Reviewerly/exHarmony huggingface dataset:
Architectural distortion detection based on superior–inferior directional context and anatomic prior knowledge in digital breast tomosynthesis

In 2020, breast cancer becomes the most leading diagnosed cancer all over the world. The burden is increasing in the prevention and treatment of breast cancer. Accurately detecting breast lesions in screening images is important for early detection of cancer. Architectural distortion (AD) is one of the breast lesions that need to be detected.To develop a deep-learning-based computer-aided detection (CADe) model for AD in digital breast tomosynthesis (DBT). This model uses the superior-inferior directional context of DBT and anatomic prior knowledge to reduce false positive (FP). It can identify some negative samples that cannot be distinguished by deep learning features.The proposed CADe model consists of three steps. In the first step, a deep learning detection network detects two-dimensional (2D) candidates of ADs in DBT slices with the inputs preprocessed by Gabor filters and convergence measure. In the second step, three-dimensional (3D) candidates are obtained by stacking 2D candidates along superior-inferior direction. In the last step, FP reduction for 3D candidates is implemented based on superior-inferior directional context and anatomic prior knowledge of breast. DBT data from 99 cases with AD were used as the training set to train the CADe model, and data from 208 cases were used as an independent test set (including 108 cases with AD and 100 cases without AD as the control group). The free-response receiver operating characteristic and mean true positive fraction (MTPF) in the range of 0.05-2.0 FPs per volume are used to evaluate the model.Compared with the baseline model based on convergence measure, our proposed method demonstrates significant improvement (MTPF: 0.2826 ± 0.0321 vs. 0.6640 ± 0.0399). Results of an ablation study show that our proposed context- and anatomy-based FP reduction methods improve the detection performance. The number of FPs per DBT volume reduces from 2.47 to 1.66 at 80% sensitivity after employing these two schemes.The deep learning model demonstrates practical value for AD detection. The results indicate that introducing superior-inferior directional context and anatomic prior knowledge into model can indeed reduce FPs and improve the performance of CADe model.
Parallel algorithms for masked sparse matrix-matrix products

Computing the product of two sparse matrices (SpGEMM) is a fundamental operation in various combinatorial and graph algorithms as well as various bioinformatics and data analytics applications for computing inner-product similarities. For an important class of algorithms, only a subset of the output entries are needed, and the resulting operation is known as Masked SpGEMM since a subset of the output entries is considered to be "masked out". In this work, we investigate various novel algorithms and data structures for this rather challenging and important computation, and provide guidelines on how to design a fast Masked-SpGEMM for shared-memory architectures.
DISSAV

This paper describes DISSAV: Dynamic Interactive Stack Smashing Attack Visualization, a program visualization tool for teaching stack smashing attacks. DISSAV is a web-based application built with ReactJS. DISSAV provides a simulated attack scenario that guides the user through a three-part stack smashing attack. Our tool allows the user to create a program, construct a payload for it, and execute the program to simulate an attack scenario. We aim to improve student learning of advanced cyber security topics, more specifically, stack smashing attacks, by increasing student engagement and interaction. We incorporate previously researched techniques of Program Visualization tools such as dynamic user input and interactive views to achieve these goals.
The development of individual self-efficiency among university students

The requirements of modern society for the training of highly qualified and personally effective specialists who can quickly adapt to changing conditions and successfully carry out their professional activities are on the increase. The purpose of our work was the development of the personal self-efficacy of students at universities of the Republic of Kazakhstan in the modern conditions of the development of society. The research collected data using an experiment. The experimental study was carried out in higher educational institutions - and the experiment involved 280 students: 140 - in the experimental group, 140 - in the control group. The results of the study confirmed the effectiveness of the proposed concept of developing students' self-efficacy. Developed and experimentally tested psychological conditions for the development of personal self-efficacy of university students can be widely used in higher education institutions.&#x0D; Keywords: achievement; development concept; psychological conditions; self-confidence; self-development; self-efficacy
Refining electronic medical records representation in manifold subspace

Electronic medical records (EMR) contain detailed information about patient health. Developing an effective representation model is of great significance for the downstream applications of EMR. However, processing data directly is difficult because EMR data has such characteristics as incompleteness, unstructure and redundancy. Therefore, preprocess of the original data is the key step of EMR data mining. The classic distributed word representations ignore the geometric feature of the word vectors for the representation of EMR data, which often underestimate the similarities between similar words and overestimate the similarities between distant words. This results in word similarity obtained from embedding models being inconsistent with human judgment and much valuable medical information being lost.In this study, we propose a biomedical word embedding framework based on manifold subspace. Our proposed model first obtains the word vector representations of the EMR data, and then re-embeds the word vector in the manifold subspace. We develop an efficient optimization algorithm with neighborhood preserving embedding based on manifold optimization. To verify the algorithm presented in this study, we perform experiments on intrinsic evaluation and external classification tasks, and the experimental results demonstrate its advantages over other baseline methods.Manifold learning subspace embedding can enhance the representation of distributed word representations in electronic medical record texts. Reduce the difficulty for researchers to process unstructured electronic medical record text data, which has certain biomedical research value.
Addendum to Linear Cryptanalyses of Three AEADs with GIFT-128 as Underlying Primitives

In ToSC 2021(2), Sun et al. implemented an automatic search with the Boolean satisfiability problem (SAT) method on GIFT-128 and identified a 19-round linear approximation with the expected linear potential being 2−117.43, which is utilised to launch a 24-round attack on the cipher. In this addendum, we discover a new 19-round linear approximation with a lower expected linear potential. However, in the attack, one more round can be appended after the distinguisher. As a result, we improve the previous optimal linear attack by one round and put forward a 25-round linear attack. Given that the optimal differential attack on GIFT-128, for now, covers 27-round, the resistances of the cipher against differential and linear attacks still have a 2-round gap.
Near-duplicate image detection based on wavelet decomposition with modified deep learning model

We aim to address the near-duplicate image (NDI) detection problem with a deep learning network. With the advancement of digital acquisition devices and easy-to-use image editing software, NDI forgery is ubiquitous nowadays. This rising problem demands robust detection algorithms that can efficiently prevent the NDI forgery and its distribution. We present a modified deep learning model to detect NDI forensics attacks based on extracted wavelet Haar features. Unlike standard deep learning models, a wavelet decomposed preprocessing layer is used before the deep learning networks, and a support vector machine classifier is employed in the classification stage of a modified deep learning model. The model is tested on distinctly available image databases. The experimental results show that the proposed model more effectively detect the NDIs. In addition, the comparison result shows that the modified model outperforms the other approaches significantly.
Adaptive Development of Parallel Power System Dynamic Simulation Application in Python

Due to its intensive computational demands for real-time operation and diagnosis, large-scale power system dynamic simulation requires high-performance computing technologies to accelerate its computation on advanced computing platforms. In this paper, leveraging high-level Python and its parallel scientific computing libraries, three parallel power system dynamic simulation applications are adaptively developed using native MPI for Python on CPU, PETSc for Python on CPU, and CuPy on GPU with dedicated data manipulation strategies and implementations, respectively. Their computational performance is compared using different sizes of testing systems and indicates that: 1) MPI and PETSc can make a decent performance for small and moderate-size systems on limited CPU resources, and 2) GPU has better potential in speeding up dynamic simulation for larger and more complex systems. The results demonstrate Python's suitability in parallelizing power system modeling and simulation with fast computational performance and easy development.
Development of Secure Electronic Cybercrime Cases Database System for the Judiciary

The recent covid-19 pandemic created a barrier to every activity that needed physical interaction and involvement, especially in the judiciary.Careful research of some courts in Nigeria shown that case records are still been manually processed and stored and some courts operate a semi-digital and semi-manual processing pattern, which also has its own shortcoming of preprocessing manual records and converting them into digital records and physical presence is required to access court records.This research develops a secure electronic Cybercrime Cases Database System (eCCDBS), for prosecuted cybercrime in the judicial service in Nigeria.The system will provide an efficient method for collecting, retrieving, preserving, and management of court case records.The Rapid Application Development (RAD) methodology is used for the system development, because of its speed and time friendliness and can be easily restructured to meet the client's requirement at any point in time during the development life span.RAD can also present a prototype of the final system software to the client.Access control mechanism and secure password hashing were used to ensure the security of the system.The system was implemented and evaluated through deployment and found to have functioned according to the specification.The application subunits of records' creation, submission, modification, deletion, retrieval, and storage functioned effectively.Hence this system provides a secure online repository specifically for cybercrime case records that have elements of confidentiality, integrity and availability.
Unveiling Magic Leap 2’s Advanced AR Platform and Revolutionary Optics

Magic Leap’s decade-long focus on premium AR has resulted in a revolutionary optical system. This presentation will provide a never before seen look at the advancements coming to market with the new Magic Leap 2 spatial AR headset including the novel projector architecture that results in the largest FOV delivered in a compact package. Precise eye tracking enables the largest, safe and comfortable display volume. Coupled with a new high performance CPU and GPU chipset and industry-leading eyepiece manufacturing process, Magic Leap 2 delivers a bright, crisp and uniform 3D display. Innovative dimming technology means developers can deliver solid virtual AR content in a wide range of ambient environments.
Realtime online courses mutated amid the COVID-19 pandemic: Empirical study in hospitality program

Real-time online courses (RTOCs), a new online learning mode, have been developed because of a longitudinal suspension of classes amid the COVID-19 pandemic worldwide. We explore an information model to review the learning process and outcomes of RTOCs, which conducted educational activities via social media. Results show that social media can be a potent mediation factor with the moderation of structural differentiation to facilitate online learning outcomes. Conclusions imply that the life-changing impact of COVID-19 has caused an evolutionary online education mode that can be hybridized with face-to-face education and massive open online courses to flourish education approaches and pedagogies.
V-ShadowGAN: generative adversarial networks for removing and generating shadows associated with vehicles based on unpaired data

We propose a method to change the shadows cast by vehicles based on unpaired data, which is used to solve the problem of insufficient datasets for target detection in autonomous driving scenarios. This method can increase the training samples of road vehicles in different time periods and reduce the time needed to make complex annotations of datasets. Direct use of generative adversarial networks with cycle-consistency constraints can achieve mapping transformation of unpaired domains, but it is difficult to completely learn the mapping between different shadow domains. To solve this problem, V-ShadowGAN is proposed. First, to train the model, we construct an unpaired dataset of vehicle shadows in different time periods. Second, the shadow mask extractor and the shadow mask discriminator are constructed to extract shadow masks and guide the generation of shadows in the network. Finally, the spatial attention mechanism is added to the generator, which improves the overall quality of the generated image by changing the channel weight of the network and deepening the network. Various experiments show that V-ShadowGAN can effectively change the vehicle shadow.
Comparison of differential evolution, particle swarm optimization, quantum-behaved particle swarm optimization, and quantum evolutionary algorithm for preparation of quantum states

Four intelligent optimization algorithms are compared by searching for control pulses to achieve the preparation of target quantum states for closed and open quantum systems, which include differential evolution (DE), particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO), and quantum evolutionary algorithm (QEA). We compare their control performance and point out their differences. By sampling and learning for uncertain quantum systems, the robustness of control pulses found by these four algorithms is also demonstrated and compared. The resulting research shows that the QPSO nearly outperforms the other three algorithms for all the performance criteria considered. This conclusion provides an important reference for solving complex quantum control problems by optimization algorithms and makes the QPSO be a powerful optimization tool.
Noise-Induced Entanglement Transition in One-Dimensional Random Quantum Circuits

A random quantum circuit is a minimally structured model to study entanglement dynamics of many-body quantum systems. We consider a one-dimensional quantum circuit with noisy Haar-random unitary gates using density matrix operator and tensor contraction methods. It is shown that the entanglement evolution of the random quantum circuits is properly characterized by the logarithmic entanglement negativity. By performing exact numerical calculations, we find that, as the physical error rate is decreased below a critical value p c ≈ 0.056, the logarithmic entanglement negativity changes from the area law to the volume law, giving rise to an entanglement transition. The critical exponent of the correlation length can be determined from the finite-size scaling analysis, revealing the universal dynamic property of the noisy intermediate-scale quantum devices.
Optimal resource allocation for flexible-grid entanglement distribution networks

We use a genetic algorithm (GA) as a design aid for determining the optimal provisioning of entangled photon spectrum in flex-grid quantum networks with arbitrary numbers of channels and users. After introducing a general model for entanglement distribution based on frequency-polarization hyperentangled biphotons, we derive upper bounds on fidelity and entangled bit rate for networks comprising one-to-one user connections. Simple conditions based on user detector quality and link efficiencies are found that determine whether entanglement is possible. We successfully apply a GA to find optimal resource allocations in four different representative network scenarios and validate features of our model experimentally in a quantum local area network in deployed fiber. Our results show promise for the rapid design of large-scale entanglement distribution networks.
Qubit Reset and Refresh: A Gamechanger for Random Number Generation

Generation of random binary numbers for cryptographic use is often addressed using pseudorandom number generating functions in compilers and specialized cryptographic packages. Using the IBM's Qiskit reset functionality, we were able to implement a straight-forward in-line Python function that returns a list of quantum-generated random numbers, by creating and executing a circuit on IBM quantum systems.
V-Gas: Generating High Gas Consumption Inputs to Avoid Out-of-Gas Vulnerability

Out-of-gas errors occur when smart contract programs are provided with inputs that cause excessive gas consumption and which will be easily exploited to perform Denial-of-Service attacks. Various approaches have been proposed to estimate the gas limit of a function in smart contracts to avoid such error. However, underestimation often occurs when the contract is complex In this work, we propose V-Gas, which automatically generates inputs that maximize the gas cost and reduce underestimation. V-Gas is designed based on static analysis and feedback-directed mutational fuzz testing. First, V-Gas builds the gas weighted control flow graph of functions in smart contracts. Then, V-Gas develops gas consumption guided selection and mutation strategies to generate the input that maximize the gas consumption. For evaluation, we implement V-Gas based on js-evm, a widely used Ethereum virtual machine written in Javascript, and conduct experiments on 736 real-world transactions recorded on Ethereum. A total of 44.02% of the transactions would have out-of-gas errors based on the estimation results given by solc, meaning that the recorded real gas consumption for those transactions is larger than the gas limit estimated by solc. In comparison, V-Gas could reduce the underestimation ratio to 13.86%. To evaluate the performance of feedback-directed engine in V-Gas, we implemented other directed fuzzing engines and compared their performance with that of V-Gas. The results showed that V-Gas generates the same or higher gas estimation value on 97.8% of the transactions with less time, usually within 5 minutes. Furthermore, V-Gas has exposed 25 previously unknown out-of-gas vulnerabilities in widely used smart contracts, 6 of which have been assigned unique CVE identifiers in the U.S. National Vulnerability Database.
ChildPredictor: A Child Face Prediction Framework With Disentangled Learning

The appearances of children are inherited from their parents, which makes it feasible to predict them. Predicting realistic children's faces may help settle many social problems, such as age-invariant face recognition, kinship verification, and missing child identification. It can be regarded as an image-to-image translation task. Existing approaches usually assume domain information in the image-to-image translation can be interpreted by “style”, i.e., the separation of image content and style. However, such separation is improper for the child face prediction, because the facial contours between children and parents are not the same. To address this issue, we propose a new disentangled learning strategy for children's face prediction. We assume that children's faces are determined by genetic factors (compact family features, e.g., face contour), external factors (facial attributes irrelevant to prediction, such as moustaches and glasses), and variety factors (individual properties for each child). On this basis, we formulate predictions as a mapping from parents’ genetic factors to children's genetic factors, and disentangle them from external and variety factors. In order to obtain accurate genetic factors and perform the mapping, we propose a ChildPredictor framework. It transfers human faces to genetic factors by encoders and back by generators. Then, it learns the relationship between the genetic factors of parents and children through a mapping function. To ensure the generated faces are realistic, we collect a large Family Face Database to train ChildPredictor and evaluate it on the FF-Database validation set. Experimental results demonstrate that ChildPredictor is superior to other well-known image-to-image translation methods in predicting realistic and diverse child faces. Implementation codes can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhaoyuzhi/ChildPredictor</uri> .
Improving Language Learning by an Interact-to-Learn Desktop VR Application: A Case Study with Peinture

Mastering vocabulary is a daunting task for second language learners. Desktop Virtual Reality (VR) can inspire and engage learners with interact-to-learn activities to facilitate memorizing vocabulary. This paper proposes a desktop VR application for color vocabulary learning called Peinture. Peinture consists of a lecturing video and an interactive coloring virtual space. The talking teacher in the lecturing video is generated automatically through text-to-speech and lip synthesis approaches. Learners can paint and interact in a theme when or after lecture learning. A preliminary user study based on performance, self-report and eye tracking data analysis shows that interact-to-learn activities provided by Peinture were beneficial for second language learning. However, there is still considerable room to upgrade such educational VR.
Evaluation of Science and Mathematics Textbooks in Context of Digital Competence

In this study, it is aimed to evaluate the content of the textbooks between the 5th- 8th grade science and mathematics textbooks in the context of digital competence. Textbooks MoNE publications in the 2020-2021 academic year were used and document analysis method was preferred. For data analysis, a checklist was created by using the digital competence criteria published in the MoNE curriculum. To the findings, there is very little attention on the content for digital competence in Science and Mathematics textbooks. The content is mostly oriented "access to information" and digital-oriented content is in the book chapters mostly took part in the "unit sections.". There is no increase in the content for digital competence as the grade level increases and no content for "retention of information" and "presentation of information" at any grade level. Also there are significant differences in the content of the textbooks at the same grade level in the context of digital competence. To fill the main gaps between the learning goals and textbooks in the context of digital competence and to create related content for the dimensions, a digital competence framework of our country should be.