Reviewerly/exHarmony huggingface dataset:
Denoise-Based Over-Sampling for Imbalanced Data Classification

Imbalanced data classification has always been a hot topic in traditional machine learning. The usual method is oversampling. Its main idea is to randomly synthesize the new minority samples between the minority samples and their neighboring samples, to put the data in a particular state of equilibrium. The existing improved methods have improved the classifier's performance to some extent, but most of the focus is on the minority sample. In this paper, a denoise-based oversampling method (DNOS) is proposed, which performs different denoise processes for the majority and minority samples. Then, it is combined with ADASYN to oversampling the data. Experimental results show that DNOS has a better classification effect than ADASYN.
Conceptions of Authorship in Early Jewish Cultures

Although early Jewish authors gave thought to the concept of literary authorship, they composed virtually no separate treatises on the subject, nor in fact did they usually address it directly. Their conceptions of authorship were presented implicitly within larger discussions relating to the ultimate form of literature in their eyes, namely the Holy Bible. Sacred Scripture, of course, stood at the heart of Judaism since Antiquity, and its interpretation has always been an essential element of Jewish learning. The science and art of Bible interpretation were systematized in the medieval period by a number of key Jewish scholars, and their writings included sporadic discussions about the nature of the authorship of the various books in sacred Scripture. In what follows, we explore selected statements from this corpus that together offer a representative sample of how literary authorship was perceived in early Jewish cultures.
Research on Business District Operation Planning Based on Machine Learning

This paper applies machine learning core algorithms to business district operation planning to explore the attributes of retail performance and operational efficiency of Business District. This paper mainly uses K-Nearest Neighbor Algorithms to sort shops by their consumption records. Besides that it also uses K-means Clustering Algorithm and Association Rules Algorithm to cluster shop consumption types and uses association rules to find consumption association routes. Based on the above operation results, it can provide more accurate and effective data for business operators to make business district operation plans.
Co-Inference Discriminative Tracking Through Multi-Task Siamese Network

In essence, visual tracking is a matching problem without any prior information about a class-agnostic object. By leveraging large scale off-line training data, recent trackers based on Siamese networks usually expect to pre-learn underlying similarity functions before a tracking task even begins. Consequently, they lack discriminative and adaptive powers. To address the issues, we propose a multi-stage co-inference tracker (named MSCI) via a multi-task Siamese network, in which a complicated tracking task is divided into three complementary sub-tasks (i.e., classification, regression and detection). Firstly, we design a novel multi-task loss function to end-to-end train the multi-task Siamese network via jointly learning from three sub-tasks. The multi-task Siamese network contains three parallel yet collaborative output layers, which correspond to three key components of our tracker (i.e., classifier, regressor and residual learning based detector). By sharing representations within the components, we not only improve each component's generalization performance, but also enhance our tracker's discriminative power. Then, we design a co-inference approach to effectively fuse the complementary components. As a result, our tracker can avoid the pitfalls of purely single components and get reliably observations to improve its adaptive power. Comprehensive experiments on OTB2013, OTB2015 and VOT2016 validate the effectiveness and robustness of our MSCI tracker.
Nonlinear System as a Stream-Cipher

A new stream cipher based on a nonlinear system. The scheme follow the structure of a Nonlinear-feedback Shift Register (NLFSR). However, it does not exhibit the cryptographic drawbacks as the NLFSR does such as an unpredictable maximal period of the generated sequences, insufficient randomness level and high probability of falling in a static state. The proposed stream uses a secret key of lentgh 256-bit and generates a keystream of length 16-bits in each iteration. The produced output sequences are pseudorandom. That means that they cannot be distinguished from a random sequence and the cipher can resist to common stream-cipher attacks like the distinguishing attack, SAC-r Diffusion test and SAC-c Diffusion test, algebraic attack and correlation analysis. Moreover, the proposed cipher surpass the AES standard and Rabbit cipher in term of encryption-decryption total time.
Investigation of the geometric meaning of the derivative using mathematical packages

Abstract The paper suggests that in the process of training mathematical disciplines in technical universities in the context of digitalization of education, applied software packages to solve practice-oriented problems are used. It is advisable to reveal basic definitions, concepts, theorems, laws on practical examples, following the wise Latin aphorism “Plus usus sine doctrina, quam c. usum doctrina valet Q/Practice without theory is more valuable than theory without practice”. The more practical examples will be used in the process of mastering the theoretical material, the stronger and more fundamental the mathematical knowledge of students will be. The paper examines the stages of investigation the geometric meaning of the derivative using the MathCad computer system. The geometric meaning of the derivative will be the most accessible and memorable when students study intersecting parabolas.
A platform application for exploiting the Digital Reference Ontology

Horizontal and vertical integration rely on flexible interaction, i.e. communication and cooperation, of heterogeneous systems and subsystems on several abstraction layers. A large ontology (the Digital Reference) that describes semiconductor supply chains and supply chains containing semiconductors is needed so as to leverage the highly distributed and decentralized data sources. The Digital Reference Platform Application (DRPA) is a tool designed to explore the power of the Digital Reference Ontology, offering features for ontology exploration and data creation along with Arrowhead Framework integration. With this, the DRPA offers interoperability between arbitrary software applications and industrial cyber-physical systems - integrated into Arrowhead Local Clouds. This approach applied for a voting wristband prototype shows the potential of the DPRA. The final target is to extend the potential of DRPA beyond Ontology exploration.
When Mini-AES Meets Machine Learning: Practice and Experience

It is interesting to investigate the decryptability of machine learning methodology for a compact block cypher: Mini-AES. In the present work, we design a neural network with three hidden layers and a moderate number of neurons. Trained by a moderate number of random pairs of plaintext and ciphertext, this neural network can successfully decrypt Mini-AES.
Accurate classification of tumor text based on attention mechanism and transfer learning

With the increase in the number of electronic medical records, precise classification of medical records can be use to accurately diagnose diseases. In medical texts, there are differences in the number of texts of different diseases, especially for specific diseases and a small number of samples have brought great challenges to medical text classification. In response to this problem, this paper proposes an accurate text classification model base on transfer learning combined with attention mechanism neural network, and finally obtains accurate category text through two-step classification. The model first uses the attention mechanism long-term and short-term cyclic neural network to extract the overall tumor sample from the mass of unbalanced medical record texts through the common characteristics of the tumor medical record, and uses the similarity between diseases to combine with the convolutional neural network through migration learning. The characteristics of each type of tumor disease are migrate to achieve the effect of precise classification training. The precise training model is used to finally realize the precise classification of tumor diseases. The model was tested on a private tumor medical data set, and the results showed that the accuracy of the tumor data set (precision) was increased by 5% compare to the average of the baseline optimal classification model. The increase in accuracy is important for accurate classification significance.
Digital Turning of Logic and Practical Paradigm: The Establishment of Big Data Model in Anthropological Field

The anthropological field driven by big data not only brings the sense of scientific research community, but also brings the transformation of logic theory and practice in innovative research theory and methods. As a paradigm shift, Digital Humanities has brought new theories and methods to traditional anthropology; On the one hand, it has realized the “horizon fusion” between horizontal disciplines at the theoretical level. This interactive integration not only brings about the omniscient and wide-angle vision of human, object and image in the space-time field of anthropological research objects, but also constructs a vertical and in-depth repetitive mining of research resources rich context by using research media technology; on the other hand, From open login of big data, related login of big data, to core login of big data in the field of humanities, this process is one of multidimensional coexistence; therefore, in the field of anthropology, this research theory and practical method can realize horizontal and vertical data collection, in-depth research and analysis, and three-dimensional interactive display.
Classification of the Mask Augsburg Speech Corpus (MASC) Using the Consistency Learning Method

This paper presents the details of our solution for the mask sub-task of the INTERSPEECH 2020 Computational Para-linguistics Challenge (ComParE). The speech production can be significantly affected when the speaker wears a face mask. The task evaluates the systems for the classification of speech recordings with and without a surgical mask. A student-teacher deep-learning neural network is proposed inspired by the well-performed consistency learning method on a lot of classification problems. In particular, the consistency regularization term is designed between out-puts of the student model and the guided teacher model. Different level Gaussian noises are respectively added into the inputs of the teacher model as model perturbations to optimize the system robustness. To take further advantage of the consistency learning, a small number of unlabeled evaluation data is utilized to be combined with the labeled data for the system training in a semi-supervised learning manner. Finally, the proposed system achieves an unweighted average recall up to 72.50% on the official evaluation dataset, increasing by 10% compared with the baseline result of 62.60%.
Modeling a Perceptron Neuron Using Verilog Developed Floating-Point Numbering System and Modules for Hardware Synthesis

The purpose of a capstone design project is to provide graduating senior students the opportunity to demonstrate understanding of the concepts they have learned during the course of their studies.As with many engineering programs, students of the computer engineering program at Utah Valley University (UVU) conclude their degree programs with a semester capstone design experience.This paper presents the details of a sample project that a student has done in this capstone course.This senior design project implements the perceptron neural network using Systems Verilog HDL module development for FPGA synthesis.A floating-point binary numbering system is developed with which all arithmetic operation modules are designed.Benefits of hardware implemented neural networks include the parallelization of computational processes that are not provided in software implementations of such networks.All modules included in the network are simulated using Altera's ModelSim platform and synthesized on Altera's DE2-115 Development Board.
CORRELATION AMONG READING INTEREST, READING SELF-EFFICACY, AND READING COMPETENCY IN THE JUNIOR HIGH SCHOOL

The current research objectives were to describe and to relate the students’ reading interest, reading self-efficacy to their reading competency in the Sekolah Menengah Pertama Negeri 8 Singaraja. The subject were the students in the eight-grade students. This research was designed a quantitative research. The data were gathered using reading interest scale, reading self-efficacy scale, and objective reading competency test. The obtained data were analyzed in two phases, namely: descriptively, that is the description consisted of central tendency and inferentially, that is the data were analyzed using pair-wise correlation and multiple correlation technique. The findings show that: 1) the students’ reading interest, reading self-efficacy, and reading competency on high-order-items are at the moderate levels;2) the relations pair-wisely between the students’ reading interest and reading self-efficacy, students reading interest and reading competency, the students’ reading self-efficacy and reading competency on high-order-items are positively and significantly correlated; and 3) the simultaneous or multiple relationships of the reading interest and reading self-efficacy to the students’ reading competency is proven to be significant.
Developing the character of a child of primary education through cooperation in the current area of the curriculum

Since the State Educational Program for Primary Education in the Slovak Republic imposes the obligation to develop not only cognitive aspects of personality, but also to ensure equally social and emotional development, in this paper we deal with possibilities of developing prosocial behaviour of student through cooperative way of teaching in the subject of Science.In the selected educational activities, we specify how to ensure the development of science literacy, but in particular how we can enable the saturation of affective component of the students´ personalities through the implementation of the cooperative learning principles.
Leveraging Linked Data in Open Education

In recent years, the processes of training, teaching, and online learning have become more widespread. In this context, open education has the role of empowering people to meet their learning goals. Aligned with this vision, educational institutions are offering open access to their educational resources. However, content is delivering from different platforms by making a challenging task for users to find accurate information. To face this issue, the academic community is adopting Semantic Web and Linked Data for publishing and sharing data. In this chapter, the author presents the main findings in this area. From a technological point of view, the contribution focuses on the main stages of the linked data publishing cycle. Also, from the perspective of the application domain, the author explains the research's contribution regarding three dimensions of Open Education. This chapter ends with some thoughts regarding the author's research path, her remaining tasks, and future trends.
Similarity-based transfer learning of decision policies

We consider a problem of learning decision policy from past experience available. Using the Fully Probabilistic Design (FPD) formalism, we propose a new general approach for finding a stochastic policy from the past data. The proposed approach assigns degree of similarity to all of the past closed-loop behaviors. The degree of similarity expresses how close the current decision making task is to a past task. Then it is used by Bayesian estimation to learn an approximate optimal policy, which comprises the best past experience. The approach learns decision policy directly from the data without interacting with any supervisor/expert or using any reinforcement signal. The past experience may consider a decision objective different than the current one. Moreover the past decision policy need not to be optimal with respect to the past objective. We demonstrate our approach on simulated examples and show that the learned policy achieves better performance than optimal FPD policy whenever a mismodeling is present.
Masked Neural Sparse Encoder for Face Occlusion Detection

This paper presents an effective way to extract low-level features based on sparse coding for facial occlusion detection. Masked Neural Sparse Encoder (MNSE) is proposed to be a sparse coding solver that brings out better feature bases for data representation and improvement in the anomaly detection task. To guarantee the representational capability of features, a set of masks is applied to force each feature basis is heeded on learning a specific stroke within a certain area. The mask set is constructed by clustering primary strokes from training samples, and represents them with corresponding centers of clusters. Hence, these masks stand for main strokes in concerned areas with higher probabilities. Experiments show MNSE contains better representational capability in data from different domains. Compared with the standard sparse coding and the auto-encoder based approaches, MNSE lifts the accuracy up around 20%.
optimization-based interactive speed planning of autonomous vehicles in structured environments

Safe and efficient interaction with other moving vehicles is crucial for autonomous vehicles. Speed planning affects the safety and smoothness of autonomous vehicles in dynamic environments, and is still an open problem. This paper focuses on how to avoid colliding with around moving vehicles by planning the speed of autonomous vehicles when its driving path is determined. The proposed method combines a heuristic search algorithm and a quadratic optimization algorithm to generate a safe and smooth speed profile for autonomous vehicles. In the interactions with other dynamic vehicles, the planning speed of the autonomous vehicles is adaptive to the changing environments. Finally, the feasibility and flexibility of the proposed method have been validated by the simulation results in two typical interaction scenarios by using Prescan Software.
Direct temporal mode measurement for the characterization of temporally multiplexed high dimensional entanglement

We report a novel method that measures directly the exact form of temporal modes. We apply the method to a pulse-pumped parametric amplifier and demonstrate temporally multiplexed multi-dimensional continuous variables quantum entanglement in telecom band.
Learning Science With Mobile Technologies: Opportunities for Enhancing Preservice Elementary Teachers’ Science Conceptual Understanding

AbstractThe use of technology is increasing rapidly in our society, and classroom teachers must recognize the impact and importance of technology in the lives of their students. It is crucial that college faculty involved in teacher training design courses that integrate mobile technologies to prepare the next generation of teachers. In this study, we investigate the effectiveness of an iPadbased curriculum app, Exploring Physics, to enhance preservice elementary teachers’ physics conceptual understanding in a physical science content course. Data were collected using a pre-andpostPhysics Conceptual Understanding (PCU) survey and open-ended questionnaires. We found significant statistical gains in participants’ (N = 73) physics conceptual understanding at the end of the course. Qualitative findings suggest that the learning experience allowed participants to experience the pedagogical affordances of mobile technologies as learners and future teachers and found themselves to be more comfortable with the idea of integrating technology into their future classrooms. The results of this study provide insights into the importance of providing preservice teachers with opportunities to experience the use of mobile technologies in their teacher preparation programs. Additional informationNotes on contributorsDeepika MenonDeepika Menon (dmenon2@unl.edu) is an assistant professor of science education in the Department of Teaching, Learning, and Teacher Education at the University of Nebraska-Lincoln in Lincoln, Nebraska.Zarah SalasZarah Salas is a senior in the early childhood program and Allison Mellendick is a senior in the elementary education program, both a: Towson University in Towson, Maryland.Allison MellendickZarah Salas is a senior in the early childhood program and Allison Mellendick is a senior in the elementary education program, both a: Towson University in Towson, Maryland.Meera ChandrasekharMeera Chandrasekhar is an emeritus faculty in the Department of Physics and Astronomy at University of Missouri-Columbia in Co-lumbia, Missouri.Dorina KosztinDorina Kosztin is a teaching professor in the Department of Physic: and Astronomy at University of Missouri-Columbia in Columbia, Missouri.
Self and Other Modelling in Cooperative Resource Gathering with Multi-agent Reinforcement Learning

In this work, we explore the application of the Self-other-Modelling algorithm (SOM) to several agent architectures for the collaborative grid-based environment. Asynchronous Advantage Actor-Critic (A3C) algorithm was compared with the OpenAI Hide-and-seek (HNS) agent. We expand their implementation by adding the SOM algorithm. As an extension of the original environment, we add a stochastic initialization version of the environment. To address the lack of performance in such an environment by all versions of agents, we made further improvements over the A3C and HNS agents, adding the module dedicated to the SOM algorithm. This agent was able to efficiently solve a stochastically initialized version of the environment, showing the potential benefits of such an approach.
Design of City Image Representation and Communication Based on VR Technology

This thesis aims to establish a VR representation system for urban images, using different computer computation methods to change the viewing angle or enhance the experience, enhancing the further dissemination of the city’s image among stratified audiences, and combining real and media reproduction to make. The image of the city is reproduced in the media world to infuse views and construct meaning, so that the audience may understand the city from a particular perspective without even realizing it.
Proofs of Data Reliability: Verification of reliable data storage with automatic maintenance†

Abstract Proofs of Data Reliability are cryptographic protocols that provide assurance to a user that a cloud storage system correctly stores her data and has provisioned sufficient redundancy to be able to guarantee reliable storage service. In this paper, we consider distributed cloud storage systems that make use of erasure codes to guarantee data reliability. We propose two novel Proof of Data Reliability schemes, namely POROS and PORTOS, that on the one hand guarantees the retrieval of the outsourced data in their entirety through the use of proofs of data possession and on the other hand ensures the actual storage of redundancy. As a result, POROS and PORTOS are compatible with the current cloud computing model where the cloud autonomously performs all maintenance operations without any interaction with the user. Moreover, POROS and PORTOS delegate the burden of generating the redundancy to the cloud as well. The security of both solutions is proved in the face of a rational adversary whereby the cheating cloud provider tries to gain storage savings without increasing its total operational cost.