Reviewerly/exHarmony huggingface dataset:
Context-Aware Algorithm for Enhancing Quality of on-the-go Media Streaming in no Signal Areas

Video streaming is continuously growing in popularity and poses the largest consumption of mobile data. Mobile users are frequently streaming content while traveling, and the rate is only going to be increased with the upcoming arrival of autonomous vehicles. Unpredictable fluctuations in throughput of the mobile data can lead to interruptions of video playback. The interruptions are caused by the inability of most HTTP Adaptive Streaming systems to either predict fluctuations or adapt to them in time. Areas without network signal are even greater threats to continuous video playback. If not detected in time, these areas can cause prolonged periods with no playback. In this paper, we offer a system that uses context information of a defined route to plan out the quality of each downloaded segment en route. The system aims to minimize video stall occurrences while delivering a higher quality stream through areas with no network signal.
The Chebyshev Collocation Least Squares Approximation Method for the Audio Data

short-paper The Chebyshev Collocation Least Squares Approximation Method for the Audio Data Share on Authors: Qian Zhang Guilin University of Electronic Technology Guilin University of Electronic TechnologyView Profile , Yonghui Qin Guilin University of Electronic Technology Guilin University of Electronic TechnologyView Profile , Ziqiang Chen Guilin University of Electronic Technology Guilin University of Electronic TechnologyView Profile , Nengxiang Wen Guilin University of Electronic Technology Guilin University of Electronic TechnologyView Profile Authors Info & Claims CONF-CDS 2021: The 2nd International Conference on Computing and Data ScienceJanuary 2021 Article No.: 135Pages 1–4https://doi.org/10.1145/3448734.3450865Published:28 January 2021 0citation12DownloadsMetricsTotal Citations0Total Downloads12Last 12 Months12Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Twist-hands and shuttle-kissing

This paper seeks to understand the skills of operating automated manufacturing machines of the C19th as craft practices, employing externally powered and automated tools around which new cultures of practice emerged. We draw upon situated/embodied/enactive/extended/distributed (SEEED) approaches to cognition to explicate the sensibilities of these practices, as well as the history of science and technology, Anthropology, STS and related fields. Our case study is a body of work focused on embodied/ embedded knowledge in the textile industry – specifically in the making of machine lace. We conclude with a proposal for multi-modal museum exhibits that provide an understanding of know-how, kinesthetic/proprioceptive skills and procedures. The authors are both long term practitioners of crafts, both traditional and industrial (see bios). This experience informs the research at every step.
Research on the current situation of ultrafiltration combined process in treatment of micro-polluted surface water

At present, most of the source water of domestic urban water supply treatment plants is taken from micro-polluted surface water. With the further improvement of drinking water sanitation standards, the traditional “coagulation-sedimentation-filtration-disinfection” process cannot meet the requirements of the new standards. Ultrafiltration, as the core technology in the field of water treatment in the 21st century, is increasingly applied to water treatment due to its advantages of green efficiency, simple operation, small footprint, and high degree of automation. However, the use of ultrafiltration membrane alone has some problems such as substandard water quality and serious membrane pollution. Therefore, the combination of ultrafiltration and traditional water treatment technology to treat micro-polluted surface water has become the mainstream choice for treatment plants. This article mainly introduces the current research status of domestic ultrafiltration combined technology, trying to explore more reasonable and effective ways to apply ultrafiltration membranes in actual projects.
Substantiation of a model for forming readiness of bachelors of the automotive profile for the implementation of future professional and pedagogical activities in SVE system

The article examines the structural-procedural model of the formation of the readiness of bachelors of the automotive profile for the implementation of professional and pedagogical activities in vocational education. The didactic principles and methodological approaches, components of the formation of a system of professional and pedagogical knowledge and skills, pedagogical conditions, stages of evaluating research results, criteria and levels of preparedness of bachelors for the implementation of future professional and pedagogical activities in the SVE system have been substantiated.
Construction Key Technology of Cast-In-Place Prestressed Box Girder for Bridge Engineering

Abstract Research topics: Based on the working platform of school-enterprise Cooperation Council, the exploration and research on the path of school-enterprise cultural inheritance and co-construction. Cast-in-place prestressed box girder construction in bridge engineering can reflect the continuous development of construction materials, box girder for higher strength concrete and steel composition, the construction process, the need to first raise the structure and apply the corresponding compressive stress, so as to make the structure work to withstand the tensile stress to be offset, to achieve the effective application of the tensile strength of steel, in this case, can promote the overall structural durability and stiffness to improve, so that the structure of the self-weight has been significantly reduced.
Thai Sentiment Lexicon Construction

Online platforms have become increasingly integrated into daily lives of Thai citizens. Besides being used to query information, online platforms are the medium to exchange opinions and discuss various topics via posts or comments. Since the number of posts and comments can be very large, understanding which topics users are talking about and grasping how users feel about those topics can be difficult and time consuming. Researchers therefore have studied how to automatically analyze posts and comments to extract topics and their sentiments using techniques such as topic modeling and sentiment analysis. There are various approaches to analyze sentiments, and many of them utilize sentiment lexicons, a set of words annotated with sentiment, to help with the analysis. Several researches have constructed sentiment lexicons for English language. This paper aims to construct sentiment lexicons for Thai language using a dictionary-based approach in combination with a crowdsourcing approach. First, words are chosen from a Thai dictionary, mostly adjectives since they usually express sentiment. Next, a web application is developed so that users can help vote whether a particular word is positive, negative, or neutral. After a number of votes have been collected, each word is scored to determine its sentiment. To validate and ensure users are voting truthfully, users are authenticated via Facebook, and simple checks are carried out against answers of each user. This process results in a sentiment lexicons for Thai words and should help form the basis for sentiment analysis of Thai text.
Playing with Drones

Human-Drone Interaction is a fast-growing subset of the field of Human-Computer Interaction as drones enable novel and interesting interactions in 3D space as they can be regarded as pixels in physical space. We examine the intersection of drones and play and explore how drones can facilitate playful bodily experiences by pervading the physical space. We focus on “Paida” play to build explorative play experiences that pervade the player's physical environment. This work resulted in three play experiences built around simple interaction methods, in addition to observations from our design process, and three design strategies; drone-based play lends itself to collaborative play, such play benefits from simple designs, and designers benefit from designing for multiple players. From these design strategies, we set a starting point for designing novel, pervasive, and playful interactions whilst regarding drones as pixels in the physical space.
Factorized CRF with Batch Normalization Based on the Entire Training Data

Batch normalization (BN) is a key component of most neural network architectures. A major weakness of Batch Normalization is its critical dependence on having a reasonably large batch size, due to the inherent approximation of estimating the mean and variance with a single batch of data. Another weakness is the difficulty of applying BN in autoregressive or structured models. In this study we show that it is feasible to calculate the mean and variance using the entire training dataset instead of standard BN for any network node obtained as a linear function of the input features. We dub this method Full Batch Normalization (FBN). Our main focus is on a factorized autoregressive CRF model where we show that FBN is applicable, and allows for the integration of BN into the linear-chain CRF likelihood. The improved performance of FBN is illustrated on the huge SKU dataset that contains images of retail store product displays.
DCAF: Dynamic Cross-Chain Anchoring Framework using Smart Contracts

Abstract Blockchain has attracted tremendous attention from both the academia and the capital market. The insertion of data in a blockchain known as anchoring makes data verifiable and independently auditable and has many potential applications. The objective of this paper is to introduce a dynamic cross-chain anchoring framework for snapshotting users’ data into multiple public blockchains with the help of smart contracts. This paper starts by proposing the framework and depicting the components and workflow. Second, we design a target chain selection algorithm that combines the states of different blockchains. Using the algorithm, users can dynamically select a suitable public blockchain for anchoring tasks. Third, the virtual chain protocol is presented and achieves data anchoring orderly and integrally. Analyzation and evaluation show that the framework can enhance user experience, robustness and performance.
Comparing GANs for Translating Satellite Images to Maps

With rapid development taking place in the world, the landscape of the Earth is constantly changing. Skyscrapers now stand in places that were once covered with water. With the advent of continuously evolving technologies, Generative Adversarial Networks are growing at a rapid pace with new variations. This engenders the need to develop and identify an efficient method to accurately convert satellite images into maps so as to adapt to the changes with ease. This is achieved using a conditional Generative Adversarial Network (cGAN) which generates an output image based on certain conditions of the input. Traditionally, image translation is performed using the Pix2Pix GAN which is a type of cGAN. However, the newly developed CycleGAN improves on the problems of the Pix2Pix GAN by making it less selective with respect to the type of data available. The proposed idea is to use both the GANs to perform image to image translation so as to convert satellite images into maps and evaluate their performance using the Frechet Inception Distance. The evaluations are then used to conclude which one is better at generating a model under similar training conditions on the same data. This can help in providing a general overlay of an area within seconds which could be useful to governments in obtaining the layout of a region on which they plan to carry out new projects, or even the armed forces in performing recon of a certain area.
Progressive AutoSpeech: An Efficient and General Framework for Automatic Speech Classification

Speech classification has been widely used in many speech-related applications. However, the complexity of speech classification tasks often exceeds the scope of non-experts, the off-the-shelf speech classification methods are urgently needed. Recently, the automatic speech classification (AutoSpeech) without any human intervention has attracted more and more attention. The practical AutoSpeech solution should be general and can automatically handle classification tasks from different domains. Moreover, AutoSpeech should improve not only the final performance but also the any-time performance especially when the time budget is limited. To address these issues, we propose a three-stage any-time learning algorithm framework called Progressive AutoSpeech for automatic speech classification under a given time budget. Progressive AutoSpeech consists of the fast stage, enhancement stage, and exploration stage. Each stage uses different models and features to ensure generalization. Additionally, we automatically construct ensembles of top-k prediction results to improve the robustness. The experimental results reveal that Progressive AutoSpeech is effective and efficient for a wide range of speech classification tasks and can achieve the best ALC score.
On Designing Experiment Teaching for Undergraduates’ Effective Learning of Logistics Distribution Center Planning and Management Course Based on PBL and BOPPPS

The experiment teaching plays an essential role in the undergraduates' effective learning of the logistics distribution center planning and management course. In this study, according to the characteristics of the course, the author proposes an experiment teaching design based on PBL and BOPPPS. In this design, considering the close connection between the contents of the course and various logistics planning problems, PBL is used to establish the framework of the experiment teaching of the logistics distribution center planning and management course. Then, to realize the PBL based experiment teaching framework, BOPPPS that carries out the PBL in a precise way is employed to design the specific teaching procedure for the experiment. The integration of PBL and BOPPPS provides a helpful design thinking to improve the effectiveness of the undergraduates' learning of the logistics distribution center planning and management course. It can also be applied in the course teaching of different specialties in the undergraduate education.
A Suggested method for detecting outliers based on a particle swarm optimization algorithm

Abstract The occurrence of tremendous developments in the field of data has led to the formation of huge volumes of data, and it is natural that this leads to the presence of outliers in this data for many reasons. The presence of outliers in the data affects the statistical analysis, so we must try to reduce their impact in various ways. On the other hand, the existence of outliers may be of great benefit in many application and of great importance in various fields. In this paper we propose a new method for detecting outliers based on the Particle Swarm Optimization algorithm (PSO). The new propose algorithm was compared with the normal distribution method, and the results obtained from the new method were very promising and encouraging.
Central Bank Digital Currencies: Balancing Public-Private Partnerships

What roles exist for public and private partnerships within the context of central bank digital currencies (CBDCs), in an increasingly digitalized global system? Do central bank digital currencies (CBDCs) serve as public goods rather than tools which should primarily remain within the realm and governance of private sector firms? What challenges or risks are presented through the use of CBDCs and how can such risks be mitigated through current existing structures - as well as models which have been propounded in relation to public – private partnerships? This paper aims to contribute to the literature on the topic through a consideration of several variants and models of CBDCs under which the public private partnership would function, namely the synthetic CBDC (sCBDC) and the two-tiered CBDC. Further, two other types of CBDCs, namely the wholesale CBDC and the retail CBDC will be distinguished - as well as the account based CBDC, which is contrasted to CBDCs based on digital tokens. Whilst concerns for privacy and security remain paramount and cannot be undermined, particularly from the perspectives of distributed ledger technologies (and blockchains – through which such platforms operate), such concerns need to be weighed against the need for identification since regulators will be better supported in their goals in enforcing the law, as well as identifying fraudulent operations, where sufficient identification procedures have been put in place.
SIMCO: SIMilarity-based object COunting

We present SIMCO, a completely agnostic multiclass object counting approach. SIMCO starts by detecting foreground objects through a novel Mask RCNN-based architecture trained beforehand (just once) on a brand-new synthetic 2D shape dataset, InShape; the idea is to highlight every object resembling a primitive 2D shape (circle, square, rectangle, etc.), Each object detected is described by a low-dimensional embedding, obtained from a novel similarity-based head branch; this latter implements a triplet loss, encouraging similar objects (same 2D shape + color and scale) to map close. Subsequently, SIMCO uses this embedding for clustering, so that different “classes” of similar objects can emerge and be counted, making SIMCO the very first multi-class unsupervised counter. The only required assumption is that repeated objects are present in the image. Experiments show that SIMCO provides state-of-the-art scores on counting benchmarks and that it can also help in many challenging image understanding tasks.
Saudi Human Awareness Needs. A Survey in How Human Causes Errors and Mistakes Leads to Leak Confidential Data with Proposed Solutions in Saudi Arabia

Recently human errors have become an increasingly high factor in security breaches that may affect confidential data and most of the cyber data breaches are caused by human errors. With one individual mistake, the attacker will gain access to the entire network and bypass the implemented access controls without any immediate detection. Unaware employees will be vulnerable to any social engineering cyber-attacks. Providing security awareness to People is part of the company protection process, the cyber risks cannot be reduced by just implementing technology, the human awareness of security will significantly reduce the risks which encourage changes in staff cyber-awareness. In this paper we will focus on human awareness and human needs to continue the required security education level. We will review human errors and introduce a solution to avoid the breach from occurring again. Recently Saudi Arabia faced many attacks with different methods of social engineering. As Saudi Arabia has become a target to many countries and individuals, we needed to initiate a defense mechanism that begins with awareness to keep our privacy and protect the confidential data against possible intended attacks.
CyRes

Existing approaches to cyber security in the automotive sector are not fit to deliver the resilience required for safe mass deployment of advanced driving functions and intelligent mobility services. This paper promotes an innovative approach to operational cyber resilience, the CyRes methodology, which aims to enable robust and resilient engineering practices in this sector from design to manufacture to operation. CyRes is based on three principles: engineered differences; detecting, understanding and acting on cyber events; and proactive updates. The aim of this short paper is to raise awareness of the problems and the many intellectual challenges in this particular sector. CyRes is an exciting opportunity for engineers and computer scientists to transfer widely studied, mature methods, such as those developed by the runtime verification community and the self-adaptive systems community, for cyber resilience.
Student-t Variational Autoencoder for Robust Multivariate Density Estimation

We propose the Student-t variational autoencoder (VAE), which is a robust multivariate density estimatorbased on the VAE. The VAE is a powerful deep generative model, and used for multivariate density estimation. Withthe original VAE, the distribution of observed continuous variables is assumed to be a Gaussian, where its mean andvariance are modeled by deep neural networks taking latent variables as their inputs. This distribution is called thedecoder. However, the training of VAE often becomes unstable. One reason is that the decoder of VAE is sensitiveto the error between the data point and its estimated mean when its estimated variance is almost zero. To solve thisinstability problem, our Student-t VAE uses a Student-t distribution as the decoder. This distribution is a heavytaileddistribution, of which the probability in the tail region is higher than that of a light-tailed distribution such as aGaussian. Therefore, the Student-t decoder is robust to the error between the data point and its estimated mean, whichmakes the training of the Student-t VAE stable. Numerical experiments with various datasets show that training ofthe Student-t VAE is robust, and the Student-t VAE achieves high density estimation performance.
Impossibility Results about Inheritance and Order of Death

If several relatives died with no will, the order of their deaths could affect the inheritance result. When the order of death is unknown, there are three approaches to determine the inheritance result: apply an inheritance method that is not affected by the order of death; artificially assign the order of death; stipulate that persons with unknown orders do not inherit each other. The last approach is adopted by the current French Civil Code (denoted as the French Approach). We prove that under some basic requirements, the French Approach is the only valid solution to the order of death problem. Therefore, the inheritance law of a country that does not adopt the French Approach either has unsolvable inheritance problems or violates basic requirements. In the appendix, we study the existence and uniqueness of inheritance methods that are invariant for different orders of death and only violate one requirement, such as gender equality.
A novel design algorithm for multifunctional drawing table

At present, although computer graphics and three-dimensional modeling design are becoming more and more popular, manual mechanical drawing, as one of the basic skills necessary for designers, still cannot be ignored. Therefore, the drawing table is one of the necessary tools for engineering designers. There are many types of drawing desks on the market, but the large multi-function is single, there are various defects, or multiple functions cannot be coordinated and used together. For example, the existing drawing desk only has the function of adjusting the elevation angle, and cannot be adjusted quickly and conveniently during use, which brings great trouble to the user. At the same time, although some drawing tables have added storage tables and storage devices, drawing tools and practical books are still placed on the desktop, and the height of the tables and chairs cannot be adjusted, which will be very inconvenient for different people to use. It is precisely for the above reasons, in order to make the most of the drawing table and play its due role, the product design concept of "multi-functional and practical drawing table" is proposed.