Reviewerly/exHarmony huggingface dataset:
Proposal for Using Digital Tools in Mitigating a Pandemic: Lessons Learned from COVID-19.

BACKGROUND: Since the COVID-19 outbreak began in Wuhan China, countries across the world have been forced to take unprecedented measures to combat it. While some countries are still grappling with the COVID-19 pandemic, others have fared better and have established relative normalcy quickly. The rapid transmission rate of the virus has shown a greater need for efficient and technologically modern containment measures. The use of digital tools to facilitate strict containment measures in countries that have faired well against the COVID-19 pandemic has sparked both interest and controversy. OBJECTIVE: In this study, we compare the precautions taken against the spread of COVID-19, particularly the use of digital tools in contact tracing, and propose policies that could be utilized in the U.S. for future COVID-19 waves or pandemics. METHODS: COVID-19 death rates data were obtained from the European Center for Disease Prevention and Control (ECDC) accessed through the Our World in Data database, and were evaluated based on population size per 100,000 from December 31, 2019 to September 6, 2020. All policies and measures enacted were obtained from their respective governmental websites. RESULTS: We found a strong association between lower death rates per capita and countries that implemented early mask use and strict border control measures that included mandatory quarantine using digital tools. There is a significant difference in the number of deaths per 100,000 when comparing Taiwan, South Korea, and Singapore versus the U.S., Spain, and Italy. CONCLUSIONS: Based on our research, it is evident that early intervention with the use of digital tools has a strong correlation for containing COVID-19. Infection rates and subsequent deaths in Italy, Spain, and more specifically the U.S. could have been much lower with early mask use and, more importantly, timely border control measures utilizing modern digital tools. Thus, we propose that the U.S. execute the following national policies should a public health emergency be declared: (1) Immediately establish a National Command responsible for enacting strict mandatory guidelines enforced by federal and state governments, including national mask use. (2) Mandate civilian cooperation with health officials in contact tracing and quarantine orders. Incoming travelers to the U.S. and those quarantined will be required to download a contact tracing app. We acknowledge the countries we studied differ in their cultures, political systems, and reporting criteria for COVID-19 deaths. Further research may need to be conducted to address these limitations; however, we believe that the proposed policies could protect the American public.
A differential evolution SAF-DE algorithm which jumps out of local optimal

The principle of differential evolutionary algorithm is easy to understand, and it has the advantages of fast convergence, simple operation and good stability, which has been favored by many researchers. However, the differential evolution algorithm is easy to fall into the local optimum, and even cause the algorithm to stagnate, the low efficiency, and the unstable convergence speed of algorithm. This paper proposes an improved differential evolution (SAF-DE) algorithm, which uses the perturbation formula to perturb the individual values in the population to make individual more diversified. So as to achieve the purpose of improving the accuracy and convergence speed in the optimization process of the differential evolution algorithm. algorithm, the improved algorithm has higher convergence speed and accuracy on some standard functions.
Spatial Joint Upsampling Based Crowd Counting

Crowd counting are playing an important role in controlling public crowd flow, maintaining public safety order, and controlling novel coronavirus (2019-nCoV). In recent years, researchers have proposed many excellent crowd counting methods, but these methods still have some problems such as insufficient information obtained in the output density map. Based on it, this paper proposed a crowd counting method based on Spatial Joint Upsampling(SJU). The method uses VGG-16 as the backbone of front-end. Meanwhile the back-end network extracts rich pixel details and spatial context information by adding cascaded joint upsampling model to combine multi-layer high-resolution feature maps, which ultimately have reached the effect of reducing the computational complexity and lifting the accuracy. Experimental results show that SJU has better performance than most crowd counting methods. This method has achieved MAE/MSE of 62.1/99.8 and 7.6/11.5 on the two parts of the public dataset ShanghaiTech A and B, respectively. The MAE/MSE after conducting curriculum learning on this dataset is 61.3/99.2. In addition, this method has also achieved excellent results on the large-scale complex crowd dataset NWPU-Crowd, with a MAE/MSE of 105.1/419.3, indicating that the Spatial Joint Upsampling(SJU) network has outstanding performance in the task of crowd counting in complex scenes.
Virtual Demonstrator for Spatial Presentations

To overcome the challenges of instructing on spatial concepts remotely, we propose Virtual Demonstrator, a virtual reality (VR) presentation software for educators. Virtual Demonstrator provides a suite of tools for creating spatial presentations in VR, analogous to 2D slide-based presentation software. Within our software, Visual Elements are combined across a spatiotemporal space, with discrete states akin to presentation slides. This educational resource is designed to address a growing need to learn without physical presence, and to leverage 3D spaces for learning complex spatial concepts. A timeline expands a 3D environment into another dimension, allowing for an expanded design space.
Effect of Spectral Bandwidths on Linear Feature Extraction: An Evaluation of Landsat ETM+ and OLI Sensors

Abstract Hitherto there have been many studies comparing the usefulness of OLI and ETM+ sensors for linear feature extraction. However, not too much attention has been paid to the differences in the bandwidth of the two sensors. In this study, the suitability of Landsat ETM+ and OLI sensors for automatic detection of linear features by LINE algorithm was compared. In this study, eight regions in northern, central and southern parts of Iran were selected based on the diversity of lithology, the pristine status, and lack of human activities for the comparison of the two datasets. Results revealed that LINE algorithm performed better on the images with higher standard deviation. The ETM+ datasets are more suitable for linear feature extraction because ETM+ panchromatic band and first principal component analysis image (PC1 image) of ETM+ datasets have higher standard deviation compared to OLI datasets.
Understanding Smart Contracts: Hype or Hope?

Smart Contracts are commonly considered to be an important component or even a key to many business solutions in an immense variety of sectors and promises to securely increase their individual efficiency in an ever more digitized environment. Introduced in the early 1990's, the technology has gained a lot of attention with its application to blockchain technology to an extent, that can be considered a veritable hype. Reflecting the growing institutional interest, this intertwined exploratory study between statistics, information technology, and law contrasts these idealistic stories with the data reality and provides a mandatory step of understanding the matter, before any further relevant applications are discussed as being ``factually" able to replace traditional constructions. Besides fundamental flaws and application difficulties of currently employed Smart Contracts, the technological drive and enthusiasm backing it may however serve as a jump-off board for future developments thrusting well in the presently unshakeable traditional structures.
SEPC: Improving Joint Extraction of Entities and Relations by Strengthening Entity Pairs Connection

Joint extraction of entities and relations aims at recognizing relational triples (subject s, relation r, object o) from unstructured text. For any entity pair (s, o) in correct relational triples, they do not appear independently, but depending on each other. While existing approaches usually model entity pairs only by sharing the encoder layer, which is insufficient to exploit entity pair intrinsic connection. To solve this problem, we propose to strengthen entity pairs connection (SEPC) by utilizing the duality property of entity pairs, which can further improve the joint extraction. The entity pairs recognization is transformed to finding subject conditioned on the object and finding object conditioned on the subject, and the dual supervised learning is introduced to model their connection. We finally demonstrate the effectiveness of our proposed method on two widely used datasets NYT and WebNLG (Code and data available: https://github.com/zjp9574/SEPC).
Big data, inferred data and the future of remaining human – between abdormission and horripilation

1. Dyson F., Is life analog or digital?, [in:] Birds and Frogs: Selected papers of Freeman Dyson, 1990– 2014, Singapur 2015. Google Scholar
Teacher-Student Competition for Unsupervised Domain Adaptation

With the supervision from source domain only in class-level, existing unsupervised domain adaptation (UDA) methods mainly learn the domain-invariant representations from a shared feature extractor, which causes the source-bias problem. This paper proposes an unsupervised domain adaptation approach with Teacher-Student Competition (TSC). In particular, a student network is introduced to learn the target-specific feature space, and we design a novel competition mechanism to select more credible pseudo-labels for the training of student network. We introduce a teacher network with the structure of existing conventional UDA method, and both teacher and student networks compete to provide target pseudo-labels to constrain every target sample's training in student network. Extensive experiments demonstrate that our proposed TSC framework significantly outperforms the state-of-the-art domain adaptation methods on Office-31 and ImageCLEF-DA benchmarks.
Incorporating Syntactic Information into Relation Representations for Enhanced Relation Extraction

Relation Extraction (RE) is a premier task of information extraction (IE) and crucial to many applications including knowledge graph completion (KGC). In recent years, some RE models have employed the topic knowledge of relations through topic words to enrich relation representations, demonstrating better performance than traditional distantly supervised paradigms. However, these models have not taken different syntactic information of relations into account, which have been proven significant in many NLP tasks. In this paper, we propose a novel RE pipeline which incorporates syntactic information into relation representations to enhance RE performance. Representations of sentence and relation in our pipeline are generated by a modified multi-head self-attention structure respectively, where the sentence is represented based on its words and the relation is represented based on the relation-specific embeddings of its topic words. Furthermore, all sentences labeled with the input relation are used to construct an entire weighted directed graph based on their dependency trees. Then, the relation-specific embeddings of words (nodes) in the graph are learned by a GCN-based model. Our extensive experiments have justified that our pipeline significantly outperforms other RE models thanks to the incorporation of syntactic information.
A Rapid Ear Modeling Method Based on Standard Template and Screening Algorithm for Portrait Product

In view of the low efficiency in the conventional portrait modeling, a rapid ear modeling method based on a standard template and screening algorithm for portrait product is proposed to achieve high efficiency in portrait modeling. Firstly, the normalized template of ears can be obtained by the operation of normalizing the photo of ear and standard template. Secondly, the feature edge of the ear and standard template can be extracted respectively by using a method of edge detection based on gray-scale morphology and local threshold segmenting. Afterwards registration is performed. Thirdly, the extracted feature edges are analyzed through the proposed screening algorithm, and the optimal ear model is obtained from the library of the standard template. Therefore, a final portrait model achieved by assembly the optimal ear model to the original model. Finally, a case study of applying the proposed methodology to a personalized portrait product modeling demonstrates the feasibility and effectiveness of the proposed method.
Adversarial Learning Siamese Network for Object Tracking

Aiming at the problem that the object tracking algorithm based on the siamese network is difficult to effectively extract the salient features of the object due to the shallow AlexNet. In this paper, we propose a siamese network tracking algorithm based on adversarial learning (AL-SiamFC). First, we construct the convolutional backbone network based on the attention dropout layer, and then let the network learn the salient features of the object in the form of adversarial learning during offline training. Finally, the trained tracking network is evaluated using the tracking benchmark data set OTBIOO, and the attention dropout layer is not used in the online tracking. The experimental results show that, without significantly reducing the tracking speed, the success rate and precision rate of the AL-SiamFC are increased by 3% and 2% compared with the SiamFC, and the adaptability and robustness have been significantly improved.
Bayesian Active Learning for Maximal Information Gain on Model Parameters

The fact that machine learning models, despite their advancements, are still trained on randomly gathered data is proof that a lasting solution to the problem of optimal data gathering has not yet been found. In this paper, we investigate whether a Bayesian approach to the classification problem can provide assumptions under which one is guaranteed to perform at least as good as random sampling. For a logistic regression model, we show that maximal expected information gain on model parameters is a promising criterion for selecting samples, assuming that our classification model is well-matched to the data. Our derived criterion is closely related to the maximum model change. We experiment with data sets which satisfy this assumption to varying degrees to see how sensitive our performance is to the violation of our assumption in practice.
The Search for Digital Information by Evaluating Four Models

As information becomes more and more abundant and accessible on the web, researchers do not have to dig through books and libraries. Web pages are rich in textual information, the web search engines provide Internet users with various files corresponding to the searched keywords. This large number of digital data makes manual sorting difficult to do, so it is necessary to automate collection of useful information using techniques based on artificial intelligence. In today's digital age, great importance is given to information retrieval techniques via Internet. Therefore, it appears essential to preconize a credible and performing system dealing with all textual information, in order to deduce structured and useful knowledge. This work focuses on four models used in the field of information retrieval, and highlights their limits of use, with a view to developing new techniques that can fill the gaps detected. At the end, the evaluation parameters will be discussed to enhance human intervention in decision making.
The Symbols of Propositional Logic

This chapter introduces a propositional language called L. The alphabet of L is constituted by three categories of symbols:
A modified Single-Shot multibox Detector for beyond Real-Time Object Detection

This works focuses on examining the performance of the Single Shot Detector (SSD) model in resource restricted systems where maintaining the power of the full model comprises a significant prerequisite. The proposed SSD variations examine the behavior of lighter versions of SSD while propose measures to limit the unavoidable performance shortage. The outcomes of the conducted research demonstrate a remarkable trade-off between performance losses, speed improvement and the required resource reservation. Thus, the experimental results evidence the efficiency of the presented SSD alterations towards accomplishing higher frame rates and retaining the performance of the original model.
The role of participatory management and social responsibility of principals in First high school teacher’s job satisfaction: creativity mediation test of principals

The role of participatory management and social responsibility of principals in First high school teacher’s job satisfaction: creativity mediation test of principals
Detecting Human-Induced Changes in Powerline Signals

Human activity in the room causes signal changes on the power-lines. Therefore, the state of a person can be inferred by measuring the signal changes on the powerlines. Previous powerline-based indoor human sensing systems suffer from expensive hardware costs and thus cannot be deployed on a large scale. In this work, we propose a more cost-effective version of the sensing system and achieve sufficiently good results in a real deployment.
Research on Simulation of Network Attack and Defense situation based on Evolutionary Game

Network attack and defense research is the focus in the field of network security, but most of the existing network attack and defense studies focus on one of the two sides of attacker and defender, which cannot accurately explain the nature of the attack-defense confrontation. It failed to describe and analyze the game behavior at the micro-level and the network attack and defense scenario at the macro level. Aiming at this, this paper firstly establishes a network attack and defense game model based on evolutionary game theory from the point of view of attack-defense confrontation and studies the quantitative methods of attack-defense income, game equilibrium, and strategic confrontation. Then based on NetLogo and improved SI (Susceptible-Infectious) model, the simulation model of network attack and defense is constructed. Finally, the effectiveness of the proposed model and method is verified by simulation experiments, and the countermeasures and suggestions to enhance the network defense capability are analyzed and summarized from the simulation results.
Principles and Interactive Tools for Evaluating and Improving the Behavior of Natural Language Processing models

While the accuracy of Natural Language Processing (NLP) models has been going up, users have more expectations than captured by just accuracy. Despite practitioners’ attempt to inspect model blind spots or lacking capabilities, the status-quo processes can be ad-hoc and biased. My thesis focuses on helping practitioners organize and explore the inputs and outputs of their models, such that they can gain more systematic insights into their models’ behaviors. I identified two building blocks that are essential for informative analysis: (1) to scale up the analysis by grouping similar instances, and (2) to isolate important components by generating counterfactuals. To support multiple analysis stages (training data assessment, error analysis, model testing), I designed various interactive tools that instantiate these two building blocks. In the process, I characterized the design space of grouping and counterfactual generation, seeking to balance the machine powers and practitioners’ domain expertise. My future work proposes to explore how the grouping and counterfactual techniques can benefit non-experts in the data collection process.
Protocol for Dynamic Load Distribution in Web-Based AR

In a Web-based Augmented Reality (AR) application, to achieve an immersive experience we require precise object detection, realistic model rendering, and smooth occlusion in real-time. To achieve these objectives on a device requires heavy computation capabilities unavailable on most mobile devices, this can be solved by using cloud computing but it introduces network latency issues. In this work, we propose a new network protocol named DLDAR (Dynamic Load Distribution in Web-based AR) that facilitates and standardizes methods for dynamic division of compute between client and server based on device and network condition to min-max latency and quality.
Architectural Decay as Predictor of Issue- and Change-Proneness

Architectural decay imposes real costs in terms of developer effort, system correctness, and performance. Over time, those problems are likely to be revealed as explicit implementation issues (defects, feature changes, etc.). Recent empirical studies have demonstrated that there is a significant correlation between architectural "smells"—manifestations of architectural decay—and implementation issues. In this paper, we take a step further in exploring this phenomenon. We analyze the available development data from 10 open-source software systems and show that information regarding current architectural decay in these systems can be used to build models that accurately predict future issue-proneness and change-proneness of the systems’ implementations. As a less intuitive result, we also show that, in cases where historical data for a system is unavailable, such data from other, unrelated systems can provide reasonably accurate issue- and change-proneness prediction capabilities.
Chapter 6: The Reasoning Process

The main element of thinking that we have singled out—analysis through synthesis that detects more and more new properties in the analyzed objects when they are incorporated into new connections—is...
On Black-Hole Numbers of Iteration of Digital Biquadratic Power Sum in the Ternary, Quinary or Quindenary System

There are different periodic and fixed points under the iteration of biquadratic sum of digits of positive integer in different carry systems. There are one fixed point and two 2-circles in ternary; four fixed points and three 4-circles in quinary; seven fixed points, two 2-circles, one 3-circle, one 9-circle and one 63-circle in quindenary.
Welcome to the Data Jungle – Our Way to Tackle AI-Supported Vehicle Development Projects

In the automotive industry, high expectations have been placed on the industrial application of Artificial Intelligence (AI) for several years. These expectations focus on both the development of vehicles and the vehicles themselves. Machine Learning should help to accelerate processes, increase quality, and even pave way to tackle previously non-feasible challenges. Indeed, AI methods hold the potential to meet these needs in most cases, provided they are applied in the right way and used in the correct context.This paper gives a descriptive overview of AI methods and their practical application in industry and research projects, especially in vehicle development. TWT is constantly developing new successful AI-based solutions in customer projects. In addition to various abstracted examples from industrial projects, exemplary results from TWT's participation in the research project AutoAkzept are presented.