Reviewerly/exHarmony huggingface dataset:
Characterizing the Role of a Single Coupling Layer in Affine Normalizing Flows

Deep Affine Normalizing Flows are efficient and powerful models for high-dimensional density estimation and sample generation. Yet little is known about how they succeed in approximating complex distributions, given the seemingly limited expressiveness of individual affine layers. In this work, we take a first step towards theoretical understanding by analyzing the behaviour of a single affine coupling layer under maximum likelihood loss. We show that such a layer estimates and normalizes conditional moments of the data distribution, and derive a tight lower bound on the loss depending on the orthogonal transformation of the data before the affine coupling. This bound can be used to identify the optimal orthogonal transform, yielding a layer-wise training algorithm for deep affine flows. Toy examples confirm our findings and stimulate further research by highlighting the remaining gap between layer-wise and end-to-end training of deep affine flows.
A Novel High-Accuracy Low-Execution Time Machine Learning- Driven Approach to Automatic Modulation Classification

The process of automatic modulation classification (AMC) has gained importance in recent years. AMC enables receivers to classify an intercepted signal's modulation scheme without any prior information about the signal. Likelihood, Distribution Test- and Feature-based approaches have been proposed in the literature for AMC. All these approaches suffer from very high execution time for the AMC process. This prevents the AMC component from operating in real-time alongside other components in the receiver. Thus, in this work, we propose the use of spiking neural network (SNN) in the labeling procedure of feature-based AMC. Implementing SNN in the labeling procedure will significantly decrease the required execution time for AMC while its classification accuracy remain highly competitive with other approaches such as convolutional and recurrent neural networks (CNN and RNN). We furthermore provide a comparison in terms of the required execution time and classification accuracy between our proposed classifier and two recently published approaches that are built upon the aforementioned neural network platforms. We utilize 16QAM, 32QAM and 64QAM modulation schemes from the real-world RadioML dataset in this comparison. The analysis demonstrates that our proposed classifier compared to RNN- and CNN-based classifiers requires on average 19.52% and 14.9% less execution time while sacrificing only 4.7% in classification accuracy on average when compared to the RNN-based classifier, and achieves on average a 15.7% higher classification accuracy compared to the CNN - based classifier.
Continuous Multi-agent Path Finding via Satisfiability Modulo Theories (SMT)

We address multi-agent path finding (MAPF) with continuous movements and geometric agents, i.e. agents of various geometric shapes moving smoothly between predefined positions. We analyze a new solving approach based on satisfiability modulo theories (SMT) that is designed to obtain optimal solutions with respect to common cumulative objectives. The standard MAPF is a task of navigating agents in an undirected graph from given starting vertices to given goal vertices so that agents do not collide with each other in vertices or edges of the graph. In the continuous version (MAPF $$^\mathcal {R}$$ ), agents move in an n-dimensional Euclidean space along straight lines that interconnect predefined positions. Agents themselves are geometric objects of various shapes occupying certain volume of the space - circles, polygons, etc. We develop concepts for circular omni-directional agents having constant velocities in the 2D plane but a generalization for different shapes is possible. As agents can have different shapes/sizes and are moving smoothly along lines, a movement along certain lines done with small agents can be non-colliding while the same movement may result in a collision if performed with larger agents. Such a distinction rooted in the geometric reasoning is not present in the standard MAPF. The SMT-based approach for MAPF $$^\mathcal {R}$$ called SMT-CBS $$^\mathcal {R}$$ reformulates previous Conflict-based Search (CBS) algorithm in terms of SMT. Lazy generation of constraints is the key idea behind the previous algorithm SMT-CBS. Each time a new conflict is discovered, the underlying encoding is extended with new to eliminate the conflict. SMT-CBS $$^\mathcal {R}$$ significantly extends this idea by generating also the decision variables lazily. Generating variables on demand is needed because in the continuous case the number of possible decision variables is potentially uncountable hence cannot be generated in advance as in the case of SMT-CBS. We compared SMT-CBS $$^\mathcal {R}$$ and adaptations of CBS for the continuous variant of MAPF experimentally.
QRing Specialties’ Mobile Course Elements to Support VET Learners’ Cognitive Level

This study aims to investigate the effectiveness of QR (Quick Response) codes in teaching and learning. More specifically, the study explores the extent to which, the use of QR codes, embedded in the learners’ class material, can improve their linguistic performance in English and enhance their interest and participation in their specialty lessons. For the purposes of the study, a number of 132 secondary vocational randomly selected school learners from four schools and eight classes, aged between 17–21 years, and four teachers took part in the research process. The study was implemented in five stages and had duration of three months. The research employed a mixed method using a quantitative and qualitative approach. Learners’ linguistic level before and after the intervention was measured with the help of the balanced state KPG test for foreign languages, while four focus groups, one per class, were also implemented. According to the results, it can be argued that the integration of the QR codes in the teaching material and process enhances both receptive English language skills but mainly reading. What is more, it seems that VET (Vocational and Technical Education) students found the use of QR codes interesting, innovative and helpful for their studies.
Optimizing Convergence for Iterative Learning of ARIMA for Stationary Time Series

Forecasting of time series in continuous systems becomes an increasingly relevant task due to recent developments in IoT and 5G. The popular forecasting model ARIMA is applied to a large variety of applications for decades. An online variant of ARIMA applies the Online Newton Step in order to learn the underlying process of the time series. This optimization method has pitfalls concerning the computational complexity and convergence. Thus, this work focuses on the computational less expensive Online Gradient Descent optimization method, which became popular for learning of neural networks in recent years. For the iterative training of such models, we propose a new approach combining different Online Gradient Descent learners (such as Adam, AMSGrad, Adagrad, Nesterov) to achieve fast convergence. The evaluation on synthetic data and experimental datasets show that the proposed approach outperforms the existing methods resulting in an overall lower prediction error.
Geodesic projection of the von Mises–Fisher distribution for projection pursuit of directional data

We investigate geodesic projections of von Mises–Fisher (vMF) distributed directional data. The vMF distribution for random directions on the (p−1)-dimensional unit hypersphere Sp−1⊂Rp plays the role of multivariate normal distribution in directional statistics. For one-dimensional circle S1, the vMF distribution is called von Mises (vM) distribution. Projections onto geodesics are one of main ingredients of modeling and exploring directional data. We show that the projection of vMF distributed random directions onto any geodesic is approximately vM-distributed, albeit not exactly the same. In particular, the distribution of the geodesic-projected score is an infinite scale mixture of vM distributions. Approximations by vM distributions are given along various asymptotic scenarios including large and small concentrations (κ→∞,κ→0), high-dimensions (p→∞), and two important cases of double-asymptotics (p,κ→∞, κ∕p→c or κ∕p→λ), to support our claim: geodesic projections of the vMF are approximately vM. As one of potential applications of the result, we contemplate a projection pursuit exploration of high-dimensional directional data. We show that in a high dimensional model almost all geodesic-projections of directional data are nearly vM, thus measures of non-vM-ness are a viable candidate for projection index.
Web-based rule-based system for early detection of anemia among pregnant mothers

Abstract Anemia among pregnant mothers is a serious issue that may cause pregnancy complications, premature birth that can lead to mortality, and low birth weight (BBLR). Monitoring for anemia among pregnant mothers is quite a phenomenon for midwives and health professionals due to time constraint as it takes time to establish a proper diagnosis. Late diagnosis establishment results in late treatment. Therefore, this paper proposes a web-based rule-based information system using the rule-based algorithm to ease monitoring and detection of anemia among high risk pregnant mothers. The method employed is online rule-based information system to diagnose anemia among pregnant mothers. Respondents input data of symptoms that will then be processed by the information system to generate diagnoses. Resulting diagnoses are displayed as they are. The rule-based method in the information system validates data of symptoms and diagnoses with experts in the field to serve as reference in providing health care information. This is a pre-experimental research with one posttest group. The intervention group (N=10) was given treatment using the information system, while the control group (N=10) was given treatment manually. Interventions were conducted for 20 days, and measurements were carried out on the 21st day using the Technology Acceptance Model (TAM). The anemia detection system for pregnant mothers developed here is proven to be effective in helping anemia detection and has been validated by experts (midwives) and hence, been declared valid. With effectiveness score of 90 %, this web-based rule-based information system is effective in detecting, monitoring, and reporting anemia among pregnant mothers.
Digital Rights Of LGBTI Communities: A Roadmap For A Dual Human Rights Framework

This paper aims to move beyond the existing narratives on digital technologies and LGBTI rights by sketching a preliminary roadmap for the development of a combined LGBTI & digital rights analytical framework and research programme. Such a framework is necessary for two reasons. First, it is vital to address the growing complexity of the implications of the Internet and digital technology for human rights that in reality go well beyond the so-called classical digital rights issues of censorship and surveillance, which has captured and even expropriated scholarly and activist attention at the expense of other pressing issues. Second, such a framework is instrumental for the LGBTI movement to expand its political spectrum beyond ‘traditional’ issues, such as marriage equality and gender identity, and confront others pressing civil rights concerns to assure the effective and full equality of LGBTI communities in the digital age. The modest goal of this paper to illuminate the problematic issues at the intersection of LGBTI communities and digital technologies and hopefully stimulate a research programme on these topics.
Effective Background and Foreground Segmentation Using Unsupervised Frequency Domain Clustering

Computer vision is the way in which the computer perceives a certain image. Background and foreground detection of an image are based on the concept of computer vision. Traditional approaches in background and foreground detection of an image imply clustering algorithms like K-means clustering, Gaussian mixture model to compute the result in the spatial domain. In spatial domain, we take into account the pixels of an image to classify them as background or foreground. In this paper, we have reviewed the process already been done in spatial domain, then we study some of the state-of-the-art background detection techniques in digital image processing and propose a novel approach to shift the domain of input images to frequency while applying the same algorithms for detection. We have used the concept in which we take into consideration the frequency rather than pixels of an image. The concept of fast Fourier transform (FFT) has been used to determine the frequency. With this solution concept, we aim at reducing the variance in input image by smoothing out the frequency domain image and experimentally demonstrate that the transition into the frequency domain outperforms the majority of techniques employed in spatial domain for background detection.
Choose Your Words Wisely: Leveraging Embedded Dialog Trajectories to Enhance Performance in Open-Domain Conversations

Human conversations are notoriously nondeterministic, and identical conversation histories can nevertheless accept dozens, if not hundreds, of distinct valid responses. In this paper, we present and expand upon Conversational Scaffolding, a response scoring method that capitalizes on this fundamental linguistic property. We envision a conversation as a set of trajectories through embedding space. Our method leverages the analogical structure encoded within language model representations to prioritize possible conversational responses with respect to these trajectories. Specifically, we locate candidate responses based on their linear offsets relative to the scaffold sentence pair with the greatest cosine similarity to the current conversation history. In an open-domain dialog setting, we are able to show that our method outperforms both an Approximate Nearest-Neighbor approach and a naive nearest neighbor baseline. We demonstrate our method’s performance on a retrieval-based dialog task using a retrieval dataset containing 19,665 randomly-selected sentences. We further introduce a comparative analysis of algorithm performance as a function of contextual alignment strategy, with accompanying discussion.
Use Cases of Quantum Optimization for Finance

In this paper we briefly review two recent use-cases of quantum optimization algorithms applied to hard problems in finance and economy. Specifically, we discuss the prediction of financial crashes as well as dynamic portfolio optimization. We comment on the different types of quantum strategies to carry on these optimizations, such as those based on quantum annealers, universal gate-based quantum processors, and quantum-inspired Tensor Networks.
A Cloud Data Classification Model Using Fuzzy Logic

In the last years, cloud computing has emerged as a new information technology (IT) model. Which provides an easy way for data processing and storage remotely through a network. The common solution to secure data in the cloud is data encryption. However, treating all data with the same security policy does not appear to be a good practice, because they do not have the same sensitivity for their owners. In this paper, we propose a model that aims to solve the problem of data sensitivity in cloud storage using the classification. The proposal is based on the use of fuzzy logic that uses the CIA (Confidentiality, Integrity and Availability) triad of information security to classify data.
A Hybrid Digital Signature Technique using Cryptosystem

Network is a node collection.The network's basic aim is to transfer information from one location to another.This information must be secured from access by third parties.The cryptography concept was based upon the necessity to secure critical data exchanged across an unsecured network.While using encryption the transmitter encrypts or encodes the information with a secret key so that only the tender recipient will understand it.Cryptanalysis, however, means unwanted access without the secret information key.The cryptography uses various techniques that are Diffie Hellman, AES, RSA, DES, IDEA, BLOWFISH, x.509, PKI, Digital Signatures convert plain texts into the respective chipper text.In different circumstances all these algorithms are important.RSA's most productive computerized signature calculation .This article presents a precise writing review of different computerized signature frameworks dependent on RSA.A basic report is completed on the key age, the creation of marks, the mark check of different computerized signature approaches.
Formation of Media Competence of the Individual in the Paradigm of the Future

Purpose: The article considers the process of formation of media competence of the individual in the paradigm of the future. In the modern education system, the issue of forming the investigated competence of future teachers, for whom activity in the media space is becoming more and more important, since the media environment and media education are the bedrock for the modernization and development of the world educational space, is acute. Design/methodology/approach: In the course of experimental work, the degree of expression of individual components of media competence of future teachers, namely, the creative and practical-operational components, was determined. Findings: The components of media competence of future teachers in the aspect of the content of professional activity are considered: motivational, cognitive, creative and practical-operational, formed in the process of project activity. Its indicators in the professional aspect are revealed. The stages of project activity and the content of work at each stage are described. Much attention is paid to the approbation of the developed media products in the course of experimental work. The degree of formation of creative and practical-operational components of media competence in the course of the experiment is identified, and diagnostic methods for determining the level of formation of its components are described. Originality/value: The results of developing our own media products are presented. It is concluded that the individual’s motivation for change is one of the most important conditions for training future teachers. The results of this research can be used by higher school teachers in the course of their project activities.
Accelerated Object Detection for Autonomous Driving in CVIS Based on Pruned CNN Architecture

While processing delay has become the primary consideration for future Cooperation Vehicle Infrastructure Systems (CVIS), different enhanced methodologies based on pruned network has widely studied. More efficient pruned architecture can be accomplished by considering prior information like motion feature prediction (optical flow, etc.) to achieve optimized feature computing. With consideration of continuous features backward propagation methods, an accelerated neural network pruning method is proposed in this paper, to achieve real-time pruning based on prior information in CNN inference time. Furthermore, proposed method can fully utilize the information from CVIS to maximize pruning ratio in feature space from the CVIS monitoring stream, with significant improvement for recall index, a more suitable performance evaluation index for object detection in autonomous driving. With comparison to general detection framework, like YOLOv3 based on darknet-52, more than 20% processing speed gain can be significantly achieved in our proposed method, with minimum loss of recall index. At the same time, the basic recall rate has also been increased by 13.7%.
Session-Based Recommendation Model Based on Multiple Neural Networks Hybrid Extraction Feature

The problem of session-based recommendation model aims to predict user actions based on anonymous sessions. Although, previous models achieved promising results, there are still some problems, for example, we are unable to take into account the effects of session sequences of different lengths. Generally speaking, the effect of long sequence is not as good as that of short sequence in the same model. The reason of above is that the characteristics of different length session will vary greatly. Generally, the shorter the session, the tighter the relationship between items, and the longer the session, the more likely there are items that have no relationship with each other. So, we propose a model named session-based recommendation model based on multiple neural networks hybrid extraction feature. This model uses different feature extractor to deal with the features of long sessions and short sessions respectively. In SR-MNN, we use Graph Convolutional Network to extract the features of long session and use Recurrent Neural Network to extract the features of short session. Each session is then represented as the composition of the global preference, the initial interest of that session, and the current interest of that session using an attention network. Experiments on two real datasets show that SR-MNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.
On the distribution of multivariate Jacobi sums

Let Fq be a finite field of q elements. We show that the normalized Jacobi sum q−(m−1)/2J(χ1,…,χm) (χ1⋯χm nontrivial) is asymptotically equidistributed on the unit circle, when χ1∈A1,…,χm∈Am run through arbitrary sets of nontrivial multiplicative characters of Fq×, if #A1≥q12+ϵ, #A2≥(log⁡q)1δ−1 for ϵ>δ>0 fixed and q→∞ or if #A1#A2/q→∞. This extends previous results of Xi, Z. Zheng, and the authors.
FaceCode: An Artistic Face Image with Invisible Hyperlink

With the widespread use of mobile social software, QR code plays an important role in acquiring messages from offline to online. However, the plain black and white blocks of QR code cannot attract users in social software, thus some popular software like Wechat beautify the QR code to improve its visual appeal. Inspired by the mentioned fact, this paper proposes a novel 2D image code named FaceCode. FaceCode is a Convolution Neural Network (CNN) based framework to embed information into a picture and beautify it simultaneously. For an input picture, FaceCode firstly uses a style transfer neural network to make the picture artistic, and then an embedding network cascading to the style transfer network embeds the personal information of users into the picture. The information is invisible but can be recognized by a phone camera. With the help of FaceCode, the users can hide their personal information in their profile pictures in social software. Experiments show that FaceCode can generate personalized profile pictures while maintaining the accuracy of recognizing the hidden information.