Reviewerly/exHarmony huggingface dataset:
On the Effect of Training Convolution Neural Network for Millimeter-Wave Radar-Based Hand Gesture Recognition

The purpose of this paper was to investigate the effect of a training state-of-the-art convolution neural network (CNN) for millimeter-wave radar-based hand gesture recognition (MR-HGR). Focusing on the small training dataset problem in MR-HGR, this paper first proposed to transfer the knowledge with the CNN models in computer vision to MR-HGR by fine-tuning the models with radar data samples. Meanwhile, for the different data modality in MR-HGR, a parameterized representation of temporal space-velocity (TSV) spectrogram was proposed as an integrated data modality of the time-evolving hand gesture features in the radar echo signals. The TSV spectrograms representing six common gestures in human–computer interaction (HCI) from nine volunteers were used as the data samples in the experiment. The evaluated models included ResNet with 50, 101, and 152 layers, DenseNet with 121, 161 and 169 layers, as well as light-weight MobileNet V2 and ShuffleNet V2, mostly proposed by many latest publications. In the experiment, not only self-testing (ST), but also more persuasive cross-testing (CT), were implemented to evaluate whether the fine-tuned models generalize to the radar data samples. The CT results show that the best fine-tuned models can reach to an average accuracy higher than 93% with a comparable ST average accuracy almost 100%. Moreover, in order to alleviate the problem caused by private gesture habits, an auxiliary test was performed by augmenting four shots of the gestures with the heaviest misclassifications into the training set. This enriching test is similar with the scenario that a tablet reacts to a new user. The results of two different volunteer in the enriching test shows that the average accuracy of the enriched gesture can be improved from 55.59% and 65.58% to 90.66% and 95.95% respectively. Compared with some baseline work in MR-HGR, the investigation by this paper can be beneficial in promoting MR-HGR in future industry applications and consumer electronic design.
Aplikasi Pengarsipan Surat Masuk dan Surat Keluar Berbasis Web pada SMP Negeri 32 Pekanbaru

Archiving incoming and outgoing mail is very important in an organization, especially for institutions such as SMP Negeri 32 Pekanbaru. The filing of letters at SMP Negeri 32 Pekanbaru is still done by writing incoming and outgoing letters on the agenda book and storing letters in filing cabinets, making it difficult to find old letter archives and often losing letters. The purpose of this research is to find the right solution so that the archiving of incoming and outgoing mail at SMP Negeri 32 Pekanbaru is more effective and efficient by making an application for archiving incoming and outgoing mail. The method used in this research is the system development model method, namely the Waterfall Model. The application design and analysis model uses the UML (Unified Modeling Language) model which is an object-oriented language or OOP (Object Oriented Programming). Application development and development uses a static programming language, namely PHP (Hypertext Pre-processor) and MySQL as application database. The results of the research on the making of incoming and outgoing mail archiving applications are very helpful and easier for SMP Negeri 32 Pekanbaru in filing incoming and outgoing mail, especially the Administration (School Administration) section because letter archiving is already stored in the database. The conclusion is that the application is very easy and helpful in archiving incoming mail and this letter is evident from the user's assessment of the application with a value of 92% more effective and efficient.
TransQuest at WMT2020: Sentence-Level Direct Assessment

This paper presents the team TransQuest’s participation in Sentence-Level Direct Assessment shared task in WMT 2020. We introduce a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures. The proposed methods achieve state-of-the-art results surpassing the results obtained by OpenKiwi, the baseline used in the shared task. We further fine tune the QE framework by performing ensemble and data augmentation. Our approach is the winning solution in all of the language pairs according to the WMT 2020 official results.
Organization and methodology for inter-disciplinary training for functional literacy in the digital economy

The advent of the digital economy and new technologies requires new human competencies, including specifically, inter-disciplinary competencies. The current education system is organized around subjects and more relevant inter-disciplinary training requires new organizational and methodological foundations. There is a need to develop functional literacy, which implies the ability of the individual to adapt and continuously develop in a dynamic and complex world, to initiate, and to implement innovations using knowledge from different industries. The aim of this research was to develop and implement a system of organizational and methodological tools for inter-disciplinary learning as a means of developing functional literacy of children and adolescents in general education organizations within Russia to help them adapt successfully to the digital economy. The research used analysis of modern theoretical and methodological approaches and practices to the organization of inter-disciplinary training in educational organizations, from observation, focus groups and experiments. Part of that functional literacy is the concept of mathematical literacy. In addition to developing cross-subject tasks, mathematical literacy was analyzed through mathematical content and mental activity. The article describes the authors' experience in developing cross-subject tasks to develop research competence, motivation for knowledge and personal development, and mathematical literacy of students in grades 5-9 (similar to the PISA-2021 research model). It then describes the methodology and foundations for creating competence-oriented cross-subject tasks and the development of functional literacy. These are added to specific, proven tools for improving the effectiveness of inter-disciplinary learning. This creates an environment and conditions for the development of a person who can think creatively using the competencies of the inter-disciplinary complex. The article will be of interest to a wide audience of specialists in the field of educational policy and practice.
Hybrid ecommerce recommendation model incorporating product taxonomy and folksonomy

In modern ecommerce platforms, product content information may have two origins: one is tree-structured taxonomy attributes, and the other is free-form folksonomy tags. This paper proposes a hybrid model to incorporate taxonomy and folksonomy information to enhance ecommerce recommendations. It first develops a tree matching algorithm to establish the overall similarity between items, where tag information is integrated for semantic analysis for taxonomy attributes. Next, it proposes a unique random walk model on a heterogeneous graph constructed by user nodes and item nodes and different types of relations — user–item preference and item–item similarity relations. The random walk model is designed to be effective to identify the nearest item nodes for a particular user node, which are seen as the best-fit items for recommendations. Empirical experiments demonstrate that the proposed model improves performance in terms of both recommendation coverage and accuracy, especially for sparse data.
Facial Recognition Technology

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Semantic-based Distance Approaches in Multi-objective Genetic Programming

Semantics in the context of Genetic Program (GP) can be understood as the behaviour of a program given a set of inputs and has been well documented in improving performance of GP for a range of diverse problems. There have been a wide variety of different methods which have incorporated semantics into single-objective GP. The study of semantics in Multi-objective (MO) GP, however, has been limited and this paper aims at tackling this issue. More specifically, we conduct a comparison of three different forms of semantics in MOGP. One semantic-based method, (i) Semantic Similarity-based Crossover (SSC), is borrowed from single-objective GP, where the method has consistently being reported beneficial in evolutionary search. We also study two other methods, dubbed (ii) Semantic-based Distance as an additional criterion (SDO) and (iii) Pivot Similarity SDO. We empirically and consistently show how by naturally handling semantic distance as an additional criterion to be optimised in MOGP leads to better performance when compared to canonical methods and SSC. Both semantic distance based approaches made use of a pivot, which is a reference point from the sparsest region of the search space and it was found that individuals which were both semantically similar and dissimilar to this pivot were beneficial in promoting diversity. Moreover, we also show how the semantics successfully promoted in single-objective optimisation does not necessary lead to a better performance when adopted in MOGP.
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning

The combination of Evolutionary Algorithms (EAs) and Deep Reinforcement Learning (DRL) has been recently proposed to merge the benefits of both solutions. Existing mixed approaches, however, have been successfully applied only to actor-critic methods and present significant overhead. We address these issues by introducing a novel mixed framework that exploits a periodical genetic evaluation to soft update the weights of a DRL agent. The resulting approach is applicable with any DRL method and, in a worst-case scenario, it does not exhibit detrimental behaviours. Experiments in robotic applications and continuous control benchmarks demonstrate the versatility of our approach that significantly outperforms prior DRL, EAs, and mixed approaches. Finally, we employ formal verification to confirm the policy improvement, mitigating the inefficient exploration and hyper-parameter sensitivity of DRL.ment, mitigating the inefficient exploration and hyper-parameter sensitivity of DRL.
Flower pollination-based K-means algorithm for medical image compression

Image compression plays a significant role in digital image storage and transmission because of limited availability of storage devices space and insufficient bandwidth and is beneficial for all multimedia applications. Magnetic resonance imaging (MRI) of a human body produces an image of huge size and is to be compressed but medical field demands high image quality for better diagnosis of disease. In this technologically advanced world, intelligence systems try to simulate human intelligence. It is applied in the field of engineering, industry, medicine and education problems and it makes decisions by using the several inputs. However, the search process is enormous and convergence time depends on algorithm structure. In this paper first time methaheuristic algorithms are used for near optimum solutions. This paper introduces flower pollination algorithm (FPA)-based vector quantisation for better image compression with better reconstructed image quality. Performance of proposed method is evaluated by using peak signal to noise ratio (PSNR), mean square error (MSE) and fitness function.
Hierarchical Reinforcement Learning by Discovering Intrinsic Options

We propose a hierarchical reinforcement learning method, HIDIO, that can learn task-agnostic options in a self-supervised manner while jointly learning to utilize them to solve sparse-reward tasks. Unlike current hierarchical RL approaches that tend to formulate goal-reaching low-level tasks or pre-define ad hoc lower-level policies, HIDIO encourages lower-level option learning that is independent of the task at hand, requiring few assumptions or little knowledge about the task structure. These options are learned through an intrinsic entropy minimization objective conditioned on the option sub-trajectories. The learned options are diverse and task-agnostic. In experiments on sparse-reward robotic manipulation and navigation tasks, HIDIO achieves higher success rates with greater sample efficiency than regular RL baselines and two state-of-the-art hierarchical RL methods. Code at: https://github.com/jesbu1/hidio.
Classification of Objects by Shape Applied to Amber Gemstone Classification

To properly and quickly evaluate an object’s shape, in a manner that is suitable for real-time applications, a set of parameters has been created and the shape parametric description (SPD) has been elaborated. This solution is focused on the classification of amber gemstones according to shape. To improve the results obtained by SPD, the most popular machine learning classification algorithms were applied and tested. The proposed method (i.e., SPD) achieved the fastest classification, requiring the least computational resources, while providing an accuracy of approximately 80%. The best results were achieved when the SPD parameters were used in a feedforward neural network (FFNN), and an accuracy of 91.5% was obtained, while the time required for the computations remained in a range that is acceptable for real-time applications.
CooPEC: Cooperative Prefetching and Edge Caching for Adaptive 360° Video Streaming

Dynamic Adaptive Streaming over HTTP (DASH) has emerged as the de facto solution for streaming 360°videos. Viewers of 360° videos view only a fraction of each video segment, i.e., the part that corresponds to their Field of View (FoV). To facilitate FoV-adaptive streaming, a segment can be divided into multiple tiles with the FoV corresponding to a subset of tiles. Streaming each segment in its entirety from the video server to a client can incur high communication overheads both in terms of bandwidth and latency. Caching at the network edge can reduce these overheads. However, as edge cache capacity is limited, only a subset of tiles encoded at a subset of supported resolutions may be present in the cache. A viewer, depending on its FoV,may experience cache hit and low download latency for some segments, and a cache miss resulting in high download latency from video server for other segments. This can result in the DASH client unnecessarily triggering quality switches for the following reason: low (high) latency download from edge cache (server, respectively) may be misinterpreted as high (low, respectively) network throughput estimate. In this paper, we propose CooPEC (COOperative Prefetching and Edge Caching), a prefetching and complementary caching solution which uses viewers' FoV entropy to: (i) enable a bitrate oscillation-free video streaming, (ii) reduce core network bandwidth consumption, and (iii) enhance QoE for users.
Mirrorlabs - creating accessible Digital Twins of robotic production environment with Mixed Reality

How to visualize recorded production data in Virtual Reality? How to use state of the art Augmented Reality displays that can show robot data? This paper introduces an opensource ICT framework approach for combining Unity-based Mixed Reality applications with robotic production equipment using ROS Industrial. This publication gives details on the implementation and demonstrates the use as a data analysis tool in the context of scientific exchange within the area of Mixed Reality enabled human-robot co-production.
Behavioral Cloning from Noisy Demonstrations

We consider the problem of learning an optimal expert behavior policy given noisy demonstrations that contain observations from both optimal and non-optimal expert behaviors. Popular imitation learning algorithms, such as generative adversarial imitation learning, assume that (clear) demonstrations are given from optimal expert policies but not the non-optimal ones, and thus often fail to imitate the optimal expert behaviors given the noisy demonstrations. Prior works that address the problem require (1) learning policies through environment interactions in the same fashion as reinforcement learning, and (2) annotating each demonstration with confidence scores or rankings. However, such environment interactions and annotations in real-world settings take impractically long training time and a significant human effort. In this paper, we propose an imitation learning algorithm to address the problem without any environment interactions and annotations associated with the non-optimal demonstrations. The proposed algorithm learns ensemble policies with a generalized behavioral cloning (BC) objective function where we exploit another policy already learned by BC. Experimental results show that the proposed algorithm can learn behavior policies that are much closer to the optimal policies than ones learned by BC.
Fast Extraction and Characterization of Fundamental Frequency Events from a Large PMU Dataset using Big Data Analytics

A novel method for fast extraction of fundamental frequency events (FFE) based on measurements of frequency and rate of change of frequency by Phasor Measurement Units (PMU) is introduced. The method is designed to work with exceptionally large historical PMU datasets. Statistical analysis was used to extract the features and train Random Forest and Catboost classifiers. The method is capable of fast extraction of FFE from a historical dataset containing measurements from hundreds of PMUs captured over multiple years. The reported accuracy of the best algorithm for classification expressed as Area Underthe receiver operating Characteristic curve reaches 0.98, which was obtained in out-of-sample evaluations on 109 system-wide events over 2 years observed at 43 PMUs.Then Minimum Volume Enclosing Ellipsoid Algorithm was used to further analyze the events.93.72% events were correctly characterized, where average duration of the event as seen by the PMU was 9.93 sec. 1
InP-Substrate-Based Quantum Dashes on a DBR as Single-Photon Emitters at the Third Telecommunication Window

We investigated emission properties of photonic structures with InAs/InGaAlAs/InP quantum dashes grown by molecular beam epitaxy on a distributed Bragg reflector. In high-spatial-resolution photoluminescence experiment, well-resolved sharp spectral lines are observed and single-photon emission is detected in the third telecommunication window characterized by very low multiphoton events probabilities. The photoluminescence spectra measured on simple photonic structures in the form of cylindrical mesas reveal significant intensity enhancement by a factor of 4 when compared to a planar sample. These results are supported by simulations of the electromagnetic field distribution, which show emission extraction efficiencies even above 18% for optimized designs. When combined with relatively simple and undemanding fabrication approach, it makes this kind of structures competitive with the existing solutions in that spectral range and prospective in the context of efficient and practical single-photon sources for fiber-based quantum networks applications.
Survey on Deep Learning Algorithms

In recent past years, deep learning algorithms are becoming more significant in object recognition, Segmentation of image, speech recognition. Deep learning also provides flexible tools for analyse and processing big data. The objective of the article is to make a survey of many deep learning algorithms which are used in emerging research. Hence, the paper focuses on three mostly used and widely applied algorithms knows as deep neural networks, convolution neural networks, recurrent neural networks. The aim is to provide better understanding of these algorithms, their efficiency, application and highlighting features.
Assessing Requirements for Agile Enterprise Architecture Management: A Multiple-Case Study

Agile enterprise architecture management (EAM) is a means to cope with the pressure for continuous architectural change in the context of short innovation cycles.However, the literature states a lack of practical EAM approaches.Therefore, this study aims at fostering the development of agile enterprise architecture frameworks (EAFs) by providing agile requirements and implementation concepts based on a literature review and an exploratory multiple-case study in three organizations of the logistics and construction tools industry.The multiple-case study's ranking by importance sets the focus on three requirements, for which implementation concepts are revealed.The gained insights make the concept and benefits of agile EAM tangible, indicate a shared understanding between the cases, and afford opportunities for future research.
Context-Based News Headlines Analysis: A Comparative Study of Machine Learning and Deep Learning Algorithms

Online news blogs and websites are becoming influential to any society as they accumulate the world in one place. Aside from that, online news blogs and websites have efficient strategies in grabbing readers’ attention by the headlines, that being so to recognize the sentiment orientation or polarity of the news headlines for avoiding misinterpretation against any fact. In this study, we have examined 3383 news headlines created by five different global newspapers. In the interest of distinguishing the sentiment polarity (or sentiment orientation) of news headlines, we have trained our model by seven machine learning and two deep learning algorithms. Finally, their performance was compared. Among them, Bernoulli naïve Bayes and Convolutional Neural Network (CNN) achieved higher accuracy than other machine learning and deep learning algorithms, respectively. Such a study will help the audience in determining their impression against or for any leader or governance; and will provide assistance to recognize the most indifferent newspaper or news blogs.
Design of a Real-Time Human-Robot Collaboration System Using Dynamic Gestures

Abstract With the development of industrial automation and artificial intelligence, robotic systems are developing into an essential part of factory production, and the human-robot collaboration (HRC) becomes a new trend in the industrial field. In our previous work, ten dynamic gestures have been designed for communication between a human worker and a robot in manufacturing scenarios, and a dynamic gesture recognition model based on Convolutional Neural Networks (CNN) has been developed. Based on the model, this study aims to design and develop a new real-time HRC system based on multi-threading method and the CNN. This system enables the real-time interaction between a human worker and a robotic arm based on dynamic gestures. Firstly, a multi-threading architecture is constructed for high-speed operation and fast response while schedule more than one task at the same time. Next, A real-time dynamic gesture recognition algorithm is developed, where a human worker’s behavior and motion are continuously monitored and captured, and motion history images (MHIs) are generated in real-time. The generation of the MHIs and their identification using the classification model are synchronously accomplished. If a designated dynamic gesture is detected, it is immediately transmitted to the robotic arm to conduct a real-time response. A Graphic User Interface (GUI) for the integration of the proposed HRC system is developed for the visualization of the real-time motion history and classification results of the gesture identification. A series of actual collaboration experiments are carried out between a human worker and a six-degree-of-freedom (6 DOF) Comau industrial robot, and the experimental results show the feasibility and robustness of the proposed system.