Reviewerly/exHarmony huggingface dataset:
Optimizing the JSM Program

AbstractSometimes the Joint Statistical Meetings (JSM) is frustrating to attend, because multiple sessions on the same topic are scheduled at the same time. This article uses seeded latent Dirichlet allocation and a scheduling optimization algorithm to very significantly reduce overlapping content in the original schedule for the 2020 JSM program. Specifically, a measure based on total variation distance that ranges from 0 (random scheduling) to 1 (no overlapping content) finds that the original schedule had a score of 0.058, whereas our proposed schedule achieved a score of 0.371. This is a huge improvement that would (i) increase participant satisfaction as measured by the post-JSM satisfaction survey, and (ii) save the American Statistical Association significant money by obviating the need for the traditional in-person meeting of the 47 program chairs and other organizers. The methodology developed in this work immediately applies to future JSMs and is easily modified to improve scheduling for any other scientific conference that has parallel sessions.Keywords: Greedy AlgorithmsLatent Dirichlet AllocationOptimal SchedulingTopic Modeling 6 Supplementary MaterialsThe supplementary materials includes three datasets used in the analysis: “abstract_info” concerns the detailed information about the sessions of JSM 2020, “seeded_words” is the table of the seeded words we used in seeded LDA, and finally “trayvon_martin” is made up of all the words of Trayvon Martin Corpus we used in data cleaning. Furthermore, “allocation_matrix_list.RData, which is a list of 100 allocation matrices we generated through our algorithm, is included to give the opportunity to avoid running its final loop.
H-FFMRA: A Multi Resource Fully Fair Resources Allocation Algorithm in Heterogeneous Cloud Computing

The allocation of multiple types of resources fairly and efficiently has become a substantial concern in state-of-the-art computing systems. Accordingly, the rapid growth of cloud computing has highlighted the importance of resource management as a complicated and NP-hard problem. Unlike traditional frameworks, in modern data centers, incoming jobs pose demand profiles, including diverse sets of resources such as CPU, memory, and bandwidth across multiple servers. Accordingly, the fair distribution of resources, respecting such heterogeneity appears to be a challenging issue. Furthermore, the efficient use of resources as well as fairness, establish trade-off that renders a higher degree of satisfaction for both users and providers. Dominant Resource Fairness (DRF) has been introduced as an initial attempt to address fair resource allocation in multi-resource cloud computing infrastructures. Dozens of approaches have been proposed to overcome existing shortcomings associated with DRF. Although all those developments have satisfied several desirable fairness features, there are still substantial gaps. Firstly, it is not clear how to measure the fair allocation of resources among users. Secondly, no particular trade-off considers non-dominant resources in allocation decisions. Thirdly, those allocations are not intuitively fair as some users are not able to maximize their allocations. In particular, the recent approaches have not considered the aggregate resource demands concerning dominant and non-dominant resources across multiple servers. These issues lead to an uneven allocation of resources over numerous servers which is an obstacle against utility maximization for some users with dominant resources. Correspondingly, in this paper, a resource allocation algorithm called H-FFMRA is proposed to distribute resources with fairness across servers and users, considering dominant and non-dominant resources. The experiments show that H-FFMRA achieves approximately %20 improvements on fairness as well as full utilization of resources compared to DRF in multi-server settings.
Concept-based Topic Attention for a Convolutional Sequence Document Summarization Model

Neural network-based document summarization often suffers from the problem of summarizing irrelevant topic content regarding the main idea. One of the main reasons leading to this problem is a lack of human common-sense knowledge which generates facts that are not decipherable. We propose a document summarization framework called Document Summarization with Concept-based Topic Triple Attention (DOSCTTA). The framework incorporates concept-based topic information into a convolutional sequence document summarization model. We propose a concept-based topic model (CTM) to generate semantic topic information using conceptual information or knowledge which is retrieved from a knowledge base. We introduce a triple attention mechanism (TAM) to not only measure the importance of each topic concept and source element to the output elements but also the importance of the topic concept to the source element. TAM presents contextual information from three aspects and then combines them using a softmax activation to acquire the final probability distribution to enable the model to produce coherent and meaningful summaries with a wide range of rich vocabulary. The experimental evaluations which are conducted over the Gigaword and CNN/Daily Mail (CNN/DM) datasets reveal that DOSCTTA surpasses the various widely recognized state-of-the-art models (WSOTA) such as Seq2Seq, PGEN, CSM and TopicCSM. DOSCTTA achieves competitive results by generating coherent and informative summaries.
Model Analysis of Collaborative-Based Recommender System

With the data growing in abundance amount and usage of social media increasing day by day, the features of Youtube videos, video streaming apps, e-commerce sites etc. have been an important concern for the companies. Every user wants the recommendation according to his or her choice. Therefore, it becomes very important for these applications and sites to have an efficient recommender system that not only recommends the result as per the user but also do not overlay excessive burden on the applications. In this paper, we would be analyzing the collaborative-based recommender system algorithms like (Pearson, cosine and matrix factorization) that are used under the different models of collaborative-based recommender systems. Following on the basis of which, we have proposed a generic book recommender system that works under the collaborative-based architecture.
On Tightly-Secure (Linkable) Ring Signatures

Tight security is increasingly gaining importance in real-world cryptography, as it allows to deploy the cryptosystem without compensating the security loss of a reduction with larger parameters. In this paper, we point out the difficulties of creating tightly-secure ring signatures (RS) and linkable ring signatures (LRS), then present solutions to overcome them. As a result, we construct a tightly-secure RS scheme that is more efficient than the previous construction (ESORICS’18), and the first tightly-secure LRS scheme. The security is based on the Computational and Decisional Diffie-Hellman assumptions in the random oracle model.
Intuitive Searching: An Approach to Search the Decision Policy of a Blackjack Agent

Porker is a game of imperfect information (Rubin and Waston, Artif Intell, 175(5-6), []), in which each player has cards hidden from other players. In previous research (Billings et al, in American Association for Artificial Intelligence Press, []; Popyack, in Blackjack-playing agents in an advanced AI course, []), Markov model serves as a powerful framework to design a Poker agent. In this paper, we propose a new approach named intuitive searching to build the Exploitative Agent AI against the Markov model Agent in the Blackjack Game. The correctness of our approach can be proved by analyzing the predictive distribution of winning rate. Our AI prevails Markov Agent in a 10000-round experiment. The performance of intuitive policy searching approach in general cases and corresponding limitations are also discussed.
Research on the History of Ancient Chinese Education Based on Artificial Intelligence

With the popularity of the Internet and the development of artificial intelligence, artificial intelligence technologies such as image recognition, voice recognition, and human-computer interaction have all found new applications in the field of education. In addition, educational applications of AI such as personalized learning, intelligent learning feedback, and robotic remote teaching are also favored, and with them comes the erosion and abandonment of traditional educational concepts, methods, and achievements. Therefore, how to combine traditional education methods with the current era is one of the major issues facing the education field. Based on this, this paper will study the development of education in ancient China, and then analyze the development of the education system in ancient Chinese schools and the imperial examination system in ancient China. The aim is to review the history and explore the development process of education in China. In this way, it will lay the foundation for the future development of education in China, so that traditional education can be adapted to the general context of artificial intelligence and make traditional culture shine in today’s wisdom era.
Scalable Conversational Intelligence for Post-COVID Health Consultation Using Flask Library and Support Vector Machine

In this paper, we put forth a model for natural language processing (NLP)-based Multilingual Conversational Bot in order to provide economic primary healthcare education, information, and advice to chronic patients. Telemedicine and Intelligent Communication Systems can be employed by Medical Practitioners, at scale, as part of their communication strategy during the recent Coronavirus outbreak in order to discourage contagion in the post-covid era. Such systems have the potential to propagate vital, valid, authenticated, up to date, information, and support, straight to the masses. The prime intention of the proposed system is to enable our already strained medical infrastructure to scale up its serviceability exponentially. Deep Learning based Conversational Intelligence Systems offer the potential for rapid and radically transforming patient care from an in-person to a remote experience. We introduce a novel computer application that enables healthcare professionals to delegate certain functions. The system was developed precisely, and it has been exhaustively trained to interact with patients alike human beings. The proposed system is based on a serverless architecture, aggregating information from a healthcare professional and providing ideas on preventative measures, home remedies, interactive counseling sessions, healthcare tips, and symptom information on diseases prevalent in rural areas of India. We leverage the resources of the Google Cloud Platform (GCP) for the provision of the said services in India, which will increase the availability of healthcare information to patients, and to leverage the immense potential of AI to eliminate the gap between the demand for healthcare services and the supply of healthcare providers.
Systematic Evaluation and Enhancement of Speech Recognition in Operational Medical Environments

Operational medical environments require reliable hands-free solutions to extract data from audio captured under noisy scenarios during rescue missions and provide timely information. However, approaches using automatic speech recognition (ASR) and natural language processing (NLP) techniques are complex as these conversations have a wide range of noise, involve medical terms from multiple speakers, and occur in high-stress environments, among others. These are further complicated by the lack of large training datasets for operational medical scenarios. To address these issues, we developed a platform that enables resilient hands-free data collection, preserves complete documentation through stages of care, and presents the information in near real-time, critical for the medical operation. Our work uniquely focused on systematic evaluation and improvement of a deep neural network-based ASR system by leveraging realistic testing data obtained from medical simulations of battlefield scenarios, which to our knowledge have not been addressed in any prior work. The system performance is shown to improve significantly using multi-style training, language model adaptation for the medical domain, speech enhancement, and NLP techniques.
Social Trust Network Embedding with Hash and Graphlet

Social trust network embedding is a useful way for efficient social trust network analysis. To get more expressive representations, the latent feature and node role feature should be preserved. In many cases, the dimension of latent feature can be large, thus latent feature has storage problem with the scale of networks increasing. As hash has a good performance on compressing, we use hash to reduce the memory needed for storing latent feature. Graphlets are useful statistics for modeling the node role. Overall, in this study, we propose a novel social trust network embedding method with the concepts of hash and graphlet (STNH). Neither of them has been researched for social trust networks in prior studies. We evaluate STNH on five realworld social trust networks with respect to the downstream task of link prediction. The results demonstrate the efficacy of STNH.
Predicting Classifiers Efficacy in Relation with Data Complexity Metric Using Under-Sampling Techniques

In imbalanced classification tasks, the training datasets may suffer from other problems like class overlapping, small disjuncts, classes of low density, etc. In such a situation, the learning for the minority class is imprecise. Data complexity metrics help us to identify the relationship between classifier’s learning accuracy and dataset characteristics. This paper presents an experimental study for imbalanced datasets wherein dwCM complexity metric is used to group the datasets based on the complexity level, thereafter the behavior of under-sampling based pre-processing techniques are analyzed for these different groups of datasets. Experiments are conducted on 22 real life datasets with different levels of imbalance, class overlapping and density of the classes. The experimental results show that these groups formed using dwCM metric can better explain the difficulty of imbalanced datasets and help in predicting the response of the classifiers to the under-sampling algorithms.
A Bayesian Interpretation of the Monty Hall Problem with Epistemic Uncertainty

The Monty Hall problem is a classic puzzle that, in addition to intriguing the general public, has stimulated research into the foundations of reasoning about uncertainty. A key insight to understanding the Monty Hall problem is to realize that the specification of the behavior of the host (i.e. Monty) of the game is fundamental. Here we go one step further and reason, in Bayesian way, in terms of epistemic uncertainty about the behavior of host, assuming subjective probabilities.
Expert and Technical Support for Investigation of Thefts Involving Malware Usage

The specific features of investigative activities related to IT crimes are the requirements for special knowledge in different areas (electronics, programming, telecommunications and others) and meticulous preparation for their application. In recent years, telecommunication networks are more commonly used for attempting to steal financial property. Thus, it has become necessary to examine technical and software opportunities and their usage for countering such legal offences. This article aims to provide forensics reasoning for measures detecting the main attributes of high-technology crimes of this category. It studies questions of applying special knowledge for CDR analysis, examination of computer devices and computer forensics.
SonoNet: Despeckling of Medical Ultrasound Scans Using Convolutional Neural Network Architecture

This paper proposes a deep learning framework using convolutional neural networks (CNNs), for the despeckling in ultrasound scans. The SonoNet model proposed in this paper consists of seven layers deep net along with activation function and batch normalization. Training of the network is carried out by utilizing synthetically modeled ultrasound frames with varying speckle content. The network uses the assumption of multiplicative noise to adopt a division approach for filtering speckle content from input frames. Finally, edge features are extracted using the Canny operator and enhanced using a twodimensional 3×3 convolutional mask. Thus, the proposed CNN method effectively accomplishes speckle elimination and visual feature preservation without inducing much blurring effects. The subjective evaluation by four subject matter experts and experimental analysis using three image quality metrics indicate that the proposed system gives a superior performance as compared to the state-of-the-art methods in the field, and therefore could find applications designed for speckle suppression in ultrasound imaging systems and real-time ultrasound video segmentation and classification algorithms.
A Comparative Study on Face Recognition AI Robot

Face recognition, the application of image processing, has gained a lot of attention. People have started researching and working on it to enhance the field of automation, security, and surveillance. The main reason behind this hype is the vast availability of commercial applications and accessibility to the latest technologies. Though the machine level recognition systems have gained a certain level of perfection, their success rate can be limited based on the application. This is because the image captured by the outdoor system is hard to detect and recognize due to change in light, different background conditions, and variations in the position of the person or object. So, we can say that the present system is far behind the perfection that a human possesses. This paper provides information on both still and moving, i.e., video-based face recognition. The main reason behind writing this review paper is to shed light on the existing literature on this topic and add some more value to knowledge gained concerning machine-based face recognition. Most of the system uses the local binary pattern (LBP) approach to perform face recognition. For detecting the face in the captured image, the Haar cascade algorithm is used where the person's facial feature is extracted and saved in a database for future reference. So, to provide an effective survey, we have classified the existing method for face recognition and explored the latest emerging technologies in this field.
The Science of Seeing Science: Examining the Visuality Hypothesis

Abstract Fundamental disciplinary differences may be traceable to the use of visual representations, with researchers in the physical and life sciences relying more heavily on visuality. Our goal was to examine how inscriptions are used by scientists in different disciplines. We analyzed 2,467 articles from journals in biology, criminology and criminal justice, gerontology, library and information science, medicine, psychology, and sociology. Proportion of page space dedicated to graphs, tables, and non-graph illustrations was calculated. A Visuality Index was defined as the proportion of page space dedicated to visual depictions of data and non-data information. An ANOVA indicated a statistically significant difference between disciplines, interaction between inscription type and discipline, with articles published in biology journals dedicating more page space to graphs. The significant overlap in inscription use and visuality indicates imperfect disciplinary demarcation, suggesting similar methodological and data analytic practices within a discipline and between subdisciplines.
Playing with the Sun: A Virtual Physics Experience for Nuclear Fusion Experimentation and Learning

Nuclear fusion has the potential to become an important energy source in the future, so we propose a virtual physics experience for learning about nuclear fusion and plasma physics with an interactive fusion experiment, and then placing the user inside the experiment and letting them run and control this. Implemented using Unreal Engine 4, users can manipulate parameters of the experiment in real-time with immediate visual results in the form of the shape and colour glow of the virtual plasma, resembling that of a real plasma.
Developing AI Bots with Minimax Algorithm for Surakarta Board Game

Surakarta Board Game is a traditional Indonesian board game that has been forgotten all over the world, especially in Indonesia where it originally came from. This game is still in its early stages of development, even though this game has been published through a book by Sid Sackson since 1970. Due to this we wanted to create an AI for this game in order to raise awareness of this beautiful Indonesian board game. Therefore, we were able to develop and create a simple Surakarta Board Game AI player implementing the Minimax algorithm. Minimax is a well-known decision-making and game-theory technique for finding the best move for a player, given that the opponent likewise plays optimally. Additionally, in this paper, we investigated the performance of our developed AI player including the chance of winning and the time taken for each game. Several experiments were carried out by opposing AI with a random player. Through developing this game, we will be able to help the survival of Indonesian culture.
Joint Weights-Averaged and Feature-Separated Learning for Person Re-identification

Although existing research has made considerable progress in person re-identification (re-id), it remains challenges due to intra-class variations across different cameras and the lack of cross-view paired training data. Recently, there are increasing studies focusing on using generative model to augment training samples. One of the main obstacles is how to use generated unlabeled samples. To address this issue, we propose a joint learning framework without label predictions, including re-id learning and data generation end-to-end. Each person encodes into a feature code and a structure code. The generative module is able to generate cross-id images by decoding structure code with switched feature code. For generated unlabeled data, we average re-id model weights instead of label predictions. Moreover, we expand the distance between inter-class feature code. Our approach improves the accuracy of the re-id model and the quality of the generated data. Experiments on several benchmark datasets shows that our method achieves competitive results with most state-of-the-art methods.
Applying Curriculum Learning on Path-based Knowledge Graph Reasoning Algorithems

In the field of knowledge graph reasoning, path reasoning based on reinforcement learning avoids using random walking methods and the inefficient search, but what follows is the false path problem. The amount of false paths is more than that of correct ones. The agent would usually reach the correct entity from the wrong paths first, and be more inclined to them in subsequent exploration. We propose to use curriculum learning to solve this problem: assuming that in the environment corresponding to the simple samples, the proportion of correct paths and the quality of paths are higher. The agent counters the sensitivity of RL models to false paths in the strategy by learning the basics knowledge out of simple sample sets. After a comprehensive evaluation on three KG datasets, our method is highly versatile and improves performance in knowledge-based question answering with almost no additional training time. And taking MINERVA as the baseline, the MRR index has increased by 1.3%, 3.7% on datasets WN18RR, NELL-995 respectively.