Reviewerly/exHarmony huggingface dataset:
A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning

Colorectal cancer is one of the most common types of cancer, and it can have a high mortality rate if left untreated or undiagnosed. The fact that CRC becomes symptomatic at advanced stages highlights the importance of early screening. The reference screening method for CRC is colonoscopy, an invasive, time-consuming procedure that requires sedation or anesthesia and is recommended from a certain age and above. The aim of this study was to build a machine learning classifier that can distinguish cancer from non-cancer samples. For this, circulating tumor cells were enumerated using flow cytometry. Their numbers were used as a training set for building an optimized SVM classifier that was subsequently used on a blind set. The SVM classifier's accuracy on the blind samples was found to be 90.0%, sensitivity was 80.0%, specificity was 100.0%, precision was 100.0% and AUC was 0.98. Finally, in order to test the generalizability of our method, we also compared the performances of different classifiers developed by various machine learning models, using over-sampling datasets generated by the SMOTE algorithm. The results showed that SVM achieved the best performances according to the validation accuracy metric. Overall, our results demonstrate that CTCs enumerated by flow cytometry can provide significant information, which can be used in machine learning algorithms to successfully discriminate between healthy and colorectal cancer patients. The clinical significance of this method could be the development of a simple, fast, non-invasive cancer screening tool based on blood CTC enumeration by flow cytometry and machine learning algorithms.
DESIGN AND ANALYSIS OF ADVANCED WALKER CUM ROLLATOR

Over the year’s walker or rollator have helped many elderly people to overcome the barrier of dependency. As the age grows, the muscles and bones suffer weakening due to lack of calcium because of which person has to depend on others for their movement. This unwanted condition creates a bad physiological and psychological impact on the victim and slowly pulls them out of all possible types of interactions with society. Thus, to make the walker more users friendly, safe, and reliable, a new design or solution is proposed. This not only addresses the problem which is prevalent in the elderly section of society but also will be helpful to people with other disabilities. The proposed design of the walker is to increase the safety and movability of the user. The design of this walker is advanced as it consists of various sensors like an Obstacle sensor, GPS, and Load Sensor. In this paper, the design and analysis of an advanced walker cum rollator is elaborated, which significantly reduces the challenges faced by the elderly and specially challenged in day-to-day life.
Digital Education to Foster the Success of Students in Difficulty in Line with the Digital Education Action Plan

Because of the Covid-19 pandemic, the need to promote digital education has been felt even more, bringing the DELTA Research Group to reflect on how digital education could optimally facilitate the creation of an educating community able to promote and support educational success exclusively through distance learning. In order to answer, in this paper the experience of the 2019/2020 edition of “Scuola dei Compiti” (a recovery project for secondary schools of the poorest suburbs of Turin) is analysed in the perspective of the priority strategies illustrated in the Digital Education Action Plan for the period 2021-2027. Results will show that this was a forerunner experience perfectly in line with the principles and directions outlined in the document. Moreover, thanks to digital education, even a peculiar edition of the project such as this one has been a success: in fact, it has allowed students to improve from a cognitive and metacognitive point of view, even during a time that was very difficult for some families.
Comparative Study of Byzantine Fault Tolerant Consensus Algorithms on Permissioned Blockchains

Blockchain is a technology that is in use increasingly. Although owing its common use to the cryptocurrencies there is more to the blockchain technology than just the monetary use. One of these uses is by smaller groups of participants under the control of a central authority, for instance at a company. Such private blockchains use permissioned blockchain consensus algorithms as the participants need the permission of the authority to be able to join the system. This paper will give an overview of the blockchain technology, investigate permissioned and permissionless blockchain, and focus on permissioned blockchains to analyze it in terms of, e.g. trust models between the nodes, incentives, number of nodes a parties involved, and scalability regarding the number of transactions.
Hermann Cohen and His Idea of the Logic of Pure Knowledge

Hermann Cohen, as it is well known, criticised the Kantian notion of the thing-in-itself. And before him the Kantian thing-in-itself was criticised by Fichte and other German idealists. Probably for this reason, Hermann Cohen is sometimes regarded as a person who said things similar to Fichte. This gives a completely wrong perspective, making it impossible to understand the philosopher's ideas. The basis for his critique of the Kantian thing-in-itself is quite different from the motives, determining the criticism of Kant in the classical German Idealism. Such interpretation does not allow to see close connection of Cohen's theoretical philosophy with revolution in physics which took place at the turn of the nineteenth and twentieth centuries. The article explains how Cohen's demand that pure thinking must form its own content is connected with transformations taking place in physics and mathematics, and the peculiarity of Cohen's understanding of idealism is demonstrated: for him, correct idealism must realize that autonomous, free thinking should work seriously with sense data. The closeness of Cohen's ideas to the postpositivist thesis of the theory-ladenness of observation is explained. For Cohen, serious work with sense data is opposite to uncritical acceptance of them as given. The origin of scientific thinking is thinking itself. It responds to the challenge of sensory material by creating its own constructs. Mathematized natural science becomes for Cohen both an example and a confirmation of this thesis. For him, what is real is what is described in the language of mathematical analysis, i.e. continuous processes, in spite of the fact that any data are discrete. It is shown that the source of Cohen's assertions on this issue is in Kant's Critique of Pure Reason, namely in the doctrine of the Principles of pure natural science and, more specifically, in the Anticipations of Perception. Cohen's conviction of the constructive character of the theories of mathematized natural science is confirmed in the article by references to the authority of A. Einstein.
An evaluation of cryptocurrency payment channel networks and their privacy implications

Cryptocurrencies redefined how money can be stored and transferred among users. However, public blockchain-based cryptocurrencies suffer from high transaction waiting times and fees. To address these challenges, the payment channel network concept is touted as the most viable solution to be used for micro-payments. The idea is exchanging the ownership of money by keeping the state of the accounts locally which provides transaction approvals in seconds. Such attention on payment channel networks has inspired many recent studies that focus on how to design them and allocate channels such that the transactions will be secure and efficient. However, as payment channel networks are emerging and reaching a large number of users, privacy issues are becoming more relevant, this raises concerns about exposing not only individual habits but also businesses' revenues. In this paper, we first propose a categorization of the existing payment networks formed on top of blockchain-backed cryptocurrencies. After discussing several emerging attacks on user/business privacy in these payment channel networks, we qualitatively evaluate them based on a number of privacy metrics that relate to our case. Based on the discussions on the strengths and weaknesses of the approaches, we offer possible directions for research for the future of privacy based payment channel networks.
Improved fine-grained object retrieval with Hard Global Softmin Loss objective

Image retrieval is a general task in computer vision, which aims at returning similar images of the query. Nowadays, extensive research has been drawn to fine-grained object retrieval, which is one of the difficult tasks of image retrieval. Compared to general image retrieval, the data of fine-grained objects show a great diversity in the same class, while a small diversity in different classes. Therefore, the key to fine-grained object retrieval resides in training models to obtain discriminative features. A kind of methods are based on local structure loss functions, e.g. pairwise and triplet loss, to generate distinguishable features for fine-grained object retrieval. However, these methods are time-consuming at the training stage and of low accuracy. To solve these problems, some methods based on global structure loss functions are proposed. Convolutional neural networks are optimized with the global centers and then generate distinguishable features. In this paper, based on the global structure loss functions and hard mining strategy, we propose the Hard Global Softmin Loss to improve the performance of fine-grained object retrieval. Furthermore, a learnable parameter is introduced into the proposed loss, which is dynamically adjusted by the network throughout the training. Lots of experiments show that the proposed loss function is effective and helpful for promoting the retrieval performance. Specifically, significant improvements are obtained over the state-of-the-art on four popular fine-grained datasets in our experiments 1 1 Our code is publicly available at https://github.com/RiyaoDong/HGSL . .
Algorithms Effectiveness comparison in solving Nonogram boards

Solving logic puzzles using selected algorithms is an interesting problem in which the efficiency of the methods is essential. One such task is solving nonograms, where board fields should be filled to meet the conditions for rows and columns, respectively. The number of methods possible to use opens the possibility to compare their efficiency and quality of operation. In this paper, we decided to analyze two algorithms belonging to different groups of methods to determine their effectiveness. A modified Depth-First Search (DFS) method and a soft computing method based on permutations generation were chosen and used to solve selected nonograms. The research was conducted on four board sizes, and the results showed that the effectiveness of the used methods mainly depends on the level of nonogram complexity. The algorithm results with permutations were stable, while in contrast to the DFS method, it did not always guarantee a complete solution.
Data Durabilities: Towards Conceptualizations of Scientific Long-Term Data Storage

With the increased requirement for open data and data reuse in the sciences the call for long-term data storage becomes stronger. However, long-term data storage is insufficiently theorized and often considered as simply short-term data that are stored longer. Interviews with scientists at a German university show that data are not in themselves durable; they are made durable. While Science &amp; Technology Studies data research has emphasized the relational character of data, always embedded in local contexts and infrastructures, we propose to add the temporal dimension of data durability to this understanding. We replace notions of long-term and short-term stored data with notions of publication data and project data, because the latter terms point to the practices through which data durability is made in a variety of ways, contingent on the kind of research phases in which the data are embedded, and on their infrastructures and practices. With the notion of data durability devices we inquire into technologies and tools, techniques and skills as well as organizational arrangements, cultural norms and relations that contribute to making data durable. We define scientific data as durable as long as they can operate in a socio-technical apparatus and uphold their capacity to make claims about the world. The scientists’ data practices revealed what we term media data durability devices and scientific data durability devices. The former were media materiality, the care for this materiality, and the compatibility between data and the data apparatus, which all contributed to shaping data durability. Scientific data durability devices, on the other hand included concealment and competition, through which data durability was prolonged, but also distributed unevenly among researchers. With these proposed concepts we hope to initiate discussions on the making of long-term data storage, just as we believe the concepts to be helpful for making realistic and relevant decisions about what data to store and for how long.
Label Importance Ranking with Entropy Variation Complex Networks for Structured Video Captioning

Structured video captioning is a fundamental yet challenging task in both computer vision and artificial intelligence (AI).The prevalent approach is to map an input video to a variablelength output sentence with models like recurrent neural network (RNN).This paper presents a new model based on an improved scene-aware bidirectional long short-term memory network (SABi-LSTM), and names the model as label importance ranking with entropy variation complex networks of structured video captions.Structured video captioning is a three-level structured system, including a multi-feature fusion level, an SABi-LSTM level, and a label importance ranking level.The system decomposes structures of multiple levels and dimensions from different perspectives to perform video captioning.This work affirms the theoretical and practical significance of label importance ranking to video caption generation, and regards entropy as a local level metric to quantify label importance.Hence, entropy variation was proposed to define label importance, namely, the variation of the network entropy through label removal.It is assumed that the removal of an important label could cause sustainable variation to the structure.Hence, the authors defined the label importance ranking with entropy variation complex network algorithm to calculate the weight model of label nodes marked by video, and obtain the final caption of the video.Empirical results on Microsoft Video Caption (MSVD) dataset and MSR-Video to Text (MSR-VTT) dataset demonstrate the superiority of our approach for structured video captioning, especially on MSVD dataset.
Image super-resolution algorithm based on RRDB model

Aiming at the problems of texture distortion and fuzzy details in the existing image superresolution reconstruction methods, a super-resolution reconstruction network based on multi-channel attention mechanism is proposed.The texture extraction module designs an extremely lightweight multichannel attention module in the network structure.Combined with one-dimensional convolution, it realizes cross-channel information interaction, focusing on important feature information; The texture restoration module introduces dense remaining blocks to restore some high-frequency texture details, improve the model performance, and generate high-quality reconstructed images.The proposed network can not only effectively improve the visual effect of the image, but the results on the benchmark data set CUFED5 are similar to the classic super-resolution (SRCNN) reconstruction method based on convolutional neural network.The peak signal-to-noise ratio (PSNR) and structure are similar.Degrees (SSIM) increased by 1.76dB and 0.062 respectively.Experimental results show that the designed network can improve the accuracy of texture migration and can effectively improve the quality of the generated image.
Voxel Transformer for 3D Object Detection

We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds. Conventional 3D convolutional backbones in voxel-based 3D detectors cannot efficiently capture large context information, which is crucial for object recognition and localization, owing to the limited receptive fields. In this paper, we resolve the problem by introducing a Transformer-based architecture that enables long-range relationships between voxels by self-attention. Given the fact that non-empty voxels are naturally sparse but numerous, directly applying standard Transformer on voxels is non-trivial. To this end, we propose the sparse voxel module and the submanifold voxel module, which can operate on the empty and non-empty voxel positions effectively. To further enlarge the attention range while maintaining comparable computational overhead to the convolutional counterparts, we propose two attention mechanisms for multi-head attention in those two modules: Local Attention and Dilated Attention, and we further propose Fast Voxel Query to accelerate the querying process in multi-head attention. VoTr contains a series of sparse and submanifold voxel modules and can be applied in most voxel-based detectors. Our proposed VoTr shows consistent improvement over the convolutional baselines while maintaining computational efficiency on the KITTI dataset and the Waymo Open dataset.
Secure and Practical Group Nearest Neighbor Query for Location-Based Services in Cloud Computing

Group nearest neighbor (GNN) query enables a group of location-based service (LBS) users to retrieve a point from point of interests (POIs) with the minimum aggregate distance to them. For resource constraints and privacy concerns, LBS provider outsources the encrypted POIs to a powerful cloud server. The encryption-and-outsourcing mechanism brings a challenge for the data utilization. However, as previous work from <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> <mo>−</mo> </math> anonymity technique leaks all contents of POIs and returns an answer set with redundant communication cost, the LBS system cannot work properly with those privacy-preserving schemes. In this paper, we illustrate a secure group nearest neighbor query scheme, which is referred to as SecGNN. It supports the GNN query with <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>n</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> <mo>≥</mo> <mn>3</mn> </mrow> </mfenced> </math> LBS users and assures the data privacy and query privacy. Since SecGNN only achieves linear search complexity, an efficiency enhanced scheme (named Sec <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msup> <mrow> <mtext>GNN</mtext> </mrow> <mrow> <mo>+</mo> </mrow> </msup> </math> ) is introduced by taking advantage of the KD-tree data structure. Specifically, we convert the GNN problem to the nearest neighbor problem for their centroid, which can be computed by anonymous veto network and Burmester–Desmedt conference key agreement protocols. Furthermore, the Sec <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msup> <mrow> <mtext>GNN</mtext> </mrow> <mrow> <mo>+</mo> </mrow> </msup> </math> scheme is introduced from the KD-tree data structure and a designed tool, which supports the computation of inner products over ciphertexts. Finally, we run experiments on a real-database and a random database to evaluate the performance of our SecGNN and Sec <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msup> <mrow> <mtext>GNN</mtext> </mrow> <mrow> <mo>+</mo> </mrow> </msup> </math> schemes. The experimental results show the high efficiency of our proposed schemes.
Target Detection Method for Low-Resolution Remote Sensing Image Based on ESRGAN and ReDet

With the widespread use of remote sensing images, low-resolution target detection in remote sensing images has become a hot research topic in the field of computer vision. In this paper, we propose a Target Detection on Super-Resolution Reconstruction (TDoSR) method to solve the problem of low target recognition rates in low-resolution remote sensing images under foggy conditions. The TDoSR method uses the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to perform defogging and super-resolution reconstruction of foggy low-resolution remote sensing images. In the target detection part, the Rotation Equivariant Detector (ReDet) algorithm, which has a higher recognition rate at this stage, is used to identify and classify various types of targets. While a large number of experiments have been carried out on the remote sensing image dataset DOTA-v1.5, the results of this paper suggest that the proposed method achieves good results in the target detection of low-resolution foggy remote sensing images. The principal result of this paper demonstrates that the recognition rate of the TDoSR method increases by roughly 20% when compared with low-resolution foggy remote sensing images.
Pronominalization and Expectations for Re-Mention: Modeling Coreference in Contexts With Three Referents

The relationship between pronoun production and pronoun interpretation has been proposed to follow Bayesian principles, combining a comprehender’s expectation about which referent will be mentioned next and their estimate of how likely it is that a potential referent will be re-mentioned using a pronoun. The Bayesian Model has received support from studies in several languages (English, Mandarin Chinese, Catalan, German), but tested contexts have been limited to two event participants, whereas natural language discourse often involves contexts with more than two event participants. In this study, we conducted three story continuation experiments to assess how the Bayesian Model performs in more complex contexts. Our results show that even in contexts with three event participants, comprehenders can behave rationally when interpreting pronouns, but that they appear to require sufficient context to build up a coherent representation of the situation to do so. In addition to testing the basic claim of the Bayesian Model (Weak Bayes), we test the central prediction of the Strong form of the hypothesis: that the two components of the model (next-mention expectations and choice of referring expression) are influenced by dissociated sets of factors. In a model comparison, Experiments 2 and 3 confirm the closest fit from the Bayesian Model, which supports Weak Bayes, and none of our experiments find evidence that the predictability of a referent affects pronominalization rates, which corroborates Strong Bayes. Finally, we test whether the rate of pronominalization is sensitive to factors related to ambiguity and argument/adjunct status of referents; we find that participants vary their production of pronouns most strongly based on the grammatical role of the antecedent (subject or not), with a smaller effect from the presence/absence of a gender-matched competitor and no effect from the syntactic position of this competing referent.