Reviewerly/exHarmony huggingface dataset:
Mitigation of block withholding attack based on zero-determinant strategy

This article focuses on the mining dilemma of block withholding attack between the mining pools in the bitcoin system. In order to obtain the higher revenue, the rational mining pool usually chooses an infiltration attack, that is, the pool will falls into the mining dilemma of the PoW consensus algorithm. Thus the article proposes to apply zero-determinant strategies for optimizing the behavior selection of the mining pool under PoW consensus mechanism to increase the total revenues of the system, so as to solve the mining dilemma. After theoretically studying the set and extortionate strategy of zero-determinant, the article devises an adaptive zero-determinant strategy that the pool can change the corporation probability of the next round based on its previous revenues. To verify the effectiveness of zero-determinant strategies, based on the actual revenue of the mining pool defined and deduced in the paper, it simulates 30 sets of game strategies to illustrate the revenue variation of the mining pools. The simulation results show that the three zero-determinant strategies can effectively improve the convergence rate of cooperation, mitigate block withholding attack and maximize the total revenues of the system. Compared with the set and extortionate strategy, the adaptive strategy can ensure more stability and more revenue.
A Study of Support Vector Regression-Based Fuzzy <i>c</i>-Means Algorithm on Incomplete Data Clustering

Support vector regression-based fuzzy c -means algorithm (SVR-FCM) clusters data according to their relationship among attributes, which can provide competitive clustering results for the dataset having functional relationship among attributes. In this paper, we study the performance of SVR-FCM on incomplete data clustering. The conventional incomplete data clustering strategies of fuzzy c -means algorithm (FCM) are first applied to SVR-FCM, and a new strategy named MIS strategy is designed to assist SVR-FCM handle incomplete data as well. A number of synthetic datasets are used to study the effect of data missing rate and missing attribute numbers on the performance of SVR-FCM based on different incomplete data clustering strategies. Several engineering datasets are used to test the performance of the current and proposed incomplete data clustering strategies for SVR-FCM. The results indicate that SVR-FCM can provide better clustering results than FCM for the dataset having functional relationship among attributes even if it has missing values, and the proposed MIS strategy can assist SVR-FCM to achieve the best clustering results for most datasets.
Q-turn: changing paradigms in quantum science

Abstract Quantum information is a rapidly-growing interdisciplinary field at the intersection of information science, computer science, mathematics, philosophy, and quantum science. This fruitful field of research is at the core of our developments of quantum technologies, while widening the frontiers of our fundamental knowledge, and has achieved remarkable progress in the last few decades. Regardless of its scientific success, quantum information is not exempt from the intrinsic features that come from the fact that scientists are humans and members of society: both the good and the bad of our social practices leak into the scientific activity. In our scientific community, diversity and equal opportunity problems are particularly difficult to observe due to social, economic, or cultural barriers, often remaining invisible. How can our lack of awareness negatively influence the progress of science in the long term? How can our community grow into a better version of itself? This article reflects on how research events—such as conferences—can contribute to a shift in our culture. This reflection draws on what we learn from Q-turn: an initiative triggered by postdoctoral researchers to discuss these questions, and by doing so raise awareness about diversity issues and equal opportunities in quantum science. In addition to the high calibre of science, one of Q-turn’s main missions is to foster an inclusive community and highlight outstanding research that may be under-appreciated in other high-impact venues due to systemic biases. As well as a scientific programme, Q-turn features talks and discussions on issues that affect the quantum information community, ranging from diversity and inclusion, health and mental health, to workers’ rights. In this perspective article, we will consider Q-turn as an example of how a research community can work to tackle systematic biases, review the successes, and identify further points for development.
An English Writing Grammar Error Correction Technology Based on Similarity Algorithm

Students are the focus of English writing instruction, which emphasizes their initiative and creativity in the classroom, fosters the development of their self-directed learning and comprehensive language skills, raises their overall English level and mastery ability, and cultivates well-rounded English learners. It is to get yourself out of the dilemma of Chinese English. This study proposes an automatic error correction method for English writing grammar based on a similarity algorithm, which is applied to students during the English writing process. To address the issue of grammatical errors, this study proposes an ontology and grammar rules based on the English writing corpus. Our experimental results demonstrate that our highlighting algorithm can effectively correct grammatical errors in written English, demonstrating the algorithm’s efficacy.
Self-Supervised Post-Correction for Monte Carlo Denoising

Using a network trained by a large dataset is becoming popular for denoising Monte Carlo rendering. Such a denoising approach based on supervised learning is currently considered the best approach in terms of quality. Nevertheless, this approach may fail when the image to be rendered (i.e., the test data) has very different characteristics than the images included in the training dataset. A pre-trained network may not properly denoise such an image since it is unseen data from a supervised learning perspective. To address this fundamental issue, we introduce a post-processing network that improves the performance of supervised learning denoisers. The key idea behind our approach is to train this post-processing network with self-supervised learning. In contrast to supervised learning, our self-supervised model does not need a reference image in its training process. We can thus use a noisy test image and self-correct the model on the fly to improve denoising performance. Our main contribution is a self-supervised loss that can guide the post-correction network to optimize its parameters without relying on the reference. Our work is the first to apply this self-supervised learning concept in denoising Monte Carlo rendered estimates. We demonstrate that our post-correction framework can boost supervised denoising via our self-supervised optimization. Our implementation is available at https://github.com/CGLab-GIST/self-supervised-post-corr.
Toward A No-reference Omnidirectional Image Quality Evaluation by Using Multi-perceptual Features

Compared to ordinary images, omnidirectional image (OI) usually has a broader view and a higher resolution, and image quality assessment (IQA) can help people to understand and improve their visual experience. However, the current IQA works cannot achieve good performance. To address this, we proposed a novel visual perception-based no-reference/blind omnidirectional image quality assessment (NR/B-OIQA) model. The gradient-based global structural features and gray-level co-occurrence matrix-based local structural features are combined together to highlight the rich quality-aware structural information. And a novel steganalysis real model-based color descriptor is extracted to reflect the color information that ignored in most IQA models. With a multi-scale visual perception, we take image entropy and the natural scene statistics features to convey the high-level semantics and quantify the unnaturalness of omnidirectional images. Finally, we apply support vector regression to predict the objective quality value based on the subjective scores and extracted all features. Experiments are conducted on OIQA and CVIQD2018 Databases, and the results illustrate that our model has more reliable performance and stronger competitiveness and receives better conformity with the subjective values.
Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box

Through the addition of humanly imperceptible noise to an image classified as belonging to a category ca, targeted adversarial attacks can lead convolutional neural networks (CNNs) to classify a modified image as belonging to any predefined target class ct≠ca. To achieve a better understanding of the inner workings of adversarial attacks, this study analyzes the adversarial images created by two completely opposite attacks against 10 ImageNet-trained CNNs. A total of 2×437 adversarial images are created by EAtarget,C, a black-box evolutionary algorithm (EA), and by the basic iterative method (BIM), a white-box, gradient-based attack. We inspect and compare these two sets of adversarial images from different perspectives: the behavior of CNNs at smaller image regions, the image noise frequency, the adversarial image transferability, the image texture change, and penultimate CNN layer activations. We find that texture change is a side effect rather than a means for the attacks and that ct-relevant features only build up significantly from image regions of size 56×56 onwards. In the penultimate CNN layers, both attacks increase the activation of units that are positively related to ct and units that are negatively related to ca. In contrast to EAtarget,C’s white noise nature, BIM predominantly introduces low-frequency noise. BIM affects the original ca features more than EAtarget,C, thus producing slightly more transferable adversarial images. However, the transferability with both attacks is low, since the attacks’ ct-related information is specific to the output layers of the targeted CNN. We find that the adversarial images are actually more transferable at regions with sizes of 56×56 than at full scale.
Sampling near neighbors in search for fairness

Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. Given a set of points S and a radius parameter r &gt; 0, the r -near neighbor ( r -NN) problem asks for a data structure that, given any query point q , returns a point p within distance at most r from q. In this paper, we study the r -NN problem in the light of individual fairness and providing equal opportunities: all points that are within distance r from the query should have the same probability to be returned. The problem is of special interest in high dimensions, where Locality Sensitive Hashing (LSH), the theoretically leading approach to similarity search, does not provide any fairness guarantee. In this work, we show that LSH-based algorithms can be made fair, without a significant loss in efficiency. We propose several efficient data structures for the exact and approximate variants of the fair NN problem. Our approach works more generally for sampling uniformly from a sub-collection of sets of a given collection and can be used in a few other applications. We also carried out an experimental evaluation that highlights the inherent unfairness of existing NN data structures.
A novel tree-based representation for evolving analog circuits and its application to memristor-based pulse generation circuit

Abstract When applying evolutionary algorithms to circuit design automation, circuit representation is the first consideration. There have been several studies applying different circuit representations. However, they still have some problems, such as lack of design ability, which means the diversity of evolved circuits was limited by the circuit representation, and inefficient transformation from circuit representation into SPICE (Simulation Program with Integrated Circuit Emphasis) netlist. In this paper, a novel tree-based circuit representation for analog circuits is proposed, which is equipped with an intuitive and three-terminal devices friendly mapping rule between circuit representation and SPICE netlist, as well as a suitable crossover operator. Based on the proposed representation, a framework for automated analog circuit design using genetic programming is proposed to evolve both the circuit topology and device values. Three benchmark circuits are applied to evaluate the proposed approach, showing that the proposed method is feasible and evolves analog circuits with better fitness and number of components while using less fitness evaluations than existing approaches. Furthermore, considering physical scalability limits of conventional circuit elements and the increased interest in emerging technologies, a memristor-based pulse generation circuit is also evolved based on the proposed method. The feasibility of the evolved circuits is verified by circuit simulation successfully. The experiment results show that the evolved memristive circuit is more compact and has better energy efficiency compared with existing manually-designed circuits.
Risk factors for stillbirth and neonatal mortality among participants in Mobile WACh NEO pilot, a two-way SMS communication program in Kenya

Globally, 2.5 million neonates die and 2 million more are stillborn each year; the vast majority occur where access to life-saving care is limited. High quality, feasible interventions are needed to reach, educate and empower pregnant women and new mothers to improve care-seeking behaviors. Mobile WACh (Mobile solutions for Women's and Children's health) NEO is a human-computer hybrid mobile health (mHealth) system that allows for two-way short message service (SMS) communication between women and healthcare workers during the peripartum period. We performed a secondary prospective cohort analysis of data from the Mobile WACh NEO pilot study to determine maternal characteristics associated with neonatal death and stillbirth and examine participant messaging associated with these events. Pregnant women were enrolled at two Kenyan public health clinics between 28-36 weeks gestation. They received personalized, educational, action-oriented SMS messages during pregnancy and through 14 weeks postpartum. Participants could message the study at any time and study nurses responded. Standardized questionnaires assessed participant characteristics at baseline and 14 weeks postpartum. Outcomes were ascertained at study visits or by SMS report. Among 798 pregnant women enrolled, median age was 24 years [IQR 21, 29], 37% were primiparous and 92% used SMS as a primary mode of communication. Seventeen neonatal deaths and 13 stillbirths occurred. Older maternal age was associated with increased risk of stillbirth [aRR 1.12 (CI 1.02-1.24), p <0.05]. We found no significant predictors of neonatal death. Participant messaging to study nurse about concerns in the week preceding death was less common prior to infant death after discharge home from facility birth (9%) than prior to stillbirth (23%). We found limited predictors of neonatal death and stillbirth, suggesting identifying women prenatally for targeted support may not be a feasible strategy. Scarce messaging from mothers whose neonates died may reflect difficulties identifying illness or rapid deterioration and needs to be better understood to design and test interventions for this high-risk period. Messaging prior to stillbirth, while at similar levels as other periods, does not appear to have an impact as most women do not experience identifiable signs or symptoms prior to the event.
SPIDER: Speeding up Side-Channel Vulnerability Detection via Test Suite Reduction

Side-channel attacks allow adversaries to infer sensitive information, such as cryptographic keys or private user data, by monitoring unintentional information leaks of running programs. Prior side-channel detection methods can identify numerous potential vulnerabilities in cryptographic implementations with a small amount of execution traces due to the high diffusion of secret inputs in crypto primitives. However, because non-cryptographic programs cover different paths under various sensitive inputs, extending existing tools for identifying information leaks to non-cryptographic applications suffers from either insufficient path coverage or redundant testing. To address these limitations, we propose a new dynamic analysis framework named SPIDER that uses fuzzing, execution profiling, and clustering for a high path coverage and test suite reduction, and then speeds up the dynamic analysis of side-channel vulnerability detection in non-cryptographic programs. We analyze eight non-cryptographic programs and ten cryptographic algorithms under SPIDER in a fully automated way, and our results confirm the effectiveness of test suite reduction and the vulnerability detection accuracy of the whole framework.
3D object deforming and manipulating through dynamic hand gestures

Deformation of 3D objects is increasingly used in human–computer interaction applications and computer games. Achieving a natural user interface like hand gestures is the most important challenge in these fields. In this paper, we propose a system for 3D object deformation through hand natural gestures. The purpose of this research is to represent the whole process of hand involvement in different tasks from start to the end, describe the challenges in each step, and resolve them in proper ways. A part of the study focuses on constraints and rules of the hand. In the proposed system, we use a camera and colored gloves to create a vision-based method to track hands. Compared to similar systems, more complex scenarios of deformations and more natural interactions are provided in our proposed framework. Furthermore, a model of the hand is created with the intention of giving a better sense of hand-object interaction to users. The hand model simulates the hand movements in the desktop environment. We conducted a set of experiments to evaluate different parts of the proposed system. The results in each step show that the proposed system can provide a precise interaction and a good experience for users to deform objects.
A Novel Exponential-Weighted Method of the Antlion Optimization Algorithm for Improving the Convergence Rate

The antlion optimization algorithm (ALO) is one of the most effective algorithms to solve combinatorial optimization problems, but it has some disadvantages, such as a long runtime. As a result, this problem impedes decision makers. In addition, due to the nature of the problem, the speed of convergence is a critical factor. As the size of the problem dimension grows, the convergence speed of the optimizer becomes increasingly significant. Many modified versions of the ALO have been developed in the past. Nevertheless, there are only a few research articles that discuss better boundary strategies that can increase the diversity of ants walking around an antlion to accelerate convergence. A novel exponential-weighted antlion optimization algorithm (EALO) is proposed in this paper to address slow convergence rates. The algorithm uses exponential functions and a random number in the interval 0, 1 to increase the diversity of the ant’s random walks. It has been demonstrated that by optimizing twelve classical objective functions of benchmark functions, the novel method has a higher convergence rate than the ALO. This is because it has the most powerful search capability and speed. In addition, the proposed method has also been compared to other existing methods, and it has obtained superior experimental results relative to compared methods. Therefore, the proposed EALO method deserves consideration as a possible optimization tool for solving combinatorial optimization problems, due to its highly competitive results.
Overlapping Cell Segmentation of Cervical Cytology Images Based on Nuclear Radial Boundary Enhancement

The accurate segmentation of cervical cell images is one of the key steps of the cervical cancer computer-aided diagnosis system. For the problem of overlapping cell and boundary blurring in cervical cell clusters, the researchers propose a segmentation algorithm based on the nuclear radial boundary enhancement for overlapping cell of cervical cytology images. This method not only suppresses the noise of cervical cytology images but also preserves the contrast of overlapping cell boundary. The researchers generate the weight graph by the candidate contour points and contour line segment attributes and utilize the dynamic programming algorithm to find the shortest path in the weight graph. The shortest path corresponds to the coarse segmentation contour in the cell image. The level set model is used to finely segment the obtained coarse cell segmentation boundary, so as to obtain the final cervical cell boundary. Through the quantitative and qualitative evaluation results, such as dice similarity coefficient, true positive rate, and false positive rate, it can be seen that the overlapping cell segmentation algorithm in this paper has achieved better segmentation results. Compared with other current overlap cell segmentation algorithms, the segmentation results obtained in this paper have greater advantages.
Modeling and forecasting of at home activity in older adults using passive sensor technology

Life expectancy in the UK has increased since the 19th century. As of 2019, there are just under 12 million people in the UK aged 65 or over, with close to a quarter living by themselves. Thus, many families and carers are looking for new ways to improve the health and care of older people. Passive sensors such as infra-red motion and plug sensors have had success as a noninvasive way to help the older people. These provide a series of categorical sensor events throughout the day. Modeling this categorical dataset can help to understand and predict behavior. This article proposes a method to model the probability a sensor will trigger throughout the day for a household whilst accounting for the prior data and other sensors within the home. We present our results on a dataset from Howz, a company helping people to passively identify changes in their behavior over time.