Reviewerly/exHarmony huggingface dataset:
Practical Dense-to-Sparse Learning for a Manageable Object Detector on the Ground

This paper presents how to construct a manageable neural network in terms of architecture and parameters with less accuracy loss to guarantee feasible model deployment on small footprint devices. We thus propose a practical dense-to-sparse learning method of using architecture rescaling and channel sparsification. From a dense detector with 7.2M parameters, we achieve promising sparse models with a compression rate of 81x-300x and an accuracy drop of 5.03%-21.26%. Our experimental result shows a high possibility of producing various variants of sparse networks for object detection.
A model-driven middleware approach to reduce the semantic gap between application domains and the generic infrastructure of smart cities

In Smart Cities, the city’s IT infrastructure must facilitate the development, deployment and operation of smart city applications for a number of different application domains that need to access a variety of heterogeneous resources. Building these applications becomes a great challenge since: they are used on large scale systems, have dynamic behavior, must access heterogeneous resources, can share resources from different domains, and must deal with the semantics of different domains. In this sense, platforms are commonly used to support application development, but they do not reduce complexity, are generic and deal with resource abstractions, while applications deal with domain abstractions. To overcome this semantic gap, this work proposes the use of domain specific middleware (DSM) with support for the development of model-driven applications defined in terms of a domain specific modeling language (DSML). To facilitate the definition of languages in different domains and, consequently, the execution of applications built according to these languages, a meta-meta language was defined that wraps the generic concepts used for the management of several domains. Furthermore, operational semantics are represented by models that describe domain-specific operations. The set of these elements associated with a middleware built according to the model-driven approach is defined as Model-Driven Domain-Specific Middleware (MDDSM). Therefore, this work proposes an integrated architecture that combines the use of MD-DSM and a smart cities platform to provide a flexible, robust and complete approach to facilitating the creation and execution of applications for different smart city domains. Thus, in the context of Smart Cities, middleware must deal with both application domain-related abstractions and platform-related abstractions (resources). Furthermore, the use of the model-driven approach allows representing such abstractions and relating domain semantics to generic resources. As a result, we have the construction of middleware and related elements in different domains and associated with a smart cities platform, demonstrating the expressiveness of the proposed approach.
Open Domain Question Answering Based on Retriever-Reader Architecture

Open-domain Question Answering (OpenQA) is a trendy research hotspot in the field of Natural Language Processing (NLP). In the past few years, with the in-depth research on Deep Learning (DL) and Pre-training Language Modules (PLM), the research on OpenQA has made rapid progress. OpenQA aims to find relevant documents from a large-scale text corpus and then extract or generate correct answers. In this paper, we introduce the development and research status of OpenQA based on “Retriever-Reader.” Firstly, we briefly expound on the relevant theories and traditional architecture. Then the function and category of the Retriever and Reader and the related existing systems are analyzed. Retriever can be regarded as an Information retrieval (IR) system. The reader can be viewed as a machine reading comprehension system (MRC). The fourth section gives a relatively simple introduction to the latest end-to-end training methods and their related types. Finally, we summarize the content of the paper and look forward to the future development trend.
Multi-Aspect Heterogeneous Graph Convolutional Network for Recommendation

Graph convolutional networks (GCN), aiming to learn meaningful representations for graph data, has been popularly used in recommender systems since user-item interactions can be represented by a bipartite graph. However, GCN often suffers from the over-smoothing issue when it goes deeper, which implies that long paths between users and items are not very useful to embedding learning in recommender systems. To deal with long paths, introducing user and item links is a promising way to shorten the average path between users and items. However, due to introducing the user and item linkages, the bipartite graph becomes a heterogeneous graph. To this end, we propose a multi-aspect heterogeneous GCN model, which is to power GCN towards recommendation tasks by introducing user-user and item-item links to build the heterogeneous user- item graph. Then, we decompose it into three subgraphs and keep the global heterogeneous graph to preserve the original structure information. Specifically, we apply GCN to learn user and item representations from both the subgraphs and the global graph. After that, we have multi-aspect attention to integrate the representations from GCN to obtain the final user and item representation. Extensive experiments on three real-world datasets show that the proposed approaches achieve significant improvements over state-of-the-art methods.
Semantic Segmentation based on Extended MobileNet with FPN

Recently, the development of Semantic Segmentation models has been actively pursued. Among them, many models are divided into Backbone module (encoder) and Head module (decoder). Feature Pyramid Network (FPN) is one of the methods to improve the accuracy. This is a module that performs feature extraction at multiple resolutions. By adding the FPN between the Backbone and the Head, we can perform fine segmentation of objects of various sizes. However, when FPNs are added to the segmentation model, the amount of computation required for prediction becomes huge. To solve this problem, we propose Reshaped Feature Pyramid Network (RFPN), which is a lightweight version of FPN. Furthermore, we propose an architecture for Segmentation that adds RFPN between Backbone and Head. In this architecture, we use MobileNet, which is a lightweight and highly accurate CNN, for the Backbone. In addition, we adopt Pyramid Pooling Module, which performs average pooling with various sizes, for the Head. The experimental results using PASCAL VOC 2012 show that the proposed method turns out to be a good trade-off between accuracy and computational time in terms of mean IoU and FLOPs, respectively.
Constraint reformulations for set point optimization problems using fuzzy cognitive map models

Abstract The selection of optimal set points is an important problem in modern process control. Fuzzy cognitive maps (FCMs) allow to construct models of complex processes using expert knowledge, which is particularly useful in situations where measuring the variables of interest online is problematic. These models can be used as constraints in optimization problems with the objective of determining optimal set points for those processes. This article presents a reformulation of the constraints imposed by the FCM models that reduces the complexity of the resulting optimization problem and enables the application of heuristic methods for its solution. Computational results show that the use of separable programming on the reformulated problem constitutes a very good alternative, both in terms of solution time and reliability in finding the optimum, enabling the application of FCM modeling to larger systems and easing the practical implementation of the approach.
Research on short video recommendation algorithm based on social network

According to the rapid development of the technological age and the rapid advancement of databases, it will be very difficult to find out the information they need in the social application and also consume a lot of time. Therefore, human-created Recommendation System. Large and small video firms, such as Douyin, Instagram, and Facebook, employ the recommendation system to allow everyone to browse their favorite material. However, as time passes and more people's needs change, large data makes it harder to convey really useful information to users, and major issues such as information overload have arisen regularly. There are currently only two solutions. The first is search engines, which are a highly prevalent information retrieval method, and the second is recommendation systems, which are the most effective information filtering technology. In the background of short video software, the Collaborative Filtering Algorithm is the most used recommendation system.
Covid Best Practices for Cyber Risk Management

Abstract If we learned anything from the year 2020, it is that we need to be more prepared for the unexpected. We need to be working to enable our business to be more resilient in the face of unexpected challenges. We strongly believe that for the industrial sector, the most effective way to enable resiliency is to ensure you have integrity in your operational technology (OT). The objective of this paper is to identify and manage the risk that arose from managing plants remotely. As a result of COVID-19, people started working and managing from home. While this needed to be done to keep businesses running, many risks were introduced as well. How to manage them effectively to reduce cyber risk to an acceptable level will be discussed. Industrial frameworks to identify security gaps, and thus risk, were considered, such as ISA-99/IEC-62443, NIST, ISO-27001, and Top CIS controls. New practices critical infrastructure followed to reduce infection rates were identified from interviews and surveys conducted by PAS, part of Hexagon, of our customers who work with critical infrastructure. These new practices were then compared to the industrial risk management framework to identify the severity of the threats. Once these were identified, mitigation plans were recommended to reduce the risk to an acceptable level. Because of this rapid shift to run the plant remotely, there was an over-provisioning of access in the early stages of the pandemic – i.e., giving more direct access to the industrial control system environment. This was not wise from a security standpoint, but the priority was to keep businesses up and running, so they were ready to take that risk. Now that some organizations have decided to continue with remote work, it is imperative to verify all remote access considers the least privileged access concept. Remote access is like a bridge that bypasses all the controls implemented. Having a remote access vulnerability will help bad actors break into the network and cause catastrophic damage. Though this paper focuses on remote access risk introduced by the COVID-19 pandemic, you can apply the findings to all remote access into critical infrastructure.
Assembling a Reducer Using VR

This project develops a virtual reality (VR) application for training in the assembly and disassembly of a mechanical speed reducer. Another important concept to work on is the idea of enhancing and improving spatial skills in terms of spatial vision and relations by rotating figures in space. This assembly procedure, together with other training activities included in the same application, will allow students to become familiar with the process of assembling mechanical units. In this case, the procedure was developed in a virtual environment in which the assembly of a speed reducer can be carried out step by step, so that the student is able to understand the process to be followed and put knowledge into practice. In addition, the application will help the user to understand how the mechanism works once it is assembled. To achieve all this, Unity was used, along with various design and 3D modelling tools.
Feed-Forward Neural Network for Graphical Symbol Recognition in Additive Noise Environment

This article proposes a feed-forward backpropagation neural network architecture for the automatic recognition of graphical symbols in the presence of high-level additive Gaussian noise based on machine learning (ML). The article presents the comparison of two different backpropagation training algorithms and presents numerical results which compare the performances of a Levenberg-Marquardt backpropagation algorithm and One step secant backpropagation algorithm. The procedures of two-layer neural network architecture creating and training are designated in detail. The neural network is depicted in MATLAB and Simulink environment [1].
The Use of Speech Recognition Systems to Select a Useful Signal in Noisy Speech at a Low Signal-To-Noise Ratio

The article considers two commonly used speech recognition systems &#x2013; Yandex Speech. Kit and Google Speech Recognition. A comparison of these systems is carried out for the recognition of noisy speech at different signal-to-noise ratios. Noise is superimposed on speech at a level of 65 dB, differing from it in level from 20 dB to -5dB with a step of 5 dB. It is noted that preliminary training of the speech recognition system is not carried out; the voices of four speakers with a total recording duration of 100 minutes are used in the experiment. It has been experimentally revealed that at a low signal-to-noise ratio, speech recognition systems do not cope well enough, therefore, they need to be improved in order to have an acceptable recognition coefficient at a low signal-to-noise ratio.
Image Stabilization Based on Panoramic Dense Optical Flow Matching for Camera on Flapping Wing

Image stabilization is a precursor to visual navigation for the flapping wing. Flapping wing is a flexible and efficient bionic aircraft which can switch flight mode freely. It can fly as flexible as rotorcraft and can also be as efficient as fixed-wing. However, violent drift is generated among the image sequence due to the reciprocating periodic flapping of its wings, being hard to be compensated for by conventional methods. In this paper, we proposed a novel image stabilization method base on panoramic dense optical flow matching and employ the method on a self-developed ultra-light flapping wing X-FLAP which weighs only 5 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$g$</tex> . The track and vibration of X-FLAP during flight is detected by the Vicon cameras for comprehension of the source of image drift. Furthermore, the proposed method is formulated using the mathematic model of the optical flow and the particle swarm optimization algorithm (PSO). The motion matching algorithm is proposed that is based on the above model and is proven by the experimental results.
Evaluation metrics for automatically constructed concept maps

Concept maps are knowledge visualization tools that allow representing the text or domain at a conceptual level. They reflect the systemic relations between the key concepts of the text and thereby contribute to a deeper understanding of its ideas, save time spent on reading and analysis. However, the very process of creating concept maps is laborious and time-consuming. At the same time, with the rapid growth of digital reading services, the automatic construction of concept maps attracts an increasing intensive research. Against that background, comparison and evaluation of methods for automatic construction of concept maps are of great importance. In this paper, we discuss popular evaluation metrics for automatically created concept maps and propose our new metric based on network centrality analysis. We test all the considered metrics by comparing an automatic concept map with a reference concept map developed manually by experts. Experiments show that our proposed metric complements existing metrics by providing information about significance degrees of concepts and relations.
Evaluating the Interoperability of C4ISR System using Cyber Six-ware Framework

Some scientists say that there are many difficulties in assessing the development of interoperability in command, control, communication, computer, intelligence, reconnaissance, and surveillance systems (C4ISR interoperability). What are the difficulties? The often encountered problem is determining the dominant factor in developing C4ISR with different platforms from various defense equipment manufacturers used by each military domain. It is an interoperability discourse that is quite challenging to study continuously according to the surrounding technology development. This study proposes a cyber-sixware framework based on six attributes: Brainware, hardware, software, infrastructure-ware, firmware, and budget-ware. In this study, we ask two questions: What are the dominant factors in developing C4ISR related to interoperability experts have studied in the last five years? What are the primary considerations in developing C4ISR related to operability issues at LEN Corp? To answer, we designed mixed-method research. Firstly, we did a systematic literature review to gather and focus the materials for questionnaires and interviews. Secondly, we conducted data collection via an “online world cafe” that involved 26 experts in two rounds of data collection. In the discussion, we designed a 60-item questionnaire based on a cyber-sixware framework with Cronbach-alpha 0.96. Thirdly, we work on data analysis to formulate conclusions and a recommendation. We analyze the data using text analysis and linear regression to predict the most influential factors of C4ISR interoperability. The findings of this study indicate that the focus of expert research in the field of C4ISR interoperability in the last five years has been on infrastructure, hardware, and brainware. Meanwhile, the significant strength of C4ISR interoperability development at LEN Corp is in software and firmware. Based on the result, we recommend LEN Corp development steps to continue emphasizing the strength of “software” besides firmware, brainware and overcome the dominant weakness, namely budgetware.
Dyn-GCN: Graph Embedding via Dynamic Evolution and Graph Convolutional Network for Personal Recommendation

Graph convolutional network (GCN) and dynamic evolutionary model are the mainstream collaborative filtering technologies in recent years. Nevertheless, the initial feature vectors selection problem of the existing recommendation algorithms based on dynamic evolutionary models leads to unstable recommendation accuracy. In addition, the collaborative filtering method of GCN does not take into account the dynamic evolution law of graph networks. Based on this, this research adopts GCN to train the initial embedding of the dynamic evolution model to perform collaborative filtering recommendation. First of all, a heterogeneous graph network is constructed by applying explicit feedback information (rating scores) of users. Secondly, the embedding of users and items are propagated through the dynamic evolution model. Then, the final embedding is obtained by weighting the embedding of each layer, and the scores are predicted. Finally, according to the Adam optimizer, the initial embedding of the dynamic evolution model is trained in the form of mini-batch to minimize the loss function. Experimental results show that the proposed algorithm is superior to several compared excellent algorithms in recommendation performance.
Simulation of Remote Control of a Hexacopter with Force Feedback in Semi-structured Environments

This project simulates the flight of hexacopters in indoor environments. The design, modeling and implementation of the environments mimic semi-structured environments with static obstacles, such as houses, churches and supermarkets. Each environment presents different levels of difficulty. The application offers the possibility to choose between two hexacopter models, achieving a higher assessment and interaction. It includes the force feedback option, implemented with a force feedback joystick, warning the user of possible collisions, and giving the operator a sense of presence. The haptic device limits the force applied. The application focuses on three-dimensional scenes in semi-structured indoor environments, reproducing the environmental variables (appearance, features, context) of a real system.
Exchanged electricity paths calculation in integrated and sustainable power systems using topological sort

In integrated and sustainable power systems, renewable energies (RE) required to satisfy the electricity demand would be generated in countries or areas with high resources, and exported to other ones. These RE productions and exchanges occur on a 24/7 basis for dozens of power systems at a time and, thus, constitute a high amount of data. Analyzing this big data and extracting out useful information require computing tools.In this work, we present a calculation model that determines power exchanges paths between areas of an integrated and sustainable power system. This would serve for many applications such as tracking power transactions, detecting grid anomalies or estimating energy costs.The model is based on the mathematical concept of topological sort. The implemented algorithm is applied to a test system and results are then exposed and discussed.
Fashion Recommendation System Reflecting Individual’s Preferred Style

The type of fashion style that people prefer varies from person to person, and the classification of fashion styles often differs from person to person as well. Against this background, many methods have been proposed that generate different outfit images from a given outfit image. However, previous methods focus on the diversity and compatibility of the generated images, and often fail to reflect individual preferences. The purpose of this paper is to develop a system which outputs outfit images with partially modified outfit of the input image according to the user’s preferred style. The generation of an outfit image with some changes in the outfit of the input image is achieved by extracting and updating the features of the color, texture and shape of the clothes from the original image and its segmentation mask. To classify images into the style of the individual’s preference, we create the user’s original dataset to learn the user’s preference in advance by asking the user to classify a group of images into 4 styles. We conducted an evaluation experiment of our system to confirm that our system reflects user’s individual preference. As a result of the evaluation experiment, it was confirmed that the same image was recommended as different styles for different users, and that the users also thought that the recommended style was match to the style that the user’s classification of the style. There are some challenges related to the bias of the data used and the learned model used to generate the images. Therefore, it is expected that improving these points will result in a system with higher recommended accuracy.
"Insight: A co-design approach to understanding energy on a university campus "

Cultivating sustainability is a complex concept that shifts beyond the technological focus of eco-efficiency, to engage with social and cultural practices.These social and cultural practices are difficult to define, and are challenging to approach through traditional design processes.A microcosm of this challenge is in reducing the energy consumption of institutional buildings.Here, a tension exists between the building as a technological system, and its performance as a site for human activity.This paper presents a co-design process that brought together a complex group of stakeholders to collaboratively explore understandings, representations, and physicalisations of 'energy consumption'.The results of this process reveal some of the key strengths of the co-design process and provide insights into the ways in which different ontological and epistemological positions can be surfaced through speculative, publicly accessible data visualisation concepts as a catalyst for creative discovery.