a novel solution by leveraging the concept of blockchain and Distributed Data Storage Service (DDSS) system to mitigate the issues mentioned above. In our model, the sensor nodes are accountable for their activity because the blockchain permanently records the logs of sensor nodes. The proposed solution provides the decentralized authentication of sensor nodes and the information stored in the fully distributed system. Furthermore, a decentralized data-auditing scheme without involving the TPA is also suggested, which finally benefits the user in terms of bandwidth and money. The formal verification of the proposed protocol is done using the Scyther simulator tool, which indicates that the protocol is safe and can protect against the relevant attacks with 100% accuracy. We have estimated the proposed scheme's time complexity and computational efficiencies and compared them with the existing one. The computation and communication overhead of the proposed method is reduced by 22.143% and 12.5% compared with the Khalid et al. (2020) scheme. Lastly, the ethereum platform simulates our solution, confirming that less than USD 2 is required to execute the proposed solution.
Kinergy: Creating 3D Printable Motion using Embedded Kinetic Energy

We present Kinergy—an interactive design tool for creating self-propelled motion by harnessing the energy stored in 3D printable springs. To produce controllable output motions, we introduce 3D printable kinetic units, a set of parameterizable designs that encapsulate 3D printable springs, compliant locks, and transmission mechanisms for three non-periodic motions—instant translation, instant rotation, continuous translation—and four periodic motions—continuous rotation, reciprocation, oscillation, intermittent rotation. Kinergy allows the user to create motion-enabled 3D models by embedding kinetic units, customize output motion characteristics by parameterizing embedded springs and kinematic elements, control energy by operating the specialized lock, and preview the resulting motion in an interactive environment. We demonstrate the potential of our techniques via example applications from spring-loaded cars to kinetic sculptures and close with a discussion of key challenges such as geometric constraints.
The Reflective Maker: Using Reflection to Support Skill-learning in Makerspaces

In recent years, while HCI researchers have developed several systems that leverage the use of reflection for skill learning, the use of reflection-based learning of maker skills remains unexplored. We present ReflectiveMaker - a toolkit for experts and educators to design reflection exercises for novice learners in makerspaces. We describe the three components of our toolkit: (a) a designer interface to author the reflection prompts during fabrication activities, (b) a set of fabrication tools to sense the user's activities and (c) a reflection diary interface to record the user's reflections and analyze data on their learning progress. We then outline future work and envision a range of application scenarios.
Flaticulation: Laser Cutting Joints with Articulated Angles

We present Flaticulation, a method to laser cut joints that clutch two cut-in-place flat boards at designated articulated angles. We discover special T-patterns added on the shared edge of two pieces allowing them to be clutched at a bending angle. We analyze the structure and propose a parametric model regarding the T-pattern under laser cutting to predict the joint articulated angle. We validate our proposed model by measuring real prototypes and conducting stress-strain analysis to understand their structural strength. Finally, we provide a user interface for our example applications, including fast assembling unfolded 3D polygonal models and adding detent mechanisms for functional objects such as a mouse and reconfigurable objects such as a headphone.
Detecting Input Recognition Errors and User Errors using Gaze Dynamics in Virtual Reality

Gesture-based recognition systems are susceptible to input recognition errors and user errors, both of which negatively affect user experiences and can be frustrating to correct. Prior work has suggested that user gaze patterns following an input event could be used to detect input recognition errors and subsequently improve interaction. However, to be useful, error detection systems would need to detect various types of high-cost errors. Furthermore, to build a reliable detection model for errors, gaze behaviour following these errors must be manifested consistently across different tasks. Using data analysis and machine learning models, this research examined gaze dynamics following input events in virtual reality (VR). Across three distinct point-and-select tasks, we found differences in user gaze patterns following three input events: correctly recognized input actions, input recognition errors, and user errors. These differences were consistent across tasks, selection versus deselection actions, and naturally occurring versus experimentally injected input recognition errors. A multi-class deep neural network successfully discriminated between these three input events using only gaze dynamics, achieving an AUC-ROC-OVR score of 0.78. Together, these results demonstrate the utility of gaze in detecting interaction errors and have implications for the design of intelligent systems that can assist with adaptive error recovery.
Using Annotations for Sensemaking About Code

Developers spend significant amounts of time finding, relating, navigating, and, more broadly, making sense of code. While sensemaking, developers must keep track of many pieces of information including the objectives of their task, the code locations of interest, their questions and hypotheses about the behavior of the code, and more. Despite this process being such an integral aspect of software development, there is little tooling support for externalizing and keeping track of developers' information, which led us to develop Catseye – an annotation tool for lightweight notetaking about code. Catseye has advantages over traditional methods of externalizing code-related information, such as commenting, in that the annotations retain the original context of the code while not actually modifying the underlying source code, they can support richer interactions such as lightweight versioning, and they can be used as navigational aids. In our investigation of developers' notetaking processes using Catseye, we found developers were able to successfully use annotations to support their code sensemaking when completing a debugging task.
MechARspace: An Authoring System Enabling Bidirectional Binding of Augmented Reality with Toys in Real-time

Augmented Reality (AR), which blends physical and virtual worlds, presents the possibility of enhancing traditional toy design. By leveraging bidirectional virtual-physical interactions between humans and the designed artifact, such AR-enhanced toys can provide more playful and interactive experiences for traditional toys. However, designers are constrained by the complexity and technical difficulties of the current AR content creation processes. We propose MechARspace, an immersive authoring system that supports users to create toy-AR interactions through direct manipulation and visual programming. Based on the elicitation study, we propose a bidirectional interaction model which maps both ways: from the toy inputs to reactions of AR content, and also from the AR content to the toy reactions. This model guides the design of our system which includes a plug-and-play hardware toolkit and an in-situ authoring interface. We present multiple use cases enabled by MechARspace to validate this interaction model. Finally, we evaluate our system with a two-session user study where users first recreated a set of predefined toy-AR interactions and then implemented their own AR-enhanced toy designs.
Audio and video editing system design based on OpenCV

With the rapid development of the Internet, a new carrier for people to perceive the world and communicate with each other - audio and video - is gradually being favoured by the public. The development of multimedia technology and artificial intelligence technology has provided a milestone for the maturity of audio and video technology. In particular, short video platforms have slowly become a new network position for various media promotions. Especially at the moment of the epidemic, the channel of understanding the world through audio and video is increasingly valued. The public has put forward higher demands on the content and presentation of audio and video. Therefore, it is particularly important to produce quality audio-video that meets the requirements of the times, which cannot be achieved without a feasible audio-video editing system. In addition, after previous research and practice, the application of artificial intelligence technology in the field of imaging has also become mature, including some applications in the direction of entertainment. Applying AI technology to the video editing process can improve the efficiency of video editing, increase the interest of video content, and allow video creators to focus on content creation without spending too much time and energy on video editing operations, thus creating better quality videos. This design is the main technology of OpenCV and front-end technology stack, such as JavaScript, React and Electron, to implement basic video editing, video filters, in addition to the development of a friendly interactive interface. The implementation of basic video editing module and video filter module are both based on OpenCV implementation. In this design, the basic video editing implements pan, zoom and rotate operations on the video, and the video filter module is implemented by changing the RGB channel values of the image. The operations on the video can be broken down into operations on each frame of the video, and OpenCV provides a way to implement these operations. The paper concludes with a summary of the shortcomings and flaws in the design, and an outlook on the next steps and future directions. This design is the main technology of OpenCV and front-end technology stack, such as JavaScript, React and Electron, to implement basic video editing, video filters, in addition to the development of a friendly interactive interface. The implementation of basic video editing module and video filter module are both based on OpenCV implementation. In this design, the basic video editing implements pan, zoom and rotate operations on the video, and the video filter module is implemented by changing the RGB channel values of the image. The operations on the video can be broken down into operations on each frame of the video, and OpenCV provides a way to implement these operations. The paper concludes with a summary of the shortcomings and flaws in the design, and an outlook on the next steps and future directions.
Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation

Contrast and texture modifications applied during training or test-time have recently shown promising results to enhance the generalization performance of deep learning segmentation methods in medical image analysis. However, a deeper understanding of this phenomenon has not been investigated. In this study, we investigated this phenomenon using a controlled experimental setting, using datasets from the Human Connectome Project and a large set of simulated MR protocols, in order to mitigate data confounders and investigate possible explanations as to why model performance changes when applying different levels of contrast and texture-based modifications. Our experiments confirm previous findings regarding the improved performance of models subjected to contrast and texture modifications employed during training and/or testing time, but further show the interplay when these operations are combined, as well as the regimes of model improvement/worsening across scanning parameters. Furthermore, our findings demonstrate a spatial attention shift phenomenon of trained models, occurring for different levels of model performance, and varying in relation to the type of applied image modification.
Exploitation of Machine Learning Algorithms for Detecting Financial Crimes Based on Customers’ Behavior

Longer-term projections indicate that today’s developing and rising nations will account for roughly 60% of the global GDP by 2030. There is tremendous financial growth and advancement in developing countries, resulting in a high demand for personal loans from citizens. Depending on their needs, many people seek personal loans from banks. However, it is difficult for banks to predict which consumers will pay their bills and which will not since the number of bank frauds in many countries, notably India, is growing. According to the Reserve Bank of India, the Indian banking industry uncovered INR 71,500 in the scam in the fiscal year 2018–2019. The average lag time between the date of the occurrence and its recognition by banks, according to the statistics, was 22 months. This is despite harsher warnings from both the RBI and the government, particularly in the aftermath of the Nirav Modi debacle. To overcome this issue, we demonstrated how to create a predictive loan model that identifies problematic candidates who are considerably more likely to pay the money back. In step-by-step methods, we illustrated how to handle raw data, remove unneeded portions, choose appropriate features, gather exploratory statistics, and finally how to construct a model. In this work, we created supervised learning models such as decision tree (DT), random forest (RF), and k-nearest neighbor (KNN). According to the classification report, the models with the highest accuracy score, f-score, precision, and recall are considered the best among all models. However, in this work, our primary aim was to reduce the false-positive parameter in the classification models’ confusion matrix to reduce the banks’ non-performing assets (NPA), which is helpful to the banking sector. The data were graphed to help bankers better understand the customer’s behavior. Thus, using the same method, client loyalty may also be anticipated.
A scoping review of the effects of a technology-integrated, inquiry-based approach on primary pupils’ learning in science

Background Since the turn of the 21st century, research on an inquiry-based approach in science education has increasingly been geared towards the integration of technology, primarily focused on secondary and tertiary education. Thus far, no study has reviewed research on a technology-integrated, inquiry-based approach in the specific context of primary science education.Purpose & Method This scoping review has two primary aims: to investigate the characteristics of the technological interventions used in previous research that employed an inquiry-based approach in science education and to examine the effects of this integrated approach on primary pupils' learning in science. Fifteen articles from science education journals based on Scimago Journal Rank in the first quartile of 2020 and the Scopus database of the years 2017 to 2021 were extracted and reviewed using narrative and thematic analysis.Findings The review navigates research that has focused on the development of inquiry-based technological tools, the assimilation of the tools with scientific knowledge, and the type of technological intervention used. The technological tools employed were either self-developed or already available on the market. They took one of two forms: a learning management system or a games/simulation. Furthermore, most of the research integrated science contents into their technological tools. In terms of the effects of the technology-integrated, inquiry-based approach, most of the studies demonstrated positive impacts on primary pupils' learning in science, including pupils' improved conceptual understanding, scientific and thinking skills, views towards science, levels of motivation and interest, and collaborative skills.Conclusions Based on this review, it is suggested that science teachers should integrate technology into an inquiry-based approach, as research has shown that this approach positively impacts pupils' learning in science. Future empirical studies should also be carried out to examine the processes needed to integrate technology into an inquiry-based approach, rather than focusing on the effects alone.
Continuous learning deraining network based on residual FFT convolution and contextual transformer module

Single image deraining work has attracted a lot of attention. So an effective single image deraining network model, RFCTNet, is designed. Specifically, the residual Fast Fourier Transform convolution module is used as a baseline for the deraining network. The advantage is its ability to capture both long and short-term information interactions and to integrate high and low-frequency background information for delivery. And a novel Transformer module, the Contextual Transformer module, is introduced. Contextual knowledge of the rain streaks neighbourhood is learned to improve the recovery of textures and information in the image background based on focusing on the rain pattern features. Because of the overlap between rain streaks, removing them in one stage is difficult, so a multi-stage progressive rain removal method is used. Considering the catastrophic forgetting problem of deep learning, a continuous learning method, PIGWM, is adopted, which is c and enables the network model to memorize of the previous data set. Also, the rain removal method RFCTNet has been proven through extensive experiments to outperform other advanced methods' evaluation metrics on both synthetic and real datasets. The source code can be found on https://github.com/SUTwu/RFCTNet
Resource management at the network edge for federated learning

Federated learning has been explored as a promising solution for training machine learning models at the network edge, without sharing private user data. With limited resources at the edge, new solutions must be developed to leverage the software and hardware resources as the existing solutions did not focus on resource management for network edge, specially for federated learning. In this paper, we describe the recent work on resource management at the edge and explore the challenges and future directions to allow the execution of federated learning at the edge. Problems such as the discovery of resources, deployment, load balancing, migration, and energy efficiency are discussed in the paper.