Reviewerly/exHarmony huggingface dataset:
RPD: A Distance Function Between Word Embeddings

It is well-understood that different algorithms, training processes, and corpora produce different word embeddings.However, less is known about the relation between different embedding spaces, i.e. how far different sets of embeddings deviate from each other.In this paper, we propose a novel metric called Relative pairwise inner Product Distance (RPD) to quantify the distance between different sets of word embeddings.This metric has a unified scale for comparing different sets of word embeddings.Based on the properties of RPD, we study the relations of word embeddings of different algorithms systematically, and investigate the influence of different training processes and corpora.The results shed light on the poorly understood word embeddings and justify RPD as a measure of the distance of embedding spaces.
Covid-19 Diagnosis Based Android Mobile Application using Certainty Factor Method

Expert Systems are part of a general category of computers known as intelligence. Created Expert System to work on a domain. To make it easier to conclude a problem, however, it requires efficient data to a certain part of basic knowledge, and human thinking procedures to implement knowledge on a given problem. In artificial intelligence, an expert system is a computer system that mimics the decision-making ability of humans who are experts in their fields. The main thrust of artificial intelligence is in the development of computer functions related to human intelligence, such as reasoning, learning, and problem solving, to build a knowledge-based system in the specific field of medicine in the domain for the purpose of diagnosing Covid-19 displayed on mobile devices, information that is quickly and precisely from an expert is needed, can be formulated a problem in the form of diagnosis Covid-19 through the application of an expert system in the form of input symptoms. This is what drives the creation of the Covid-19 diagnostic system using a certainty factor based on Android mobile application.
Designing Stronger AI Personalities

In this invited industry case study, Tanya X.Short introduces eight mechanisms that designers can utilize to better harness procedural character personalities in games. Tanya is co-founder and captain of Kitfox Games, a Montreal-based independent game studio, and a veteran developer and designer known for her expertise in procedural generation and systems-driven game design. With Tarn Adams, she edited the 2017 volume Procedural Generation in Game Design. As Tanya explains in this paper, there is an emerging pattern in game design that utilizes character personality as a central gameplay system. Using a number of examples spanning her experiences as both developer and player, in this paper Tanya distills her collected design knowledgeinto an actionable recipe for building better character personality systems.
Living Identity Verification via Dynamic Face-Speech Recognition

In this paper, we investigate how to achieve living identity verification based on face and speech jointed recognition. Summarily, the person is required to make several expressions and read several short words, while the orders and contents are dynamically generated. Then we compare the facial components with registered ones by integrated deep neural networks (DNNs), and recognize the speeches by MVDR-MFCC features. At last, according to the results of face and speech recognition, the living identity can be verified. The innovations are as follows: firstly, the dynamic acoustic contents and faces with different expression can efficiently ensure the target is living, thus the safety of identification can be increased. Secondly, an effective component-based method is proposed for face recognition, and synthesized multiple DNNs can help reflect intensities of different components. Thirdly, MVDR spectrum is used in acoustic classification, which can effectively enhance MFCC feature. Comparative experiments demonstrate that our algorithm outperforms traditional methods in face and speech recognition accuracy, and our algorithm has particularly preponderance that it can verify the liveness of targets, so it can achieve higher security.
KNOWLEDGE TRANSFORMING IN WRITING: AN ANALYSIS OF READ-TO-WRITE PROCESS

&lt;p&gt;Academic writing depends on critical reading. Writers need to read materials to be used as support and evidence in their writing. The writing process of an academic writer mirrors the knowledge transforming model. The link between reading and writing has been established by past studies. The read-to-write process is the academic writing process that writers go through but focused more on the reading activities to prepare for materials to be included in the essay. This study is done to explore the perception of writers in their reading stage (read-to-write process). 176 undergraduates taking academic writing course were randomly chosen to respond to the instrument. The instrument is a 5 Likert-scale with 34 items on topics pertaining to variables found in the knowledge transforming model. Findings reveal that writers take alternative roles and reader and writer in the writing process. Findings also reveal interesting pedagogical implications in the teaching and learning of both academic reading and writing.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/lit/0136/a.php" alt="Hit counter" /&gt;&lt;/p&gt;&lt;a href="https://doi.org/10.5281/zenodo.3883649"&gt;&lt;img src="https://zenodo.org/badge/DOI/10.5281/zenodo.3883649.svg" alt="DOI" /&gt;&lt;/a&gt;
CONCEPT AND SIGNS OF DIGITAL TRACES THAT FORM DURING CYBERCRIMES

The article considers scientific views on terminology in the field of forensics and forensic examination for the investigation of cybercrime, as well as taking into account the specific use of information and telecommunication technologies in forensics, the validity of the term’s use “digital traces” is substantiated. The author’s definition of the concept of digital traces that are formed during the commission of cybercrimes is proposed. Its specific signs and properties are presented and analyzed as well. The necessity of using special knowledge and appropriate expert equipment to search for, detect and record digital tracks has been noted. It is emphasized the importance of the speed of detection and investigation of digital traces in the process of investigating cybercrimes. The necessity of developing the concept of digital tracks, research of new, different from traditional methods, methods and means of their detection, fixation, analysis and preservation is substantiated.
Automatic registration method using EM sensors in the IoT operating room

Abstract The Internet of Things (IoT) in the operating room can aid to improve the quality of the computer-aided surgical system. Patient-to-image registration is an important issue for computer-aided surgical systems. Automating the procedure of patient-to-image registration could increase tracking accuracy and lower the time consumed for performing the procedure of registration. Therefore, we propose an automatic registration method to address this issue by constructing a wireless sensor network system for surgery. A plastic fiducial object combing with specific markers is developed to perform registration in that the ultimate purpose is to integrate them into a surgical robotic system for surgical navigation. The specific markers are designed to localize the position of the small EM sensor and can be automatically detected in CT/MRI images by an automatic algorithm. The positions of the EM tracking sensors can be calibrated during the procedure of registration. Some experiments are designed and performed, and the experimental results demonstrate that the proposed registration method is robust and accurate. The proposed registration method is a foundational link of the surgical robots combing with virtual or augmented reality technology that all these technologies will be performed in further surgical navigation.
Minecraft as a Platform for Project-Based Learning in AI

Undergraduate courses that focus on open-ended, project-based learning teach students how to define concrete goals, transfer conceptual understanding of algorithms to code, and evaluate/analyze/present their solution. However, AI, along with machine learning, is getting increasingly varied in terms of both the approaches and applications, making it challenging to design project courses that span a sufficiently wide spectrum of AI. For these reasons, existing AI project courses are restricted to a narrow set of approaches (e.g. only reinforcement learning) or applications (e.g. only computer vision).In this paper, we propose to use Minecraft as the platform for teaching AI via project-based learning. Minecraft is an open-world sandbox game with elements of exploration, resource gathering, crafting, construction, and combat, and is supported by the Malmo library that provides a programmatic interface to the player observations and actions at various levels of granularity. In Minecraft, students can design projects to use approaches like search-based AI, reinforcement learning, supervised learning, and constraint satisfaction, on data types like text, audio, images, and tabular data. We describe our experience with an open-ended, undergraduate AI projects course using Minecraft that includes 82 different projects, covering themes that ranged from navigation, instruction following, object detection, combat, and music/image generation.
A Blockchain Based Buyer-seller Watermark Protocol with Trustless Third Party

Background: With the development and innovation of digital information technologies and new-generation Internet information platforms, new types of information exchange methods have been spawned. It has broken the restriction of the traditional internet boundary, and integrated all round connections between people and objects. Methods: Based on the above progresses, digital multimedia contents distributed or published much more convenient on the internet than before and most of them without any copyright protection. The dishonest owner can easily copy and distribute digital multimedia content without reducing any perceptual quality. According to the relative concerns, watermark protocol networks play a very important role in the usage tracking and copyrights infringement authentication, etc. However, most of the watermark protocols always require a “fully trusted third party”, which has a potential risk to suffer conspiracy attack. Results: Therefore, in this paper, we focus on designing a watermark protocol with trustless third party via blockchain for protecting copyrights of owners that they want to publish or distribute on the internet. The proposed watermark protocol includes three sub-protocols which covers the negotiation process, transaction process and identification processes. Conclusion: In addition, this paper also provides a fully detail analysis that describes the benefits and weaknesses of current solution.
The Memorable Image Encryption Algorithm Based on Neuron-Like Scheme

A new image cryptosystem based on neuron-like scheme is proposed in this paper. As a non-linear component of image cryptosystem, neuron-like learning algorithm can realize a kind of memorable diffusion algorithm, thus realizing the plaintext-related image encryption algorithm. Through the feedback operation, the inputs and weights of the neuron are regulated by the information of the image, and then they are used to diffuse the image information. According to the needs of application scenarios, the number of inputs of the neuron can be adjusted so that the image cryptosystem can be applied to different security requirements. The simulation results show that the proposed image encryption system is extremely sensitive to the secret keys, the equivalent keys and the plain images, and also possesses the high security and adaptive characteristics. The image cryptosystem can be applied to the actual applications of image information security.
Analysis of Vulnerability Levels to the Flash Flood Based on Social Economic and Institutional Factors in Wasior, Teluk Wondama, West Papua

One form of disaster mitigation is to know the vulnerability of areas that are at risk of disaster socially and economically. Studies on vulnerability to flash floods are necessary, as it might prevent material losses and fatalities. Wasior District at Teluk Wondama Regency experienced a flash flood in 2010 causing negative impacts such as fatalities and large material losses. To anticipate flash floods that might occur in the future, studies to evaluate the vulnerability to the flash flood are needed. This study aimed to: 1) analyze the level of vulnerability to flash floods in Wasior based on socioeconomic factors post the 2010 flash floods, 2) analyze the level of vulnerability to flash floods in Wasior based on institutional factors. The method used in this study was a combination of qualitative and quantitative methods. Data collection was conducted by observation, interview, and documentation. Data were analyzed by: 1) identifying the socioeconomic indicators of the community and institutions around the watershed, 2) providing weighting scores to the social economic criteria and institutional criteria from the most vulnerable to the least vulnerable, 3) assessing the level of community and institution vulnerabilities in the study site to the impact of flash floods based on the calculation of the weighting scores of socio-economic and institutional indicators. The results showed that the level of socioeconomic vulnerability to flash floods were categorized as moderate (total score 2.084), while the level of institutional vulnerability was low (total score 2.251). The results of this study can be used as a basis for considerations in the implementation of flash flood mitigation in Wasior. Keywords: vulnerabilities, social, economic, institutional, flash flood
UNIVERSITY JOURNALS. Consolidating institutional repositories in a digital, free, open access publication platform for &lt;i&gt;all&lt;/i&gt; scholarly output

Funders increasingly mandate researchers to publish their scientific articles in open access and to retain their copyright. Universities all over the world have set up institutional repositories and use repositories for the preservation and dissemination of academic production of their institutions, including scientific articles, reports, datasets, and other research outputs. However, in general, authors do not find institutional repositories very attractive and accessible as an open access publication platform since repositories and open access are not part of the rewarding system. We expect that researchers are more likely to publish and deposit their scientific papers in a repository, once they have the appearance, recognition and dissemination of a scientific journal. That is why we took the initiative to set up a repository based journal ‘University Journals’ in which universities collaborate. The paper will explain the&nbsp;University Journals&nbsp;project and how the involved universities want to facilitate a valuable alternative publication platform that complies with Plan S principles and enables publication and dissemination of all research outcomes. By establishing&nbsp;University Journals&nbsp;as a publication platform, university libraries are instrumental (and crucial) in achieving the ambitions of Open Science, and universities gain control over the publication process.
IWE-Net: Instance Weight Network for Locating Negative Comments and its application to improve Traffic User Experience

Weakly supervised learning aims at coping with scarce labeled data. Previous weakly supervised studies typically assume that there is only one kind of weak supervision in data. In many applications, however, raw data usually contains more than one kind of weak supervision at the same time. For example, in user experience enhancement from Didi, one of the largest online ride-sharing platforms, the ride comment data contains severe label noise (due to the subjective factors of passengers) and severe label distribution bias (due to the sampling bias). We call such a problem as ‘compound weakly supervised learning’. In this paper, we propose the CWSL method to address this problem based on Didi ride-sharing comment data. Specifically, an instance reweighting strategy is employed to cope with severe label noise in comment data, where the weights for harmful noisy instances are small. Robust criteria like AUC rather than accuracy and the validation performance are optimized for the correction of biased data label. Alternating optimization and stochastic gradient methods accelerate the optimization on large-scale data. Experiments on Didi ride-sharing comment data clearly validate the effectiveness. We hope this work may shed some light on applying weakly supervised learning to complex real situations.
Time Registration Algorithm Based on Multi-Missiles Cooperative Target Detection

Aiming at the problems of time delay of fusion center in multi-missile cooperative target detection system due to the different starting time of sensor measurement of each missile, a time registration algorithm based on modified gain extended Kalman filter (MGEKF) is introduced. In this algorithm, the extended Kalman filter (EKF) is improved to carry out high-precision unbiased estimation of the target motion state, which is used for time compensation of the target motion state estimation of the missiles, and data fusion is conducted from the estimation registration of the missiles motion state to the fusion moment. The simulation results show that the algorithm can effectively reduce the system time registration error, improve the system time registration accuracy, and provide a good foundation for data fusion.
A Comparative Study of Analysis and Investigation using Digital Forensics

Data Analysis and Investigation using Digital forensics from Digital Storage Devices, is a defined way towards effective data backup strategies, as well as a key aspect in Data Privacy and Confidentiality.Digital storage Devices like Hard Drives (internal or external), USB Drives, floppy disks, etc. provide a good medium for better utilisation and storage of data and information.So, the main task is to retrieve the stolen or lost data from these devices.Digital forensics provides the exact concept for this data extraction, in a systematic and effective manner.Now, there can be various conditions of a damaged digital storage device like it may be burnt, wet or physically damaged parts, all these conditions play a significant role in Data Extraction.Since, Data is the most important asset for any organisation, so compromising with its Security and Confidentiality, may be wrong or devastating option, for future.Just spending thousands and millions of dollars in finding the vulnerability (large-scale or small-scale), is not a solution for being secure.There has to be proper and effective choice of ways and tools for it.
Implementing Injunctive Social Norms Using Defeasible Reasoning

Believability requires video game characters to consider their actions within the context of social norms. Social norms involve a broad range of behavioral defaults, obligations, and injunctions unrelated to strictly causal reasoning. Defeasible reasoning involves rationally compelling but deductively invalid arguments, such as reasoning with rules that allow exceptions. This paper investigates having video game characters use defeasible reasoning to consider injunctive social norms when selecting and planning actions.
Frustratingly Easy Multilingual Grapheme-to-Phoneme Conversion

In this paper, we describe two CU Boulder submissions to SIGMORPHON 2020 Task 1 on multilingual grapheme-to-phoneme conversion (G2P).Inspired by the high performance of a standard transformer model (Vaswani et al., 2017) on the task, we improve over this approach by adding two modifications: (i) Instead of training exclusively on G2P, we additionally create examples for the opposite direction, phoneme-to-grapheme conversion (P2G).We then perform multi-task training on both tasks.(ii) We produce ensembles of our models via majority voting.Our approaches, though being conceptually simple, result in systems that place 6th and 8th amongst 23 submitted systems, and obtain the best results out of all systems on Lithuanian and Modern Greek, respectively.
Third-Person Imitation Learning via Image Difference and Variational Discriminator Bottleneck (Student Abstract)

Third-person imitation learning (TPIL) is a variant of generative adversarial imitation learning and can learn an expert-like policy from third-person expert demonstrations. Third-person expert demonstrations usually exist in the form of videos recorded in a third-person perspective, and there is a lack of direct correspondence with samples generated by agent. To alleviate this problem, we improve TPIL by applying image difference and variational discriminator bottleneck. Empirically, our new method has better performance than TPIL on two MuJoCo tasks, Reacher and Inverted Pendulum.
Chicken-Moth Search Optimization-based Deep Convolutional Neural Network for Image Steganography

Image steganography is considered as one of the promising and popular techniques utilized to maintain the confidentiality of the secret message that is embedded in an image. Even though there are various techniques available in the previous works, an approach providing better results is still the challenge. Therefore, an effective pixel prediction based on image stegonography is developed, which employs error dependent Deep Convolutional Neural Network (DCNN) classifier for pixel identification. Here, the best pixels are identified from the medical image based on DCNN classifier using pixel features, like texture, wavelet energy, Gabor, scattering features, and so on. The DCNN is optimally trained using Chicken-Moth search optimization (CMSO). The CMSO is designed by integrating Chicken Swarm Optimization (CSO) and Moth Search Optimization (MSO) algorithm based on limited error. Subsequently, the Tetrolet transform is fed to the predicted pixel for the embedding process. At last, the inverse tetrolet transform is used for extracting the secret message from an embedded image. The experimentation is carried out using BRATS dataset, and the performance of image stegonography based on CMSO-DCNN+tetrolet is evaluated based on correlation coefficient, Structural Similarity Index, and Peak Signal to Noise Ratio, which attained 0.85, 46.981dB, and 0.6388, for the image with noise.&#x0D;
The future of travel

Most experts would agree that recovery from the COVID-19 crisis will be slow (see Figure 6.2), in large part due to the impact that the crisis has had on the global travel and tourism industry (Romei, 2020). Until there is vaccine, the virus will influence nearly every sector of travel from transportation, destination and resorts, to the accommodations, attractions, events and restaurants. The first section of this chapter looks at the future for these different sectors, a future heavily influenced by technology and a heightened emphasis on health and safety. The second part of the chapter focuses on a theme that has been prevalent in this book – the need for adaptability or ‘COVID-aptability’. Consumer demands and behavior will be permanently altered by the pandemic, and all stakeholders in the travel industry will need to adapt. One part of adaptability is redesigning servicescapes – a necessity for many after the lockdown, and this is the subject of the penultimate section of the chapter. The conclusion looks at lessons learned from this crisis.