Reviewerly/exHarmony huggingface dataset:
Exploring Product/Part Longevity in Open Design of Small Kitchen Appliances

Emerged at the intersection of open-source, do-it-yourself and maker movements, and blurring of the lines among users, designers and producers, open design suggests a continuous process of co-designing open to everyone and demonstrates opportunities for repair, reuse and upgrading through transparent processes and design sharing. While such opportunities are conceptualized in literature, how they can (or should) be reflected in design is an open question worth exploring. This paper presents an exploratory study on product/part longevity, personalization and reuse to find out the implications of open design for transforming an already established product category like small kitchen appliances. For this purpose, research through co-designing methodology was developed and utilized through two design workshops on practices shaped around small kitchen appliances. The study revealed sustainable design considerations for idea-generation, open part properties that respond to them, the strategies for iterating open designs and their implications for product/part longevity, personalization and reuse.
Isea: An Interesting Application of Chemistry Education Based on AR

The project bases on the research of a mobile device application, integrating “AR” technology and elementary education, in order to forge an intuitive teaching and motivate the student-oriented learning. Firstly, creating an educating mode benefitted from “AR”, which can provide assistance for the Internet+primary-level education undertakings is good for innovation of vitalizing the textbooks to arouse students' interest and activity. Meanwhile, it is economical and convenient for teaching to beyond the borders of training aids, specific venues relatively as well as some special facilities such as glasses. Furthermore, it offers an opportunity to expand the scale of “AR” technology application, with changing the stereotype that “AR” products always connected to entertainment.
Speech to Mexican Sign Language for Learning with an Avatar

This work proposes the combination of natural language processing techniques and the use of graphic development engines to create an avatar to display information translated into Mexican Sign Language (MSL). It describes the application of the guarantee analysis carried out on Mexican Sign Language in the implementation of a rules-based automatic translation system. Taking into account the graphic character of sign language, the generated translation is represented through the use of an avatar developed in a virtual environment Unity. The translation is based on a restricted vocabulary associated with the learning of history of Mexico for the fourth level of primary education. The grammatical structures most used in the history book were also analyzed and included in the rules system. The application was developed that, through a cloud server that will use Natural Language Processing (NLP) and Automatic Translation (AT), transforms the voice into MSL. The signs will be made by a digitally animated avatar on a mobile device .
Evaluation of a User Finger Movement Capturing Device for Control of Self-standing Omnidirectional Robot

Convenient systems and equipments to support humans are in great need for ever-growing populations of the elderly and those with disabilities caused by illness or injury. One of these system is the wheelchair, which can provide the user with many benefits such as maintaining mobility, continuing or broadening community and social activities, conserving strength and energy, and enhancing quality of life. However, when users use wheelchairs, they have to frequently stand and sit. This increases the physical burden on the user. For this reason, we proposed a self-standing omnidirectional robot. In order to support the user, the robot body must be able to flexibly make different movements and should be capable to deal with various control methods to meet diverse needs. In this paper, we present evaluation of a user finger movement capturing device for control of self-standing omnidirectional robot.
Macro Discourse Relation Recognition via Discourse Argument Pair Graph

Most previous studies used various sequence learning models to represent discourse arguments, which not only limit the model to perceive global information, but also make it difficult to deal with long-distance dependencies when the discourse arguments are paragraph-level or document-level. To address the above issues, we propose a GCN-based neural network model on discourse argument pair graph to transform discourse relation recognition into a node classification task. Specifically, we first convert discourse arguments of all samples into a heterogeneous text graph that integrates word-related global information and argument-related keyword information. Then, we use a graph learning method to encode argument semantics and recognize the relationship between arguments. The experimental results on the Chinese MCDTB corpus show that our proposed model can effectively recognize the discourse relations and outperforms the SOTA model.
Asymmetric Convolution Networks Based on Multi-feature Fusion for Object Detection

Real-time object detection on mobile platforms is a crucial but challenging computer vision task. However, it is widely recognized that although the lightweight object detectors have a high detection speed, the detection accuracy is relatively low. To address this problem, in this paper, we propose a new lightweight one-stage generic object detector, named ACFNet. The design goal of ACFNet is to improve the detection accuracy of object detectors while maintaining a high detection speed. In the backbone part, we design an asymmetric convolution unit, namely AC block, which employs three asymmetric convolution branches to enhance multi-feature representation of CNNs. We follow the backbone part design in ShuffleNetV2, and present a backbone network called ACNet that exploits AC blocks to replace the standard convolutional layers, e.g., 3×3 layers. Besides, in order to make all feature maps stronger semantically, we design a deconvolution block. In the prediction block, spatial attention can make the network better locate the feature distribution. Following this principle, we add a spatial attention building block to the residual block of each prediction layer. Experiments on PASCAL VOC 2007 and VOC 2012 datasets demonstrate the effectiveness of our proposed method and can run at 19 FPS on a single GPU.
Fast Trajectory Optimization for Quadrotor Landing on a Moving Platform

This paper addresses the trajectory optimization problem of quadrotor landing on a moving platform. Various state and control constraints are imposed, and a combined optimization objective of minimum-energy and minimum-time is considered. The contribution of the paper is to convert the original nonconvex optimal control problem into a second-order cone programming (SOCP) mainly utilizing the change of variables, equivalent transformation, and linearization. And the SOCP problem can be solved by the interior point method quickly. Then, a successive solution algorithm is proposed, which is found to be able to converge quickly. Especially, the algorithm is guaranteed to converge when the moving platform has a constant velocity vector. Numerical examples have been provided to show the efficiency and effectiveness of the proposed algorithm for quadrotor landing on moving platforms.
Autonomous Teammates for Squad Tactics

The United States Department of Defense seeks to integrate small unmanned aircraft systems (UAS) into infantry squads and develop tactics, techniques, and procedures using unmanned systems. Through an iterative design process consisting of live-fly tactical exercises, this research investigates the teaming of humans with unmanned aerial systems. Exercises involve force on force engagements to encourage the development of tactics and procedures for the future operating environment. Three successful mission tactics for leveraging UAS in missions are defined. In addition to autonomy, teams leverage convolutional and artificial deep neural networks running real time on aerial video feeds to identify and classify combatants and friendly forces.
A three-stage shearlet-based algorithm for vessel segmentation in medical imaging

Dictionaries are known tools used in different branches of image processing like edge detection, inpainting and, etc. Segmentation is the task of extracting an object as the part of a particular image. The common drawback of different segmentation methods is that they perform the extraction task incompletely. Tasks like edge detection, denoising and smoothing, as the parts of segmentation, can be done through applying the dictionaries. In this paper, we propose three new contrast stretching function. Based on one of the stretching functions and shearlets as a dictionary, we improved the previous version of a method that has been used in binary segmentation for magnetic resonance angiography images (MRI). We also introduce a three-stage binary image segmentation algorithm for vessel segmentation in MRI images. There are some disadvantages in recent proposed methods when dealing with extracting vessels of medical images. Our algorithm does the task with a more accurate extraction in detecting vessels having low intensity and weak edges in MRI.
Learning Multilingual Topics with Neural Variational Inference

Multilingual topic models are one of the most popular methods for revealing common latent semantics of cross-lingual documents. However, traditional approximation methods adopted by existing probabilistic models sometimes do not effectively lead to high-quality multilingual topics. Besides, as the generative processes of these models become more expressive, the difficulty of performing fast and accurate inference methods over parameters grows. In this paper, to address these issues, we propose a new multilingual topic model that permits training by backpropagation in the framework of neural variational inference. We propose to infer topic distributions via a shared inference network to capture common word semantics and an incorporating module to incorporate the topic-word distribution from another language through a novel transformation method. Thus, the networks of cross-lingual corpora are coupled together. With jointly training the coupled networks, our model can infer more interpretable multilingual topics and discriminative topic distributions. Experimental results on real-world datasets show the superiority of our model both in terms of topic quality and text classification performance.
Game Theory for Mobile Location Privacy

With the rapid growth of mobile network and infrastructures, location-related services accross multiple domains have received a great deal of attention. However, privacy is always an important problem that has a great influence on services. In order to research the attack and defence of privacy, we present the existing game-theoretic literatures for their interactions about mobile location privacy problems. We first demonstrate the necessity of game theory applied to location privacy. Then, we divide the literatures into four types according to different game players. Next, we describe the detailed content and analyse the equilibrium of privacy games. In addition, we also provide the works based on mechanism design to motive the defenders to increase the defence in various contexts. Finally, we also discuss the possible trends and challenges of the future research. Our survey provides a systematic and comprehensive understanding about location privacy preservation problems in mobile network.
Semi-supervised Learning to Remove Fences from a Single Image

When taking photos in the outdoors, e.g., playgrounds, gardens and zoos, fences are often inevitable to interfere with the perception of the background scenes. Thus it is an interesting topic on removing fences from a single image, which is dubbed image de-fencing. Generally, image de-fencing can be tackled within the two-stage framework, i.e., first detecting fences and then restoring background image. In existing methods, detecting fences mask cannot guarantee accuracy due to the diverse patterns of fences. Meanwhile, state-of-the-art supervised learning-based restoration methods usually fail in well handling inaccurate fences mask, yielding artifacts in restored background image. In this paper, we propose a semi-supervised framework for removing fences from a single image, where a simple yet effective recurrent network is proposed for supervised learning to detect fences mask and unsupervised learning is employed for robust restoration of background image. Specifically, we propose a recurrent network for fences mask detection, where convolutional LSTM is adopted for progressive detection. Instead of strict fences mask detection using standard \(\ell _1\) loss, we adopt an asymmetric loss to make detected fences mask be more inclusive of true fences. Then motivated by the success of deep image prior (DIP) for several image restoration tasks, we propose to employ unsupervised learning of three DIP networks for modeling background image, fences layer and fences mask, respectively. Moreover, we propose to adopt Laplacian smoothness loss function for refining the detected fences mask, making the restoration of background image be more robust to detection errors of fences mask. Experimental results validate the effectiveness of our semi-supervised image de-fencing approach.
Lightweight Security Data Streaming, Based on Reconfigurable Logic, for FPGA Platform

Alongside the rapid expansion of Internet of Things (IoT), and network evolution (5G, 6G technologies), comes the need for security of higher level and less hardware demanding modules. New cryptographic systems are developed, in order to satisfy the special needs of security, that have emerged in modern applications. In this paper, a novel lightweight data streaming system, is proposed, which operates in alternative modes. Each one of them, performs efficiently as one of three in total, stream ciphering modules. The operation of the proposed system, is based on reconfigurable logic. It aims at a lower hardware utilization and good performance, at the same time. In addition, in order to have a fair and detailed comparison, a second one design is also integrated and introduced. This one proposes a conventional architecture, consisting of the same three stream ciphering modes, implemented on the same device, as separate operation modules. The FPGA synthesis results prove that the proposed reconfigurable design achieves to minimize the area resources, from 18% to 30%, compared to the conventional one, while maintaining high performance values, for the supported modes.
A Hybrid Motion Planning Algorithm for Multi-robot Formation in a Dynamic Environment

Motion planning with the multi-robot system is complex and challenging in a dynamic environment with unknown obstacles. This paper proposes a two-stage motion planning strategy to guarantee the formation of multi-robot when they pass through some obstacle areas. First, the multi-robot formation is constructed by a leader-follower strategy. Then a two-level algorithmic framework is introduced to optimize both global and local path planning. A Self-adaptive Dynamic Window Algorithm (SDWA) is proposed to dynamically search both global and local optimal path, which aims to balance the speed and safety of the multi-robot system. In SDWA, the weight parameter of the objective function is adaptively controlled based on the distance between the multi-robot system and obstacles. Finally, results of experiments demonstrate convincingly that the SDWA can avoid dynamic obstacles by switching between global path planning and local path planning modes, which makes the trajectory more reasonable in a complex environment, and ensure the smooth path and safety of multirobot formation.
Chinese Sign Language Key Action Recognition Based on Extenics Immune Neural Network

In order to solve the problem of low accuracy of key action capture and feature recognition in Chinese sign language human-computer interaction, a large scale continuous sign language data set is established with Kinect equipment. By combining extension analysis theory with immune neural network (INN), extension immune detector is designed to locate the key actions of the sign language effectively. Hu invariant moment feature and Hamming rule are combined to improve immune neural network for accurate sign language image matching. The experimental results show that the sign language key motion capture and recognition accuracy based on extension immune neural network has greater advantages than the classical immune algorithm.
Survey of Open-Source Clouds Capabilities Extension

In this paper, we present a survey, which would be beneficial to anybody considering private or hybrid cloud solution implementation or implementing custom scheduling optimization algorithm on popular cloud computing platforms. The cloud computing platform can be deployed as a private cloud on-premises or in dedicated data centre space, as a public service or as a combination of both. In this survey, we review similarities and differences of most popular private cloud implementation platforms and their compatibility with public cloud solutions. The survey reveales the prevalence of resource scheduling and service deployment algorithms within some popular open-source clouds.
The Large-Small Group-Based Evolutionary Game on Knowledge Sharing in Uncertain Environment under the Background of Telemedicine Service

Group decision-making is an effective method to deal with complex unstructured problem in uncertain environment, and it has been widely used in many fields such as medical decision-making. This is a novel study that considers the decision-makers as different groups in the group decision-making problems in uncertain environment. This paper aims to present a novel method combined with evolutionary game for decision-making problem of knowledge sharing in uncertain environment between the large and the small groups in Telemedicine service. For this purpose, the evolutionary game model is constructed to solve decision-making problem of large-small group. Through analyzing the evolutionary path and balance, the influencing factors of the selection strategies and objective and subjective factors restricting the establishment of knowledge sharing between the large and small groups cloud be determined. Finally, a numerical simulation experiment is conducted with Matlab to demonstrate the feasibility of the proposed method. In this study, the range of decision groups in the research of group decision-making problem has been expanded, and the complicated factors of knowledge sharing between hospitals in uncertain environment under the background of Telemedicine service have been discussed.
Coherently Driven <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>N</mi> </math> Number of Degenerate Three-Level Atoms with Parametric Amplifier

We have analyzed the squeezing and statistical properties of the cavity light beam produced by a coherently driven degenerate three-level laser with a degenerate parametric amplifier (DPA) in an open cavity and coupled to a vacuum reservoir via a single-port mirror. We have carried out our analysis by putting the noise operators associated with the vacuum reservoir in normal order. Applying the solutions of the equations of evolution for the expectation values of the atomic operators and the quantum Langevin equation for the cavity mode operator, the mean photon number and the quadrature squeezing of the cavity light are calculated. And a large part of the mean photon number is confined in a relatively small frequency interval. Furthermore, we also obtain the antinormally ordered characteristic function defined in the Heisenberg picture. With the aid of the resulting characteristic function, we determine the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>Q</mi> </math> function which is then used to calculate the photon number distribution.
On the Cyclical Properties of Hamilton's Regression Filter and Refinements

Hamilton (2017) criticises the HP filter because of three drawbacks (i. spurious cycles, ii. end-of-sample bias, iii. ad hoc assumptions regarding the smoothing parameter) which, moreover, apply to other popular time series filters as well. As an alternative filter, he proposes a regression filter. I demonstrate that Hamilton's regression filter is partially subject to the same drawbacks (i. and iii.). Furthermore, I illustrate that Hamilton's regression filter does not fulfil established criteria for the desired properties of filters. For instance, in contrast to the HP filter, Hamilton's filter does not approximate the ideal band pass filter, does not have constant cyclical properties across time series, and induces phase shifts. I discuss two refinements to bridge Hamilton's and the established view. For extracting business or financial cycles, I find that a one-sided HP filter and a 1-quarter regression filter can best reconcile both perspectives.
HadoopSec 2.0: Prescriptive analytics-based multi-model sensitivity-aware constraints centric block placement strategy for Hadoop

Like many open-source technologies such as UNIX or TCP/IP, Hadoop was not created with Security in mind. Hadoop however evolved from the other tools over time and got widely adopted across large enterprises. Some of Hadoop’s architectural features present Hadoop its unique security issues. Given this security vulnerability and potential invasion of confidentiality due to malicious attackers or internal customers, organizations face challenges in implementing a strong security framework for Hadoop. Furthermore, given the method in which data is placed in Hadoop Cluster adds to the only growing list of these potential security vulnerabilities. Data privacy is compromised when these critical and data-sensitive blocks are accessed either by unauthorized users or for that matter even misuse by authorized users. In this paper, we intend to address the strategy of data block placement across the allotted DataNodes. Prescriptive analytics algorithms are used to determine the Sensitivity Index of the Data and thereby decide on data placement allocation to provide impenetrable access to an unauthorized user. This data block placement strategy aims to adaptively distribute the data across the cluster using innovative ML techniques to make the data infrastructure extra secured.
Key Competencies in the Digital Age and Transformation of Education

Abstract Due to the influence of digitalization and other changes affecting different spheres of people’s lives, it became clear that the modern education system must undergo a serious transformation, because the traditional education system is seriously outdated. It is expected that in the near future a significant part of education will be based on modern digital technologies, and the educational process will be implemented with the use of global educational platforms that provide students with the opportunity for global interaction and advanced knowledge and experience. The aim of the study was to formulate the features of industrial and modern education systems, to compare them, as well as to identify a set of competencies that people need to form in order to effectively cope with their professional tasks in the digital age. The issue of the optimal combination of standard educational approaches with the new technologies used in education, which allow to develop competencies that need to be formed among people in order to be in demand in the labour market, is becoming topical.