Reviewerly/exHarmony huggingface dataset:
Tumor Characterization Using Unsupervised Learning of Mathematical Relations Within Breast Cancer Data

Despite the variety of imaging, genetic and histopathological data used to assess tumors, there is still an unmet need for patient-specific tumor growth profile extraction and tumor volume prediction, for use in surgery planning. Models of tumor growth predict tumor size and require tumor biology-dependent parametrization, which hardly generalizes to cope with tumor variability among patients. In addition, the datasets are limited in size, owing to the restricted or single-time measurements. In this work, we address the shortcomings that incomplete biological specifications, the inter-patient variability of tumors, and the limited size of the data bring to mechanistic tumor growth models. We introduce a machine learning model that alleviates these shortcomings and is capable of characterizing a tumor’s growth pattern, phenotypical transitions, and volume. The model learns without supervision, from different types of breast cancer data the underlying mathematical relations describing tumor growth curves more accurate than three state-of-the-art models. Experiments performed on publicly available clinical breast cancer datasets, demonstrate the versatility of the approach among breast cancer types. Moreover, the model can also, without modification, learn the mathematical relations among, for instance, histopathological and morphological parameters of the tumor and, combined with the growth curve, capture the (phenotypical) growth transitions of the tumor from a small amount of data. Finally, given the tumor growth curve and its transitions, our model can learn the relation among tumor proliferation-to-apoptosis ratio, tumor radius, and tumor nutrient diffusion length, used to estimate tumor volume. Such a quantity can be readily incorporated within current clinical practice, for surgery planning.
Psychological and Pedagogical Characteristics of Students’ Demands While Self-studying at the Institution of Higher Education

In the conditions of rapid growth of new scientific and application information, the volume of knowledge that a graduate of a higher school has to master is constantly increasing. Educational standards, curricula, educational and methodological resources, textbooks and teaching aids despite being constantly reconsidered aren’t able to catch up with all the current changes. In this context a large gap between scientific achievements and the content of university courses arises which reduces the quality of training students. The solution to this contradiction is the development of students’ need for self-studying along with the improvement of the methodology and methods of teaching certain courses at the university. Based on the analysis of psychological and pedagogical research and personal experience, the characteristics of three levels of students’ readiness for self-studying are revealed. Potentialities of teachers’ activity for the organization of students’ controlled cognitive activity are determined. It is proved that the need for independent acquisition of certain subject matter and modules of the course under study inevitably leads to a shift in priorities from memorizing and reproducing the acquired knowledge to their independent acquisition and mastering. Particular attention is drawn to the need for organizing independent laboratory research and virtual laboratory experiments in physics which provide supportive environment for the formation of students’ need for self-study.
Games with Full, Longitudinal, and Transverse Observability

Design and control of multi-agent systems correspond to the synthesis of winning strategies in games that model the interaction between the agents. In games with full observability, the strategies of players depend on the full history of the play. In games with partial observability, strategies depend only on observable components of the history. We survey two approaches to partial observability in two-player turn-based games with behavioral winning conditions. The first is the traditional longitudinal observability, where in all vertices, the players observe the assignment only to an observable subset of the atomic propositions. The second is the recently studied transverse observability, where players observe the assignment to all the atomic propositions, but only in vertices they own.
ABOUT THE SITUATION WITH THE TEACHING COMPUTER SCIENCE IN CERTAIN COUNTRIES

The digital environment of children is not an extra option, but a habitat. When a person does not have a knowledge in coding, he is forced to accept the devices and software provided by the developers. This puts him at a disadvantage: he spends a lot of time in a digital environment, where the rules of conduct have not been developed by himself. Knowledge of coding, according to the formed opinion, will help the user to better "navigate the situation". Digitally literate children will stop judging websites by their attractiveness and range of services only — they will be capable to critically and purposefully build their interaction with the websites. The dynamics of the situation with teaching informatics in schools in the United States of America from 2005 to the present and in Estonia from 2012 to the present has been analyzed in the article. The features of the approach to the formation of educational programs in informatics in schools of different states and the role of external organizations that are not related to the education system have been considered.
RETRACTED: Research on Innovation of Network Information Security Technology Based on Cloud Computing

Abstract Cloud computing has become a new network service mode, which can provide a series of resources for many users and carry out intelligent computing according to their needs and daily habits. While technology development brings positive effect, it will also have negative effects. More and more hackers and viruses are trying to invade computers, endangering network security to provide users with storage and data processing services. Based on the widely used environment of cloud computing, this paper studies the innovation of network information security technology with the development of science and technology.
PERCEIVED EFFECTIVENESS OF INFORMATION SOURCES IN MEETING INFORMATION NEEDS OF RICE GROWERS IN NEPAL

A study was done to assess the effectiveness of different information sources in meeting information needs and utilization behavior of rice growers in western midhills of Nepal, taking 101 sample households. Survey design with a structured questionnaire was used for data collection under the pragmatic paradigm of social research. The study showed that 30 percent of farmers were high information seekers while about 59 percent were utilizing information highly implying that most farmers were using the information highly though they don't seek information by themselves. Input management and climatic information were highly sought and utilized. Though fellow and contact farmers were the more used source of information, however, respondents felt that extension personnel and agro-vets were more credible. An assessment of the effective index showed that fellow farmers were highly effective followed by extension personnel and agro-vets while social media and ICTs were least effective. There was a significant positive correlation between information variables and education, ethnicity, number of crops grown and accessibility to information sources. However, poor accessibility of farmers to these sources was a major problem. Thus, a suitable information delivery system accessible to most farmers according to their time and needs should be designed regarding rice cultivation.
Security of Public Key Encryption Against Resetting Attacks

Ciphertext indistinguishability under chosen plaintext attacks is a standard security notion for public key encryption. It crucially relies on the usage of good randomness and is trivially unachievable if the randomness is known by the adversary. Yilek (CT-RSA’10) defined security against resetting attacks, where randomness might be reused but remains unknown to the adversary. Furthermore, Yilek claimed that security against adversaries making a single query to the challenge oracle implies security against adversaries making multiple queries to the challenge oracle. This is a typical simplification for indistinguishability security notions proven via a standard hybrid argument. The given proof, however, was pointed out to be flawed by Paterson, Schuldt, and Sibborn (PKC’14). Prior to this work, it has been unclear whether this simplification of the security notion also holds in case of resetting attacks. We remedy this state of affairs as follows. First, we show the strength of resetting attacks by showing that many public key encryption schemes are susceptible to these attacks. As our main contribution, we show that the simplification to adversaries making only one query to the challenge oracle also holds in the light of resetting attacks. More precisely, we show that the existing proof can not be fixed and give a different proof for the claim. Finally, we define real-or-random security against resetting attacks and prove it equivalent to the notion by Yilek which is of the form left-or-right.
Recognition and Segmentation of Gastric Tumor Based on Deep Residual Network

In order to better complete the task of tumor segmentation, enterprises need to identify tumors in cardiac CT images. First, the CT image is preprocessed, including denoising, segmentation, amplification and gray value. In this paper, the deep residual network framework is built, and the pre-processed CT images are made into data sets and trained. The deep residual network is characterized by fast speed, good convergence and high robustness. The trained model is applied to the test data, and the predicted result and the weight coefficient are weighted as the final result, which is then used as the parameter to judge whether there is a tumor, so as to complete the tumor recognition.
Research on the Technology of Intelligent Basketball Shooting Training Vehicle

Abstract The basketball training car only has the function of storing basketballs and training equipment. During training, the basketball is taken out of the training car and scattered on the ground. The athlete needs to bend down to pick up the ball for shooting training, which is not conducive to the continuity of shooting action training, and affects the intensity of shooting training and the shooting accuracy improvement. It is necessary to manually place the basketball on the swing seat, it can not be automatically served, and the function is low. Therefore, this article conducts technical research on the basketball shooting training vehicle, and sets up a telescopic motor to drive the upper tee to move up and down and other technical studies, in order to intelligently upgrade the existing shooting training vehicle, which is conducive to the continuity of shooting action training and the intensity of shooting training, can improve the fixation of the shooting hand and improve the shooting percentage.
Application of orthogonal fringe patterns in uniaxial microscopic 3D profilometry

This research presents a novel uniaxial microscopic 3D profilometry method with the application of orthogonal fringe patterns in a structured light system. Specifically, the projector alternately projects vertical and horizontal stripes, and subtraction of two adjacent shot images is applied to eliminate the influence of the background information. This method requires only one-tenth of our previous method in collection and data processing volume, but the accuracy can achieve almost the same as that method. We will describe the principle of this uniaxial microscopic 3D profilometry and demonstrate the accuracy of the proposed measurement framework by comparing it with the ten-step phase-shifting method and the Fourier transform method. It shows that approximately 5.22 µ m root-mean-square (RMS) error with a depth range of 1100 µ m can be achieved by the proposed approach.
Integrating visual-vestibular mismatch and posture instability in Virtual Reality: A cyber-sickness study

Virtual reality (VR) displays can introduce nausegenic symptoms, often known as cyber-sickness. One dominant hypothesis explained the malady by the mismatch between afferent signals from visual, vestibular, and other systems, and the discrepancy with the predictions made based on real-world experiences. Another proposed that sickness occurs when one’s ability to maintain a stable bodily orientation is challenged. To date, only a few studies have been conducted to examine the interactions between them. Here, we explore the effects of visual-vestibular mismatch and posture instability through measuring cyber-sickness discomfort level and symptoms. The independent variables involving two levels of: sensory conflict (with or without illusory self-motion elicited visually by an HTC Vive head-mounted display) and postural challenge (with or without low-frequency vibration induced while a participant stands on a 3DoF motion platform), were manipulated alone or in combination. Together with the control condition, participants (n = 12) experienced one of the four conditions for 4.4 min each day in four separate days. During the stimulus presentation, the participant engaged in a visual search task that promote similar eye movement across conditions while the discomfort was measured using Misery Scale (MISC) every minute and Simulator Sickness Questionnaire (SSQ) before and after each condition. Results showed that higher discomfort levels and more symptoms were associated with the combined and the sensory conflict only condition compared to other conditions. In addition, spontaneous postural instability was measured while the participant stood on a Wii balance board for 1 min with their eyes closed. The standard deviation of the center of pressure (CoP) displacement in the anterior-posterior (AP) direction was higher after experiencing the combined condition than before the exposure. Our results suggest that the visual-vestibular mismatch might be one of the requisite causes for cyber-sickness, while postural stability might be a moderator variable.
A Novel Hybridization of Self-adaptive Multi-verse Optimizer over K-Means for Data Clustering

Although clustering algorithms are a popular way to find the relationship among a collection of data, these algorithms have to deal with various types of challenges such as slow convergence rate, converging to local optima, and requiring the number of clusters in advance. In order to solve these drawbacks in one of the most famous clustering algorithms called K-Means, this paper has been presented a novel method based on nature-inspired algorithms in combination with clustering technique to construct a hybrid method for solving the clustering as an optimization problem in a reasonable time. Many nature-inspired algorithms have been successfully used to solve non-linear optimization problems. This paper proposed an algorithm which uses a recently introduced nature-inspired algorithm called Multi- Verse Optimizer (MVO) over K-Means to minimize the cluster integrity and maximize the distance between clusters by finding the optimal number of clusters (K) as well as initial centroid for clusters. The proposed method has been tested using ten datasets and compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), K-Means with random initial centroids, and K-Means++ to show the considerable improvement of clustering by using the proposed method. The results have shown that our new self-adaptive method outperforms other comparing nature-inspired algorithms both in cluster integrity and convergence rate.
RETRACTED: Early warning expert decision system of water resource management based on Cloud Computing

Abstract In order to solve various problems caused by the lack of timely warning in water resources management, based on the current water resources informatization, an early warning expert decision-making system for water resources management based on cloud computing architecture is proposed. The system collects real-time data through the Internet of things interface layer, uses Web GIS to display geographic information and Ajax (asynchronous Javascript + XML) asynchronous interactive processing mode to speed up the response speed. Finally, an early-warning expert decision system for water resource management based on the cloud computing architecture is realized, which can monitor both the command center and the mobile terminal at the same time.
Identifikasi Pengenalan Wajah Perokok Menggunakan Metode Principal Component Analysis

Cigarettes are one of the biggest contributors to preventable causes of death in society. Cigarette smoke contains various chemicals that can cause various diseases such as chronic coughs, lung cancer, and other health problems. Cigarette smoke not only harms the health of the smoker itself but also the health of others. Sometimes written warnings about smoking bans are often not followed by active smokers. This study aims to identify smokers 'facial recognition in order to recognize and identify smokers' faces who do not obey the rules by using dimensional reduction techniques oriented to the Principal component Analysis (PCA) method. Principal Component Analysis will later be integrated with the Eigenface and Eucladean analysis algorithms to reduce the image size in obtaining the best value vectors to simplify the face image in the input image space and look for the threshold value which is the threshold that the test data must pass so that it can prove the data value. testing becomes recognizable data through the calculation of the distance for each weight. In this study, there were 8 smoker faces with 5 different facial poses that were tested for 40 face recognition experiments and resulted in 34 correct smoker face recognition and 6 wrong smoker face recognition with an accuracy rate of 92.5% and a long face recognition process time of 80. second. This test has proven that the Eigenface and Euclidean distance in the Principal Component Analysis (PCA) are able to handle and recognize smoker's facial image data well.
MACHINE LEARNING AND BIG DATA PROCESSING IN A MODERN SCHOOL

The modern world is filled with a variety of data, and their volume is increasing many times every year. On the one hand, Big Data is data that cannot be processed on one computer, it is difficult to structure it. On the other hand, these are special tools, approaches and data processing methods that are used so that a person can get specific and necessary results for their further effective use. Where does big data come from? From the Internet — data from social networks, blogs, media, forums, sites, the Internet of things. From archives and databases — corporate data, transactional business information. From all sorts of instruments and devices — readings from sensors, meters, recorders, for example, data from the meteorological service and cellular operators. Billions of devices are connected to the Internet today. It is expected that within a few years millions of new devices will be connected to the global network, which will generate several tens of millions of terabytes of data every day. Such figures should interest those young people who are looking for their own way in the field of information technology. It is obvious that in the near future, specialists who know how to work with Big Data will be in demand in the labor market. "Machine learning and Big Data" is one of the competencies of WorldSkills Junior (Future Skills). The article presents the experience of preparing for competitions of this kind without going beyond the school subject "Informatics".
The use of cloud services to enhance information interaction in e-learning to improve the quality of educational results

The problem and the aim of the study. New challenges and requirements of society and the state to the e-education system necessitate the formation of students' skills for active information interaction and cognitive activity in the digital space. The authors propose to use cloud services for organizing the educational process, e-learning participants integration and communication, expanding didactic tools and improving the quality of graduate training in the electronic educational environment. Research methods. The main methods are theoretical and methodological analysis and generalization of fundamental scientific works on the research problem, processing of the results of control events and the content of the Google Classroom cloud service (assigned tasks, announcements, instructions, links to additional resources). The pedagogical experiment involved 52 students (35% of girls and 65% of boys) from the Law Institute of Vyatka State University. The G sign criterion is used as a statistical processing method. Results. The features of the organization of e-learning in a digital school have been formulated to enhance information interaction based on the use of cloud technologies through the Google Classroom service: the integration of students and teachers into a single information and educational space, expanding the possibilities of learning and communication; "synchronization" of educational and cognitive activities in different digital resources; the orientation of the service content to the peculiarities of the modern student’s perception, the ability to work in the format of mobile applications activates cognitive interest, research activities and communication practice are supported. The empirical value Gemp=3&lt;Gcr=10 (for p=0.01) obtained during the experiment confirms that the shift towards improving the quality of educational results after using the Google Classroom cloud service is not accidental. Conclusion. The use of cloud services in organizing information interaction in the e-learning system will improve the quality of educational results while providing a set of conditions: enhancing cognition, connecting students to information interaction in new ways, expanding the boundaries of the educational environment, modifying the roles of participants in the didactic process, and using mobile applications.
Informative Ensemble Kalman Learning for Neural Structure

We characterize the stochastic dynamics of Neural Learning to develop Informative Ensemble Kalman Learning. Specifically, an adaptive Ensemble Kalman Filter replaces backpropagation to quantify uncertainty and maximize information gain during Learning. Demonstrating competitive performance on standard datasets, we show Structure Learning using the Informative Ensemble Kalman Learner quickly recovers the dynamical equations of the Lorenz-63 system ab initio from data. Results indicate that extending DDDAS key informative paradigm to optimize Learning Systems is promising.
The Construction of Human-oriented Applied “New Finance and Economics” Talent Training Model Based on Big Data

Abstract The cultivation of applied talents is the essential function of colleges and universities. Building a high-level application-oriented talent training system is the basic engineering requirement for deepening the talent training work in the era of computer big data. The construction of personnel training mode of new finance and economics is an important support for the renewal of financial technology in the new era [1] . It is also the main stage of the progress of the applied talents training system. This paper briefly describes the basic content of talent training system. This paper describes the main relationship between the new financial concept and the financial personnel training system. In addition, this paper puts forward the main point of view on how to build a high-level financial personnel training system in the era of computer big data.