Reviewerly/exHarmony huggingface dataset:
MicroMaps: a unified StarCraft 2 micromanagement AI testbed

The real-time strategy game represented by StarCraft is the frontier and hot spot of Artificial Intelligence (AI) research in recent years, in which micromanagement is the basic and core key problem. The micromanagement technological breakthrough has great and wide application prospects in the field of swarm intelligence game confrontation. At present, in the research of micromanagement in StarCraft, the verification scenarios proposed by various researchers for their own concerns are scattered and independent, and lack of a unified training and testing scenario testbed. In this regard, we propose a unified testbed named micromaps. According to reasonable principles, we systematically and comprehensively design a variety of homogeneous and heterogeneous micromanagement scenarios, and support the comprehensive comparison of AI algorithms in many aspects, such as winning rate, generalization and convergence speed. Finally, the future research direction of StarCraft 2 micromanagement is prospected.
Keynotes

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. With the wide/rapid spread of distributed systems for information processing, such as cloud computing and social networking, not only transmission but also processing is done on the intemet. However, cloud environments have some serious issues for end users, such as unauthorized access, data leaks, and privacy compromise, due to unreliability of providers and some accidents. Accordingly, we first focus on compressible image encry ption schemes, which have been proposed for encryption-then-compression (EtC) systems, although the traditional way for secure image transmission is to use a compression-then encry ption (CtE) system. EtC systems allow us to close unencrypted images to network providers, because encrypted images can be directly compressed even when the images are multiply recompressed by providers. Next, we address the issue of leamable encryption. Huge training data sets are required for machine leaming and deep leaming algorithms to obtain high performance. However, it requires large cost to collect enough training data while maintaining people's privacy.
Tampering Localization in Double Compressed Images by Investigating Noise Quantization

Noise is uniformly distributed throughout an untampered image. Tampering operations destroy this uniformity and introduce inconsistency in the tampered region. Hence, noise discrepancy is often investigated in forensic analysis of uncompressed digital images. However, noise in compressed images has got very little attention from the forensic experts. The JPEG compression process itself introduces uniform quantization noise throughout an image, making this investigation difficult. In this paper, the authors have proposed a new noise compression discrepancy model, which blindly estimates this discrepancy in the compressed images. Considering the smaller tampered region, SVM classifier was trained using noise features of test sub-images and its nonaligned recompressed versions. Each of the test sub-images was further classified using this classifier. Experimental results show that in some cases, the proposed approach can achieve better performance compared with other JPEG artefact based techniques.
A Comprehensive Big Data Analytics Based Framework for Premature Recognition of Breast Cancer

Healthcare industry is fast growing and expanding in rapid pace. The volume and veracity of data generated in the industry is massive and requires huge storages and handling capability. Big data is empowered with such robust abilities and hence most suitable for handing large amount of data. Further, hese data could be utilized towards building predictive and forecasting models. Breast cancer is a deadly form of cancer majorly affecting women around the globe. The concept of big data and predictive analytics is being explored in the paper towards early diagnosis of breast cancer. This paper surveys various literatures available on application of big data analysis for breast cancer. Subsequently a comprehensive framework is being proposed based on the gaps identified. Different machine learning algorithms which can be applied in the framework is also detailed in the paper. Such frameworks when implemented will greatly help in handling the massive data available and aid in early detection of breast cancer.
Basics of Fluid Construction Grammar

Fluid Construction Grammar (FCG) is a fully operational computational platform for developing grammars from a constructional perspective. It contains mechanisms for representing grammars and for using them in computational experiments and applications in language understanding, production and learning. FCG can be used by grammar writers who want to test whether their grammar fragments are complete and coherent for the domain they are investigating (for example verb phrases) or who are working in a team and have to share grammar fragments with others. It can be used by computational linguists implementing practical language processing systems or exploring how machine learning algorithms can acquire grammars. This paper introduces some of the basic mechanisms of FCG, illustrated with examples.
Effect of Urban Computing on the Public's Perception of Place, Community, and Infrastructure

The work presented in this chapter delineates the longitudinal experience of deploying an urban computing system that enables citizens to share and interact with digital content about the urban environment and experiences of people with it. It is part of an emerging and novel aspect of urban computing that expands research beyond simple optimisations of city functions towards a social and cultural approach that seeks to orchestrate complex socio-technical ensembles. Offering Collective City Memory as a service to citizens and enabling them to interact with it via diverse novel interfaces has uncovered the implications for city life that the introduction of urban computing brings such as the redefinition of spatial and temporal proximity and the effects on the perception of city space, fostering of social interactions, contribution to shared resources and participation in collective efforts.
Digital Forensics of Cybercrimes and the Use of Cyber Forensics Tools to Obtain Digital Evidence

This chapter evaluates the most relevant methodologies and best practices for conducting digital investigations, preserving digital forensic evidence and following chain of custody (CoC) of cybercrimes. Cybercriminals are assuming new strategies to launch their sophisticated cyberattacks within the ever-changing digital ecosystems. The authors recommend that digital investigations must continually shift to tackle cybercrimes and prosecute cybercriminals to increase international collaboration networks, to share prevention knowledge, and to analyze lessons learned. They also establish a cyber forensics model for miscellaneous ecosystems called cyber forensics model in digital ecosystems (CFMDE). This chapter also reviews the most important categories of tools to conduct digital investigations. Nevertheless, as the cybercrime sophistication keeps improving, it is also necessary to harden technologies, techniques, methodologies, and tools to acquire digital evidence in order to support and make cyber investigation cases stronger.
Visual Event Computing I

In surveillance, we need present a story of a moving object. This story is called event which is the best way to describe the motion of this object. In this chapter, we will critically compare and evaluate the major components of a surveillance event, understand the event as a basic semantic unit of intelligent surveillance. We will introduce the algorithms how to detect and recognize an event.
Privacy-Preserving Orchestrated Web Service Composition with Untrusted Brokers

This article describes how by using web service composition to model different business processes is a usual tendency in the industry. More specifically, web service composition enables to separate a certain process in different activities that must be executed following a certain order. Each activity has its own set of inputs and outputs and is executed by a certain web service hosted by a service provider which can be completely independent. Among all the applications in which web service composition may be applied, this article focuses on a cloud-based scenario in which a business wishes to outsource the execution of a certain complex service in exchange for some economical compensation. It is for this reason, among the different composition approaches that exist in the literature, this article focuses on the orchestrated one, in which a broker coordinates the composition. One of the main issues of orchestrated systems is the fact that the broker receives and learns all the input data needed to perform the requested complex service. This behavior may represent a serious privacy problem depending on the nature of the business process to be executed. In this article, a new privacy-preserving orchestrated Web service composition system based on a symmetric searchable encryption primitive is proposed. The main target of this new scheme is to protect the privacy of the business that wish to outsource their operations using a cloud-based solution in which the broker is honest but curious, this is, this entity tries to analyze data and message flows in order to learn all the possible sensitive information from the rest of participants in the system.
Data Analysis in Earth Sciences

The book introduces some of the most important methods of data analysis employed in earth sciences and illustrates their use through examples using the MATLAB® software package. This chapter deals with data acquisition (Sect. 1.2), the various types of data (Sect. 1.3) and the appropriate methods for analyzing earth science data (Sect. 1.4). We first explore the characteristics of typical data sets and subsequently investigate the various ways of analyzing data using MATLAB.
Implications of System on Health and Public Health Practice and Research

Chapters 10 and 11 were about the methodology and results, respectively, on the system's impact on compliance of the healthcare providers with the maternal health guidelines. Chapter 12 now considers the broader consequences of the BACIS program study. First, a discussion of the results of the BACIS program study in relation to the safe motherhood programme is undertaken. This is followed by a discussion of the issue of non-utilisation of health services, which is an issue that impacts on outcomes and programme success. After this follows a section on the discussion of the results of the BACIS program study against other e-health interventions that are in use or have been proposed, then next against other public health interventions in general, not just e-health systems. The final section discusses the study's results against the backdrop of the broader topic of primary healthcare functioning.
Research on Micro Video Character Recognition Based on Target Detection Technology

With the rapid development of Internet short video platform, micro video is loved by more and more users because of its advantages such as small traffic and time period. However, the content of micro video on the Internet is complex, including much illegal and illegal content information. This research is an improved algorithm based on target detection technology and aiming at the characteristics of difficult character recognition in micro video. The algorithm combines Gabor wavelet transform algorithm and 2DPCA algorithm. Through MATLAB simulation analysis, it can be seen that the optimization algorithm proposed in this paper has better recognition rate and recognition efficiency.
A Reusable Software Architecture for Spacecraft Control System

High-density launches have become normal, and spacecraft software productization is imperative. In order to realize the productization of spacecraft control system software, the domain-specific software architecture was introduced into the development process, and a reusable software architecture was designed. Based on the functional requirements of spacecraft control system, the domain analysis was carried out, and the domain foundation and layered structure of the reusable software architecture were discussed. Using the reusable software architecture, the reuse rate of software products is increased, the scalability is enhanced, and the scale is reduced. At the same time, the software development process can be optimized and the development cycle can be shortened. Project practices prove that the reusable software architecture can be promoted.
Semantic Based Annotation for Surveillance Big Data Using Domain Knowledge

Video surveillance technology is playing a more and more important role in traffic detection. Vehicle's static properties are crucial information in examining criminal and traffic violations. Image and video resources play an important role in traffic events analysis. With the rapid growth of the video surveillance devices, large number of image and video resources is increasing being created. It is crucial to explore, share, reuse, and link these multimedia resources for better organizing traffic events. With the development of Video Surveillance technology, it has been wildly used in the traffic monitoring. Therefore, there is a trend to use Video Surveillance to do intelligent analysis on vehicles. Now, using software and tools to analyze vehicles in videos has already been used in smart cards and electronic eye, which helps polices to extract useful information like plate, speed, etc. And the key technology is to obtain various properties of the vehicle. This paper provides an overview of the algorithms and technologies used in extracting static properties of vehicle in the video.
Cybersecurity Leadership

Technology drives today’s public and private organizations and has become the core of their business. Hence, in this technology driven organization environment, confidentiality, integrity, and availability of technology, and information systems have become crucial to today’s digital transformation in public and private organizations. However, cybercrime is the greatest threat and vulnerability to every public and private organization, making cybersecurity the most important issue today. Therefore, cybersecurity leadership is essential to deal with which requires a holistic approach and roadmap for leaders to ponder and to follow an effective cybersecurity program in public and private organizations.
Athena Diagnostics, Inc. v. Mayo Collaborative Servs. United States Court of Appeals of the Federal Circuit, 2019 927 F.3d 1333

Biotechnology Law ReportVol. 38, No. 5 Case in PointAthena Diagnostics, Inc. v. Mayo Collaborative Servs. United States Court of Appeals of the Federal Circuit, 2019 927 F.3d 1333Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:11 Oct 2019https://doi.org/10.1089/blr.2019.29143.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 38Issue 5Oct 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:Athena Diagnostics, Inc. v. Mayo Collaborative Servs. United States Court of Appeals of the Federal Circuit, 2019 927 F.3d 1333.Biotechnology Law Report.Oct 2019.303-323.http://doi.org/10.1089/blr.2019.29143.cmhPublished in Volume: 38 Issue 5: October 11, 2019PDF download
Lived experience of literature teachers and students in the new normal

This study was conducted to explore the lived experience of literature teachers and students in the new normal, particularly in dealing with Online Distance learning modality.The participants of the study were the Grade 9 Literature teachers and Grade 9 students from selected Junior High Schools in the private schools of Batangas.The study utilized qualitative research with interpretative phenomenology analysis as the approach in order to explore in detail the participants personal experience and its meaning.A validated self-made interview questionnaire was used as the primary data gathering instrument.The data collected were transcribed and analyzed to formulate themes.The themes were as follows: Diverse
DeshengNet: An Information Extraction Model for Table in Digital Documents

In the scholarly literature, tables carry a huge amount of information. In the traditional information extraction of the table, researcher often spend a lot of manpower to intergrate information. Using computer vision technology can imporve the efficiency of gathering data. However, in the literature of academic, too many negative samples often lead to poor result. In response to the previous problem, this paper proposes DeshengNet based on deep learning table information extraction in digital documents. Firstly, the feature map of literature pictures is obtained through the deep residual network. Then, the multi-scale features are merged with the feature pyramid network. Afterwards, the class box subnet is used for table positioning. Aiming at the problem of too many negative samples in the paper. The focal loss method is used for training. After detection, the spatial features of the table are used for heuristic extraction. The experimental results show that the method proposed in this paper can be applied to the industrial field and serve the researcher.
A Transformer-based Dual Position Attention Network for Recognizing Human-object Interaction

In this paper, a transformer-based dual position attention network (TDPAN) is proposed for recognizing human-object interaction. The dual attention module embedding position information is designed to scan the entire area of an image space and adaptively aggregate crucial features. Moreover, the transformer architecture is adopted for effectively extract essential region features in a binary pairwise manner from the sequence image data. Compared to the CNN-based method, TDPAN feature aggregation does not require prior modification of the region of interest, but also focus more on the important context information in images. The experiments demonstrate that the TDPAN outperforms previous methods on two datasets (the HICO-DET dataset and the V-COCO dataset). Specifically, the recognition accuracy is increased by 4.3% compared with the prior convolutional neural network methods in V-COCO dataset.
Innovative Techniques for the Formation of Metadisciplinary Learning Outcomes of Younger Schoolchildren

The article discusses the ways to implement in the pedagogical practice the requirements of the Federal State Educational Standard of Primary General Education to form the basis for the younger schoolchildren of the ability to learn and the ability to organize their activities, which refers to metasubjective results of learning. It offers techniques that allow to level the difficulties and eliminate potential problems of primary school graduates in performing tasks based on working with information and associated with the development of logical and algorithmic thinking, the construction of reasoning, predictions and conclusions.
Research of Effective Structural Engineering Course Teaching Based on Artificial Intelligence

Summarizing and analysing the inherent nature of structural engineering intelligent teaching, the effective method of intelligent teaching is proposed. Teaching effectiveness with intellectualization is greatly improved. After the analysis of the status of students, the inherent nature and characteristics of teaching are pointed out. Based on the use of complex features of old and new knowledge with artificial intelligence method, the progress and logic and mental model of the traditional mode of engineering course teaching on theory and practice are probed into. Computer program has been used to carry out engineering structure design and engineering course teaching and information editing.
Evolutionary Algorithms

Evolutionary algorithms (EAs) are computational methods that simulate natural evolutionary processes for the purpose of solving optimization problems. Each EA contains a set of individual solutions to the problem being addressed. The individual solutions are typically initialized on a random basis and then iterative processes including selection, recombination, and mutation are employed to operate on these individuals so that they will evolve toward optimal or near-optimal conditions. EAs have been widely used in a variety of application domains, including operations research, pattern analysis, data mining, and spatial decision analysis.
A Lightweight Detection Model for Typical Objects in Streetscape Dataset

This paper proposes a sparse training strategy which the weighting factor decays globally and dynamically, and a channel pruning method for models with shortcut connection. The detection model trained on yolov5 for the streetscape dataset needs to be pruned. To improve the detection accuracy of the pruned model, this paper presents a knowledge distillation loss function based on the idea of greed for object detection. By virtue of these strategies, we obtain a relatively lightweight detection model with smaller size, fewer parameters and higher detection accuracy of the typical objects in our streetscape dataset, providing reference for future application of detection models to in-vehicle embedded devices for street objects monitoring.
Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.
Informatization of Constructive English Learning Platform Based on Machine Learning

In today's era, with the rapid development of network communication technology, people have generally accepted the model of using learning platforms to carry out learning. With the popularity of English in the world, the demand for English in daily life is also increasing, and the application of English learning platform(ELP) is also increasing. In recent years, the research of machine learning(ML) methods has developed rapidly, and has been applied in many fields to solve corresponding problems. This article mainly adopts experimental analysis and data analysis methods, intending to use ML to analyze user behavior patterns and gain insights into user needs from the massive user behavior data on the ELP, so as to promote the realization of the informatization of the constructive ELP. This paper conducts an experimental research on the realization of the informatization of a constructive ELP based on ML, tests and analyzes the server performance. According to experimental research results, as the number of concurrent users increases, the platform's network utilization, average memory usage, CPU usage, and denial of service rate also increase. On the whole, the ELP still has a certain Practicality and stability, but there are still some functional defects in the platform.