Reviewerly/exHarmony huggingface dataset:
Formalization of Transfinite Induction in Coq*

Transfinite induction is a proof method of mathematics and has important applications in computer science. In this paper, a formal proof of transfinite induction is proposed based on Morse-Kelley set theory. It is the first Coq formalization about transfinite induction to our knowledge. Furthermore, according to the minimum principle, we formally prove mathematical induction which is a special form on non-negative integers of transfinite induction. The whole proofs in this paper are verified in Coq proof assistant. The Morse-Kelley set theory is also formalized in Coq. The work shows that the formal proof of mathematical theorems is credible and religious. The formal work in this paper has important applications in set theory, algebra, and topology. In addition, according to transfinite induction, we can formally prove the general well-founded structures in computer science.
Migrating from ILLiad to Tipasa

Tennessee State University was a decade long user of OCLC’s ILLiad platform. 2019 marked the year when the relationship with that system ended as our two interlibrary loan departments migrated to Tipasa, OCLC’s new cloud-based system. The platform change did not stem from dissatisfaction with ILLiad but from being informed that Tipasa would eventually replace ILLiad. This article covers three areas of the implementation: communication, staff training, and outreach. Explained is the communication had between library staff and OCLC representatives, what our participation in online cohort training involved, and included are visuals used to inform our campus about Tipasa.
Prestasi Belajar Mahasiswa Penerima Bidik Misi

This research is a descriptive study that aims to see a picture of the learning achievement of Bidik Misi recipients in the academic year 2017/2018 who studies at Prof. University. Dr. Hazairin, SH (UNIHAZ) in the first semester. The population and sample in this study were all students receiving Bidik Misi. Data collection technique is a documentary technique by taking secondary data in the form of a GPA of one semester in the beginning of Bidik Misi recipient students who have been archived in each study program. Data obtained in this study were analyzed using descriptive statistical analysis and categorized based on Academic Guidelines. Based on research data obtained, it is known that the average learning achievement of Bidik Misi recipient students is in the very satisfactory category with an average GPA of 3.42. When viewed individually based on categorization according to the 2017 UNIHAZ academic guidelines, there are 55.09% of students whose learning achievement is in the honors category (GPA&gt; 3.50), 43.22% of students whose learning achievement is in the very satisfactory category (GPA 3.00-3.49), and 1.69% of students whose learning achievement is in the satisfactory category (GPA 2.50-2.99). In conclusion, the average learning achievement of Bidik Misi recipients is in the very satisfactory category with an average GPA of 3.42.&#x0D; Keywords: Learning Achievement, Aiming Mission
Solving Traveling Salesman Problem with Image-Based Classification

Combinatorial optimization is a problem with various application in the real world, and development of high-quality algorithms for solving it is important. We focus on the traveling salesman problem (TSP) which is one of typical combinatorial optimization problems, and introduce algorithms applying deep learning. One way to apply to combinatorial optimization problems is to make decisions in finding a solution using neural networks. Pixel-mapped Classification Network (PCN) we propose treats a problem as an image by mapping each vertex onto an image, and approximate evaluation of vertices to construct a tour. This can be regarded as a classification that selects the appropriate vertex. We consider construction algorithms of greedy selecting and using beam-search according to the evaluation by PCN. We conduct experiments to examine the performance of these methods, and verify the effectiveness of improving quality of solutions.
The Role and Role of Fairy Tales in Child Rearing

The model of pedagogical improvement in preschool educational establishments has been improved on the basis of integration and reflexive integration of the components of moral and educational activities (target-content, organizational-methodological, control-rating) with spheres of development. (intellectual-creative, know-how, socio-emotional, communicative and physical).
Image or Video Contrast Enhancement and Performace Analysis using Image Decomposition based Technique

Due to poor lighting and atmospheric conditions, the images are often under or over-exposed which results in low contrast or quality. In this paper, an intrinsic decomposition algorithm is applied for image as well as video contrast enhancement. Intrinsic decomposition priors are used as constraints on decomposed illumination and reflectance layers to yield a better solution. It implies that neighbourhood pixels with same colour would have similar illumination and reflectance. Hence this method is designed as an optimization problem by diminishing an energy function. This decomposition model performs on HSV space in order to minimize computing complexity and avoid colour artifacts in enhanced images. Images with different lighting and atmospheric conditions are enhanced using this algorithm and quality is analysed using various image quality metrics. This method is also applied to video enhancement. Experimental results provide better enhancement results for both images and videos.
Effects of nonideal features of silicon photomultiplier on the measurements of quantum correlations

We analyze the effects of some stochastic deviations from the ideal response in silicon photomultipliers that prove to be detrimental for the measurement of quantum properties of light. We demonstrate that an optimized operation mode can overcome some limitations of the detectors allowing the characterization of entanglement in mesoscopic twin-beam states.
Effectiveness, Efficiency and Instruction Appeal of Blended Learning Model

The practice of learning, to acquire success, has so far been measured by the level of effectiveness with a percentage range from 0% to 100% and converted into values E to A. While in the science of knowledge, learning outcome is measured by effectiveness, efficiency, and attractiveness. Dwiyogo's research [1][2] and 2015 developed a tuition design model that combines face-to-face schooling using offline resources (print, audio, video, computer) and online knowledge training (internet and smart phones) known as Blended Learning-Based Instruction Design Models. Numerous studies have been conducted to test the potency, capability, and fascination of learning. However, the objectives of this study with respect to Dwiyogo’s model are: (1) examining experts on it usage, (2) applying the model in courses, and (3) testing the productivity, and capability. This research makes use of the survey and quasi-experimental design methods. The data consists of a total of 3 experts, 113 students, and 6 teachers with result showing that the blended learning model using eight items produced huge output (= 4.750, SD = 0.361). Based on the components of the expert test, it can be concluded that the learning model developed is appropriate and can be implemented. Student opinion with respect to this model showed a high level of agreement (= 3.415, SD = 0.536). This shows that students who applied blended learning based instruction models responded positively to its implementation. Therefore, this technique can enhance knowledge thereby, making the act of efficient, effective, and instruction appeal.
On Predictive Information in RNNs

Certain biological neurons demonstrate a remarkable capability to optimally compress the history of sensory inputs while being maximally informative about the future. In this work, we investigate if the same can be said of artificial neurons in recurrent neural networks (RNNs) trained with maximum likelihood. Empirically, we find that RNNs are suboptimal in the information plane. Instead of optimally compressing past information, they extract additional information that is not relevant for predicting the future. We show that constraining past information by injecting noise into the hidden state can improve RNNs in several ways: optimality in the predictive information plane, sample quality, heldout likelihood, and downstream classification performance.
Modeling and Verifying Dynamic Architectures with FACTum Studio

With the emergence of ambient and adaptive computing, dynamic architectures have become increasingly important. Dynamic architectures describe an evolving state space of systems over time. In such architectures, components can appear or disappear, and connections between them can change over time. Due to the evolving state space of such architectures, verification is challenging. To address this problem, we developed FACTum Studio, a tool that combines model checking and interactive theorem proving to support the verification of dynamic architectures. To this end, a dynamic architecture is first specified in terms of component types and architecture configurations. Next, each component type is verified against asserted contracts using nuXmv. Then, the composition of the contracts is verified using Isabelle/HOL. In this paper, we discuss the tool’s extended features with an example of an encrypted messaging system. It is developed with Eclipse and active on Github.
Few-Shot Learning for Monocular Depth Estimation Based on Local Object Relationship

Monocular depth estimation has gained great momentum and achieved growing success recently. Nonetheless, due to the intrinsic difficulty associated with large-scale RGB-D data capture for training purpose and the inefficient utilization of existing training datasets, it is still challenging to accommodate flexibly-changing scenarios. To ameliorate, we propose a fewshot learning method for monocular depth estimation augmented by local object-object relationship. Our method is based on the insight that the depth changing between neighboring objects is relatively stable across diverse but similar scenarios. At the technical front, we first learn the object relationship based on the relative distance between single objects. Towards this goal, we design a CNN architecture to simultaneously encode the object spatial context into object-object relationship features and encode the original image into global context features. Hence we can complementally leverage few-shot dataset with only a few samples for depth estimation while preserving the global depth changing range and respecting the local object-object depth details. As a result, our novel approach could estimate depth from various indoor RGB images, which greatly alleviates the training dataset dependency in monocular depth estimation. Finally, we conduct extensive experiments and comprehensive evaluations on the widely-used public benchmarks, and all the experiments confirm that, our method outperforms the state-of-the-art depth estimation methods, especially for the cases where only smallscale training samples are available.
Authenticated Encryption Based on Lesamnta-LW Hashing Mode

Authenticated encryption refers to symmetric cryptography providing both privacy and authenticity. It is most common to construct it as a block-cipher mode of operation. Another promising approach is to construct it based on cryptographic hashing. This paper proposes a nonce-based authenticated encryption scheme based on the Lesamnta-LW hashing mode. Lesamnta-LW is a block-cipher-based iterated hash function, which is specified in the ISO/IEC 29192-5 lightweight hash-function standard. This paper also shows that the proposed scheme is secure if the underlying block cipher is a pseudorandom permutation. Both of the other ISO/IEC 29192-5 mechanisms, PHOTON and SPONGENT, are hardware-oriented sponge-based hash functions, and nonce-based authenticated encryption schemes can also be constructed based on them. On the other hand, Lesamnta-LW is a software-oriented Merkle-Damgård hash function. Thus, the proposed scheme is a new option for authenticated encryption based on lightweight cryptographic hashing.
Performance Analysis of Coin Matching Using Local Texture Features

At present coin recognition and classification tasks are performed manually by the numismatists. This process requires lot of time and proper preservation of hard copies of source documents such as printed catalog. Hence automation in this area is very much helpful and supportive. In this paper machine vision based automatic coin recognition using local texture features such as SIFT (Scale Invariant Feature Transform) and SURF (Speed Up Robust Features) algorithms are proposed to complement the conventionalmanual classification and recognition process. This work is carried out on ancient Roman coins and the performance is compared.
Design of An Analog Circuit-Based Artificial Neural Network

In this paper, a feed-forward artificial neural network with a single hidden layer has been realized using analog circuit blocks designed in 90 nm UMC technology. The network is capable of solving non-linearly separable problems and successfully realizes the XOR gate, which is one of the most basic and common non-linear classification problems. The inputs and the weights of the network are represented by the amplitudes of the transient signals. The weights have been calculated through the back-propagation (BP) algorithm. The analog circuit-based learning implementation yields accurate results with the expected outputs.
External Knowledge-Based Weakly Supervised Learning Approach on Chinese Clinical Named Entity Recognition

Automatic extraction of clinical named entities, such as body parts, drugs and surgeries, has been of great significance to understand clinical texts. Deep neural networks approaches have achieved remarkable success in named entity recognition task recently. However, most of these approaches train models from large, high-quality and labor-consuming labeled data. In order to reduce the labeling costs, we propose a weakly supervised learning method for clinical named entity recognition (CNER) tasks. We use a small amount of labeled data as seed corpus, and propose a bootstrapping method integrating external knowledge to iteratively generate the labels for unlabeled data. The external knowledge consists of domain specific dictionaries as well as a bunch of handcraft rules. We conduct experiments on CCKS-2018 CNER task dataset and our approach achieves competitive results comparing to the supervised approach with fully labeled data.
Quadruplet-Center Loss for Face Verification

Deep learning for face verification applications has proven to be productive. Most existing face verification methods focus on enhance the discriminative power of the deeply learned features with softmax loss or learning discriminative features with deep metric learning, which have their own advantages, but the combination of these two directions is more or less ignored. In this paper, a novel loss named quadruplet-center loss is proposed to learn more discriminative features for face verification task. The proposed quadruplet-center loss learns a center for deep features of each class, which forces the distances between the samples and centers from different classes are larger than those from same class and regardless of whether they contain different probe images or not. It is worth mentioning that a dynamic margin is presented based on the average distance between samples and corresponding centers for loss functions in a batch. Our method is evaluated on two widely-used benchmarks for face verification, which outperforms most of the state-of-the-art algorithms. The experimental results clearly demonstrate the effectiveness of our proposed classification loss.
Technology of Perfection of Knowledge Transfer and Acquisition on the Basis of Computer Simulation Models

This project explores the design and implementation of virtual resources in the learning process on the basis of computational simulation models for higher educational establishments.
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems

The proliferation of personalized recommendation technologies has raised concerns about discrepancies in their recommendation performance across different genders, age groups, and racial or ethnic populations. This varying degree of performance could impact users' trust in the system and may pose legal and ethical issues in domains where fairness and equity are critical concerns, like job recommendation. In this paper, we investigate several potential factors that could be associated with discriminatory performance of a recommendation algorithm for women versus men. We specifically study several characteristics of user profiles and analyze their possible associations with disparate behavior of the system towards different genders. These characteristics include the anomaly in rating behavior, the entropy of users' profiles, and the users' profile size. Our experimental results on a public dataset using four recommendation algorithms show that, based on all the three mentioned factors, women get less accurate recommendations than men indicating an unfair nature of recommendation algorithms across genders.
Semantic segmentation of very high resolution remote sensing images with residual logic deep fully convolutional networks

This paper describes a deep learning approach to semantic segmentation of very high resolution remote sensing images. We introduce RLFCN, a fully convolutional architecture based on residual logic blocks, to model the ambiguous mapping between remote sensing images and classification maps. In order to recover the output resolution to the original size, we adopt a special way to efficiently learn feature map up-sampling within the network. For optimization, we employ the equally-weighted focal loss which is particularly suitable for the task for it reduces the impact of class imbalance. Our framework consists of only one single architecture which is trained end-to-end and doesn't rely on any post-processing techniques and needs no extra data except images. Based on our framework, we conducted experiments on a ISPRS dataset: Vaihingen. The results indicate that our framework achieves better performance than the current state of the art, while containing fewer parameters and requires fewer training data.
A New Word Discovery Method Based on Constrainted and DSG

Accurate identification of risk places is of great significance for risk identification, risk analysis, risk response planning and other work in risk areas. Therefore, we will combine the word formation features of risk place and statistical algorithms to discover new words in this paper. Firstly, we preprocess the data based on conditional random field (CRF), and develop a professional dictionary with the help of expert. Secondly, segment the test set, and form a set of new words to be selected based on segmentations and their left-right words. Finally, we make word vectors based on directional skip-gram (DSG), and calculate the distance between two words to discover new words. The results show that the algorithm can accurately identify new places, and it is suitable for discovering professional vocabulary which word-formation features is compound.
A History of Surface Guidance Methods in Radiation Therapy

Modern SGRT technology has witnessed a rapid and widespread adoption in the radiation therapy clinic. SGRT provides real-time position information during simulation, patient setup, and treatment delivery including respiratory-correlated techniques. The versatility of the technology holds the promise of creating broad quality and safety benefits in the clinic. This chapter begins with a historical review of optical-based approaches to localization that lead to the development of modern SGRT, from early efforts at verifying patient setup and monitoring patient position with television cameras, to modern real-time in-room guidance and continual localization with advanced 3D imaging and processing technology. The theories and basic principles underpinning current surface imaging technologies are described, including calibration and registration algorithms. The aim of this chapter is to provide the reader with a thorough appreciation of the history and trajectory of SGRT technologies, including their role in the context of modern IGRT, so that the diverse applications of SGRT in the clinic can be fully appreciated.
Conditional expectation of a Markov kernel given another with some applications in statistical inference and disease diagnosis

Markov kernels play a decisive role in probability and mathematical statistics theories, and are an extension of the concepts of σ-field and statistic. Concepts such as independence, sufficiency, completeness, ancillarity or conditional distribution have been extended previously to Markov kernels. In this paper, the concept of conditional expectation of a Markov kernel given another is introduced, setting its first properties. An application to clinical diagnosis is provided, obtaining an optimality property of the predictive values of a diagnosis test. In a statistical framework, this new probabilistic tool is used to extend to Markov kernels the theorems of Rao-Blackwell and Lehmann-Scheffé. A result about the completeness of a sufficient statistic is obtained in passing by properly enlarging the family of probabilities. As a final statistical scholium, a generalization of a result about the completeness of the family of nonrandomized estimators is given.
Computing the racing line using Bayesian optimization

A good racing strategy and in particular the racing line is decisive to winning races in Formula 1, MotoGP, and other forms of motor racing. The racing line defines the path followed around a track as well as the optimal speed profile along the path. The objective is to minimize lap time by driving the vehicle at the limits of friction and handling capability. The solution naturally depends upon the geometry of the track and vehicle dynamics. We introduce a novel method to compute the racing line using Bayesian optimization. Our approach is fully data-driven and computationally more efficient compared to other methods based on dynamic programming and random search. The approach is specifically relevant in autonomous racing where teams can quickly compute the racing line for a new track and then exploit this information in the design of a motion planner and a controller to optimize real-time performance.
Mediating Role of Employee Engagement inthe Relationship between PO-Fit, PJ-Fit, and Employee Performance: A Conceptual Perspective for Future Research Agenda

The purpose of this study is to explore the relationships between person-organisation (PO) fit, person-job (PJ) fit, employee engagement, organizational citizenship behaviour (OCB) and employee performance. PO fit refers to opinions among employees abouthow well their unique traits are blended into the organizational culture, while PJ fit relates to the alignment between the employees interests and expertise and their job requirements.In different studies, the results of PO fit and PJ fit have a positive effect on organizational commitment, job satisfaction, and OCB. In addition, the role of employee engagement is also important to these the relationships. Employees involved in the organisation will have a special desire to lead to organizational progress, expressing high energy, motivation, intensity and anticipation in line with organizational objectives. Despite being an interesting topic in the science of management, there are still a few who examine these relationships. In-depth, the results of this study can provide the meaningful inputs to the work environment.