Reviewerly/exHarmony huggingface dataset:
Analysis and Classification of Biomedical Data Using Machine Learning Techniques

Breast cancer research has been the center of discussion on health information technology in recent years, as it is the second largest cause of cancer deaths in women. A biopsy in which tissue is microscopically extracted and analyzed will be used for the detection of breast cancer. The cancer cells are either categorized as cancer or non-cancer. There are various methods for classifying and predicting breast cancer. The proposed work uses a breast cancer dataset from Wisconsin (Diagnostic). The data collection used to track the effectiveness of different machine learning approaches with respect to main parameters including accuracy, F1-score, specificity and recall. The three basic classification models in this paper are i.e., Naïve Bayes, KNN, Decision Tree was used to forecast cancer. The comparative tests show that the proposed Naïve Bayes classifier gives the accuracy of 98.1%, f1 score of 98.3%, specificity of 97.5%, recall of 98.3%.
Efficient simulatability of continuous-variable circuits with large Wigner negativity

Discriminating between quantum computing architectures that can provide quantum advantage from those that cannot is of crucial importance. From the fundamental point of view, establishing such a boundary is akin to pinpointing the resources for quantum advantage; from the technological point of view, it is essential for the design of nontrivial quantum computing architectures. Wigner negativity is known to be a necessary resource for computational advantage in several quantum-computing architectures, including those based on continuous variables (CVs). However, it is not a sufficient resource, and it is an open question under which conditions CV circuits displaying Wigner negativity offer the potential for quantum advantage. In this work we identify vast families of circuits that display large, possibly unbounded, Wigner negativity, and yet are classically efficiently simulatable, although they are not recognized as such by previously available theorems. These families of circuits employ bosonic codes based on either translational or rotational symmetries (e.g., Gottesman-Kitaev-Preskill or cat codes) and can include both Gaussian and non-Gaussian gates and measurements. Crucially, within these encodings, the computational basis states are described by intrinsically negative Wigner functions, even though they are stabilizer states if considered as codewords belonging to a finite-dimensional Hilbert space. We derive our results by establishing a link between the simulatability of high-dimensional discrete-variable quantum circuits and bosonic codes.
Suppressing Outlier Reconstruction in Autoencoders for Out-of-Distribution Detection

While only trained to reconstruct training data, autoencoders may produce high-quality reconstructions of inputs that are well outside the training data distribution. This phenomenon, which we refer to as outlier reconstruction, has a detrimental effect on the use of autoencoders for outlier detection, as an autoencoder will misclassify a clear outlier as being in-distribution. In this paper, we introduce the Energy-Based Autoencoder (EBAE), an autoencoder that is considerably less susceptible to outlier reconstruction. The core idea of EBAE is to treat the reconstruction error as an energy function of a normalized density and to strictly enforce the normalization constraint. We show that the reconstruction of non-training inputs can be suppressed, and the reconstruction error made highly discriminative to outliers, by enforcing this constraint. We empirically show that EBAE significantly outperforms both existing autoencoders and other generative models for several out-of-distribution detection tasks.
Работа над иллюстрациями и оформлением книги А. Д. Митяева «Рассказы о русском флоте». Замысел оформления и начало работы над макетом книги

The article describes in detail the step-by-step illustration and design process of the book, “Russian Fleet Stories” by A. D. Mityaev (the School Library series of the Children’s Literature (Detskaya Literatura) Publishing House) taking into account the main parameters set by the publisher. By perceiving the book as an integral spatial composition with harmonious proportions tantamount to a small-scale architectural form, the author puts forward his conceptualization of its graphic arrangement. The article maps out the sequence of the book layout development, standard templates used to layout the key elements of the publication: foresketches of the cover, front page, page imposition. It further touches upon the questions of the ratio between the text page format and full-page illustration. Attention is paid to the tasks of collecting material for illustrations using information from paper and digital media sources, as well as to the study of an array of visual materials on the given topic and step-by-step execution of the aforementioned undertakings. The article is illustrated with layout templates for the principal components of the publication, presketches and illustrations consequently created on their basis. The work method proposed by the author is recommended for teaching the basics of book graphics in art schools within the framework of higher and secondary education programmes. The topics reviewed by the author are also relevant for professional book artists and art historians.
Deep segmentation of abdominal organs from MRI: off-the- shelf architectures and improvements

Deep Learning outperforms prior art in medical imaging tasks. It has been applied to segmentation of Magnetic Resonance Imaging (MRI) scans, where consecutive slices capture relevant body structures for visualization and diagnosis of medical condition. In this work we investigate experimentally the factors that improve segmentation performance of MRI sequences of abdominal organs, including network architecture, pre-training, data augmentation and improvements to loss function. After comparing segmentation network architectures, we choose the best performing one and experimented improvements (data augmentation, training choices). Finally, metrics are fundamental and IoU of each organ in particular, therefore we change loss function to IoU and evaluate the resulting quality. We show that DeepLabV3 is better than competitors by 20 percentage points (pp) or more (depending on the competitor), data augmentation and further enhancements improve performance of DeepLabV3 by 12 percentage points (pp) in average, and that loss function improves performance by up to 13pp as well. Finally, we discuss challenges and further work.
CETAK IPTEK BAGI MASYARAKAT (IBM) PAKAN IKAN NILA DI DESA KALITENGAH MENGGUNAKAN IOT

Abdimas Tilapia Cultivation in Kalitengah Village, Tanggulangin Sidoarjo is the location oftilapia breeders. Currently has 10 ponds of tarpaulin fish. To feed fish, fish farmers must provide fishfeed at least every three hours. Because non-continuous feeding can interfere with the developmentof fish growth, because fish growth is greatly influenced by feed. The bigger and the more fish thatare cultivated, the more busy the fish farmers are. To find a solution for the need to feed fishcontinuously, continuously periodically, so as to manage the time from various busy activities indeveloping a business. For this reason, an automatic tool is made that can help tilapia farmingowners busy by designing an automated internet-based feeding system where the system canautomatically provide tilapia food. With an automatic feed system that can be controlled, the fishcan get food within the time set by the fish farmer
Deep Reconstruction-Arrival Picking Networks: Transfer Learning from Seismic P-Wave to Ultrasonic Logging Imaging

Increasing the accuracy of the first arrival time extraction of the echo signal is the decisive technology of ultrasonic logging imaging. However, the performance of the existing first arrival time detection algorithms is inadequate, and wrong picking locations lead to indistinct local details and lack of scene adaptation, which makes it challenging to analyze and interpret the geological features. In this paper, a novel domain adaptation algorithm based on deep learning for one-dimensional time-domain ultrasonic signal reconstruction and first arrival time picking is proposed. We build the model called Deep Reconstruction-Arrival Picking Network (DRAPN), in which the source domain performs P-wave picking and waveform reconstruction on seismic signals, but the target domain detects the first arrival time of the unlabeled ultrasonic echo data. A formal analysis is provided to demonstrate the effectiveness of the algorithm in the domain adaptation context and imaging performance, which shows that DRAPN offers better accuracy overall than conventional algorithms.
The impact of Google on discovering scholarly information: managing STM publishers’ visibility in Google

The purpose of this paper is to discuss Google visibility of five large STM publishers (Elsevier, Emerald Publishing, Springer, Taylor & Francis and John Wiley & Sons) with the aim to focus on and investigate various upcoming current issues and challenges of the publishing industry regarding discoverability, promotion strategies, competition, information-seeking behavior and the impact of new information technologies on scholarly information.,The study is based on data retrieved through two commercial online tools specialized in retrieving and saving the data of the domain's visibility in search engines: SEMrush (“SEMrush – Online Visibility Management Platform”) and Ahrefs (“Ahrefs – SEO Tools & Resources To Grow Your Search Traffic”). All data gathering took place between April 15 and the May 29, 2019.,The study exhibits the significance of Google visibility in the STM publishing industry taking into consideration current issues and challenges of the publishing activity.,This is a “new” trend, certainly of great significance in the publishing industry. The research is conducted in this paper and the theoretical background will be offered to the study of this issue.
An ontological foundation for multi-paradigm modelling for cyber-physical systems

This chapter introduces the different background material needed to understand the next three Chapters respectively presenting the Cyber-Physical Systems (CPS), Multi-Paradigm Modelling (MPM) and Multi-Paradigm Modelling for Cyber-Physical Systems (MPM4CPS) ontologies. It first introduces the approach and tools that have been used to define the ontologies. It then provides an overview of the ontological framework and a description of the shared ontology, which provides concepts defined in order to frame in a more general context some of the more specific notions of the other ontologies. Then, the chapter presents a brief introduction of the two CPS engineering environments employing MPM that were used to explore the domains covered by the ontologies and to guide readers through our exploration path.
Teaching as a decision-making model: strategies in mathematics from a practical requirement

The need in the current social context to adopt teaching methods that can stimulate students and lead them towards autonomy, awareness and independence in studying could conflict with the needs of students with specific learning disorders, especially in higher education, where self-learning and self-orientation are required. In this sense, the choice of effective teaching strategies becomes a decision-making problem and must therefore be addressed as such. This article discusses some mathematical models for choosing effective methods in mathematics education for students with specific learning disorders. It moves from the case study of a student with specific reading and writing disorders enrolled in the mathematical analysis course 1 of the degree course in architecture and describes the personalized teaching strategy created for him.
Analysis and Evaluation of ERP Information System User Satisfaction PT. Bozzetto Indonesia Using Pieces Framework

This research will evaluate the ERP application which has implemented in PT. Bozzeto Indonesia.  The application of the information system used only by some of department. Some of them are money accounting department, sales marketing, and production department. As known that inside ERP system there are so many modules in it. Every department using different module for inputting the data, to determine the component of information system running well need the evaluation process. this research aimed to evaluate the running system and to analysis the strength and the weakness of ERP PT. Bozzeto Indonesia information system which used Framework pieces by measuring six variables they are performances information/data, economic, control, efficiency, and services. Which has an analysis point is evaluation referential and information system analysis also to determined what is the impact of profit rate that increase in system used for make the company follow up from the business prospect for facing the global defiance. The results of the research show that ERP PT. Bozzeto Indonesia information system that is using by the company has many advantages and strength that support the business company but also found the disadvantages and weakness so that ERP PT. Bozzeto Indonesia information system need to improve.
STUDI LITERATUR PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE NUMBERED HEADS TOGETHER (NHT) TERHADAP HASILBELAJAR SISWAPADA MATA PELAJARAN PKN KELAS VII DI SMP

Due to the low learning outcomes of PKN students due to several problems, examples of students are less active in class, the use of learning models is less precise. The purpose of this research is to know the significant applicability of NHT-type cooperative models towards student learning outcomes in PKN subjects. The study used literature studies. The type of data used is descriptive analysis. The results showed that NHT learning models were effective to increase student activity, develop students ' cognitive abilities, improve motivation, and make learning atmosphere more enjoyable so that it has a significant influence on the students ' IPA learning outcomes.
Media Ecology and Digital Media Literacy

The article is an attempt to highlight some basic features in the meaning of the terms “media ecology” and “digital media literacy”. On thе basis of a brief theoretical observation the indicators are identified and they are combined with the rhetorical analysis, the aim being to identify, on the one hand, changes in the Bulgarian blogosphere, in the business social network Linkedin and in SlideShare. On the other hand, the focus of the investigation is on elements of digital media literacy in a different media environment, and their opportunities for using of digital tools not only by professional journalists.
Video Prediction with Variational Temporal Hierarchies

Deep learning has shown promise for accurately predicting high-dimensional video sequences. Existing video prediction models succeeded in generating sharp but often short video sequences. Toward improving long-term video prediction, we study hierarchical latent variable models with levels that process at different time scales. To gain insights into the representations of such models, we study the information stored at each level of the hierarchy via the KL divergence, predictive entropy, datasets of varying speed, and generative distributions. Our analysis confirms that faster changing details are generally captured by lower levels, while slower changing facts are remembered by higher levels. On synthetic datasets where common methods fail after 25 frames, we show that temporally abstract latent variable models can make accurate predictions for up to 200 frames.
Optimal allocation of data across training tasks in meta-learning

Meta-learning models transfer the knowledge acquired from previous tasks to quickly learn new ones. They are tested on benchmarks with a fixed number of data-points for each training task, and this number is usually arbitrary, for example, 5 instances per class in few-shot classification. It is unknown how the performance of meta-learning is affected by the distribution of data across training tasks. Since labelling of data is expensive, finding the optimal allocation of labels across training tasks may reduce costs. Given a fixed budget b of labels to distribute across tasks, should we use a small number of highly labelled tasks, or many tasks with few labels each? In MAML applied to mixed linear regression, we prove that the optimal number of tasks follows the scaling law sqrt{b}. We develop an online algorithm for data allocation across tasks, and show that the same scaling law applies to nonlinear regression. We also show preliminary experiments on few-shot image classification. Our work provides a theoretical guide for allocating labels across tasks in meta-learning, which we believe will prove useful in a large number of applications.
Alpha-DAG: a reinforcement learning based algorithm to learn Directed Acyclic Graphs

Directed acyclic graphs (DAGs) are widely used to model the casual relationships among random variables in many disciplines. One major class of algorithms for DAGs is called `search-and-score', which attempts to maximize some goodness-of-fit measure and returns a DAG with the best score. However, most existing methods highly rely on their model assumptions and cannot be applied to the more general real-world problems. This paper proposes a novel Reinforcement-Learning-based searching algorithm, Alpha-DAG, which gradually finds the optimal order to add edges by learning from the historical searching trajectories. At each decision window, the agent adds the edge with the largest scoring improvement to the current graph. The advantage of Alpha-DAG is supported by the numerical comparison against some state-of-the-art competitors in both synthetic and real examples.
PERAN WALI KELAS TERHADAP PENINGKATAN KECERDASAN ADVERSITAS ANAK KORBAN BULLYING SISWI KELAS VII F SMP SWASTA GALIH AGUNG PESANTREN DARUL ARAFAH RAYA.

The role of homeroom teacher in increasing intelligence adversity of child victims of bullying is an effort to help child victims of bullying in order to solve problems or difficulties wisely and be resilient if treated with bullying.This study aims to provide an explanation of the Role of Homeroom Classes in Increasing the Intelligence Intelligence of Children of Bullying Victims of Class VII F Students of Galih Agung Private Middle School of the Islamic Boarding School Darul Arafah Raya, with a focus of research that includes(1) What is the role of the homeroom teacher for the adversity intelligence of VII F grade students of the Galih Agung Private Middle School of the Islamic Boarding School Darul Arafah Raya ?,(2) How is the adversity intelligence of children who are victims of bullying of VII F students of the Private Galih Agung Middle School of the Islamic Boarding School Darul Arafah Raya ?,(3) What is the role of the homeroom teacher in increasing the adversity intelligence of children who are victims of bullying students of class VII F of the Galih Agung Private Middle School of the Islamic Boarding SchoolDarul Arafah Raya?.This study uses qualitative research with a type of multi-site research. Data collection methods used in this study include (1) Interviews, (2) Observations, (3) Documentation. Data collected from the three methods are then analyzed to obtain research findings. Research findings show that (1) Adversity intelligence can build self-confidence and resilience in all tests encountered in daily life. (2) Children who are victims of bullying must be able to withstand the difficulties they face because all problems must be resolved, they must not run away from existing problems. (3) The role of homeroom teacher in increasing the intelligence of child victims of bullying, because every child has different intelligence and adversity intelligence is very important in children's lives so that children can survive all difficulties and can solve these difficulties intelligently.
Self-organisation migration technique for enhancing the permutation coded genetic algorithm

Genetic algorithm (GA) is well-known optimisation algorithm for solving various kinds of the optimisation problems. GA is based on the evolutionary principles and effectively solves the large-scale problem. In addition, it incorporates the variety of hybrid techniques to achieve the best performance in complex problems. However, self-organisation is one of the popular model, which acquire global order from the local interaction among the individuals. The combined version of self-organisation and genetic algorithm are adopted to improve the performance in attaining the convergence. This paper proposes a bi-directional self-organisation migration technique for improving the genetic algorithm which achieves the convergence and well-balanced diversity in the population. The experimentation is conducted on the standard test-bed of travelling salesman problem and instances are obtained from TSPLIB. Thus, the proposed algorithm has shown its dominance with the existing classical GA in terms of various parameter metrics.
Programming by Moving: Interactive Machine Learning for Embodied Interaction Design

Interactive Machine Learning is a promising approach for designing movement interaction because it allows creators to implement even complex movement designs by simply performing them with their bodies. We introduce a new tool, InteractML and accompanying ideation method, being developed to make movement interaction design faster, adaptable and accessible to creators of varying experience and backgrounds. By closing the gap between ideation and implementation stages of designing movement interaction, we hope to apply embodied sketching all the way through the creation process, supporting the design of more expressive and reflective range of movement interaction.
Pengaruh Kualitas Siakad Terhadap Kepuasan Mahasiswa Dengan Perceived Usefulness Sebagai Variabel Intervening

The purpose of this research is to measure and test the variables and indicators that influence the success of academic information system (SIAKAD) in universities with STIE Mandala Jember as a case study. Adopting Delone &amp; Mclean IS Success research model, a set of structured questionnaires were distributed to 100 respondents. Exogenous variables include System Quality, Information Quality, Service Quality, and endogenous variables Student Satisfaction. Perceived usefulness variable used as a intervening variable proved it could mediate the quality of information, the quality of system and the quality of services to user satisfaction. Path analysis is employed as analitical technic. The result shows that there is a significant effect on the quality of the system, quality of the information, the quality of the services to the students’ satisfaction. Perceived usefulness variable, can mediate between exogenous variables to endogenous variables. This conclusion support the prediction of many previous researches.This indicates that the model used is not proper because the model is built on the construct of implementation of academic information system onstudents’ satisfaction.&#x0D; Keywords: System Quality, Information Quality, Service Quality, and Perceived Usefulness.