Reviewerly/exHarmony huggingface dataset:
Improved nucleus segmentation process based on knowledge based parameter optimization in two levels of voting structures

Digital analysis and biomedical image processing has become important part within modern medicine and biology. Digital pathology is just one of many medicine areas that is being upgraded by constant biomedical engineering research and development. It is very important that some of disciplines as nucleus detection, image segmentation or classification become more and more effective, with minimum human intervention on these processes, and maximum accuracy and precision. Improved optimization of nucleus segmentation methods parameters based on two levels of voting processes is presented in this paper. First level includes hybrid nucleus segmentation based on 7 segmentation algorithms: OTSU, Adaptive Fuzzy-c means, Adaptive K-means, KGB (Kernel Graph Cut), APC (Affinity Propagation Clustering), Multi Modal and SRM (Statistical region merging) based on optimization of algorithms parameters along with implemented first level voting structure. Second level voting structure includes segmentation results obtained in the first level of voting structure in combination with 3rd party segmentation tools: ImageJ/Fiji and MIB (Microscopy Image Browser). A definite segmented image of a nucleus could serve as a generic ground truth image because it is formed as a result of a consensus based on several different methods of segmentation and different parameter settings, which guarantees better objectivity of the results. In addition, this approach can be used with great scalability on 3D-stack image datasets.
First-Order and Higher-Order Power Analysis: Computational Approaches and Aspects

Side-channel analysis pose a serious threat to many modern cryptosystems. Using Correlation power analysis, attacker may be able to recover the cipher key and therefore jeopardize the whole cryptosystem, which is why many countermeasures are being developed. These countermeasures are typically effective against first-order attacks. However, protected implementations may still be vulnerable to higher-order analysis. In this paper, we compare different approaches to the higher-order analysis regarding their mathematical and performance properties. We focus on Correlation power analysis attack and the test vector leakage assesment using Welch's t-test, we optimize and accelerate discussed algorithms using CPU and GPU, and we present our experimental results and remarks.
Dynamic Data Publishing with Differential Privacy via Reinforcement Learning

Differential privacy, which is due to its rigorous mathematical proof and strong privacy guarantee, has become a standard for the release of statistics with privacy protection. Recently, a lot of dynamic data publishing algorithms based on differential privacy have been proposed, but most of the algorithms use a native method to allocate the privacy budget. That is, the limited privacy budget is allocated to each time point uniformly, which may result in the privacy budget being unreasonably utilized and reducing the utility of data. In order to make full use of the limited privacy budget in the dynamic data publishing and improve the utility of data publishing, we propose a dynamic data publishing algorithm based on reinforcement learning in this paper. The algorithm consists of two parts: privacy budget allocation and data release. In the privacy budget allocation phase, we combine the idea of reinforcement learning and the changing characteristics of dynamic data, and establish a reinforcement learning model for the allocation of privacy budget. Finally, the algorithm finds a reasonable privacy budget allocation scheme to publish dynamic data. In the data release phase, we also propose a new dynamic data publishing strategy to publish data after the privacy budget is exhausted. Extensive experiments on real datasets demonstrate that our algorithm can allocate the privacy budget reasonably and improve the utility of dynamic data publishing.
On the potential of evolution strategies for neural network weight optimization

Artificial neural networks typically use backpropagation methods for the optimization of weights. In this paper, we aim at investigating the potential of applying the so-called evolutionary strategies (ESs) on the weight optimization task. Three commonly used ESs are tested on a multilayer feedforward network, trained on the well-known MNIST data set. The performance is compared to the Adam algorithm, in which the result shows that although the (1 + 1)-ES exhibits a higher convergence rate in the early stage of the training, it quickly gets stagnated and thus Adam still outperforms ESs at the final stage of the training.
Improved Parity-Based Error Estimation Scheme in Quantum Key Distribution

Quantum Key Distribution (QKD) is a promising technique to distribute unconditional secure keys for the remote two parties. In order to improve the final secure key rate of a QKD system, the Quantum Bit Error Rate (QBER) needs to be estimated as accurate as possible with minimum information leakage. In this paper, an improved parity-based error estimation scheme is proposed. The core part of the scheme is the proposed optimal block length calculation method. Simulation results show that the proposed scheme improves the accuracy of QBER estimation with less information leakage.
Research on Alert Strategy of Unmanned surface Vessel Based on Typical Missions

In a typical patrol alert mission, taking the circular patrol protecting fixed targets as an example, we propose an alert strategy under different detection ranges and warning circle ranges. When the unmanned vessel has global detection capability, based on the elevator algorithm, the maximum interception number and the optimal interception path is obtained. When the unmanned vessel has local detection capability, in the case of non-threat target intrusion, according to the radar detection probability model, the enemy interception quantity is maximized and the optimal patrol speed is obtained. For multiple threat target intrusions, a threat risk model is established and an optimal alert strategy is provided. In this paper, we build models for different situations, which are more specific and reasonable. The simulation test verifies that the alert strategy proposed in this paper is applicable to the patrol alert mission in various situations of unmanned surface vessel.
Shape Prior Embedded Level Set Model for Image Segmentation

This paper presents an optimized level set evolution (LSE) without reinitialization (LSEWR) model and a shape prior embedded level set model (LSM) for robust image segmentation. Firstly, by performing probability weighting and coefficient adaptive processing on the original LSEWR model, the optimized image energy term required by the proposed model is constructed. The purpose of the probability weighting is to introduce region information into the edge stop function to enhance the model’s ability to capture weak edges. The introduction of the adaptive coefficient enables the evolution process to automatically adjust its amplitude and direction according to the current image coordinate and local region information, thus completely solving the initialization sensitivity problem of the original LSEWR model. Secondly, a shape prior term driven by kernel density estimation (KDE) is additionally introduced into the optimized LSEWR model. The role of the KDE-driven shape prior term is to overcome the problem of image segmentation in the presence of geometric transformation and pattern interference. Even if there is obvious affine transformation in the shape prior and the target to be segmented, the target contour can still be reconstructed correctly. The extensive experiments on a large variety of synthetic and real images show that the proposed algorithm achieves excellent performance. In addition, several key factors affecting the performance of the proposed algorithm are analyzed in detail.
Methods for Designing Systems with Benefits of Inconvenience

This paper introduces the concept of “benefits of inconvenience (BI)” and proposes ideation methods for designing human-machine interaction (HMI) involving BI. HMI is the superset of HCI. Based on the assumption that convenience means saving time and reducing effort, efficiency, and high functionality provide convenience to users. On the other hand, for discussing HCI, interaction is essential that requires humans’ time and efforts. In other words, inconvenience in the sense of above-mentioned assumption is essential for human-machine interactions. In this case, machines do not substitute humans but interact with humans, and inconvenience will be beneficial. This paper categorizes methods for designing systems with benefits of inconvenience into three classes.
Comparison of the Legal Principle Foundation of Chinese and American Secret Information Access Control Technique

This paper firstly put forward the concept of secrecy legal principle foundation for information security and Secrecy Techniques. Secondly, the variance of the secret information access control technique between China and American was analyzed using contrast analysis. Thirdly, analysis of American sensitive compartmented information based on the lattice which was not suitable in China was given. Finally, according to the limitations of China control scheme in access scope, the suggestions for establishing secret information access control mechanism and model with China legal principle foundation is put forward.
A Survey of Data Hiding Based on Vector Quantization

With the development of computers and networks, digital data can be transmitted quickly to anywhere in the world. Information security has become the focus of research for several researchers as it is essential to protect the information that is being transferred over the Internet. In 1980, Linde et al. proposed vector quantization (VQ), a simple compression technique with good image quality and compression rate. We explore vector quantization (VQ) in this paper for embedding watermark to achieve the goal of data hiding. Data hiding schemes for the encoded vector quantization (VQ) index table are studied from five papers and analyzed in terms of the characteristics of different methods. A comparison of image quality and the amount of embedded information has been presented and discussed.
DEVELOPMENT OF TEENAGE CLUB ACTIVITY IN THE SOVIET OUTOF- SCHOOL WORK IN ORENBURG REGION

Purpose: To study the development of teenage club activity in the Orenburg region from 1931 till 1958 in the Soviet out-of-school work in a national context. Methodology: The main stages are defined and their descriptive characteristic is given on the basis of proposed and formulated criteria via comparative qualitative research methods. Main Findings: The leaders of the club associations where children from families with difficulties and problems can be met and given qualified consultation. The development of the teenage club activity in the Orenburg region is represented by three periods of development, decline and flourishing. Applications: This research can be used at universities, historical organizations, and culture-literature institutes and by graduate students in history. Novelty/Originality: The paper explored the history of the regional system of teenage club activities in the Orenburg region which is becoming of particular importance as it allows to think critically and borrow the most advanced things for the further development of the club activities and for the further improvement of the education system.
Synchronie en diachronie in de Nederlandse zinsbouw

Amsterdam University Press is a leading publisher of academic books, journals and textbooks in the Humanities and Social Sciences. Our aim is to make current research available to scholars, students, innovators, and the general public. AUP stands for scholarly excellence, global presence, and engagement with the international academic community.
The Moderating Role of Public Governance on the Relationship Between the Entrepreneurship and Economic Growth

This study aims to investigate the role played by public governance in the relation between entrepreneurship and economic growth in the United Arab Emirates (henceforth UAE). To achieve its goals, the study used a time series that extends from (1996-2015), a span of 20 years. The study came up with the following results: there was a positive impact of entrepreneurship activities on the economic growth in UAE within the framework of the public governance. Based on the previous results, the study came up with a set of recommendations that focuses on supporting entrepreneurship activities that play a significant role in the economic growth.
Research on optimal path of power wiring based on intelligent algorithm

Power wiring is the cornerstone of urban expansion and social progress. In recent decades, the traditional route routing method takes the first place, that is, to minimize the construction pressure without considering the cost first. However, this method is often restricted by many unexpected factors, and it often takes a long time to choose an appropriate path, which seriously affects the construction progress. Combined with the intelligent algorithm and the understanding of the real situation, we designed the power wiring system (PW system) and cabling optimization system (CO system). Through computer programming, the actual problems are digitized, and the actual problems are transformed into virtual points. The swarm intelligence algorithm is used to generate the optimal path through the computer. At the same time, the use of the computer can compare and choose the problems and factors that are difficult to be considered at the same time in the traditional wiring. Moreover, the flexibility and convenient manipulation of the computer also create convenient conditions for the later maintenance, which can effectively improve the efficiency of power wiring and reduce the wiring cost. Through the research on intelligent cabling, it aims to provide more effective methods for power cabling and help solve the problem of power demand arising from the rapid development of society.
Autonomous driving on a direct perception system with deep recurrent layers

Controlling a car in traffic is a complex task that is hard to treat with classical algorithms. With the fast development of computer vision techniques, deep artificial neural networks have been use on this task with the use of a frontal camera on a car. Few research projects with these techniques, however, go beyond the perception task and are tested on a driving environment.
Automatically Estimating Meaning Ambiguity of Emoticons

Emoticons are one of the means of communication widely used by Internet users. Emoticons are used to express information that cannot be fully transmitted only by text, such as emotions or feelings. However, not all emoticons are easily understandable. In this research, we aimed at estimating such general level of comprehensibility, or “meaning ambiguity”, defined by their correspondence with linguistic expressions, such as onomatopoeia. The method could help users apply more meaningful and understandable emoticons in their everyday communication and improve their online communication.
Adaptation of Machine Learning Frameworks for Use in a Management Environment

The combination of person and location recognition provides numerous new fields of possible applications, such as the development of approaches for detecting missing persons in public spaces using real-time monitoring. It is necessary to use frameworks of both domains on given data sets and to merge the acquired results. For this purpose Thomanek et al. [11] developed an evaluation and management system for machine learning, which allows the interconnection of different frameworks and the fusion of result vectors [11]. This paper discusses the EMSML in terms of interfaces and components to develop a generic workflow that supports the integration of different frameworks for people and location recognition. In this context, the focus is on the required adaptation of existing frameworks to the implemented infrastructure. A generic workflow concept can be deduced from the analysis results. This concept can be applied to two typical frameworks for evaluation and implemented as prototypes. Subsequently, developed test cases are used to demonstrate the functional validity of the prototypes and the applicability of the concept.
Generic level polymorphic n-ary functions

Agda's standard library struggles in various places with n-ary functions and relations. It introduces congruence and substitution operators for functions of arities one and two, and provides users with convenient combinators for manipulating indexed families of arity exactly one.
Recognition of Listener’s Nodding by LSTM Based on Movement of Facial Keypoints and Speech Intonation

Communication between humans and robots is crucial to achieve successful cooperation in real-life scenarios. The robot must understand not only linguistic expressions, but also non-linguistic expressions such as nodding and gestures. In this research, we examine whether a listener nods in response to a speaker’s utterance. Our proposed method judges nodding based on the movement of the listener’s facial keypoints and the speaker’s speech intonation. The proposed method achieves approximately 84.4% recognition accuracy when we input the movement and intonation simultaneously. This improves nodding recognition accuracy by 8.8% over movement only approach. This result indicates that the movement of the listener’s facial keypoints and the speaker’s intonation are important information in nodding recognition.
Atmospheric Light Estimation by Edge Detection

In this article, we put forward a novel method for atmospheric light estimation by detecting edge information. This method based on the observation that the visibility in the haze-opaque area of the foggy image is low on account of the degradation of edge information. We detect the dense foggy area of the image by extracting the edge information of the image and cutting the expanding region with large edges in the corresponding place of the original image. Then, the global atmospheric light can be estimated directly. Experimental results on a variety of hazy images demonstrate the efficiency of the proposed method.
Geometrically Intuitive Rendering of High-Dimensional Data

Visualization is arguably the most powerful manual analytic method. However, as enterprise data have grown in size and complexity, supporting fast and intuitive data visualization has been become increasingly difficult. In particular, when the data being analyzed have many dimensions (e.g., feature vectors having many attributes), conventional methods, when they can be used at all, often make representational compromises that produce non-intuitive and misleading graphical products. Three conventional methods for displaying sets of high-dimensional vectors on a flat computer display are described: pair-plots, feature-winnowing, and parallel coordinate plots. These are compared with two methods the authors have formulated, Recursive Stereographic Projection (RSP), and Tensor Projection (TP), for producing visual displays of high-dimensional discrete data that are fast, intuitive, navigable, and informative, and tend to preserve geometric characteristics of the original high-dimensional data such as, occlusion, proximity, intersection, relative scale, spatial extent, shape, and coherence. Several real-world examples of intuitive 2-dimensional renderings of high-dimensional data are given. Suggestions for future research are made, and references provided.
Batch Attendance System Based on Face Recognition

Biometric identification technology has been adopted to develop batch attendance system of electric power enterprises in this paper. The face is a typical biological characteristic, which can be adopted to promote the identity recognition efficiency without any touch. The face recognition technology includes three processes: face detection, face learning, and face recognition. The system has been deployed in Android platform, which has the highest market share of mobile operating system. The principal component analysis (PCA) has been adopted as the characteristic extraction method, and Java CV as the visual processing library. The experiment results show that the system achieves the higher accuracy with the more input photos and the fewer people in the photo, which helps the electric power enterprises to finish the staff attendance management.
DirectFlow: A Robust Method for Ocular Torsion Measurement

Measuring involuntary eye movement under specific stimuli is an important way to identify diseases such as balance disorders. Exams based on video-oculography (VOG) equipment are able to detect horizontal and vertical displacements of the pupil. However, detecting torsional movements is still a challenge. Although conventional methods have good accuracy, their results can be influenced by artifacts, such as a torsion center displacement, interference by illumination, reflections, and changes in the pupil dilation. We propose a novel method which improves the robustness of this measurement by applying the Lucas-Kanade Pyrm (LKP) optical flow technique to the captured image, directly over the iris, rather than making a polar transformation. Retaining this additional information allows multiple features over the iris to be analyzed individually and as a group, providing correction of the torsion center displacements, filtering features with reflections and adapting to different pupil dilations before the torsion angle is calculated. The accuracy and performance of this method were evaluated by comparing it against a conventional method when detecting torsional movements on videos with a known ground truth. Moreover, a simplified version of the proposed method is also evaluated, in order to analyze the impacts of a torsion center displacement. Results show that the proposed method has higher accuracy and equivalent performance to the conventional method.
Engineering Self-adaptive Systems: From Experiences with MUSA to a General Design Process

Designing and developing complex self-adaptive systems require design processes having specific features fitting and representing the complexity of these systems. Changing requirements, users’ needs and dynamic environment have to be taken in consideration, also considering that, due of the self-adaptive nature of the system, the solution is not fixed at design time but it is a run-time outcome. Traditional design approach and life cycles are not suitable to design software systems where requirements continuously change at runtime. A new design process paradigm is needed to design such systems. In this Chapter, we present a retrospective analysis based on three projects developed in the last five years with the middleware MUSA in order to identify specific features of the design process for supporting continuous change and self-adaptation. The result is a general approach allowing to reduce the gap between design time and run-time.
New Cryptographic Algorithm Based on CFA

In the modern era of Information and communication network security becomes a major concern for researchers. Unauthorized users may interrupt the communication; therefore, researchers are trying to protect data during information exchange using various cryptographic techniques. Cryptography is the science of encrypting data which keeps the message safe from unauthorized users. In this paper, we have presented a new technique CFA, which is based on ASCII values and coin flipping. Moreover, the proposed coin flipping method is a new cryptographic technique which cannot be found in the literature.