Reviewerly/exHarmony huggingface dataset:
Extending a Conventional Chatbot Knowledge Base to External Knowledge Source and Introducing User-Based Sessions for Diabetes Education

Chatbots or conversational agents are computer programs that interact with users using natural language through artificial intelligence in a way that the user thinks he is having dialogue with a human. One of the main limits of chatbot technology is associated with the construction of its local knowledge base. A conventional chatbot knowledge base is typically hand constructed, which is a very time-consuming process and may take years to train a chatbot in a particular field of expertise. This chapter extends the knowledge base of a conventional chatbot beyond its local knowledge base to external knowledge source Wikipedia. This has been achieved by using Media Wiki API to retrieve information from Wikipedia when the chatbot's local knowledge base does not contain the answer to a user query. To make the conversation with the chatbot more meaningful with regards to the user's previous chat sessions, a user-specific session ability has been added to the chatbot architecture. An open source AIML web-based chatbot has been modified and programmed for use in the health informatics domain. The chatbot has been named VDMS – Virtual Diabetes Management System. It is intended to be used by the general community and diabetic patients for diabetes education and management.
n- Refined Neutrosophic Rings

The aim of this paper is to introduce the concept of n-refined neutrosophic ring as a generalization of refined neutrosophic ring. Also, wepresent concept of n-refined polynomial ring. We study some basic concepts related to these rings such as AH-subrings, AH-ideals, AH-factors, and AH-homomorphisms.
Geophysics Bright Spots

It has been an interesting challenge to be the Bright Spots coordinator. Relatively speaking, it is an easy assignment in that the Geophysics editors do all the hard work of the paper review process and then nominate the papers showcased here. I just have to coordinate getting the summaries. Unfortunately, maybe due to being retired, I do not always allow enough time to get help from the authors, or my request for assistance disappears into a spam filter. For the most part, I have written the summaries myself, which is challenging since they represent such a diverse range of topics. For this column, I was able to reach four of the six authors to get their help in writing the summaries for what should be an improved column. Check out the interesting papers below.
Fundamental Ruby

If you are new to Ruby, I would like to welcome you to this amazing community of awesome, easygoing engineers who uphold the MINASWAN principle, which states that “Matz is nice, and so we are nice.”
An Integrated Framework for Semantic Service Composition Using Answer Set Programming (SSC-ASP)

Notwithstanding the advancement of service computing in recent years, service composition is still a main issue in this field. In this chapter, the authors present an integrated framework for semantic service composition using answer set programming. Unlike the AI planning approaches of top-down workflow with nested composition and combining composition procedure into service discovery, the proposed framework integrates a designed service workflow with automatic nested composition. In addition, the planning is based on service signature while validating through service contract. Moreover, a unified implementation of service discovery, selection, composition, and validation is achieved by answer set programming. Finally, the performance of proposed framework is demonstrated by a travel booking example on QWSDataset.
Organizer Logo

List of Organizer Logo, Sponsor Logo images are available in this pdf.
Salonica

The Yale ReviewVolume 108, Issue 1 p. 145-146 POETRY Salonica Paul Muldoon, Published thirteen collections of poetry, including Frolic and Detour. Among his awards are a Pulitzer Prize and the Queen's Gold Medal for Poetry.Search for more papers by this author Paul Muldoon, Published thirteen collections of poetry, including Frolic and Detour. Among his awards are a Pulitzer Prize and the Queen's Gold Medal for Poetry.Search for more papers by this author First published: 20 April 2020 https://doi.org/10.1111/yrev.13612 AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume108, Issue1Spring 2020Pages 145-146 RelatedInformation
Solar Panel Tracker Using PIC Microcontroller 16F628A

Energy sources are of two types renewable and non-renewable. ‘Renewable’ resources are those that recover their capacity in a time significant by human needs. Examples are sun light or wind power, when natural phenomenon that are the primary source of energy are going and not depleted by human demands. Now days, sometimes we complete our demand of electricity with the help of solar energy. This is done with the help of solar panels. By maintaining the solar panel always perpendicular to the incident sun rays we can receive maximum amount of energy. For this purpose, we have to set the tracker below the solar panel which always sets the panel orientation towards maximum intensity.
Image Secret Sharing using IWT (Integer Wavelet Transform) Algorithm

Because of the importance of digital images and their extensive application to digital watermarking, block chain, access control, identity authentication, distributive storage in the cloud and so on ,image secret sharing(ISS) is attracting ever-increasing attention .Share authentication is an important issue in its practical application. However, most ISS schemes with share authentication ability require a dealer to participate in the authentication. To design an ISS for a(k ,n)-threshold with separate share authentication abilities of both dealer participatory authentication and dealer non participatory authentication .the advantage of polynomial-based ISS and visual secret sharing(VSS) are skilfully fused to achieve these two authentication abilities Without sending a share by using a screening operation. In addition, the designed scheme has the characteristics of low decryption (authentication) complexity, lossless decryption and no pixel expansion .Experiments and theoretical analyses are performed to show the effectiveness of the designed scheme.
Holey Eclogite!

Scientists have found holes filled with minerals that indicate fluid-filled pores exist many tens of kilometers below Earth’s surface. But no, The Core fans, you still can’t get amethyst-laden geodes in the mantle.
Vulnerability Analysis of Chinese Digital Passwords Related to ATM PIN Using Deep Learning

Human-made digital passwords are a dominant form of ATM card authentication. However, it is vulnerable to guessing attacks. Unfortunately, previous studies have focused on letters-only passwords or passwords that include both letters and digits, and few empirical studies have been conducted on the security of digital passwords, let alone the regional differences in digital passwords. In this paper, we studied the vulnerability of pure six-digit passwords extracted from the leaked dataset of the Chinese website. We used the Pearson chi-square test to check whether each digit in the password obeyed a uniform distribution. We showed regional conventions for Chinese digital passwords. We proposed using a recurrent neural network (RNN) to model password resistance to guessing attacks and explore how different architectures and training methods impact the effectiveness of neural networks. The experimental results on five website datasets demonstrate the superior performance of the proposed approach over state-of-the-art deep learning techniques in terms of both learning efficiency and matching accuracy.
Reward-Based Environment States for Robot Manipulation Policy Learning

Training robot manipulation policies is a challenging and open problem in robotics and artificial intelligence. In this paper we propose a novel and compact state representation based on the rewards predicted from an image-based task success classifier. Our experiments, using the Pepper robot in simulation with two deep reinforcement learning algorithms on a grab-and-lift task, reveal that our proposed state representation can achieve up to 97% task success using our best policies.
LP Formulations of sufficient statistic based strategies in Finite  Horizon Two-Player Zero-Sum Stochastic Bayesian games

This paper studies two-player zero-sum stochastic Bayesian games where each player has its own dynamic state that is unknown to the other player. Using typical techniques, we provide the recursive formulas and sufficient statistics in both the primal game and its dual games. It's also shown that with a specific initial parameter, the optimal strategy of one player in a dual game is also the optimal strategy of the player in the primal game. To deal with the long finite Bayesian game we have provided an algorithm to compute the sub-optimal strategies of the players step by step to avoid the LP complexity. For this, we computed LPs to find the special initial parameters in the dual games and update the sufficient statistics of the dual games. The performance analysis has provided an upper bound on the performance difference between the optimal and suboptimal strategies. The main results are demonstrated in a security problem of underwater sensor networks.
Digital Fundamental Groups and Edge Groups of Clique Complexes

In previous work, we have defined---intrinsically, entirely within the digital setting---a fundamental group for digital images. Here, we show that this group is isomorphic to the edge group of the clique complex of the digital image considered as a graph. The clique complex is a simplicial complex and its edge group is well-known to be isomorphic to the ordinary (topological) fundamental group of its geometric realization. This identification of our intrinsic digital fundamental group with a topological fundamental group---extrinsic to the digital setting---means that many familiar facts about the ordinary fundamental group may be translated into their counterparts for the digital fundamental group: The digital fundamental group of any digital circle is $\mathbb{Z}$; a version of the Seifert-van Kampen Theorem holds for our digital fundamental group; every finitely presented group occurs as the (digital) fundamental group of some digital image. We also show that the (digital) fundamental group of every 2D digital image is a free group.
In Memoriam

Other| May 01 2022 In Memoriam: Tributes to Joel Myerson (1945–2021) Nathaniel Hawthorne Review (2022) 48 (1): 117–119. https://doi.org/10.5325/nathhawtrevi.48.1.0117 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation In Memoriam: Tributes to Joel Myerson (1945–2021). Nathaniel Hawthorne Review 1 May 2022; 48 (1): 117–119. doi: https://doi.org/10.5325/nathhawtrevi.48.1.0117 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectivePenn State University PressNathaniel Hawthorne Review Search Advanced Search I first met Joel Myerson in an elevator at an American Literature Association Conference in Boston: he was wearing his famous Hawaiian shirt. Thereafter, I recognized his presence at various Alcott venues at ALA conferences: he always had something significant to remark upon after the talks were given. When I became more involved with the Alcott Society, I realized how sociable, warm, witty, and helpful he was at Alcott sessions and dinners. I remember an organized outing from an ALA meeting in Boston to the Alcott Orchard House in Concord, and he was clearly the life of the party.In terms of my scholarship on Louisa May Alcott, I could not have done as much without his co-edited collections (with Daniel Shealy) on Alcott’s letters and journals. This also holds true for his significant work on Margaret Fuller: I found his Margaret Fuller, Critic: Writings from the New York–Tribune, 1844–1846... You do not currently have access to this content.
Over-Sampling Effect in Pre-Training for Bidirectional Encoder Representations from Transformers (BERT) to Localize Medical BERT and Enhance Biomedical BERT (Preprint)

<sec> <title>BACKGROUND</title> Pre-training large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing. With the introduction of transformer-based language models, such as bidirectional encoder representations from transformers (BERT), the performance of information extraction from free text has significantly improved for both the general and medical domains; however, it is difficult to train specific BERT models that perform well for domains in which there are few publicly available databases of high quality and large size. </sec> <sec> <title>OBJECTIVE</title> We hypothesize that this problem can be addressed by over-sampling a domain-specific corpus and using it for pre-training with a larger corpus in a balanced manner. In this study, we verify our hypothesis by developing pre-training models using our method and evaluating their performance. </sec> <sec> <title>METHODS</title> Our proposed method was based on simultaneous pre-training after over-sampling. We conducted three experiments in which we generated (1) English biomedical BERT from a small biomedical corpus, (2) Japanese medical BERT from a small medical corpus, and (3) enhanced biomedical BERT pre-trained from complete PubMed abstracts in a balanced manner and compared their performance with the conventional models. </sec> <sec> <title>RESULTS</title> We first confirmed that our English BERT pre-trained using both general and small medical-domain corpora performed sufficiently well for practical use in the biomedical language understanding evaluation (BLUE) benchmark. Moreover, our proposed method was more effective than conventional methods for each different biomedical corpus size with the same corpus size for the general domain. Next, our Japanese medical BERT outperformed the other BERT models built using a conventional method concerning the medical document classification task. It demonstrated the same trend as in the first experiment in English. Lastly, our enhanced biomedical BERT model, in which clinical notes were not used during pre-training, achieved both clinical and biomedical scores on the BLUE benchmark that were 0.3 points above those of the model trained without our proposed method. </sec> <sec> <title>CONCLUSIONS</title> Well-balanced pre-training by over-sampling instances derived from a corpus appropriate for the target task allowed us to construct a high-performance BERT model. </sec>
Message passing multitarget tracking with out-of-sequence measurements

This paper considers multitarget tracking under out-of-sequence measurements (OOSMs), i.e. when the measurements processed by the tracker might be out of order. In order to fully exploit information provided by the sensor, OOSMs should be re-utilized rather than being simply discarded so as to improve tracking performance. To this end, this paper proposes a message passing (MP) multitarget tracking algorithm under OOSMs, where MP is adopted to perform efficient association between target and (in-sequence and out-of-sequence) measurements. Simulation experiments show that, compared to simply discarding OOSMs, the accuracy in terms of target number and state estimates can be greatly enhanced by incorporating OOSMs, thus demonstrating the effectiveness of the proposed approach.
Irregularity Detection of Daily Behavior Pattern Based on Regularity Feature Extraction for Home Elderly

Daily behavior irregularity detection is important for assessment of the health status for the elderly in homecare. This paper proposes a Daily Behavior Irregularity Detection (DBID) mechanism which outputs the irregularity probability of daily behaviors based on the extracted regularity features using unsupervised learning algorithm. The regular behaviors which satisfy the time-regular and frequency-regular properties are identified as the regularity of daily behaviors. Then, the irregularity probability of the daily behaviors in one days can be calculated based on the selected regular behaviors. Experiments show that the proposed DBID has a good performance in terms of F measure, compared the existing mechanisms.
Confidence based class weight and embedding discrepancy constraint network for partial domain adaptation

Partial domain adaptation (PDA) is a special domain adaptation task where the label space of the target domain is a subset of the source domain. In this work, we present a novel adversarial PDA method named Confidence Based Class Weight and Embedding Discrepancy Constraint Network (CEN). Specifically, we design a robust weighting scheme that takes sample confidence and class information into account. It can automatically distinguish outlier samples in the source domain and reduce their importance. Besides, we consider the relationship between feature norm and domain shift. We limit the expectation of the feature norms of both domains to an adaptive value. By this means, we can align the feature distributions and help the deep model learn domain-invariant representations. Comprehensive experiments on three domain adaptation datasets Office-31, Office-home, and Visda2017 show that our approach surpasses state-of-the-art methods on various PDA tasks.
Rancang Bangun Sistem Informasi Manajemen Pada PT Mitra Jaya Keramik

Running a business requires science and technology. Businesses need knowledge to know what will be developed and will be run and technology to manage and produce data that is precise, accurate and efficient. the application of information technology in business is to use a computer-based information system for both staff and managers. One of the computer-based information systems that can assist managers in obtaining information in their respective units is the Management Information System. The method used in the development of the system used to analyze and design this system is the Iteration methodology (Iterative model). Management information systems can be used by managers in which there is data relating to sales, purchases, inventories and others where the data can produce information that will be useful for managers to know the results of the company's development. This system is intended to make it easier for managers to check purchasing information, sales information, inventory information, inventory information and information to be reported. The results of this system, so that managers can see sales results in detail because they can find out the total sales per day, month and year. The accounts receivable section can view detailed and detailed information on receivables that have been paid off and those that are still due. The purchasing department can see information about debts that have been paid and those that are still due.
청렴교육의 청렴도에 대한 효과성 연구

Anti-corruption education is widely accepted as an effective tool for curbing corruption. However, due to a lack of data, the current literature has not yet been able to comprehensively evaluate the effectiveness of anti-corruption education on corruption or integrity. Using the Integrity Assessment dataset and the Anti-Corruption Education dataset, this paper empirically tests the impact of anti-corruption education on the level of integrity amongst 51 public organizations in South Korea from 2014 to 2018. A fixed-effects model and time-lagged regression enable this study to capture the impacts of anti-corruption education on integrity. This paper finds that if ten percent of employees in a public organization receive anti-corruption education, then the level of comprehensive integrity within that organization will increase by about 0.8 points in the following year. This study also finds that in some conditions, anti-corruption programs have no effect on reducing corruption. These results suggest that anti-corruption education programs are indeed effective in fighting corruption. Public workers should participate in anti-corruption education programs regularly because the program's impact does not last long. Anti-corruption programs should be developed to increase external integrity and internal integrity.
Personified Robotic Chatbot Based On Compositional Dialogues

We present a novel chatbot architecture implemented in the humanoid robot Pepper that is capable to communicate in natural language in a compositional fashion. Compared to the previous solutions based either on question-answering pairs or dialogue trees, we propose a novel method which constructs the dialogue from atomic blocks that can be applied interchangeably. It thus allows the creation of hundreds of meaningful dialogues from only a few building blocks. Most of the dialogues are focused on the user preferences and opinions which are stored in a database and translated into feature vectors. The chatbot is able to compare these preferences with other users or with data from the internet so that it can be used as a crowd-based recommendation system. To improve the user experience, the chatbot has its own personification in the form of predefined opinions and preferences for each question asked during the communication. The embodiment of the chatbot in the humanoid robot enables further improvements in the interaction. We report results from a preliminary evaluation with a group of volunteers that compared our chatbot with a different system.
Modelling and computing decision-making processes and temporal derivatives with electromagnetic waves

The arbitrary manipulations of fields and waves for analogue and quasi-digital computing processes has become a new paradigm for computing applications in recent years. In this talk, we will show and discuss our most recent findings in this field by exploiting light-matter interactions for fundamental decision-making processes (such as if-then-else) and analogue computation of temporal derivatives.
Increment prediction for video anomaly detection

The Appearance-based and motion-based dual-stream anomaly detection algorithm uses optical flow to extract motion features in normal video, but optical flow only considers the pixel displacement relationship between two adjacent frames, while ignoring the latent semantic association of continuous changes in multi-frame images. This paper proposes an incremental prediction-based video anomaly detection algorithm (IP-VAD), which first uses an encoder to extract corresponding features of consecutive frames, and then reconstructs feature maps from appearance and motion directions, respectively. In the appearance direction, the last frame is reconstructed by the appearance decoder; in the motion direction, the feature differences of successive frames are stacked and fed into the motion decoder, which then predicts the increment of future and last frames. Finally, future frames are obtained using the reconstructed last frame and the predicted increment. During the test, the peak signal-to-noise ratio is used to measure the reconstruction quality of future frames, and the threshold is used to determine whether it is abnormal. The experimental results on the public datasets UCSD Ped2 and CUHK Avenue show that the AUC indicators of our method are improved by 1.0% and 0.2%, respectively.