Reviewerly/exHarmony huggingface dataset:
Optimizing Space Communications Using Deep Learning

The paradigm shift from standard communication models to artificially intelligent solutions continues to be an exciting avenue of research. Through applying neural networks to a communication-learning model, the autonomous development of robust signal constellations emerges; however, optimal training architectures for these algorithms have not been determined. This work focuses on improving the modulation and coding solutions produced by deep neural networks by evaluating various training methods and configurations. The communications system is modeled with an autoencoder, an auto-associative neural network that learns how to transmit and recover input data, representing transmitter and receiver. Effective training of an autoencoder-based system encourages the model to learn modulation and coding schemes through optimizing symbol placement, which is achieved in this work through proper network normalization and adding channel noise. A normalization layer bounds symbol energy in a learned constellation, but these layers can impair the reproducibility of the training processes. This work assesses the effect of various normalization layers on the minimum distance between symbols in a learned constellation. Furthermore, a nonlinear noise technique, which intentionally mislabels symbols during training, is implemented to encourage symbol separation. This work concludes with a comparison between bit-wise versus symbol-wise training to determine if the learned constellation can achieve gray coding-like results.
Lessons On-(the)-Line Blended Learning and Pedagogy of (the) Digitally Oppressed

In this essay, I explore the meanings and implications of blended learning in an era of global pandemic by extending Paulo Freire’s notion of a “pedagogy of the oppressed” into the digital milieu and COVID-19 era of the 21st century. In doing so, I critically meditate on how Freire’s cue is reformulated in the context of online teaching while situating questions about online learning in the context of the Blended Learning Online South Africa (BLOSA) project based at the University of Witwatersrand. I do so as a means for tracking how, in material practice, blended learning operates in the context of knowledge dissemination and postcolonial poverty.
Module Integration Using Graph Grammars (MIGRATE)

Software, whether desktop, mobile or web, is becoming more and more connected. Software development is also becoming more connected with ecosystems comprised of networks of millions of packages. Engineering software today is writing code that weaves together libraries, services and applications. Such fabrics are under constant changes due to both internal requests, e.g. new features, or external demands, e.g. dependency updates. Avoiding integration bugs in this scenario can be a big challenge regardless of common strategies such as testing and versioning. We propose an approach, called Module Integration using Graph Grammars (MIGRATE), to describe/analyze integration points among software modules. We define module nets, a formalism to capture the essential information regarding module integration, whose semantics is defined in terms of graph transformations. This allows us to use the rich theory of graph transformation, specially critical pair analysis, to analyze the coupling among different modules and create warnings in case of possible integration problems. The approach is organized in three phases: (i) transformation of code into module nets (model extraction), (ii) translation of module nets into graph grammars (semantics of integration) and (iii) verification of module integration. We have built a prototype that implements the MIGRATE approach.
Practical Bandwidth Allocation for Video QoE Fairness

Nowadays, the QoE unfairness problem exists under the scenario of multiple clients sharing bottleneck links, as clients just maximize their Quality of Experience (QoE) independently via adaptive bitrate algorithm and congestion control algorithms provide only connection-level fairness. Improving the QoE fairness among clients, the video providers would jointly optimize the QoE of multiple clients. Nevertheless, existing solutions are impractical due to either deployment issues or heavy computation overhead. Therefore, we propose the practical bandwidth allocation (PBA) mechanism for video QoE fairness in this paper. Specifically, PBA formulates QoE by considering the impacts of both network conditions and devices and reconfigures congestion control algorithm according to the piggybacked QoE. In this distributive manner, PBA can rapidly converge to the bandwidth allocation with good QoE fairness. Real-world experiments confirm that PBA improves the QoE fairness and accordingly increases the minimum QoE of video streams by at least 13% and 15% compared with Copa and Cubic, respectively. Moreover, the performance advantage of PBA becomes significant under dynamic wireless networks.
Comments on “A Gibbs Sampler for a Class of Random Convex Polytopes”

In this comment we discuss relative strengths and weaknesses of simplex and Dirichlet Dempster-Shafer inference as applied to multi-resolution tests of independence.
DFL-Net: Effective Object Detection via Distinguishable Feature Learning

The one-stage anchor-based approach has been an efficient and effective approach for detecting objects from massive image data. However, it neglects many distinguishable features of objects, which will lower the accuracy of object detection. In this paper, we propose a new object detection approach that improves existing one-stage anchor-based methods via a Distinguishable Feature Learning Network (DFL-Net). DFL-Net integrates distinguishable features into the learning process to improve the accuracy of object detection. Notably, we implement DFL-Net by a full-scale fusion module and an attention-guided module. In the full-scale fusion module, we first learn the distinguishable features at each scale (layer) and then fuse them in all layers to generate full-scale features. This differs from prior work that only considered one or limited scales and limited features. In the attention-guided module, we extract more distinguishable features based on some positive or negative samples. We conduct extensive experiments on two public datasets, including PASCAL VOC and COCO, to compare the proposed DFL-Net with several one-stage approaches. The results show that DFL-Net achieves a high mAP of 83.1% and outperforms all its competitors. We also compare DFL-Net with three two-stage algorithms, and the results also suggest the superiority of DFL-Net.
PoemAI: Text Generator Assistant for Writers

Text Generation has become one of the functional areas of Natural Language Processing (NLP) and is considered a major advancement in this generation of creative texts such as literature. Every year, the field is receiving increased future research. With the advent of Machine Learning and the Deep Neural Network, it has become possible to process and find hidden patterns from huge amounts of information (Big Data). Our research is focused on the use of the Neural Network Model to construct an AI assistant for writers, primarily poets, using Bi-Directional Long Short-Term Memory (LSTM), a variant of the Recurrent Neural Network (RNN), to generate poetry in the English language by providing it with a huge corpus of poems compiled specifically for research by renowned poets. With two unique themes, love and nature, the dataset is specifically chosen. Most of the poems are from the Renaissance and Modern period. This method will function in two ways, firstly by creating full-length poems and sonnets, and secondly as a provocative tool for the writer to make it a hybrid system in which a creative piece of work is produced by both the writer and AI. Some issues with previous versions and models have been improved, such as lack of coherence and rhyme, along with a newly designed use case introduced in our report, such as AI assistant. This model can produce unique poetry along with providing meaningful suggestions with a suggestion of rhyming with appropriate precision in some outputs.
Online Teachers Training: Increasing Elementary School Teachers’ Ability in Daily Speaking English for Bilingual

Bilingual class (Indonesian and English) for some lessons must be done in Islamic Elementary School of Al Azhar 17 Bintaro. Therefore, English Training for teacher was conducted. This program was a challenge for all teachers of SD Al Azhar 17 Bintaro to improve their competence in teaching by using two languages and to improve the ability of using English for daily communication with students. The aims of the training was to prepare the implementation of bilingual classes that was done 10 meetings and each meeting was done 4 hours. English Teacher Training Program include: games and icebreaking, group and class discussions, role-play, sharing session, microteaching (presentation), and feedback evaluation. Participants were always enthusiastic in doing all activities. Feedback from other participants was very good. At the end of the training, there was an increase in the competence of all participants in using English expressions for teaching purposes. They seem more confident and communicate more fluently in English. The school hopes that the bilingual training program in schools will continue to be a success.
Fleet-Level PV Modeling with Realistic Sub-Hourly Solar Power Variability

Simulating the generation of solar power plants is necessary for resource integration planning studies and flexibility analysis. However, most simulation tools fail to capture realistic sub-hourly variability and have limited usability. Here we present work on modeling sub-hourly variability at the plant and fleet levels, with an emphasis on usability for system planners. We validate our approach on a year of 6-second power measurements for a PV fleet with a total installed capacity of approximately 1,750 MW. Additionally, we report on efforts to develop a user-friendly tool integrating the approach, which will be released as an open-source Python 3 library and accompanying graphical user interface (GUI).
An Information-Theoretic Scheme for Multi-Party Private Set Intersection

We investigate the problem of multi-party private set intersection (MP-PSI). In MP-PSI, there are <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> parties, each storing a data set <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{P}_{i}$</tex> over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N_{i}$</tex> replicated and non-colluding databases, and we want to calculate the intersection of the data sets <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\cap_{i=1}^{M}\mathcal{P}_{i}$</tex> without leaking any information beyond the set intersection to any of the parties. For a specific communication protocol, we propose an information-theoretic scheme for MP-PSI based on the connection between the PSI problem and the multi-message symmetric private information retrieval (MM-SPIR) problem. Our scheme is a non-trivial generalization of the 2-party PSI scheme as it needs an intricate design of the shared common randomness. Interestingly, our scheme does not incur any penalty due to the more stringent privacy constraints in the MP-PSI problem compared to the 2-party PSI problem.
Introduction to blockchain and distributed systems—fundamental theories and concepts

Blockchain is among the foremost significant technical innovation during latest period. It’s a clear money exchange system that changed the approach an occupation is held. It’s becoming widespread due to its indisputable security and skills to offer full resolution to digital identity problems. Blockchain serves as a digital ledger in a P2P network. This chapter gives an in-depth background on blockchain technology, its history, design, working, benefits, and challenges associated with it and its application in several industries. It is a long-time ledger that permits transactions occur in a very decentralized way. The applications of blockchain are arising, covering varied fields together with finance, IoT, etc. However, there are yet numerous challenges such as scalability and safety-related issues which are yet to be resolved. In this chapter, a comprehensive outline on blockchain technology and all of its aspects is explained.
DGVAE:An End-to-end Model for Link Prediction in Directed Graphs

Graph neural network(GNN) models have been widely used in graph-structured data which can be seen everywhere but difficult to deal with. Link prediction is a kind of typical graph learning task which has been widely used in many applications such as knowledge graph completion, social recommendation, etc. Most existing link prediction models need preprocessing to preserve properties of graph such as asymmetric transitivity, node proximity, etc., which make models inconvenient to use. In this paper, we focus on link prediction in directed graphs and propose an end-to-end model called directed graph variational auto-encoder(DGVAE). DGVAE uses variational auto-encoder(VAE) as the whole framework and uses a two-layer GNN model FDGCN as the encoder. We design a brand new decoder for asymmetric output. Experiments are conducted on several different datasets and existing state-of-the-art models. Results show that DGVAE has the best performance in most cases.
Four-level double V-type atomic system solved by virtue of supersymmetric unitary transformation

We consider a four-level double V-type atom with two closely-separated top levels and two closely-separated lower levels interacting with a single mode field via multi-photon processes and in the presence of Kerr medium. We show that this atomic system possesses supersymmetric structure and construct its supersymmetric generators. We diagonalize the Hamiltonian of this system using supersymmetric unitary transformation and obtain the corresponding eigenstates and eigenvalues. The atom–field wave functions are obtained when the atom and the field mode are initially in two different cases. The evolution of both the quasi-probability distribution Q-function and the Mandel Q-parameter of the field are studied when the input field is in a coherent state. The influence of the Kerr medium and the detuning parameters on the behavior of these quantum effects is analyzed. The results show that they play a prominent role on the Poissonian statistics of the field. Also, the Kerr medium changes the behavior of the quasi-probability distribution Q-function. We end with discussion and conclusions.
A novel Blockchain-based Access Control Manager to Electronic Health Records (EHRs)

The main objective of this chapter is to propose a novel blockchain-based access control manager to Electronic Health Records. Our system gives patients a complete, immutable log and seamless access to their medical information across healthcare providers. The proposed system facilitates authentication, authorization, confidentiality, privacy-preserving, and data sharing in crucial conditions when handling sensitive information. Blockchain access control manager integrates with providers’ existing, local data storage, facilitating interoperability, and making our system convenient and flexible. The proposed system motivates medical stakeholders (patients, doctors, healthcare providers, pharma industry, insurance companies, and researchers) to participate in the network as a blockchain entity. The blockchain access control manager would facilitate continuous monitoring of high-risk patients that would notify caregivers and health providers if a patient reached a critical threshold for action. Caregivers could reach out to the patient and coordinate treatment options for early intervention. The main objective of this chapter is to provide a working protocol for blockchain-based automated access control managers that do not require trust in a third party instead it is going to execute instructions and queries such as storing, retrieval, and data sharing through which we analyze and discuss our approach.
Considerations Across Three Cultures: Parametric Regressions, Interpretable Algorithms, and Complex Algorithms

We consider an extension of Leo Breiman's thesis from "Statistical Modeling: The Two Cultures" to include a bifurcation of algorithmic modeling, focusing on parametric regressions, interpretable algorithms, and complex (possibly explainable) algorithms.
Cryptographic Primitives Optimization Based on the Concepts of the Residue Number System and Finite Ring Neural Network

Data encryption has become a vital mechanism for data protection. One of the main challenges and an important target for optimization is the encryption/decryption speed. In this paper, we propose techniques for speeding up the software performance of several important cryptographic primitives based on the Residue Number System (RNS) and Finite Ring Neural Network (FRNN). RNS&FRNN reduces the computational complexity of operations with arbitrary-length integers such as addition, subtraction, multiplication, division by constant, Euclid division, and sign detection. To validate practical significance, we compare LLVM library implementations with state-of-the-art, high-performance, portable C++ NTL library implementations. The experimental analysis shows the superiority of the proposed optimization approach compared to the available approaches. For the NIST FIPS 186-5 digital signature algorithm, the proposed solution is 85% faster, even though the sign detection has low efficiency.
Mask Scene Text Recognizer

Scene text recognition is a challenging sequence modeling problem. In this paper, a novel mask scene text recognizer (MSTR) is proposed to incorporate a supervised learning task of predicting text image mask into a CNN (convolutional neural network)-Transformer framework for scene text recognition. The incorporated mask predicting branch is connected in parallel with the CNN backbone, and the predicted mask is used as attention weights for the feature maps output by the CNN. We investigate three variants of the incorporated mask predicting branches, i.e. a) mask branch which predicts text foreground image mask; b) boundary branch which predicts boundaries of characters in the input images; c) fused mask and boundary branches with different fusion schemes. Experimental results on seven commonly used scene text recognition datasets show that our method with fused mask and boundary branches has outperformed previous state-of-the-art methods.
Implementing Virtual Reality–Enhanced Tasks in Chinese Language Teaching

Virtual Reality (VR) has great potential to enable immersive, interactive, and intuitive language learning. Several studies have discussed the effectiveness of VR in language learning (Collentine in Language Learning & Technology. 15:50–67, 2011; Schwienhorst in Computer Assisted Language Learning 15:221–239, 2002), yet such attempts in teaching Chinese as a foreign language are still rare. Using the VR technologies Unity and Google Cardboard to enhance language learning, this study explores the design and effects of VR-incorporated tasks in Chinese language classrooms. Twenty-seven American college students participated in the study, with proficiency ranging from novice-high to intermediate-low levels. A combination of quantitative and qualitative analysis was employed to assess learners’ perception of their language learning experience and evaluate their motivation, engagement, and confidence. The results show that learners’ self-perceived experiences in VR-incorporated language learning were quite positive, and that VR-incorporated tasks enhanced their motivation, engagement, and confidence. Reflections on the use of the VR technology and directions for future study are discussed at the end of the study.
Infusing ML into VM Provisioning in Cloud

In cloud computing, provisioning virtual machines (VMs) fast and reliably is a fundamental yet challenging problem, particularly in cloud environments of changing workloads and shifting demand patterns. A trivial solution of instantiating a VM from scratch per customer request may not satisfy business SLA's and would degrade customer experience. Hence, provisioning VMs in advance motivated with machine learning (ML) infused into the cloud computing system to predict upcoming VM request demands is an advisable solution. In this paper, we first describe a number of system integration challenges including 1) how to achieve low latency provisioning to quickly adjust to customer demand pattern shifts, 2) how to efficiently scale to serve a large number of VM configurations supported in the cloud environment, and 3) how to reliably consume recommended prediction results for VM provision despite of anticipated operation failures and timeout. We then present the high level solution design with discussions of our developed system to address the aforementioned challenges. Our system has been implemented and deployed successfully in Microsoft Azure exhibiting significant improvements for VM provisioning experience with regards to latency and reliability requirements.
Increased Reinforcement Learning Performance through Transfer of Representation Learned by State Prediction Model

Reinforcement Learning (RL) is known for its high sample complexity, particularly in sparse reward settings. This is in part due to biased temporal-difference (TD) targets, particularly in the early stages of training. Our work proposes using state change predictions as an unbiased and non-sparse supplement for TD-targets. By training a forward model which shares a Q-network's initial layers, we allow transfer learning from model dynamics prediction to Q value function approximation. We discuss two variants, one doing this only in the initial steps of the training and another one using it throughout the training process. Both variants can be used as enhancements to state-of-the-art RL algorithms. Our results show that enhancing Double DQN (DDQN) and TD3 with this approach outperforms vanilla versions when applied to the Acrobot, MountainCar, and Cartpole RL benchmarks for DDQN and the HalfCheetah and Walker2D RL benchmarks for TD3. This result is particularly significant as it shows the power of transfer learning even without a priori knowledge.
PeakVis: a Visual Analysis Tool for Social Network Data and Video Broadcasts

Television and live internet broadcasts coexist with a stream of social network commentary, engaging audiences in more complex ways than traditional ratings can represent, demanding convergent tools to analyze such contemporary media. In this work, we present PeakVis, an interactive tool that syncs a video recording with a responsive line graph representing the total number of tweets from each moment, the top messages, a dynamic word cloud, and a semantic graph showing word correlation. Thus, this novel interactive approach allows the analysis of broadcast highlights identified through Twitter posts peaks. We explored this freely available tool’s applicability by analyzing two different case studies: the season finale of a reality show and the final episode of a telenovela. We could observe that it allows us to quickly identify the most relevant segments and grasp the discussed subjects in-depth through the obtained results, showing its value in broadcast analysis.
Netflix, Amazon chiefs blast Hollywood foreign press over diversity, group responds

Netflix has announced that it will not work with the group that awards the Golden Globes until further measures are taken