Reviewerly/exHarmony huggingface dataset:
Novel Constructions for Ciphertext-Policy Attribute-Based Searchable Encryption

More often, cloud services are being used for out-sourcing data storage. Nonetheless, as storage providers are untrustworthy, two major security concerns for data owners are confidentiality and access control. A naive solution to achieve these security goals is to encrypt before outsourcing the data to the cloud. However, such approach entails a major problem for data consumers: searching over unintelligible data becomes quite difficult. In this context, Searchable Encryption (SE) is a powerful cryptographic technique that lets data owners store encrypted data on untrusted external servers while keeping the ability to perform searching and retrieval tasks from the encrypted format. In this paper, we present two novel constructions for searching over encrypted data on the basis of Attribute-Based Searchable Encryption (ABSE). Our constructions are implemented over asymmetric pairings (Type-III), enabling SE deployments for security levels higher than the common and obsolete 80-bit security found in the literature. The proposed constructions consider the two most common realizations of Attribute-Based Encryption (ABE) for the access structure: tree- and matrix-based. The experimental validation and evaluation over the LISA benchmark revealed the suitability of ABSE in practical applications, its correctness, and efficacy.
Hardware Architecture for Adaptive Edge-Directed Interpolation Algorithm

Demosaicing refers to the reconstruction of full color image by the incomplete color samples produced by the single-chip image sensor. So there is a need of interpolation to obtain the missing color pixels. In this work a hardware architecture has been proposed for the adaptive edge-directed interpolation algorithm which uses an edge estimator for the interpolation. The proposed hardware architecture is implemented in Verilog HDL (Hardware Description Language) and synthesized using Cadence Genus compiler with 90nm technology in typical mode. For the proposed architecture, the power dissipation is found to be 26 mW, delay is 7.2 ns and requires 2.3 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area. The demosaiced images obtained using the proposed architecture is observed to have better image quality in terms of peak signal-to-noise ratio and structural similarity while comparing with existing architectures.
Artificial Intelligence-Powered Contactless Face Recognition Technique for Internet of Things Access for Smart Mobility

A contactless system became necessary for smart mobility during the COVID-19 pandemic. There are many touchpoints in private and public areas where contact is essential, such as intelligent transportation systems for vaccine carriers, patient ambulances, elevators, metros, buses, hospitals, and banks. A secured contactless device reduces the chances of COVID-19 infection spread. Several devices use smart cards, fingerprint identification, or code-based access. Most of these devices require some form of touch. The cost of such devices varies, depending on their capability and intended use. Sensors developed by using artificial intelligence (AI) to provide secured access are an emerging area. This paper presents an AI-powered contactless face recognition system. The solution has the Internet of Things (IoT) enabled access system. To identify a person, it uses AI assistance for face recognition with the help of Python Dlib’s facial recognition network. Dlib offers a wide range of functionality across several machine learning sectors and is open-source. The Arduino Uno (ATmega328P) and STK500 protocol has been used for communication to testify and validate the performance of the proposed technique. The objective is to detect and recognize faces by the proposed contactless approach. The obtained result shows 92% accuracy, 94% sensitivity, 96% precision and FRR 6% for face detection. There is a significant improvement in FRR in our work compared to the published 27.27%. The implemented solution in this paper provides accurate and secure contactless access to conventional, readily available techniques in public health safety.
Interactive Scientific Visualization of Fluid Flow Simulation Data Using AR Technology-Open-Source Library OpenVisFlow

Computational fluid dynamics (CFD) are being used more and more in the industry to understand and optimize processes such as fluid flows. At the same time, tools such as augmented reality (AR) are becoming increasingly important with the realization of Industry 5.0 to make data and processes more tangible. Placing the two together paves the way for a new method of active learning and also for an interesting and engaging way of presenting industry processes. It also enables students to reinforce their understanding of the fundamental concepts of fluid dynamics in an interactive way. However, this is not really being utilized yet. For this reason, in this paper, we aim to combine these two powerful tools. Furthermore, we present the framework of a modular open-source library for scientific visualization of fluid flow “OpenVisFlow” which simplifies the creation of such applications and enables seamless visualization without other software by allowing users to integrate the visualization step into the simulation code. Using this framework and the open-source extension AR-Core, we show how a new markerless visualization tool can be implemented.
An Unconscious Universal in the Mind is Like an Immaterial Dinner in the Stomach. A Debate on Logical Generalism (1914–1919)

Abstract The debate on the a fortiori and the universal that took place between April 1914 and April 1919 in the journal Mind has a double interest for argumentation theorists. First, the discussion is an example of a philosophical polylogue that exhibits the characteristics of a quasi-engaged dialogue (Blair Blair, J. A. (2012 [1998]). “The Limits of the Dialogue Model of Argument”. Argumentation 12, pp. 325–339. Reprinted in J.A. Blair, Groundwork in the Theory of Argumentation, pp. 231–244. Dordrecht: Springer, 2012.), confirming Blair’s hypothesis that journal papers and scholarly monographs can be analyzed as turns in non-engaged or quasi-engaged dialogues. It could be said that philosophical argumentation is dialectical but not dialogical. Second, the debate is a discussion in argumentation theory. Generalism in the theory of argument claims that the very possibility of arguing depends on a suitable supply of general rules that specify what kinds of conclusions can be drawn from what kinds of data, while particularism denies this. Although the terminology may be alien, I will also show that the debate on the a fortiori and the universal was a debate on generalism and particularism.
A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation

Motion segmentation is one of the fundamental steps for detection, tracking, and recognition, and it can separate moving objects from the background. In this paper, we propose a spatial-motion-segmentation algorithm by fusing the events-dimensionality-preprocessing algorithm (EDPA) and the volume of warped events (VWE). The EDPA consists of depth estimation, linear interpolation, and coordinate normalization to obtain an extra dimension (Z) of events. The VWE is conducted by accumulating the warped events (i.e., motion compensation), and the iterative-clustering algorithm is introduced to maximize the contrast (i.e., variance) in the VWE. We established our datasets by utilizing the event-camera simulator (ESIM), which can simulate high-frame-rate videos that are decomposed into frames to generate a large amount of reliable events data. Exterior and interior scenes were segmented in the first part of the experiments. We present the sparrow search algorithm-based gradient ascent (SSA-Gradient Ascent). The SSA-Gradient Ascent, gradient ascent, and particle swarm optimization (PSO) were evaluated in the second part. In Motion Flow 1, the SSA-Gradient Ascent was 0.402% higher than the basic variance value, and 52.941% faster than the basic convergence rate. In Motion Flow 2, the SSA-Gradient Ascent still performed better than the others. The experimental results validate the feasibility of the proposed algorithm.
Significant Factors in Agile Software Development of Effort Estimation

The Agile effort estimation involves project-related and people-related factors. This research objective is to find the factors that influence Agile effort estimation significantly through path analysis using a structural equation model. This research built an agile effort estimation path coefficient model from six constructs from theories and previous studies. Project-related factors represent by requirement and design implementation constructs. People-related factors are measured by the construct of experience, knowledge, and technical ability. The last construct is the effort itself. SmartPLS is employed for the confirmatory composite analysis and the structural model assessment. The confirmatory composite analysis indicated that all constructs are reliable and valid. Furthermore, the structural model assessment found that all factors of project-related constructs have a positive relationship and significant influence, showing a coefficient path value of 59.1% between requirement and design implementation constructs. All constructs represent people-related factors indicated by the coefficient path value of 67% between experience and knowledge, 42.6% between experience and technical ability, and 54.4% between knowledge and technical ability. In addition, all constructs proved influential simultaneously to effort by 31.1%. Positively contribute provided by requirement, experience, and technology’s ability. Significantly influenced provided by constructs of the developer’s knowledge and technical ability. The largest effect is given by technical ability, knowledge, and experience on medium and small scales. Contrarily, both constructs from project-related effects can be negligible because there was no influence. Based on the result, this study concludes that the significant factors in Agile effort estimation are technical ability, knowledge, and experience.
Sim2Word: Explaining Similarity with Representative Attribute Words via Counterfactual Explanations

Recently, we have witnessed substantial success using the deep neural network in many tasks. Although there still exist concerns about the explainability of decision making, it is beneficial for users to discern the defects in the deployed deep models. Existing explainable models either provide the image-level visualization of attention weights or generate textual descriptions as post hoc justifications. Different from existing models, in this article we propose a new interpretation method that explains the image similarity models by salience maps and attribute words. Our interpretation model contains visual salience maps generation and the counterfactual explanation generation. The former has two branches: global identity relevant region discovery and multi-attribute semantic region discovery. The first branch aims to capture the visual evidence supporting the similarity score, which is achieved by computing counterfactual feature maps. The second branch aims to discover semantic regions supporting different attributes, which helps to understand which attributes in an image might change the similarity score. Then, by fusing visual evidence from two branches, we can obtain the salience maps indicating important response evidence. The latter will generate the attribute words that best explain the similarity using the proposed erasing model. The effectiveness of our model is evaluated on the classical face verification task. Experiments conducted on two benchmarks—VGGFace2 and Celeb-A—demonstrate that our model can provide convincing interpretable explanations for the similarity. Moreover, our algorithm can be applied to evidential learning cases, such as finding the most characteristic attributes in a set of face images, and we verify its effectiveness on the VGGFace2 dataset.
Formalising Basic Topology for Computational Logic in Simple Type Theory

We present a formalisation of basic topology in simple type theory encoded using the Isabelle/HOL proof assistant. In contrast to related formalisation work, which follows more ‘traditional’ approaches, our work builds upon closure algebras, encoded as Boolean algebras of (characteristic functions of) sets featuring an axiomatised closure operator (cf. seminal work by Kuratowski and McKinsey & Tarski). With this approach we primarily address students of computational logic, for whom we bring a different focus, closer to lattice theory and logic than to set theory or analysis. This approach has allowed us to better leverage the automated tools integrated into Isabelle/HOL (model finder Nitpick and Sledgehammer) to do most of the proof and refutation heavy-lifting, thus allowing for assumption-minimality and less-verbose interactive proofs.
Context‐guided ground truth sampling for multi‐modality data augmentation in autonomous driving

Abstract Data augmentation is an important pre‐processing step for object detection in 2D image and 3D point clouds. However, studies on multimodal data augmentation are extremely limited compared to single‐modal work. Moreover, simultaneously ensuring consistency and rationality when pasting both image and point cloud samples is a major challenge in multimodal methods. In this study, a novel multimodal data augmentation method based on ground truth sampling (GT sampling) is proposed for generating content‐rich synthetic scenes. A GT database and scene ground database based on the raw training set is initially built, following which the context of the image and point cloud is used to guide the paste location and filtering strategy of the GT samples. The proposed method can avoid the cluttered features caused by random pasting of samples; the image context information can help the model to learn the correlation between the object and the environment more comprehensively, and the point cloud context information can reduce occlusion in the case of long‐distance objects. The effectiveness of the proposed strategy is demonstrated on the publicly available KITTI dataset. Utilizing the multimodal 3D detector MVXNet as an implementation tool, our experiments evaluate different superimposition strategies ranging from context‐free sample pasting methods to context‐guided new training scenes. In comparison with existing GT sampling methods, our method exhibits a relative performance improvement of 15% on benchmark datasets. In ablation studies, our sample pasting strategy achieves a +2.81% gain compared with previous work. In conclusion, considering the multimodal context of modelled objects is crucial for placing them in the correct environment.
An Accurate Unsupervised Liver Lesion Detection Method Using Pseudo-lesions

Anomaly detection using an unsupervised learning scheme has become a challenging research topic. Unsupervised learning requires only unlabeled normal data for training and can detect anomalies in unseen testing data. In this paper, we propose an unsupervised liver lesion detection framework based on generative adversarial networks. We present a new perspective that learning anomalies positively affect learning normal objects (e.g., liver), even if the anomalies are fake. Our framework uses only normal and pseudo-lesions data for training, and the pseudo-lesions data comes from normal data augmentation. We train our framework to predict normal features by transferring normal and augmented data into each other. In addition, we introduce a discriminator network containing a U-Net-like architecture that extracts local and global features effectively for providing more informative feedback to the generator. Further, we also propose a novel reconstruction-error score index based on the image gradient perception pyramid. A higher error-index score indicates a lower similarity between input and output images, which means lesions detected. We conduct extensive experiments on different datasets for liver lesion detection. Our proposed method outperforms other state-of-the-art unsupervised anomaly detection methods.
Parameter-Free Latent Space Transformer for Zero-Shot Bidirectional Cross-modality Liver Segmentation

In this paper, we address the domain shift in cross CT-MR liver segmentation task with a latent space investigation. Domain adaptation between modalities is of significant importance in clinical practice, as different diagnostic procedures require different imaging modalities, such as CT and MR. Thus, training a convolutional neural network (CNN) with one modality may not be sufficient for application in another one. Most domain adaptation methods need to use data and ground truths of both source and target domain in the training process. Different from these techniques, we propose a zero-shot bidirectional cross-modality liver segmentation method by investigating a parameter-free latent space through the prior knowledge from CT and MR images. Experiments on the CHAOS, the subset of LiTS and the local TACE datasets demonstrate that our method can well deal with the problem of CNN failure caused by domain shift and yields promising segmentation results.
TranSQ: Transformer-Based Semantic Query for Medical Report Generation

Medical report generation, which aims at automatically generating coherent reports with multiple sentences for the given medical images, has received growing research interest due to its tremendous potential in facilitating clinical workflow and improving health services. Due to the highly patterned nature of medical reports, each sentence can be viewed as the description of an image observation with a specific purpose. To this end, this study proposes a novel Transformer-based Semantic Query (TranSQ) model that treats the medical report generation as a direct set prediction problem. Specifically, our model generates a set of semantic features to match plausible clinical concerns and compose the report with sentence retrieval and selection. Experimental results on two prevailing radiology report datasets, i.e., IU X-Ray and MIMIC-CXR, demonstrate that our model outperforms state-of-the-art models on the generation task in terms of both language generation effectiveness and clinical efficacy, which highlights the utility of our approach in generating medical reports with topics of clinical concern as well as sentence-level visual-semantic attention mappings. The source code is available at https://github.com/zjukongming/TranSQ .
Block-Based Discrete Cosine Approaches for Removal of JPEG Compression Artifacts

Image compression plays an important role in different fields, such as medical imaging, digital photography, multimedia, interactive Television, mobile communications. The main function of image compression is to reduce the size of image, transmission cost and also occupy less storage space. It maintains the perceptual quality of an image without ant loss of important information. Therefore, space and time are two main components of image compression. Moreover, Image compression is a successful and popular technique which reduces the size of image and display tangible information of given data. Lossless, lossy and hybrid compression are the different types of techniques which used to reduce the size of images and videos. In this paper, advantages and efficiency of image compression have been discussed. There are two types of blocking artifacts filters that are discussed along with types of blocking artifacts.
Enabling Secure and Transparent Crowd Funding Approach Powered by Blockchain

Now a day, crowdfunding is the most prominent area which the people are using to generate and create the funds. This is not only popular in developed countries, but also popular in developing countries. In India, the scope of the crowdfunding is increasing exponentially. Blockchain is one of the best way to do the fundraising and Crowdfunding. Many MSE and organizations are practicing the crowdfunding to raise funds for their infrastructures and the projects. The large number of people can easy contribute their funds electronically. Traditionally, the task was done in form of bank loans and partnerships, now the digital world has given so many options to raise the funds. Entrepreneur can easily generate the fund through the crowdfunding platforms. The proposed system addressed the various issues and their solutions to use the crowdfunding platforms. The article shows an approach and methodology which is secure and transparent to raise the fund using blockchain architecture.
Anomaly-Aware Multiple Instance Learning for Rare Anemia Disorder Classification

Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations. Multiple Instance Learning (MIL) has shown to be an effective solution, yet it suffers from low accuracy and limited explainability. Although the inclusion of attention mechanisms has addressed these issues, their effectiveness highly depends on the amount and diversity of cells in the training samples. Consequently, the poor machine learning performance on rare anemia disorder classification from blood samples remains unresolved. In this paper, we propose an interpretable pooling method for MIL to address these limitations. By benefiting from instance-level information of negative bags (i.e., homogeneous benign cells from healthy individuals), our approach increases the contribution of anomalous instances. We show that our strategy outperforms standard MIL classification algorithms and provides a meaningful explanation behind its decisions. Moreover, it can denote anomalous instances of rare blood diseases that are not seen during the training phase.
Local-global visual interaction attention for image captioning

Image captioning is a typical cross-modal task, which aims to automatically describe the main content of an image with a complete and natural sentence. Existing attention based approaches treat local feature and global feature in the image individually, neglecting the intrinsic interaction between them that provides important guidance for generating caption. To alleviate the above issue, in this work we propose a novel Local-Global Visual Interaction Attention (LGVIA) structure that novelly explores the intrinsic interactions between local feature and global feature in the image. Specifically, we devise a new visual interaction graph network that mainly consists of visual interaction encoding module and visual interaction fusion module. The former implicitly encodes the visual relationships between local feature and global feature to obtain an enhanced visual representation containing rich local-global feature relationship. The latter fuses the previously obtained multiple relationship features to further enrich different-level relationship attribute information. In addition, we introduce a new relationship attention based LSTM module to guide the word generation by dynamically focusing on the previously output fusion relationship information. Extensive qualitative and quantitative experimental results show that the superiority of our LGVIA approach on the large-scale MSCOCO dataset. More remarkably, LGVIA outperforms the related state-of-the-art methods on the small-scale Flickr30k dataset.