Reviewerly/exHarmony huggingface dataset:
An Endoscope Image Enhancement Algorithm Based on Image Decomposition

The visual quality of endoscopic images is a significant factor in early lesion inspection and surgical procedures. However, due to the interference of light sources, hardware, and other configurations, the endoscopic images collected clinically have uneven illumination, blurred details, and contrast. This paper proposed a new endoscopic image enhancement algorithm. The image decomposes into a detail layer and a base layer based on noise suppression. The blood vessel information is stretched by channel in the detail layer, and adaptive brightness correction is performed in the base layer. Finally, Fusion obtained a new endoscopic image. This paper compares the algorithm with six other algorithms in the laboratory dataset. The algorithm is in the leading position in all five objective evaluation metrics, further indicating that the algorithm is ahead of other algorithms in contrast, structural similarity, and peak signal-to-noise ratio. It can effectively highlight the blood vessel information in endoscopic images while avoiding the influence of noise and highlight points. The proposed algorithm can well solve the existing problems of endoscopic images.
An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers

Image denoising is a classic but still important issue in image processing as the denoising effect has a significant impact on subsequent image processing results, such as target recognition and edge detection. In the past few decades, various denoising methods have been proposed, such as model-based and learning-based methods, and they have achieved promising results. However, no stand-alone method consistently outperforms the others in different complex imaging situations. Based on the complementary strengths of model-based and learning-based methods, in this study, we design a pixel-level image combination strategy to leverage their respective advantages for the denoised images (referred to as initial denoised images) generated by individual denoisers. The key to this combination strategy is to generate a corresponding weight map of the same size for each initial denoised image. To this end, we introduce an unsupervised weight map generative network that adjusts its parameters to generate a weight map for each initial denoised image under the guidance of our designed loss function. Using the weight maps, we are able to fully utilize the internal and external information of various denoising methods at a finer granularity, ensuring that the final combined image is close to the optimal. To the best of our knowledge, our enhancement method of combining denoised images at the pixel level is the first proposed in the image combination field. Extensive experiments demonstrate that the proposed method shows superior performance, both quantitatively and visually, and stronger generalization. Specifically, in comparison with the stand-alone denoising methods FFDNet and BM3D, our method improves the average peak signal-to-noise ratio (PSNR) by 0.18 dB to 0.83 dB on two benchmarking datasets crossing different noise levels. Its denoising effect is also greater than other competitive stand-alone methods and combination methods, and has surpassed the denoising effect of the second-best method by 0.03 dB to 1.42 dB. It should be noted that since our image combination strategy is generic, the proposed combined strategy can not only be used for image denoising but can also be extended to low-light image enhancement, image deblurring or image super-resolution.
Diffraction Efficiency of MEMS Phase Light Modulator, TI-PLM, for Quasi-Continuous and Multi-Point Beam Steering

The recent development of the Micro Electromechanical System (MEMS) Phase Light Modulator (PLM) enables fast laser beam steering for lidar applications by displaying a Computer-Generated Hologram (CGH) without employing an iterative CGH calculation algorithm. We discuss the application of MEMS PLM (Texas Instruments PLM) for quasi-continuous laser beam steering by deterministically calculated CGHs. The effect on the diffraction efficiency of PLM non-equally spaced phase levels was quantified. We also address the CGH calculation algorithm and an experimental demonstration that steered and scanned the beam into multiple regions of interest points, enabling beam steering for lidar without sequential raster scanning.
Towards a Low-Cost Teacher Orchestration Using Ubiquitous Computing Devices for Detecting Student’s Engagement

The ubiquitous devices and technologies to support teachers and students in a learning environment include the Internet of things (IoT), learning analytics (LA), augmented or virtual reality (AR/VR), ubiquitous learning environment (ULE), and wearables. However, most of these solutions are obtrusive, with substantial infrastructure costs and pseudo-real-time results. Real-time detection of students’ activeness, participation, and activity monitoring is important, especially during a pandemic. This research study provides a low-cost teacher orchestration solution with real-time results using off-the-shelf devices. The proposed solution determines a teacher’s activeness using multimodal data (MMD) from both teacher and student’s devices. The MMD extracts different features from data, decodes them, and displays them to the instructor in real time. It allows the instructor to update their teaching methodology in real time to get more students on board and provide a more engaging learning experience. Our experimental results show that real-time feedback about the classroom’s current status helped improve learning outcomes by about 45%. Also, we investigated a 50% increase in classroom engaging experience.
DualCF: Efficient Model Extraction Attack from Counterfactual Explanations

Cloud service providers have launched Machine-Learning-as-a-Service (MLaaS) platforms to allow users to access large-scale cloud-based models via APIs. In addition to prediction outputs, these APIs can also provide other information in a more human-understandable way, such as counterfactual explanations (CF). However, such extra information inevitably causes the cloud models to be more vulnerable to extraction attacks which aim to steal the internal functionality of models in the cloud. Due to the black-box nature of cloud models, however, a vast number of queries are inevitably required by existing attack strategies before the substitute model achieves high fidelity. In this paper, we propose a novel simple yet efficient querying strategy to greatly enhance the querying efficiency to steal a classification model. This is motivated by our observation that current querying strategies suffer from decision boundary shift issue induced by taking far-distant queries and close-to-boundary CFs into substitute model training. We then propose DualCF strategy to circumvent the above issues, which is achieved by taking not only CF but also counterfactual explanation of CF (CCF) as pairs of training samples for the substitute model. Extensive and comprehensive experimental evaluations are conducted on both synthetic and real-world datasets. The experimental results favorably illustrate that DualCF can produce a high-fidelity model with fewer queries efficiently and effectively.
Sequential analysis in Fourier probabilistic neural networks

In this paper, the computational complexity of the probabilistic neural network for the classification of high-dimensional data is improved. At first, the class probability densities are estimated by using only a few principal components of an observed point. The Gaussian–Parzen kernel is replaced by the orthogonal series estimates of class-conditional densities for each principal component using the Fourier series to speed up a decision-making procedure. The unreliable classes are found for which a likelihood ratio to the maximal likelihood does not exceed a certain threshold. The next components are used to refine these estimates only for other classes, and such a sequential analysis of principal components is repeated until only one reliable class is obtained. Experimental study for image recognition with features extracted by deep convolutional neural networks including EfficientNets demonstrates that our approach is more accurate and 15–60 times faster than the baseline instance-based learning methods.
Video‐blogs and linguistic simplification for students with intellectual disability

The Internet provides individuals with intellectual disability with access to information and participation in a broader society, but it also presents risks when content is difficult to comprehend. This study aimed to test whether students with intellectual disability enhanced their comprehension of online blogs as a function of the format (text vs. video) and linguistic simplification.Young students with intellectual disability read or watched text and video-blog posts about environment, health, society, and technology in their original version or a linguistically simplified/easy-to-read version. Then, they completed tests that assessed their reading processing (e.g., calibration) and comprehension of blog content.Participants predicted that they would correctly answer 80% of the comprehension questions, but their average comprehension of the blog posts was 55%, regardless of the condition. Previous levels of students' reading comprehension skills predicted their blog comprehension scores and interacted with format and simplification. Those participants with higher reading comprehension skills learned more from non-simplified than from simplified blog posts while those with lower levels of reading comprehension skills did not benefit from linguistic simplification nor video-blog format although the difference with regard to higher comprehenders was lower in the video and simplified conditions.Improving reading comprehension abilities of students with intellectual disability is essential to prevent the digital divide while linguistic simplification is not a useful accommodation and even counterproductive for higher reading comprehenders.
BMAM: complete the missing POI in the incomplete trajectory via mask and bidirectional attention model

Abstract Studies on the checked-in point-of-interests have become an important means to learn user’s behavior. Nevertheless, users do not sign in to all visited locations. There are unobserved check-in locations in the generated POI trajectory. Such the trajectory is called an incomplete trajectory, and unobserved point is called missing point. However, incomplete trajectory has a negative impact on downstream tasks such as personalized recommendation system, criminal identification and next location prediction. It is a challenge to use the forward sequence and backward sequence information of the missing point to complete the missing POI. Therefore, we propose a bidirectional model based on mask and attention mechanism (BMAM) to solve the problem of missing POI completion in user’s incomplete trajectory. The context information of trajectory checked in by user can be mined to connect the missing POI with the forward sequence and backward sequence information. Therefore, the model learns the order dependence between each location according to the user trajectory sequence and obtain the user’s dynamic preference to identify the missing POI in the sequence. Besides, the attention mechanism is used to improve the user's representation feature, that is, the preference for POI categories. The experimental results demonstrate that our BMAM outperforms the state-of-the-art models for completion on missing POI of user’s incomplete sequence.
Graph Neural Network for Context-Aware Recommendation

Abstract Recommendation problems are naturally tackled as a link prediction task in a bipartite graph between user and item nodes, labelled with rating information on edges. To provide personal recommendations and improve the performance of the recommender system, it is necessary to integrate side information along with user-item interactions. The integration of context is a key success factor in recommendation systems because it allows catering for user preferences and opinions, especially when this pertains to the circumstances surrounding the interaction between users and items. In this paper, we propose a c ontext-aware G raph C onvolutional M atrix C ompletion which captures structural information and integrates the user’s opinion on items along with the surrounding context on edges and static features of user and item nodes. Our graph encoder produces user and item representations with respect to context, features and opinion. The decoder takes the aggregated embeddings to predict the user-item score considering the surrounding context. We have evaluated the performance of our model on 14 five publicly available datasets and compared it with state-of-the-art algorithms. Throughout this we show how it can effectively integrate user opinion along with surrounding context to produce a final node representation which is aware of the favourite circumstances of the particular node.
Blockchain Adoption in Banking Systems

Blockchain technology is a core, underlying technology that stimulates possible applications in the banking sector. The study examines blockchain, the nascent technology that reinforces Bitcoin and other cryptocurrencies, to determine what it is and how it can disrupt and alter the banking services. It emphasizes the technology's properties and explains why they can significantly impact the banking industry, including reimbursements, payments, outflows, credentials facilities, and novel products based on smart contracts. The study also considered the work that needs to be done by the industry for blockchain applications to become a mainstream part of the financial landscape. It emphasizes that this is not a technology that a single company can perfect to obtain an advantage over competitors. Instead, it may assist the entire sector by speeding up and securing transactions. However, it can only reach its full potential if there is a wide-ranging alliance across the industry to explore practices and develop shared standards.
Portraying What is in Front of You: Virtual Tours and Online Whiteboards to Facilitate Art Practice during the COVID-19 Pandemic

The arts and education fields recognize the importance of museums and art galleries as not only buildings that house hundreds, often thousands, of specialized objects, artworks, research, and conservation but rather as institutions sharing the history of people and their environment, thus they play a substantive role in modern society. For art students, practitioners, and hobbyists, these institutions are often visited to provide inspiration and practice, although the COVID-19 pandemic has had a profound impact. National lockdowns and restrictions, and social distancing measures meant museums and art galleries underwent mandated temporary closures, and socially distanced specified visitor routes often meant onsite art practice was no longer permitted. This exploratory pictorial takes a first-person research method to present a collection of reflections, experiences, and example artworks by facilitators of an art practice group that moved from on-site to online practice during the pandemic.
The improvement of the wavefront modulation accuracy of SLMs based on a grayscale segmentation of the phase modulation curve

It is known that there are obvious fly-back zone errors in the area of 2π phase resets of spatial light modulators(SLMs). It reduces the diffraction efficiency of the SLM, as a result, the accuracy of the wavefront modulation will be reduced. To reduce the fly-back zone error, this paper proposes a zoning variable module phase wrapping method based on the grayscale segmentation, which is marked as the module-n1π-n2π method. The experimental results show that the root-mean-square (RMS) value of the astigmatism term's deviation is from 0.0413λ reduced to 0.0277λ, which proves that the proposed method can improve the reconstruction accuracy of the non-rotationally symmetric aberrations and can effectively reduce the fly-back zone error. Compared with the traditional module-2π method, the module-n1π-n2π method does not need to consider the 2π correction of the modulation depth, therefore, it is more flexible and convenient. With the improvement of the wavefront modulation accuracy of SLMs, it will play a more important role in adaptive interferometry for optical freeform surfaces.
Integration of Blockchain Tokenisation in Real Estate

Real estate is among the top reliable investments because it provides a reliable source of income through rent and leases. While there are many benefits, one of the main negatives of real estate investment is the lack of liquidity and transparency. Blockchain technology holds real potential to address this issue, making the market accessible for investors. Due to the utility and flexibility of constructing security tokens secured by real assets, real property can be liquidated through the use of special purpose vehicles. Blockchain tokenisation technology will revolutionize the real estate market across all aspects: ownership selling, managing, and investment. Tokenisation shifts physical real estate into the digital world and could lead to significant savings in the cost of the pre- and post-tokenisation phases. Policymakers and developers in India have started talking about shifting from the traditional investment towards new blockchain tokenisation. Tokenisation may prove to be a viable funding source for those relatively poorly capitalized financial markets.
Knowledge Organisation in Academic Libraries

Linked Data (LD) emerged as an innovation in libraries over a decade ago. It refers to a set of best practices for publishing and linking structured data using existing Semantic Web technologies. Knowledge organisation in academic libraries can use the advantages of LD technologies to increase availability of library resources on the world wide web. Existing methods of descriptive cataloguing are based on describing metadata and constructing unique authorized access points as text strings. However, this strings-based approach works well in the closed environment of a traditional library catalogue and not in an open environment where data are shared and linked. This chapter investigates the introduction of LD in the organization of knowledge in academic libraries, as literature shows that students prefer to search the internet for their information needs. Secondary literature was reviewed and analysed. Findings indicated that libraries that adopted LD increased the visibility of their products on the internet.
Why Am I Not Seeing It? Understanding Users’ Needs for Counterfactual Explanations in Everyday Recommendations

Intelligent everyday applications typically rely on automated Recommender Systems (RS) to generate recommendations that help users make decisions among a large number of options. Due to the increasing complexity of RS and the lack of transparency in its algorithmic decision-making, researchers have recognized the need to support users with explanations. While many traditional Explainable AI methods fall short in disclosing the internal intricacy of recommender systems, counterfactual explanations provide many desirable explainable features by offering human-like explanations that contrast an existing recommendation with alternatives. However, there is a lack of empirical research in understanding users' needs of counterfactual explanations in their usage of everyday intelligent applications. In this paper, we investigate whether and when to provide counterfactual explanations to support people's decision-making with everyday recommendations through a question-driven approach. We conducted a preliminary survey study and an interview study to understand how existing explanations might be insufficient to support users and elicit the triggers that prompt them to ask why not questions and seek additional explanations. The findings reveal that the utility of decision is a primary factor that may affect their counterfactual information needs. We then conducted an online scenario-based survey to quantify the correlation between utility and explanation needs and found significant correlations between the measured variables.
Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?

This paper contains images and descriptions that are offensive in nature.
Modding the Pliable Machine: Unpacking the Creative and Social Practice of Upkeep at the Makerspace

As digital fabrication machines have become more accessible and widely available, practitioners in maker communities have become increasingly responsive to the opportunities to achieve bespoke modifications, known colloquially as 'modding'. Drawing on interviews with five experienced makers who engage in modding a laser cutter, along with ethnographic observations of maker-machine interactions, we analyse makers' experiences and 'war stories' to frame modding as a prevalent but less explored maker activity. We highlight how makers care for machines, how they cope with risks when engaging in modding, and how mods are essentially creative projects. Based on our findings, we present the conceptualisation of the 'pliable machine' – a socio-technical system constituted by, (1) an accessible machine that can be altered, (2) maker skills that go beyond intended use, and (3) a surrounding 'maker culture' of caring, sharing and experimentation. Treating the machine as a material offers an alternative perspective on our interactions with technology; we show how the laser cutter becomes pliable in the hands of those who mod.
ComAI: Enabling Lightweight, Collaborative Intelligence by Retrofitting Vision DNNs

While Deep Neural Network (DNN) models have transformed machine vision capabilities, their extremely high computational complexity and model sizes present a formidable deployment roadblock for AIoT applications. We show that the complexity-vs-accuracy-vs-communication tradeoffs for such DNN models can be significantly addressed via a novel, lightweight form of "collaborative machine intelligence" that requires only runtime changes to the inference process. In our proposed approach, called ComAI, the DNN pipelines of different vision sensors share intermediate processing state with one another, effectively providing hints about objects located within their mutually-overlapping Field-of-Views (FoVs). CoMAI uses two novel techniques: (a) a secondary shallow ML model that uses features from early layers of a peer DNN to predict object confidence values in the image, and (b) a pipelined sharing of such confidence values, by collaborators, that is then used to bias a reference DNN’s outputs. We demonstrate that CoMAI (a) can boost accuracy (recall) of DNN inference by 20-50%, (b) works across heterogeneous DNN models and deployments, and (c) incurs negligible processing, bandwidth and processing overheads compared to non-collaborative baselines.
Leveraging Spanning Tree to Detect Colluding Attackers in Federated Learning

Federated learning distributes model training among multiple clients who, driven by privacy concerns, perform training using their local data and only share model weights for iterative aggregation on the server. In this work, we explore the threat of collusion attacks from multiple malicious clients who pose targeted attacks (e.g., label flipping) in a federated learning configuration. By leveraging client weights and the correlation among them, we develop a graph-based algorithm to detect malicious clients. Finally, we validate the effectiveness of our algorithm in presence of varying number of attackers on a classification task using a well-known Fashion-MNIST dataset.