Reviewerly/exHarmony huggingface dataset:
New Methodology and Checklist of Wi-Fi Connected and App-Controlled IoT-Based Consumer Market Smart Home Devices

Since its invention, the Internet has changed the world, but above all, it has connected people. With the advent of the Internet of Things, the Internet connects things today much more than people do. A large part of the Internet of Things consists of IoT controlled Smart Home devices. The Internet of Things and the Smart Home have become an increasingly important topic in recent years. The growing popularity of Smart Home devices such as Smart TVs, Smart Door Locks, Smart Light Bulbs, and others is causing a rapid increase in vulnerable areas. In the future, many IoT devices could be just as many targets. The many new and inexperienced manufacturers and the absence of established uniform standards also contribute to the precarious situation. Therefore, new methods are needed to sensitize and detect these threats. In this paper, different existing approaches like those of the National Institute of Standards and Technology (NIST) and the Open Web Application Security Project (OWASP) are combined with concepts of this work like the Smart Home Device Life Cycle. In the context of this paper, a universal 31-page question-based test procedure is developed that can be applied to any Smart Home device. Based on this new, innovative security checklist, the communication between device, app, and the manufacturer's servers, as well as the firmware of IoT devices, can be analyzed and documented in detail. In the course of this paper, also a handout in the abbreviated form will be created, which serves the same purpose.
Using conceptual incongruity as a basis for making recommendations

We evaluate the possibility of using within-item measures of meta-data similarity to improve recommendation rankings along psychologically salient dimensions of incongruity and creativity. Our approach contrasts with recently developed methods at introducing diversity into recommendations which rely on across-item measurements of dissimilarity, while sharing several formal and algorithmic elements. We show that semantic distance based operationalizations of psychological constructs show substantial correlation with empirical data. We further show that incongruity predicts variability in satisfaction as measured by movie ratings in a large corpus. Empirical results from a two month-long user study demonstrate that incongruity-based recommendations attract considerably more interaction from users, and users expressed significantly greater satisfaction given these recommendations. Based on these observations, we propose that using incongruity to diversify recommendations may be useful in expanding recommendation repertoires along interesting psychological dimensions, complementing relevance-based search.
Campus Landscape Design Under the Background of Internet +

Since the beginning of the new century, the Internet and its surrounding technologies have reached unprecedented heights, especially the popularity of mobile smartphones in the past 10 years has brought tremendous impact on our lifestyle. From the initial online chat to the present Internet of Things, the Internet’s thinking is almost full of all aspects of our lives. Based on this background of the times, I am thinking that the landscape can also be intelligent and digital. Based on the traditional campus landscape planning system, citing new concepts and technologies not only changes the campus landscape from the interactive way, but also changes the traditional campus landscape from all aspects of design management. This paper briefly introduces the application of information technology in campus landscape design and promotes the way of ecological management technology through the basic principles of the pursuit of contemporary campus landscape design.
Road Intersection Path Planning Based on Q-learning for Unmanned Ground Vehicle

This paper concentrates on the road intersection path planning problem of unmanned ground vehicle (UGV). First, the interaction between UGV and environment is established as a Markov decision process (MDP) model. Considering the feasibility of path, the kinematic model is also utilized to update the states of UGV, such as position, velocity and attitude. Then, the optimal driving strategy and path are generated by Q-learning algorithm. Reward function is designed to reflect the gain and loss of the chosen action. Finally, simulations demonstrate the feasibility of Q-learning in path planning of UGV.
P‐123: Tiled Display on a Textile Base

We propose to produce the tiled display from woven tiles, each tile can be active‐ or passive‐matrix display. Such tiled display can be comparatively cheap, very flexible and unlimited in size.
An Explorative Design Process for Game Map Generation Based on Satellite Images and Playability Factors

This paper proposes a tool for 3D Procedural Terrain Generation in games. The main objective of this study is to assess the quality of the generated terrains and thus the experience of the user. The tool measures how traversable a terrain is, how long it takes to walk across and how much of it is accessible. The produced results can be used to further refine the generation method. The evaluation consists of multiple terrains being produced, based on Generative Adversarial Networks (GANs) trained on satellite images. A path finding algorithm is then used to evaluate the traversability of each terrain between two points on the map. Results show that the proposed method can generate quite diverse and interesting maps and that the proposed metric can be used for evaluation of playability.
Dynamic Multi-view Combination for Image Classification

Abstract Multi-view learning is widely used in image classification tasks to better explore the discriminative information of different views. However, existing multi-view methods commonly rely on some pre-defined assumptions or fail to fully take advantage of the combination commonality between individual images. This paper presents an efficient dynamic multi-view combination approach to dynamically combine the discriminative power of different views. Specially, we firstly utilize a group of pre-trained CNNs to extract different views of an image. Secondly, we apply a dynamic gating module to the image, which will generate a weight vector of these views to model the image-level information for the multi-view learning. Finally, the weight vector and the views are combined for the classification. Experimental results and analysis on CIFAR-10 and ImageNet show the effectiveness of the proposed dynamic multi-view combination method (DMVC) for visual classification.
Gloria Corpas Pastor and Jean-Pierre Colson (eds). 2020. <i>Computational Phraseology</i>

Computational Phraseology is a compilation of a number of articles and essays dealing with recent research on phraseology, the study of multiword expressions ranging from collocations to formulas and proverbs. The articles mainly deal with the computational aspects of phraseology, in particular the automatic extraction of collocations, which has been a hot topic in linguistics and in lexicography for over 30 years, going back to Church and Hanks’ seminal 1990 paper centred on the concept of mutual information, which is used to identify relations between words which co-occur more often than chance. Over 10 years ago, I noted elsewhere that ‘statistical tools to extract collocations have become such a hot topic that several workshops and conferences have been organised over the last few years to address the role of collocations in lexicography, in applied and computational linguistics and to compare the various approaches that have been proposed’ (in Fontenelle 2008: 12). This is still true today and the range of statistical techniques and practical tools made available to researchers keeps expanding, allowing the latter to identify, extract, formalize, and represent a whole range of ‘prefabricated units’ for a variety of applications (NLP systems, machine translation, dictionaries…).
A 2.46M Reads/s Seed-Extension Accelerator for Next-Generation Sequencing Using a String-Independent PE Array

Advances in DNA-sequencing technology have far outpaced Moore's law, imposing significant challenges on the computationally intensive secondary analysis in the sequencing pipeline. An accelerator for seed extension, a critical and computationally intensive step in genome sequencing, is proposed. The accelerator, implementing a string-independent automata, consists of a triangular array of $25\times 2$ custom-designed processing elements. It performs 2.46 million reads per second (MRPS), achieving a $1581\times $ improvement in power efficiency and $165.5\times $ smaller silicon footprint compared to a system setting with dual-socket Xeon E5-2697 v3 server processors.
The Creation and Case Study of Smart Learning Environment

With the continuous development of education informatization, how to integrate wisdom education into teaching has important practical significance today. After years of development, the campus network has been basically popularized, and the application of informatization has gradually penetrated into teaching, management, and services. The field has increasingly become an important channel for teachers and students to obtain information, enrich knowledge, and learn to communicate. It has played an active role in promoting education reform and development, promoting ideological and cultural exchanges, and enriching the spiritual life of teachers and students. This paper analyzes the background, concepts, characteristics of the smart learning environment, and analyzes the smart learning environment. Finally, taking the creation of a smart classroom as an example, it provides insights on how to create a smart learning environment.
Integrating Agile Teams Into the Organization

The purpose of this chapter to is examine the issues involved in the integration of Agile teams into existing organizations that have not previously incorporated Agile teams, and suggest mechanisms that can make the integration process more efficient and effective. Issues and solutions discussed include conflicting environmental viewpoints, differing project end-point definitions, organizational reporting, budgeting and financial performance reporting challenges, and the special challenge of legacy technologies to Agile teams. Methods discussed for successful integration of Agile teams into existing organizations include leadership selection, use of cross-functional team members, appropriate performance measures, funding for the team, and effective communication within and among the organizational members.
Graph Convolutional Network Using a Reliability-Based Feature Aggregation Mechanism

Graph convolutional networks (GCNs) have been proven extremely effective in a variety of prediction tasks. The general idea is to update the embedding of a node by recursively aggregating features from the node's neighborhood. To improve the training efficiency, modern GCNs usually sample a fixed-size set of neighbors uniformly or sample according to nodes' importance, instead of using the full neighborhood. However, both the sampling strategies ignore the reliability of a link between the target node and its neighbor, which can be implied by the graph structure and may seriously impact the performance of GCNs. To deal with this problem, we present a Graph Convolutional Network using a Reliability-based Feature Aggregation Mechanism called GraphRFA, where we sample the neighbors for each node according to different kinds of link reliability and further aggregate feature information from different reliability-specific neighborhoods by a dual feature aggregation scheme. We also theoretically prove that our aggregation scheme is permutation invariant for the graph data, and provide two simple but effective instantiations satisfying such scheme. Experimental results demonstrate the effectiveness of GraphRFA on different datasets.
NLP and the Representation of Data on the Semantic Web

One of the goals of data scientists and curators is to get information (contained in text) organized and integrated in a way that can be easily consumed by people and machines. A starting point for such a goal is to get a model to represent the information. This model should ease to obtain knowledge semantically (e.g., using reasoners and inferencing rules). In this sense, the Semantic Web is focused on representing the information through the Resource Description Framework (RDF) model, in which the triple (subject, predicate, object) is the basic unit of information. In this context, the natural language processing (NLP) field has been a cornerstone in the identification of elements that can be represented by triples of the Semantic Web. However, existing approaches for the representation of RDF triples from texts use diverse techniques and tasks for such purpose, which complicate the understanding of the process by non-expert users. This chapter aims to discuss the main concepts involved in the representation of the information through the Semantic Web and the NLP fields.
Laser-Raman spectral analysis of the Baoxingchang Cu-Mo deposit, Yunnan Province, SW China

Abstract Baoxingchang Cu-Mo deposit is one of several Cu-Mo-(Au) polymetallic deposits have been found in the Tethys metallogenic province. Laser-Raman spectral analysis shows that the vapour-phase components of fluid inclusions in quartz are mainly composed of water vapour and a little CO 2 , while the fluid-phase components contain Na + and Cl − metallogenic fluid, which generally belongs to a NaCl-H 2 O system. In conclusion, the characteristics of fluid inclusions in porphyry-type Cu-Mo polymetallic deposits in Baoxingchang are closely related to multi-stage intrusions along the Jinshajiang-Red River shear zone.
SOLAR: Fusing Node Embeddings and Attributes into an Arbitrary Space

Network embedding has attracted lots of attention in recent years. It learns low-dimensional representations for network nodes, which benefits many downstream tasks such as node classification and link prediction. However, most of the existing approaches are designed for a single network scenario. In the era of big data, the related information from different networks should be fused together to facilitate applications. In this paper, we study the problem of fusing the node embeddings and incomplete node attributes provided by different networks into an arbitrary space. Specifically, we first propose a simple but effective inductive method by learning the relationships among node embeddings and the given attributes. Then, we propose its transductive variant by jointly considering the node embeddings and incomplete attributes. Finally, we introduce its deep transductive variant based on deep AutoEncoder. Experimental results on four datasets demonstrate the superiority of our methods.
A Solution Algorithm for Exploring Shop Scheduling Problems

Combinatorial optimization is widely used in assignment problems, such as traveling salesman problem (TSP), scheduling problems, et al. In this study, an evolutionary-based block model (EBBM) was proposed to enhance the speed of convergence so as to avoid premature convergence problem. The main idea of blocks is to find key blocks from chromosomes and use these blocks to improve evolutionary algorithms (EAs) to solve combinatorial optimization problems (COPs). Block is a kind of information that explores the effects of individual genes on the evolution of chromosomes, containing information that is helpful for evolution and information that hinders evolution. In this paper, these two different kinds of information are stored and used. The evolution direction of the information providing algorithm, through the influence of two different information, not only improves the convergence speed of the algorithm, but also improves the diversity of the algorithm solution, so as to achieve goals of high stability and good solution quality. The block mechanism proposed includes building a probability matrix, generating blocks by associated rule and applying blocks to construct artificial chromosomes. Since the block is used as the basic unit for constructing the artificial solution. In this paper, the blocks mined by the association rules not only have diversity, but also control the block in formation strength required for the evolution process according to the set confidence level. Finally, in order to confirm the quality of solutions, the proposed approach is experimentally implemented on permutation flow-shop scheduling problem (PFSP). According to the average error rate, the optimal error rate, and the convergence curve, the solution effect of the algorithm is discussed, and the experimental results show that the block mechanism by positive and negative information is useful to improve the speed of convergence and avoid premature convergence problem. In additional, the results also demonstrate that BBEM is applicable and efficient to the COPs.
A clinical coding recommender system

Clinical coding of hospital admissions can erroneously omit diagnosis and procedure codes. A consequence of these omissions is that the condition and treatment of the patient are not fully captured by the entered codes, which can then also impact hospital revenue. One way to prevent these errors is through a real-time recommender system which suggests the addition of codes at the point of coding when it appears they have been omitted. Association analysis uncovers patterns between codes, forming a basis for coding recommendations. Combining association analysis with manual expert validation produces more useful recommendations (we refer to this as the expert validated list), but is labour-intensive. In this study, we propose an approach using Bayesian Networks to determine the conditional relationships between codes. Performance is evaluated using a testing strategy which simulates errors through the random removal of codes from episodes of patient care and counts how many of the removed codes are recommended to coders by each recommender. Performance is also based on how many recommended codes were not removed (superfluous recommendations) which we seek to minimise. We develop a recommender system which generates 96% of the number of correct recommendations produced by the expert validated list, while having 68% fewer superfluous recommendations. Our proposed methodology provides a high performance recommender while reducing dependence on labour-intensive effort by clinical coding experts.
Adapt Swarm Path Planning for UAV Based on Artificial Potential Field with Birds Intelligence Extensions

Artificial potential field (APF), a concept from physic field, has been successfully adopted for path planning of unmanned aerial vehicle (UAV). The cooperation between the expulsion against obstacle and the gravity from target, ensures a global planning optimization considering obstacle avoidance. Unfortunately, under different UAV-to-UAV distance conditions, the APF also has a weakness unable to support swarm path planning for multiple UAVs due to the highly dynamic shift between the repulsion and the gravity. To utilize APF to realize robust swarm planning, we redesign the APF and embed it into the swarm avoidance mechanism from bird intelligence involving group collision avoidance (GCA) and individual collision avoidance (ICA), forming two kinds of a APF-based swarm planning respectively: GCA-APF and ICA-APF. We then propose an adapt switch mechanism for dynamically choosing GCA-APF or ICA-APF in contexts of different obstacle environment. Experiments show the effectiveness of our approach, 15.25% higher planning efficiency than that of original GCA and avoiding certain polynomial cost increase from original ICA.
13‐2: <i>Late‐News‐Paper:</i> How to Reduce Harmful Blue Light on OLED Devices

This study shows how to reduce the harmful blue light of OLED displays. The results demonstrate that the ratio of harmful blue light depends on the blue dopant‐host spectrum and the resonance strength. We define the harmful blue light spectrum to cover between 415 nm and 455 nm. The ratio of harmful blue light decreases as the blue dopant‐host spectrum shifts toward longer wavelengths. However, a color change could occur from the original one, which contains a shorter spectrum area. In order to maintain the other properties, we simultaneously change the full width at half maximum (FWHM) and the peak wavelength (Wp). As a result, the harmful blue light ratio is reduced from 12% to 7% compared with previous materials. This new technology is already being applied in mass production.
Application of UHF RFID Technology for Identification of Closely Spaced Medical Equipment

The global healthcare industry is transforming very quickly. For many years, healthcare providers have been addressing how to reduce errors that increase costs, lower quality of life for patients, and in a number of cases, have caused patient death. The paper discusses options for automated identification based on UHF RFID technology on stacked or very near objects, especially for use in healthcare. When using UHF RFID on closely spaced objects, such as test tubes, a particular problem arises. When two common UHF tag antennas are placed very near and parallel to each other, the structure becomes very similar to a folded dipole antenna. A solution based on testing the use of UHF RFID technology to identify medical test tubes is proposed in this paper.
Big data, differential privacy and national statistical organisations

Differential privacy (DP) has emerged in the computer science literature as a measure of the impact on an individual’s privacy resulting from the publication of a statistical output such as a frequency table. This paper provides an introduction to DP for official statisticians and discuss its relevance, benefits and challenges from a National Statistical Organisation (NSO) perspective. We motivate our study by examining how privacy is evolving in the era of big data and how this might prompt a shift from traditional statistical disclosure techniques used in official statistics – which are generally applied on a cell-by-cell or table-by-table basis – to formal privacy methods, like DP, which are applied from a perspective encompassing the totality of the outputs generated from a given dataset. We identify an important interplay between DP’s holistic privacy risk measure and the difficulty for NSOs in implementing DP, showing that DP’s major advantage is also DP’s major challenge. This paper provides new work addressing two key DP research areas for NSOs: DP’s application to survey data and its incorporation within the Five Safes framework.
A CNN Based Method for Quantitatively Evaluating 3D Face Reconstruction Algorithms

Abstract 3D face reconstruction is an attractive topic in computer vision. We have seen dramatic rise in its development recently. Now the state-of-the-art method can reconstruct a face from a single 2D face image. Since the work conditions and cost of a 3D scanner is limited, these 3D reconstruction algorithms are tough to evaluate. Some researchers had proposed different algorithms by measuring the distances between reconstructed images and true depth face previously. Inspired by CNN’s powerful representative ability, here we investigate the issue from CNN’s perspective by comparing the performances of various 3D face reconstruction algorithms on two classification tasks. One is an identity classification task for evaluating the intra-dataset discrimination and another is a binary classification for reconstructed faces and 3D ground truth faces for evaluating the inter-dataset discrimination. To accomplish this, we adopt hourglass neural network as backbone to perform comparisons for three 3D reconstructed algorithms on two datasets. In the end, we show the pros and cons of various algorithms and explicit the mechanisms of the results.