Reviewerly/exHarmony huggingface dataset:
Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation

The emergence of the recommendation system has effectively alleviated the information overload problem. However, traditional recommendation systems either ignore the rich attribute information of users and items, such as the user’s social-demographic features, the item’s content features, etc., facing the sparsity problem, or adopt the fully connected network to concatenate the attribute information, ignoring the interaction between the attribute information. In this paper, we propose the information fusion-based deep neural attentive matrix factorization (IFDNAMF) recommendation model, which introduces the attribute information and adopts the element-wise product between the different information domains to learn the cross-features when conducting information fusion. In addition, the attention mechanism is utilized to distinguish the importance of different cross-features on prediction results. In addition, the IFDNAMF adopts the deep neural network to learn the high-order interaction between users and items. Meanwhile, we conduct extensive experiments on two datasets: MovieLens and Book-crossing, and demonstrate the feasibility and effectiveness of the model.
Implementation of Cloud Computing System in Learning System Development in Engineering Education Study Program

The development of technology demands a variety of activities that can be easily accessed regardless of space and time. The development of information technology has become an innovative, dynamic, and economically profitable solution. Information technology is the answer to the problems and challenges faced by the world of education. Cloud computing is changing the way information technology services are provided and distributed so that institutions have the opportunity to access the educational and scientific information. Cloud computing technology is a new paradigm in the delivery of computing services, which has many advantages compared to conventional systems. This study discusses cloud computing in general and some examples of the application of cloud computing services and their benefits in the Electrical Engineering Education Study Program environment. The case study taken is the application of cloud computing services in the Electrical Engineering Education Study Program, where cloud computing is a computing model, as resources such as processors or computing power, storage, networks, and software are abstracted and provided as services on a network or internet with using the remote access pattern. The purpose of this study was to determine whether cloud computing might be applied to learning, as well as the benefits provided by cloud computing in the development of learning systems. This study uses a qualitative descriptive method with data collection techniques in the form of literature studies. Based on the research conducted, the results show that the services provided by cloud computing have various benefits, namely increasing efficiency and effectiveness in the use of cloud computing-based technology in the learning process.
EMaxPPE: Epoch’s Maximum Prediction Probability Ensemble Method for Deep Learning Classification Models

As deep learning (DL) is evolving rapidly, implementing the knowledge of DL into various fields of human life and the effective usage of existing data insights are becoming crucial tasks for a majority of DL models. We are proposing to ensemble maximum prediction probabilities of different epochs and the epoch which achieved the highest accuracy for classification problems. Our suggestion contributes to the improvement of DL models using the pre-trained and skipped results from epochs. The maximum prediction probability ensemble of epochs increases the prediction space of the entire model if the intersection of prediction scope of any epoch is smaller than the one that has the biggest prediction scope. Using only the best epoch’s prediction probabilities for classification cannot use the other epochs’ knowledge. To avoid bias in this research, a simple CNN architecture with batch normalization and dropout was used as a base model. By ensembling only maximum prediction probabilities of different epochs, we managed to use 50% of the lost data insight from the epochs, thereby increasing the total accuracy by 4–5%.
Enterprise Cloud Native Automation

Software engineering’s main objective is to unify the development of software (Dev) and subsequent operations (Ops), and it is this combination of cultural values, practices, and tools that allow an organization to deliver software applications quickly.
Keynote 1: Compute Express Link (CXL) changing the game for Cloud Computing

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. High-performance workloads demand heterogeneous processing, tiered memory architecture, persistent memory support, infrastructure accelerators such as smart NICs, and infrastructure processing units to meet the demands of the emerging compute landscape. Applications such as Artificial Intelligence, Machine Learning, Analytics, 5G, automotive, and high-performance computing are driving significant change in infrastructure such as Cloud, Edge, and client computing. Interconnect is a key pillar in this evolving computational landscape. The recent advent of Compute Express Link (CXL), a new open standard for cache-coherent interconnect, with its memory and coherency semantics has made it possible to pool computational and memory resources at the rack level using low-latency, higher-throughput, and memorycoherent access mechanisms. CXL is adopting networking features such as multi-host connectivity, pooled memory, persistence flows, and fabric manager while keeping its low-latency load-store semantics intact. The load-store I/O interconnects such as PCI Express (PCIe) and CXL are evolving to provide efficient access mechanisms across multiple nodes with advanced atomics, acceleration, smart NICs, persistent memory support etc. In this talk we will explore how synergistic evolution across load-store interconnects and fabrics can benefit the compute infrastructure of the future.
Improving The Quality Of Education - A Strategic Framework For Sustainable Development

The key role of education in achieving the 17 sustainable development goals identified by the UN for the period up to 2030 as the most important factors in transforming the world is considered. At the same time, improving the quality of education stimulates the development of the knowledge economy branches, that provides decisive competitive advantages in modern socio-economic processes. The authors show the choice of public priorities on the example of the development and implementation of the Strategy for the economic and social development of St. Petersburg for the period up to 2035. One of the most important priorities is the development of education to ensure sustainable development of the city's economy, increase the competitiveness of St. Petersburg in Russia and on foreign markets through the purposeful formation and accumulation of intellectual capital. A comparative analysis of the state of the knowledge economy branches in Russia and a number of other countries is presented, the activities of the UNESCO Chair "Quality Management in Education for Sustainable Development" at Peter the Great St. Petersburg Polytechnic University on conducting research in accordance with the global goals of UNESCO and Russia's national priorities in the field of improving the quality of education are shown.
Dynamic Multi-Goal Motion Planning with Range Constraints for Autonomous Underwater Vehicles Following Surface Vehicles

Autonomous underwater vehicles (AUVs) are often required to reach multiple goal locations while staying within the communication range of a surface vehicle. The goals, which could be dispersed throughout the environment, are dynamically discovered by the surface vehicle as it moves along a predefined trajectory. As the goals are discovered, they are communicated to the AUV. This paper develops an efficient multi-layered planner that generates collision-free and dynamically-feasible trajectories that enable the AUV to reach as many goals as possible while always staying within the communication range of the surface vehicle. The planner relies on a roadmap to capture the connectivity of the environment in order to facilitate navigation. The high-level layer is based on discrete search to find paths over the roadmap that increase the sum of the rewards to the known goals while maintaining the range constraints. The low-level layer relies on sampling-based motion planning to expand a tree of feasible motions along paths computed by the discrete layer. These layers interact with each other to update the planned motions as new goals are dynamically discovered. Experiments using 3D environments, second-order AUV models, and an increasing number of goals, demonstrate the efficiency of the planner to solve dynamic multi-goal motion-planning problems with range constraints.
Quantum Computation - A Sign of Quantum Supremacy

Quantum computation as the name suggests is the use of quantum phenomena to perform computational tasks. As predicted by Richard Feynman, an extraordinarily brilliant physicist of the 20th century, quantum computers can do many tasks that an ordinary computer cannot. In this paper, an attempt is made to obtain the electronic energy levels of hydrogen molecule and lithium hydride by employing quantum algorithm based on variational principle. An introduction to basic ideas of quantum computation and qubit Hamiltonian for these molecules are summarized. It is a common misconception that the term ‘quantum’ is associated with ‘complicated’, and complex mathematical equations and hence cannot be learnt easily. The objective of this paper is to eliminate this misconception and that any quantum phenomenon can be understood with very little mathematics and very few equations.
Pixel Difference Networks for Efficient Edge Detection

Recently, deep Convolutional Neural Networks (CNNs) can achieve human-levelperformance in edge detection with the rich and abstract edge representationcapacities. However, the high performance of CNN based edge detection isachieved with a large pretrained CNN backbone, which is memory and energyconsuming. In addition, it is surprising that the previous wisdom from thetraditional edge detectors, such as Canny, Sobel, and LBP are rarelyinvestigated in the rapid-developing deep learning era. To address theseissues, we propose a simple, lightweight yet effective architecture named PixelDifference Network (PiDiNet) for efficient edge detection. Extensiveexperiments on BSDS500, NYUD, and Multicue are provided to demonstrate itseffectiveness, and its high training and inference efficiency. Surprisingly,when training from scratch with only the BSDS500 and VOC datasets, PiDiNet cansurpass the recorded result of human perception (0.807 vs. 0.803 in ODSF-measure) on the BSDS500 dataset with 100 FPS and less than 1M parameters. Afaster version of PiDiNet with less than 0.1M parameters can still achievecomparable performance among state of the arts with 200 FPS. Results on theNYUD and Multicue datasets show similar observations. The codes are availableat https://github.com/zhuoinoulu/pidinet.
The Impact of Building Core Capabilities in Development of Job Performance (An Applied Study on the Iraqi Tourism Authority)

The researchers aim through the current research through their investigations to verify the relationship and impact of the core capabilities as an independent variable in the development of functional performance as a responsive variable, in the tourism authority of the city of Baghdad, and to try to come up with a set of recommendations that contribute to the promotion of the practice and adoption of the two variables in the body under study, and based on the importance of the subject to the research community and its fundamental impact in their activities and services, has been based on the descriptive analytical approach in the completion of this research, including the research community (from the Director General and his assistants and heads of state). Departments, department heads and people's officials in the Tourism Authority) where the researchers distributed (119) the identification of the research community, and provided them with interviews and field observations as tools to strengthen them. the research adopted the program (Spss V.25, Amos V.25) with the adoption of descriptive and inference statistics methods (linear testing, natural distribution test, emphasis analysis, structural model, computational medium, percentages, The standard deviation, relative importance, the coefficient of variation, the Pearson correlation coefficient, the simple regression coefficient, the track analysis, and the Sobel test) concluded to test its hypotheses, and the study concluded to highlight its conclusions that showed the validity of the hypotheses represented by the direct impact of the intrinsic capabilities in the development of functional performance.
User Satisfaction Reward Estimation Across Domains: Domain-independent Dialogue Policy Learning

Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years. While most work that is based on reinforcement learning employs an objective measure like task success for modelling the reward signal, we propose to use a reward signal based on user satisfaction. We propose a novel estimator and show that it outperforms all previous estimators while learning temporal dependencies implicitly. We show in simulated experiments that a live user satisfaction estimation model may be applied resulting in higher estimated satisfaction whilst achieving similar success rates. Moreover, we show that a satisfaction estimation model trained on one domain may be applied in many other domains that cover a similar task. We verify our findings by employing the model to one of the domains for learning a policy from real users and compare its performance to policies using user satisfaction and task success acquired directly from the users as reward.
A Study of Fairness Functionals for Smooth Path Planning in Mobile Robots

Smoothness of mobile and vehicle navigation has become relevant to ensure the safety and the comfortability of riding. The robotics community has been able to render smooth trajectories in mobile robots by using non-linear optimization approaches and well-known fairness metrics considering the curvature variations along the path. In this paper, we evaluate the possibility of computing smooth paths from input reference trajectories by using higher order non-linear fairness functionals. Our approach is potential to enable the generation of simple and computationally-efficient path planning and smoothing for navigation in mobile robots.
The Review of Image Processing Based on Graph Neural Network

Convolutional neural networks have ushered in significant advancements in the field of image processing. Convolutional neural networks, on the other hand, operate well with European geographic data, whereas graph neural networks function better with non-European geographical data. This paper summarizes the application of image processing based on graph neural network. First, this article introduces the development history of graphs and graph neural networks and then explains the graphs’ concept and structure. Secondly, it focuses on the graph convolutional neural network, and details the graph convolutional neural network of spectrum and space. Finally, for graph convolutional neural network application in image processing, three processing methods are concluded.
A New Electronic System for Comparative Analysis of Verse and Prose

This paper will focus on the development of a new computational system, Prosimetron, which enables comparative statistical studies of the rhythm of verse and prose in different languages (currently 10 languages are operative, with the possibility of adding more).The results of the analysis can be used not only for studying the processes for the genesis, expansion, and modification of various versification systems, but also for commenting on and interpreting the verse rhythm in different national poetic traditions in comparison with their foreign sources and language prosody.In addition, the possibility to model various processes of poetic speech generation and to analyze rhythmic vocabularies of prose allows hypotheses about the cognitive mechanisms of verse generation.This system operates in a semiautomatic mode and, by minimizing errors and enabling the processing of large amounts of data, provides a unique tool for computer research on the rhythm of different modes of speech.
The development of the Open Machine-Learning-Based Anti-Spam (Open-MaLBAS)

Spam e-mails are unsolicited e-mails received by users of the e-mail service. Spam e-mails cause serious harm to organizations, for they waste, among other things, their computational and networking resources. To reduce the damage caused by them, organizations use anti-spams. Anti-spams are software systems that classify e-mails in order to separate legitimate from spam e-mails. The best current commercial and open-source anti-spams, and in particular the well-known commercial anti-spam CanIt-PRO, make use of various techniques, such as blacklists and/or SMTP extensions, to classify e-mails. Unfortunately, both blacklists and SMTP extensions have serious drawbacks, such as low scalability and high computational and network costs. This paper introduces the Open Machine-Learning-Based Anti-Spam (Open-MaLBAS). Unlike the best current anti-spams, Open-MaLBAS does not make use of blacklists and SMTP extensions, but only of machine learning models for e-mail classification. Open-MaLBAS was compared to CanIt-PRO in a series of experiments on a database composed of 862,227 real e-mails, collected over three months at the Federal University of Itajubá, Brazil. The e-mails were previously classified by CanIt-PRO. From the experiments, it was observed that Open-MaLBAS was able to correctly classify 81.48% and 98.13% of the e-mails in the database, using, respectively, the two models — Multi-Layer Perceptron and Random Forest — evaluated. In addition, it managed to obtain times of up to 88% shorter than those of CanIt-PRO to classify all e-mails in the database. Open-MaLBAS is implemented in Java language, under free software license, for free use. It is available on GitHub.
A neural network for underwater polarization dehazing imaging

The underwater polarization dehazing imaging has attracted a lot of interest due to the potential applications in corresponding fields. There are some progress on the underwater polarization dehazing imaging by introducing the deep learning into polarization dehazing imaging. In this work, the underwater active polarization dehazing imaging based on the deep learning model is studied. A modified All-in-One Dehazing Network model with three input channels is designed under the framework of TensorFlow. The polarization image data of three different polarization components are designed as the training set with the convolution neural network (CNN).This light-weight CNN is designed to achieve underwater dehazing imaging of different targets with different turbidity. Experiment results indicate that the prediction and estimation using modified AOD-Net have better accuracy than that of the traditional dehazing model.
Wavelet Modeling of Control Stochastic Systems at Complex Shock Disturbances

This article is devoted to the development of methodological supports and experimental software tools for accuracy analysis and information processing in control stochastic systems (CStS) with complex shock disturbances (ShD) by means of wavelet Haar–Galerkin technologies. Basic new results include methods and algorithms of stochastic covariance analysis and modeling on the basis of the Galerkin method and wavelet expansion for linear, linear with parametric noises, and quasilinear CStS with ShD. Results are illustrated by an information-control system at ShD. New stochastic effects accumulation for systematic and random errors are detected and investigated.
A Multi-Cache System for On-Chip Memory Optimization in FPGA-Based CNN Accelerators

In recent years, FPGAs have demonstrated remarkable performance and contained power consumption for the on-the-edge inference of Convolutional Neural Networks. One of the main challenges in implementing this class of algorithms on board an FPGA is resource management, especially with regard to memory. This work presents a multi-cache system that allows for noticeably shrinking the required on-chip memory with a negligible variation of timing performance and power consumption. The presented methods have been applied to the CloudScout CNN, which was developed to perform cloud detection directly on board the satellite, thus representing a relevant case study for on the edge applications. The system was validated and characterized on a Xilinx ZCU106 Evaluation Board. The result is a 64.48% memory saving if compared to an alternative hardware accelerator developed for the same algorithm, with comparable performance in terms of inference time and power consumption. The paper also presents a detailed analysis of the hardware accelerator power consumption, focusing on the impact of data transfer between the accelerator and the external memory. Further investigation shows that the proposed strategies allow the implementation of the accelerator on FPGAs with a smaller size, guaranteeing benefits in terms of power consumption and hardware costs. A broader evaluation about the applicability of the presented methods to other models demonstrates valuable results in terms of memory saving with respect to other works reported in the literature.
Unadorned Gabor based Convolutional Neural Network Overrides Transfer Learning Concept

The efficiency of Convolutional Neural Networks (CNN) is highly influenced by the size of dataset.To train CNN systems from the scratch, dataset of very large size is essential.Transfer learning concept was introduced to overcome this deficiency of CNN.Even though in recent years transfer learning networks has attained high popularity, the adaption of transfer learning networks to entirely different dataset would produce very low recognition results.In this work a Gabor based CNN network is introduced which works highly efficient compared to transfer learning networks.Another deficiency of CNN is that it is not robust to rotation.Even though the notion of Gabor filter being induced in CNN has been suggested earlier, this work introduces an entirely different and very simple Gabor based CNN which produces high recognition efficiency in dataset of very small size and works invariant to rotation.This work would shed new light in deep Learning research where researchers are forced to focus on and build highly complex CNN network structures.Small subset of MNIST dataset with rotated images is used to learn the proposed architecture.