Reviewerly/exHarmony huggingface dataset:
An isogeny-based quantum-resistant secret sharing scheme

In a secret sharing scheme, a secret is distributed among several participants in such a way that only any authorized subset of participants is able to recover the secret. So far, the security of many secret sharing schemes has been based on the hardness of some mathematical problems, such as discrete logarithm and factorization. These problems can be solved in polynomial time using Shor?s algorithm for a quantum computer. In this paper, we propose an efficient multi-secret sharing scheme based on the hardness of computing isogenies between supersingular elliptic curves. The proposed scheme is based on De Feo and Jao key exchange protocol. We prove that our scheme is secure under computational assumptions in which there is no known efficient quantum algorithm.
Fully Attentional Network for Skeleton-Based Action Recognition

With the extraordinary ability by representing human body structure as a spatial graph, graph convolution networks (GCNs) have progressed much in skeleton-based action recognition. However, these methods usually use a predefined graph to represent human body structure, which is limited to 1-hop neighborhood with fixed weights. To handle these limitations, we propose a fully attentional network (FAN). It dynamically computes the edge weights for each input sample between graph nodes, thus avoiding the predefined fixed weights. Besides, it could attend to distant nodes by calculating their edge weights based on their similarities, thus avoiding the limited spatial receptive field. As an effective feature extractor, FAN achieves new state-of-the-art accuracy on three large-scale datasets, i.e., NTU RGB+D 60, NTU RGB+D 120 and Kinetics Skeleton 400. Visualizations are given to verify that FAN could dynamically emphasize the graph nodes that are important in expressing an action.
On the Political Use of the Reportative Conditional in Italian Newspapers

The labels condizionale riportivo (reportative conditional) or condizionale citativo (quotative conditional) are employed to describe certain uses of the conditional mood in Italian, particularly when the conditional marks the non-firsthand nature of a given piece of information. This use of the conditional is particularly frequent in the language of newspapers. However, reportative conditionals may also carry epistemic overtones, and they are used by speakers when they want to stress that they are not committed to the truthfulness of the information reported. Depending on the context, a reportative conditional can be either a purely evidential marker, or an evidential marker with epistemic overtones. Some Italian (trained) speakers exploit this ambiguity as a strategy to play on the two possible interpretations. This strategy is particularly significant in contexts in which the speaker is supposed to be neutral and, as such, must refrain from expressing her/his epistemic stance when recounting events. In this paper, after an analysis of the different sections of newspapers in which reportative conditionals occur, I will discuss one particular use of reportative conditionals in Italian newspapers, i.e. the “political” use of conditionals. To this end, I will carry out a qualitative analysis of some excerpts from newspapers published between the second half of the 19th century and today. In particular, it will be shown that journalists often take advantage of the ambiguities underlying reportative conditionals in Italian, in order to discredit the political position of a person (or group) without overtly questioning it.
Ultrasound‐guided breast biopsy using an adapted automated cone‐based ultrasound scanner: a feasibility study

Among available breast biopsy techniques, ultrasound (US)-guided biopsy is preferable because it is relatively inexpensive and provides live imaging feedback. The availability of magnetic resonance imaging (MRI)-3D US image fusion would facilitate US-guided biopsy even for US occult lesions to reduce the need for expensive and time-consuming MRI-guided biopsy. In this paper, we propose a novel Automated Cone-based Breast Ultrasound Scanning and Biopsy System (ACBUS-BS) to scan and biopsy breasts of women in prone position. It is based on a previously developed system, called ACBUS, that facilitates MRI-3D US image fusion imaging of the breast employing a conical container filled with coupling medium.The purpose of this study was to introduce the ABCUS-BS system and demonstrate its feasibility for biopsy of US occult lesions.The biopsy procedure with the ACBUS-BS comprises four steps: target localization, positioning, preparation, and biopsy. The biopsy outcome can be impacted by 5 types of errors: due to lesion segmentation, MRI-3D US registration, navigation, lesion tracking during repositioning, and US inaccuracy (due to sound speed difference between the sample and the one used for image reconstruction). For the quantification, we use a soft custom-made polyvinyl alcohol phantom (PVA) containing eight lesions (three US-occult and five US-visible lesions of 10 mm in diameter) and a commercial breast mimicking phantom with a median stiffness of 7.6 and 28 kPa, respectively. Errors of all types were quantified using the custom-made phantom. The error due to lesion tracking was also quantified with the commercial phantom. Finally, the technology was validated by biopsying the custom-made phantom and comparing the size of the biopsied material to the original lesion size. The average size of the 10-mm-sized lesions in the biopsy specimen was 7.00 ± 0.92 mm (6.33 ± 1.16 mm for US occult lesions, and 7.40 ± 0.55 mm for US-visible lesions).For the PVA phantom, the errors due to registration, navigation, lesion tracking during repositioning, and US inaccuracy were 1.33, 0.30, 2.12, and 0.55 mm. The total error was 4.01 mm. For the commercial phantom, the error due to lesion tracking was estimated at 1.10 mm, and the total error was 4.11 mm. Given these results, the system is expected to successfully biopsy lesions larger than 8.22 mm in diameter. Patient studies will have to be carried out to confirm this in vivo.The ACBUS-BS facilitates US-guided biopsy of lesions detected in pre-MRI and therefore might offer a low-cost alternative to MRI-guided biopsy. We demonstrated the feasibility of the approach by successfully taking biopsies of five US-visible and three US-occult lesions embedded in a soft breast-shaped phantom.
Deep learning based brain MRI registration driven by local‐signed‐distance fields of segmentation maps

Deep learning based unsupervised registration utilizes the intensity information to align images. To avoid the influence of intensity variation and improve the registration accuracy, unsupervised and weakly-supervised registration are combined, namely, dually-supervised registration. However, the estimated dense deformation fields (DDFs) will focus on the edges among adjacent tissues when the segmentation labels are directly used to drive the registration progress, which will decrease the plausibility of brain MRI registration.In order to increase the accuracy of registration and ensure the plausibility of registration at the same time, we combine the local-signed-distance fields (LSDFs) and intensity images to dually supervise the registration progress. The proposed method not only uses the intensity and segmentation information but also uses the voxelwise geometric distance information to the edges. Hence, the accurate voxelwise correspondence relationships are guaranteed both inside and outside the edges.The proposed dually-supervised registration method mainly includes three enhancement strategies. Firstly, we leverage the segmentation labels to construct their LSDFs to provide more geometrical information for guiding the registration process. Secondly, to calculate LSDFs, we construct an LSDF-Net, which is composed of 3D dilation layers and erosion layers. Finally, we design the dually-supervised registration network (VMLSDF ) by combining the unsupervised VoxelMorph (VM) registration network and the weakly-supervised LSDF-Net, to utilize intensity and LSDF information, respectively.In this paper, experiments were then carried out on four public brain image datasets: LPBA40, HBN, OASIS1, and OASIS3. The experimental results show that the Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD) of VMLSDF are higher than those of the original unsupervised VM and the dually-supervised registration network (VMseg ) using intensity images and segmentation labels. At the same time, the percentage of negative Jacobian determinant (NJD) of VMLSDF is lower than VMseg . Our code is freely available at https://github.com/1209684549/LSDF.The experimental results show that LSDFs can improve the registration accuracy compared with VM and VMseg , and enhance the plausibility of the DDFs compared with VMseg .
Iteratively reweighted least square for kernel expectile regression with random features

To overcome the computational burden of quadratic programming in kernel expectile regression (KER), iteratively reweighted least square (IRLS) technique was introduced in literature, resulting in IRLS-KER. However, for nonlinear models, IRLS-KER involves operations with matrices and vectors of the same size as the training set. Thus, as the training set becomes large, nonlinear IRLS-KER needs a long training time and large memory. To further alleviate the training cost, this paper projects the original data into a low-dimensional space via random Fourier feature. The inner product of the random Fourier features of two data points is approximately the same as the kernel function evaluated at these two data points. Hence, it is possible to use a linear model in the new low-dimensional space to approximate the original nonlinear model, and consequently, the time/memory efficient linear training algorithms could be applied. This paper applies the idea of random Fourier features to IRLS-KER, and our testing results on simulated and real-world datasets show that, the introduction of random Fourier features makes IRLS-KER achieve similar prediction accuracy as the original nonlinear version with substantially higher time efficiency.
NFA: A neural factorization autoencoder based online telephony fraud detection

The proliferation of internet communication channels has increased telecom fraud, causing billions of euros in losses for customers and the industry each year. Fraudsters constantly find new ways to engage in illegal activity on the network. To reduce these losses, a new fraud detection approach is required. Telecom fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic. Developing an effective strategy to combat fraud has become challenging. Although much effort has been made to detect fraud, most existing methods are designed for batch processing, not real-time detection. To solve this problem, we propose an online fraud detection model using a Neural Factorization Autoencoder (NFA), which analyzes customer calling patterns to detect fraudulent calls. The model employs Neural Factorization Machines (NFM) and an Autoencoder (AE) to model calling patterns and a memory module to adapt to changing customer behaviour. We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods. Our results show that our approach outperforms the baselines, with an AUC of 91.06%, a TPR of 91.89%, an FPR of 14.76%, and an F1-score of 95.45%. These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks. © 2022 Published by Elsevier Ltd.
A fast face recognition system based on annealing algorithm to optimize operator parameters

In order to improve the recognition rate and operational efficiency of the system, a method in which the image features compensation coefficients are optimized by using an improved simulated annealing algorithm is proposed. Firstly, eight computational factors with low computational complexity are given, which can be used to compensate image features. Secondly, the design flow of face recognition algorithm is presented. Thirdly, an improved simulated annealing algorithm is designed to solve the optimal combination of feature compensation coefficients in the face recognition system. Fourthly, the results of the feature compensation coefficients recommended by the improved simulated annealing algorithm are applied to the Efficient Face Recognition Algorithm (EFRA) in this paper, and verified on the simulation platform. Experiments show that the recognition rate can reach 100% when the training images are 6 in ORL. The proposed algorithm also performs well in MU_PIE dataset.
Optimal 2-traceability codes of length 4

Chor, Fiat and Naor proved a significant sufficient condition for traceability codes that an error correcting code of high minimum distance is a traceability code. Their result has been the basis of most constructions of traceability codes since then. This paper improves their result by showing that an error correcting code under a weaker requirement of minimum distance is still a traceability code by taking into account the effect of intersection of codewords. Furthermore, we show that the proposed weaker sufficient condition can be attained on 2-traceability codes by a new construction based on resolvable affine planes. In particular, this leads to a q-ary 2-traceability code of length 4 containing 3q codewords; moreover, we prove that the size 3q is optimal.
A Bibliometric Study of Scientific Production on Self-Sovereign Identity

Context: Self-sovereign identity (SSI) enables the creation of user-centric, privacy-by-design, secure, and decentralized identity-management systems. The aim of this paper is to carry out a bibliometric analysis of the scientific production on SSI during the period 2017-2022.Method: A complete bibliometric analysis of all publications on SSI indexed in Scopus and Web of Science was carried out. A corpus of 143 articles was examined by processing their bibliographic metadata on a bibliometric tool. In order to do this, the Bibliometrix packageand the R programming language were used.Results: A bibliometric characterization of the publications on SSI was obtained for the 2017 − 2022 period. The most important keywords used in these publications were identified, as well as their use tendencies throughout this period. Moreover, the most influential authors in the area and the most relevant publication sources were also identified.Conclusions: The results of the bibliometric analysis show that Lotka’s and Bradford’s laws apply for academic publications on SSI, which means that the most relevant publications in this area are concentrated in a relatively small group of authors and journals. Paul Jenkins, Nitin Naik, Yang Liu and Aijun An were the most impactful authors, and Lecture notes in computer science, Frontiers in blockchain and IEEE were the most influential journals. Finally, the keyword analysis showed that Blockchain, Authentication, Identity management, Electronic document identification systems, and Digital identity are currently the most relevant concepts for SSI.
Elaborate multi-task subspace learning with discrete group constraint

In multi-task learning (MTL), multiple related tasks can be learned simultaneously under the shared information to improve the generalization performance. However, most of MTL methods assume that all the learning tasks are related indeed and appropriate for joint learning. In some real situations, this assumption may not hold and further lead to the problem of negative transfer. Therefore, in this paper, we not only focus on researching the problem of robustly learning the common feature structure shared by tasks, but also discriminate with which tasks one task should share. By combining with the idea of subspace learning, we propose an elaborate multi-task subspace learning model (EMTSL) with discrete group structure constraint, which can cluster the learned tasks into a set of groups. By introducing the Schatten p-norm instead of trace norm, our model EMTSL can better approximate the low-rank constraint and also avoid the trivial solution. Furthermore, we design an efficient algorithm based on the re-weighted method to solve the proposed model. In addition, the convergence analysis of our algorithm is given in this paper. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our method.
Depth-based 3D human pose refinement: Evaluating the refinet framework

In recent years, Human Pose Estimation has achieved impressive results on RGB images. The advent of deep learning architectures and large annotated datasets have contributed to these achievements. However, little has been done towards estimating the human pose using depth maps, and especially towards obtaining a precise 3D body joint localization. To fill this gap, this paper presents RefiNet, a depth-based 3D human pose refinement framework. Given a depth map and an initial coarse 2D human pose, RefiNet regresses a fine 3D pose. The framework is composed of three modules, based on different data representations, i.e. 2D depth patches, 3D human skeletons, and point clouds. An extensive experimental evaluation is carried out to investigate the impact of the model hyper-parameters and to compare RefiNet with off-the-shelf 2D methods and literature approaches. Results confirm the effectiveness of the proposed framework and its limited computational requirements.
Avoiding Bikeshedding: An Eye Toward the Existential

The bicycle-shed effect (also known as Parkinson’s law of triviality or bikeshedding) is a belief that humans assign disproportionate weight to trivial issues, thus devoting excessive resources (e.g., time, energy, money, etc.) to more insignificant issues to the detriment of the significant <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . Humans are believed to prefer to focus on simple and comprehendible subjects (e.g., which color to paint the company Bike-Shed) at the expense of far more significant, yet often complex quandaries (e.g., determining the location of the nuclear power plant) <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> .
Ply level strain measurement in composites using digital image correlation

Application of digital image correlation technique for full field strain characterization has gained widespread interest. However, characterization of the strain and damage at very small length scales using this technique is still difficult and development in this regard would add a great value in understanding the material behavior, particularly for in-homogeneous materials such as composites. This article describes the work done primarily on continuous fiber reinforced polymer composites and ceramic matrix composites, towards applying the digital image correlation (DIC) technique at very small length scale, of the order of 100–200 μm. A novel technique was developed to generate a fine and randomly distributed speckle pattern. Actual examples of the application of digital image correlation at small length scales for strain and damage characterization are briefly described such as ply level strain measurement near ply drop in a fiber reinforced composite, local carbon-glass fiber joint strain measurement in hybrid composites, and short beam shear strength validation using ply level strain measurement in carbon glass fiber joints in hybrid composites. Limitations of digital image correlation technique for small scale strain measurement are also discussed.
Research on network cognition model and mechanism of intelligent information network

Intellectualization has been an inevitable trend in the information network, allowing the network to achieve the capabilities of self-learning, self-optimization, and self-evolution in the dynamic environment. Due to the strong adaptability to the environment, the cognitive theory methods from psychology gradually become an excellent approach to construct the intelligent information network (IIN), making the traditional definition of the intelligent information network no longer appropriate. Moreover, the thinking capability of existing IINs is always limited. This paper redefines the intelligent information network and illustrates the required properties of the architecture, core theory, and critical technologies by analyzing the existing intelligent information network. Besides, we innovatively propose a novel network cognition model with the network knowledge to implement the intelligent information network. The proposed model can perceive the overall environment data of the network and extract the knowledge from the data. As the model's core, the knowledge guides the model to generate the optimal decisions adapting to the environmental changes. At last, we present the critical technologies needed to accomplish the proposed network cognition model.
Medical Helper Using OCR

-In Today's Day to Day Life, more often patients may forget to take their prescribed medications on time, which may lead to delayed in their recovery from that disease. Therefore, it is very important for every patient to take right dosage of medication in right time. So our platform is based on an android application in which user can take a picture of the medicines and with the help of OCR the system will automatically print the text on the screen and save the details of the medicines, it’s dosage and it’s time. The reminder will remind the patient to take their medicine. Key Words: Medical Helper Using OCR, Android, Medicine, MS SQL 2008, Java.
Dynamic semantics versus dynamic propositionalism

Una Stojnić's Context and Coherence: The Logic and Grammar of Prominence offers a series of interesting criticisms of the classical dynamic paradigm in natural language semantics and offers a sophisticated alternative outlook, one that does recognize a dynamic, context change inducing dimension of meaning but at the same preserves the idea that (declarative) utterances express propositions in context. The purpose of this note is to set the record straight: existing dynamic analyses of modals and conditionals compare favorably with Stojnić's dynamic propositionalism.
THE IMPORTANCE OF THE CHAIN OF CUSTODY OF EXPERT EVIDENCE IN DIGITAL AND DIGITIZED PROCESSES IN THE CIVIL SPHERE

The research will bring an approach to the importance and problem of the non-existence or breaking of the chain of custody of evidence in civil procedure law, especially in digital or digitized expert evidence. It will demonstrate the need to implement and program together with civil procedural procedures an educational culture to demonstrate the need for the existence of the chain of custody of the traces, from the moment that all documentation enters the court secretariat, when it goes to the hands of the auxiliaries of justice – expert and attorney – until its return to the procedural records, to finally reach the judicial appreciation. Such acts are necessary to guarantee the transparency, fairness and integrity of the evidence, granting the broader right of defense regulated by the Federal Constitution. It will present at first a general conceptual approach. For the development of the work, documentary research, articles in the area and other bibliographies within the theme were used. Making sure that there is an enormous weakness in security with digital evidence in the civil procedural context, making the existence of a chain of custody necessary. Finally, it is worth noting the importance of implementing the chain of custody, together with the organizational culture to adapt a new way of functioning for the preservation of digital expert evidence in the civil sphere.