Reviewerly/exHarmony huggingface dataset:
Velocity-Tuned Oscillators for NeuroSLAM and Spatial Navigation

In this paper, we present the implementation of Velocity Controlled (or tuned) Oscillators (VCO) to model spatial coding and navigation in the mammalian hippocampus. Specifically, we demonstrate these spatial cells by representing a spatial firing map of grid, place, and border cells. Since the VCO is the basis for Oscillatory Interference (OI) models based on the Spatial Envelope Synthesis (SES) approach of hippocampal and entorhinal navigation, we use these models in our hardware implementation to construct more complex spatial cells from simple interference between VCOs. We develop the design of a VCO ASIC chip containing up to 128 independently tuned VCOs.
A novel application integration architecture for the education industry

Since the Ministry of Education launched Education Informatization 2.0, the digitalization of colleges and universities has entered a stage of rapid growth. However, after more than 20 years of construction, problems such as system barriers and information islands have emerged in the digital construction of university systems. In order to solve such problems between the university systems, this paper proposes an easily expandable and configurable open information integration architecture by considering traditional information integration methods and combining with Web service technology. The architecture handles user service invocation information through a service layer, and manages the registration and invocation of services through a service module. The permission module manages user permissions to prevent information leakage and security issues. The data module abstracts data-related services to provide a basis for the deep use of data. And other optional development services are designed to satisfy special requirements for different platforms. The architecture proposed in this paper can integrate different heterogeneous subsystems in colleges and universities, eliminating the problem of system barriers and information islands, and providing specifications for the construction of new applications.
Improvement parallelization in Apriori Algorithm

When people look for the internal relationship of massive data, the classical Apriori association algorithm can not meet the mining scenario of massive data in terms of algorithm efficiency and I/O performance of data processing process. When mining and analyzing the classical Apriori association algorithm, all transactions are always located in the file system or database, and each iteration needs to scan the transaction database; when mining frequent sets by Apriori algorithm, a large number of project sets will be generated, resulting in excessive I/O overhead and excessive system resources. This paper proposes a distributed apriori algorithm based on MapReduce. The Hadoop distributed file system HDFS is used to automatically realize the distributed storage (fragmentation) of big data. Combined with MapReduce computing framework, map and reduce are used for parallel processing in generating candidate itemsets and computing frequent itemsets respectively. The distributed system is fully utilized to improve the processing ability, At the same time, the monotonicity of frequent itemsets is used to optimize the transaction database. Experimental analysis, the addition of compute nodes can significantly provide mining algorithm performance. At the same time, the improved Apriori algorithm has a great improvement in operating efficiency when it conducts association rule analysis on the data set with a large amount of item indexes.
Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples.

In a number of situations, collecting a function value for every data point may be prohibitively expensive, and random sampling ignores any structure in the underlying data. We introduce a scalable optimization algorithm with no correction steps (in contrast to Frank-Wolfe and its variants), a variant of gradient ascent for coreset selection in graphs, that greedily selects a weighted subset of vertices that are deemed most important to sample. Our algorithm estimates the mean of the function by taking a weighted sum only at these vertices, and we provably bound the estimation error in terms of the location and weights of the selected vertices in the graph. In addition, we consider the case where nodes have different selection costs and provide bounds on the quality of the low-cost selected coresets. We demonstrate the benefits of our algorithm on the semi-supervised node classification of graph convolutional neural network, point clouds and structured graphs, as well as sensor placement where the cost of placing sensors depends on the location of the placement. We also elucidate that the empirical convergence of our proposed method is faster than random selection and various clustering methods while still respecting sensor placement cost. The paper concludes with validation of the developed algorithm on both synthetic and real datasets, demonstrating that it outperforms the current state of the art.
Fast Fréchet Inception Distance

The Frechet Inception Distance (FID) has been used to evaluate thousands of generative models. We present a novel algorithm, FastFID, which allows fast computation and backpropagation for FID. FastFID can efficiently (1) evaluate generative model *during* training and (2) construct adversarial examples for FID.
AR 3D Magic Book

AR 3D Magic Book(Augmented Reality 3-Dimensional Magic Book) is a 3D book device based on AR image recognition technology, mainly including three parts, i.e. AR image recognition software, a 3D book device, a picture book or a paper book. AR 3D Magic Book can scan images from a picture book to identify corresponding digital information, which can stimulate the interest of teenagers and children in reading, help improve their eyesight, enhance students' experience when reading, and allow them to have a better understanding of knowledge from books, as well as making some complicated instructions more readable.
Bias in machine learning - what is it good for?

In public media as well as in scientific publications, the term bias is used in conjunction with machine learning in many different contexts, and with many different meanings. This paper proposes a ...
Machine Learning Techniques to Evaluate Whether Twitter Accounts Are Human or Robot

As social media becomes more relevant in daily life; efforts have been made to try to harness this relevancy and use it for nefarious goals. Bot accounts have been created by third-party organizations to try to influence public opinion, impersonate humans, and perform other forms of exploitation. Researchers have been concentrating efforts to attempt to recognize these accounts and label them so that the public can differentiate between genuine accounts and bot accounts. Our contributions to this research include the study of various classification algorithms on our dataset to identify the best performing algorithm for this sort of data. We also studied the inclusion of tweet time gap variance to attempt to capture potential unnatural patterns or consistency in time between tweets. Our experimental evaluation shows the feasibility of the proposed approach.
Experiments in non-personalized future blood glucose level prediction

In this study we investigate the need for training future blood glucose level prediction models at the individual level (i.e. per patient). Specifically, we train various model classes: linear models, feed-forward neural networks, recurrent neural networks, and recurrent neural networks incorporating attention mechanisms, to predict future blood glucose levels using varying time series history lengths and data sources. We also compare methods of handling missing time series data during training. We found that relatively short history lengths provided the best results: a 30 minute history length proved optimal in our experiments. We observed long short-term memory (LSTM) networks performed better than linear and feed-forward neural networks, and that including an attention mechanism in the LSTM model further improved performance, even when processing sequences with relatively short length. We observed models trained using all of the available data outperformed those trained at the individual level. We also observed models trained using all of the available data, except for the data contributed by a given patient, were as effective at predicting the patient's future blood glucose levels as models trained using all of the available data. These models also significantly outperformed models trained using the patient's data only. Finally, we found that including sequences with missing values during training produced models that were more robust to missing values.
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles.

While feedback loops are known to play important roles in many complex systems, their existence is ignored in a large part of the causal discovery literature, as systems are typically assumed to be acyclic from the outset. When applying causal discovery algorithms designed for the acyclic setting on data generated by a system that involves feedback, one would not expect to obtain correct results. In this work, we show that---surprisingly---the output of the Fast Causal Inference (FCI) algorithm is correct if it is applied to observational data generated by a system that involves feedback. More specifically, we prove that for observational data generated by a simple and $\sigma$-faithful Structural Causal Model (SCM), FCI is sound and complete, and can be used to consistently estimate (i) the presence and absence of causal relations, (ii) the presence and absence of direct causal relations, (iii) the absence of confounders, and (iv) the absence of specific cycles in the causal graph of the SCM. We extend these results to constraint-based causal discovery algorithms that exploit certain forms of background knowledge, including the causally sufficient setting (e.g., the PC algorithm) and the Joint Causal Inference setting (e.g., the FCI-JCI algorithm).
The Obstacles to Remote Learning for Undergraduate, Graduate, and Professional Students

Author(s): Soria, Krista M; Chirikov, Igor; Jones-White, Daniel | Abstract: The majority of students at research universities—96% of undergraduate students and 88% of graduate students—experienced at least one obstacle in their transition to remote learning, according to the recent Student Experience in the Research University (SERU) Consortium survey of 22,519 undergraduate students and 7,690 graduate and professional students at five public research universities. The biggest obstacle among undergraduate, graduate, and professional students was the lack of motivation for remote learning during the pandemic. Other common obstacles included lack of interaction with other students, inability to learn effectively in an online format, and distracting home environments or lack of access to appropriate study spaces. The obstacles vary by students’ social class background and major/program.
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks

In this paper, we present a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network classifier, extending prior work on the Bayesian Dark Knowledge framework. The proposed framework takes as input teacher and student model architectures and a general posterior expectation of interest. The distillation method performs an online compression of the selected posterior expectation using iteratively generated Monte Carlo samples. We focus on the posterior predictive distribution and expected entropy as distillation targets. We investigate several aspects of this framework including the impact of uncertainty and the choice of student model architecture. We study methods for student model architecture search from a speed-storage-accuracy perspective and evaluate down-stream tasks leveraging entropy distillation including uncertainty ranking and out-of-distribution detection.
The Monitoring Infectious Diseases Diffusion through GIS

Awareness of the spread of the disease becomes a part that must monitor carefully. This monitoring requires solutions, one of which is through Geographic Information Systems (GIS). This paper discusses the use of GIS in controlling the spread of infectious diseases. The method used is to study previous research. This study provides an understanding of the application of GIS in various situations of the range of contagious diseases. This study includes GIS technology, which monitored the spread of infectious diseases, the application layer of GIS technology, the technology that supports development, and various types of map information displayed. This research provides an essential reference in the event of GIS, especially in the development of map-based software on the spread of infectious diseases.
Design and Implementation of College Internet + English Education System Platform Based on HTML5

With the continuous progress and development of Internet technology, it has gradually become an important part of front-end development technology. At present, the main technical software of front-end technology and rich client technology is HTML5, which is transferred to the client through the combination of HTML5 and JavaScript software and the processing of the underlying logic. HTML5 has features such as animation, multimedia and three bits to replace Flash and Silverlight software and improve processing efficiency. Therefore, HTML5 technology is chosen as the implementation of college English teaching system.
Understanding the Effects of Explanation Types and User Motivations on Recommender System Use

It is becoming increasingly common for intelligent systems, such as recommender systems, to provide explanations for their generated recommendations to the users. However, we still do not have a good understanding of what types of explanations work and what factors affect the effectiveness of different types of explanations. Our work focuses on explanations for movie recommender systems. This paper presents a mixed study where we hypothesize that the type of explanation, as well as user motivation for watching movies, will affect how users respond to recommendation system explanations. Our study compares three types of explanations: i) neighbor-ratings, ii) profile-based, and iii) event-based, as well as three types of user movie-watching motivations: i) hedonic (fun and relaxation), ii) eudaimonic (inspiration and meaningfulness), and iii) educational (learning new content). We discuss the implications of the study results for the design of explanations for movie recommender systems, and future novel research directions that the study results uncover.
Analysis of Differences in Core Competencies According to Major, Grade and Gender of Korean University Students

In order to achieve global competitiveness, university students should have core competency as creative convergent talent that can actively respond to changes, recreate culture with new ideas, and play a leadership role in a constantly diversifying society. Therefore, it is very important to analyze the core competencies of university students according to their major, grade, and gender, and to develop differentiated and systematic curriculum based on this. In this study, a core competency test (by S University, 2016) was conducted on 5770 students in years 1 to 4 at “S University” in Seoul. We analyzed the core competencies of students (creative competence, convergence competence, community competence, communication competence, leadership competence, and global competence) according to their major, grade, and gender. The results showed that there were differences in the 6 core competencies among college students according to their majors(p&lt;.05): creativity and convergence competence were the highest in Art and the lowest in Law and Sports; communication and leadership competence were the highest in the convergence specialized free majors, and the lowest in Art; the highest communication competence was found in the humanities, and the lowest in convergence specialized free majors; global competence was the highest in the humanities, and lowest in the sports majors; Overall the six core competencies of Soongsil are the highest among those students in the convergence specialization. In addition, there were differences in core competencies among Korean university students according to the year of study students were(p&lt;.05) in all 6 core competencies (p&lt;.05), with the students in the 4th year being the highest in all 6 core competencies including the core competency total. The core competence of students was found to be higher in males than females in all areas of creativity, convergence, global competence and core competence, except communication. Based on these results, universities should develop appropriate curriculum considering majors, grade, and gender in order to effectively cultivate core competencies of students.
Hybrid features and exponential moth-flame optimization based deep belief network for face recognition

Face recognition finds its application in various areas, like biometrics, person identification through their identity cards, Closed-Circuit Television (CCTV) cameras, and so on. Among various biometrics, such as fingerprint, palm print, iris, etc., face plays an important role. Hence, a face recognition technology was developed in the previous papers, with contributions in the feature extraction and classification phases. In this paper, an approach is developed for face recognition using the proposed Exponential Moth-flame Optimisation (Exponential MFO) based Deep Belief Network (DBN). Initially, the images in the database undergo feature extraction, where the features, such as K-SIFT, m-Co-HOG, along with Active Appearance Models (AAM) features are extracted from the image. Then, the classification is done using the proposed EMFO-DBN. The proposed EMFO-DBN is designed by integrating the Exponential Weighted Moving Average (EWMA) into the update process of the Moth flame optimisation (MFO) algorithm. The experimentation of the proposed method has been done using the CVL database, and the proposed method outclassed other state of the art techniques with the values of 0.98, 0.0073, and 0.0083, respectively, for accuracy, FAR, and FRR, respectively.
An Analysis of Requirements-Related Problems that Occurred in an organization Using a Mature Requirements Engineering Process

Plenty has been written on the necessary elements of a requirements engineering (RE) process. When an organization experiences product development problems and is found to lack an effective RE process, it is relatively easy to find potential causes for the problems. For example, changes may not have been managed, or requirements and other artifacts may not have been traced to each other. Nevertheless, what can be said of RE process failures that occur despite an organization's using a reasonably mature RE process? This paper will examine a set of RE-related problems that occurred in one such organization. It will also describe a solution that has been proposed to avoid such problems in the future.
The Impact Activity on the Quality of Life of Seniors Living in Retirement Homes

Objective:The aim of this research to find out whether physical activity (PA) affects the quality of life (QoL) of seniors living in retirement homes.Design: Prospective study. Participants:The study group consisted of 47 seniors.The
An Involution Function-Based Symmetric Stream Cipher

The use of cryptography meaning the art of secret writing for securing information shared over an open medium is an old methodology but its importance in the present scenario has increased tremendously. With the increase in the computing power of an eavesdropper, it has become unavoidable to look for a better and better cryptographic primitive that provides a level of security while keeping a check upon the time consumed to encrypt and decrypt the data. We have used an involution function to produce a symmetric stream cipher. Involution functions have the property that when functioned again they outputs the original variable. We have analyzed our work with respect to the execution time and compared it with the standard AES-128 algorithm. The result shows a much lesser time is required to encrypt data of same size with the proposed approach as compared to AES-128 and other block ciphers.
Prediction of performance of 2D DCT-based filter and adaptive selection of its parameters

To enhance images, one often has to apply a filtering operation (denoising). However, there are several issues within the denoising. One of them is that sometimes denoising can be not efficient. Another issue regards a selection of an appropriate filter and setting of its parameters. As a particular case, we consider a 2D DCTbased filter with 8x8 pixel fully overlapping blocks where one of the parameters is a proportionality factor (PF) used in the threshold setup. We show that a performance of the considered filter in the sense of standard PSNR and visual quality metric PSNR-HVS-M can be predicted before applying image filtering procedure. This prediction is sufficiently faster than the denoising itself and accurate enough. We demonstrate that, having DCT statistics in a limited number of image blocks, such a prediction can be done for several values of PF. This allows deciding is it worth applying filtering to an image at hand. If the denoising is desired, it is also possible to select the PF optimal value for the considered image and noise intensity. Such a procedure, in some cases, can result in improvement of output PSNR or PSNR-HVS-M by up to 1 dB in comparison to the default parameters setup.
Generation of Multipartite Entangled States Using Switchable Coupling of Cooper-Pair-Boxes

We propose a scheme for a switchable coupling between several Cooper-pair boxes in the charge regime.The switch is embodied in a SQUID element contained in the center conductor of a transmission-line resonator.Altering the flux bias through the SQUID allows for changing the effective resonator length.Thus the position of the nodes and anti-nodes of the relevant eigenmodes changes and leads to a variable interaction strength between qubit and cavity vacuum field.For the coupled qubits the interaction is dispersive.An example for the application of this switch is the generation of multipartite entangled states for three and four charge qubits.Although used as a discrete switch in the present proposal, the combined system of SQUID module integrated into the transmission line may be operated continuously as well.
Brief review of the mathematical models for analyzing and forecasting transmission of COVID-19

Coronaviruses are one of the dangerous sources of human infection. To counter such a source, it isimportant to obtain reliable statistics. It is also necessary to carry out appropriate data analysis, which helps to findsolutions to various issues. To do this, use data analysis and forecasting models. Thus, this work is devoted to thereview of mathematical models for the analysis and prediction of the distribution of COVID-19. Such a reviewshowed that there are models of different directions. This allows you to make more effective decisions.