Reviewerly/exHarmony huggingface dataset:
Belief Space Planning for Mobile Robots With Range Sensors Using iLQG

In this work, we use iterative Linear Quadratic Gaussian (iLQG) to plan motions for a mobile robot with range sensors in belief space. We address two limitations that prevent applications of iLQG to the considered robotic system. First, iLQG assumes a differentiable measurement model, which is not true for range sensors. We show that iLQG only requires the differentiability of the belief dynamics. We propose to use a derivative-free filter to approximate the belief dynamics, which does not require explicit differentiability of the measurement model. Second, informative measurements from a range sensor are sparse. Uninformative measurements produce trivial gradient information, which prevent iLQG optimization from converging to a local minimum. We densify the informative measurements by introducing additional parameters in the measurement model. The parameters are iteratively updated in the optimization to ensure convergence to the true measurement model of a range sensor. We show the effectiveness of the proposed modifications through an ablation study. We also apply the proposed method in simulations of large scale real world environments, which show superior performance comparing to the state-of-the-art methods that either assume the separation principle or maximum likelihood measurements.
DBMS-Friendly Searchable Symmetric Encryption: Constructing Index Generation Suitable for Database Management Systems

Searchable symmetric encryption enables users with the secret key to conduct keyword search on encrypted data without decryption. Recently, dynamic searchable symmetric encryption (DSSE) which provides secure functionalities for adding or deleting documents has been studied extensively. Many DSSE schemes construct indexes in order to efficiently conduct keyword search. On the other hand, the indexes constructed in DSSE are complicated and independent to indexes supported by database management systems (DBMSs). Plug-in developments over DBMSs are often restricted, and therefore it is not easy to develop softwares which can deploy DSSE schemes to DBMSs. In this paper, we propose a DBMS-friendly searchable symmetric encryption scheme which can generate indexes suitable for DBMSs. Our index can narrow down encrypted data which should be conducted keyword search, and be combined with well-used indexes supported by many DBMSs. Our index consists of a small portion of an output value of a cryptographic deterministic function (e.g. pseudo-random function or hash function). We also show an experiment result of our scheme deployed to DBMSs.
A Blockchain-based Collaboration Framework for Educational Material Sharing

Education institutes have a long history of collaboration. But when comes in creating educational materials, teachers need to take precaution in making use of existing educational contents or being referred as other's original creation. While some works can be reused and redistributed freely for educational purposes but some cannot due to copyright ownership issue. In order to provide more freedom in using existing work, we explore blockchain technology and propose a collaboration system to record multiple authorship when using other people's work. So that authors can know when their works are used and claim for economic benefit etc. We first propose to denote a corresponding block to each work so a work can be reused. Then an educational material contents various works can be denoted into a blockchain by linking work's corresponding blocks together. When update the educational material, new blocks can be added to the chain. We also propose to store the file off chain to keep the block size small. Existing online file access management function can be added to control distribution of educational material. This blockchain-based framework can be extended to support other open collaboration in reusing existing works.
Instructional Video Summarization Using Attentive Knowledge Grounding

This demonstration considers the scenario of summarizing an instructional video, for query such as "how to cook galbi", to efficiently obtain a skillset. Specifically, we use the query to retrieve both the relevant video and the external procedural knowledge, such as a recipe document, and show summarization is more effective with such augmentation.
On Interim Cognitive Diagnostic Computerized Adaptive Testing in Learning Context

Interim assessment occurs throughout instruction to provide feedback about what students know and have achieved. Different from the current available cognitive diagnostic computerized adaptive testing (CD-CAT) design that focuses on assessment at a single time point, the authors discuss several designs of interim CD-CAT that are suitable in the learning context. The interim CD-CAT differs from the current available CD-CAT designs primarily because students' mastery profile (i.e., skills mastery) changes due to learning, and new attributes are added periodically. Moreover, hierarchies exist among attributes taught sequentially and such information could be used during item selection. Two specific designs are considered: The first one is when new attributes are taught in Stage II, but the student mastery status of the previously taught attributes stays the same. The second design is when both new attributes are taught, and previously taught attributes can be further learned or forgotten in Stage II. For both designs, the authors propose an individual prior, which considers a person's learning history and population learning model, to start an interim CD-CAT. Simulation results show that the Stage II CD-CAT using individual prior outperforms the methods using population priors. The GDINA (generalized deterministic inputs, noisy, "and" gate) diagnostic index (GDI) is extended to accommodate item hierarchies, and analytic results are provided to further illustrate the types of items that are most popular during item selection. As the first study that focuses on the application of CD-CAT in a learning context, the methods and results present herein showed the great promise of using CD-CAT to monitor learning.
Data duplication using Amazon Web Services cloud storage

Since Big Data has become a worldwide phenomenon, both decision-makers and experts in data and analysis have been concerned about data scalability. They have been too reliant on the theory of better data in the digital age. Data duplication is an occurrence where a business has more than one copy of the same source of data. To a layman, data replication appears like a basic issue which could be avoided by any professional data scientist or experienced network administrator. Needless to say, the replication of data is also a very common issue. Cloud computing is a big part of the business sector today, where computing services are supplied to consumers via the Internet on demand. Data storage is one of the most common services provided by cloud computing. The key benefit of using cloud storage from the perspective of consumers is that they are only liable for the required quantity of data, which can be scaled and decreased on demand, to minimize their costs in buying and maintaining their data infrastructure. A decrease in data sizes will help providers minimize costs by using large storage facilities and minimize energy usage by rising data size in cloud computing. It has contributed to the implementation of data replication technology to increase cloud storage capacity. Because of the volatile existence of data in cloud storage and cloud overtime shifts, some data chunks can also be read in time but cannot be accessed in another time. Many databases can be accessed or modified simultaneously by several users and others need a high consistency level for accuracy. The author has suggested a dynamic cloud storage virtualization scheme to maximize storage capacity and preserve fault tolerance redundancy.
Application of Computer Virtual Technology in Basketball Training

Abstract With the rapid development of information technology, various industries are facing great opportunities and challenges for upgrading, as is the sports industry. Combining the Internet of things, big data, artificial intelligence, VR/AR, 3D printing with sports products, stadiums and sports services will bring new changes to the sports fitness industry. Computer virtual technology is a new technology produced under the background of the all-round development of science and technology. This technology is mainly composed of sensing technology, computer technology, simulation technology and so on, so as to better simulate the real world. By simulating real vision and various actions, we can deepen the user’s understanding of something, feel all kinds of phenomena constructed by the virtual world, strengthen the communication between the user and the virtual environment, and feel more the connotation of the real world [1] . Athletes can interact with their own limbs and environment through computer virtual technology, which is the application of virtual method in real sports training, so that athletes can better exercise their sports skills and improve their sports ability.
Random style transfer for person re-identification with one example

<abstract> Person re-identification with only one labeled image for each identify can not eliminate the style variations of different cameras in the same dataset. In this paper, we propose a random style transfer strategy that randomly transforms the labeled images on the one-example person re-identification task. In this strategy, we focus on twofolds: 1) Randomly transform the camera style of labeled images and unlabeled images during the training stage and 2) use the average feature of labeled data and its camera style transform data to estimate pseudo label on unlabeled data. Notably, our strategy exhibits state-of-the-art performance on large-scale image datasets and its Rank-1 accuracy outperforms the state-of-the-art method by 10.3% points on Market-1501, and 8.4% points on DukeMTMC-reID. </abstract>
PS3: Partition-Based Skew-Specialized Sampling for Batch Mode Active Learning in Imbalanced Text Data

While social media has taken a fixed place in our daily life, its steadily growing prominence also exacerbates the problem of hostile contents and hate-speech. These destructive phenomena call for automatic hate-speech detection, which, however, is facing two major challenges, namely i) the dynamic nature of online content causing significant data-drift over time, and ii) a high class-skew, as hate-speech represents a relatively small fraction of the overall online content. The first challenge naturally calls for a batch mode active learning solution, which updates the detection system by querying human domain-experts to annotate meticulously selected batches of data instances. However, little prior work exists on batch mode active learning with high class-skew, and in particular for the problem of hate-speech detection. In this work, we propose a novel partition-based batch mode active learning framework to address this problem. Our framework falls into the so-called screening approach, which pre-selects a subset of most uncertain data items and then selects a representative set from this uncertainty space. To tackle the class-skew problem, we use a data-driven skew-specialized cluster representation, with a higher potential to "cherry pick" minority classes. In extensive experiments we demonstrate substantial improvements in terms of G-Means, and F1 measure, over several baseline approaches and multiple datasets, for highly imbalanced class ratios.
The Effects of Fear of Cybercrime and Information Systems Security Policy on National Vigilance

The aim of this study was to examine the effect of information system security policy and fear of cybercrime on national vigilance in the Indonesian Ministry of Defense of the Republic of Indonesia. Cybercrime threat could happen in many forms and these are real threats to the Indonesian national security and sustainability as a nation. This is one of the reasons why this study was conducted. The study has been conducted since October 2015 in the Research and Development Body of the Ministry of Defense of the Republic of Indonesia, and Universitas Pembangunan Nasional “Veteran” Jakarta (University of National Development “Veteran” Jakarta). A quantitative method was employed in this study. Military officers, civilian employees working in the Research and Development Body of the Ministry of Defense, university lecturer as well as students of Universitas Pembangunan N asional “Veteran” Jakarta were selected as respondents. The result shows that information systems security policy has the biggest effect on national vigilance. As the result of this study, information security policy could play an important role in combating cybercrime. Conducting awareness program on the danger of cybercrime is another way to make public aware of the danger of cybercrime.
A Study of Aging-Related Bugs Prediction in Software System

Software aging refers to the problem of the deteriorated performance and increased failure rate in the long-running software systems. Typically, aging-related bugs (ARBs) that are activated due to the runtime garnering of error conditions caused the software aging problem. ARBs are difficult to detect when software testing is carried out. Therefore, early identification of these bugs can help in building a robust software system. However, one major issue in ARBs prediction is the skewness of the dataset (class imbalance problem) that may cause bias in the learning of classification algorithms and thus may lead to the higher misclassification rate. This paper aims to investigate the effect of instance filtering (resampling) and standardization techniques with various classification algorithms when predicting aging-related bugs in the software system. The experimental study is performed by using four different classification algorithms namely logistic regression, support vector classifiers (SVC), random forest, and artificial neural network (ANN) with Softmax function for three different datasets available in the open-source software repository. Results of the analysis show an increment in the performance of the used classification algorithms with reduced misclassification rate.
A Novel Bilateral Oblivious Transfer Protocol Based on Cut-and-Choose Technique

The Yao's protocol based on garbled circuits is the foundation of secure two-party computation protocols, and Oblivious Transfer (OT), as a important tool in modern cryptography, plays a crucial role in transmitting garbled keys in the Yao's protocol. In the past decades, considerable attentions have been paid to the Cut-and-Choose technique which is applied to OT protocols to prevent malicious P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> (called circuit generator) from constructing wrong garbled circuits. Additionally, Bilateral OT becomes a focus point, which can simultaneously transmit P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> 's and P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> 's (called circuit evaluator) garbled keys to guarantee input consistency of two parties and improve transmission efficiency. As can be seen, some Cut-and-Choose Bilateral OT (CCBOT) protocols have been proposed. Since CCBOT has emerged, the probability of malicious P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> constructing wrong garbled circuits without being found becomes lower and the input consistency problem is properly resolved too. However, the security of most CCBOT protocols are facing challenges since they are based on the Decisional Diffie-Hellman (DDH) assumption, Quadratic Residues (QR) assumption and Decisional Composite Residuosity (DCR) assumption. In this paper, we present a novel CCBOT protocol with the security against malicious adversaries based on the Computational Diffie-Hellman (CDH) assumption, and analysize the correctness, efficiency and security of the constructed protocol. Besides, we analysize the cheating probability of P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> in the CCBOT and extend the CCBOT to Single-Choice CCBOT to make the protocol more applicable.
Combining Temporal Event Relations and Pre-Trained Language Models for Text Summarization

In this paper, we introduce an innovative high performing deep learning architecture for text summarization using pre-trained language models. Language model (LM) pre-training has provided an impressive performance on many of language understanding tasks. However, it remains much for experimentation to efficiently utilize these pre-trained models for specific tasks. We propose a novel architecture(ENEMAbst) effectively utilizing pre-trained LM MASS for summarization. We also propose that the use of temporal relations across event representations can significantly improve language generation and inference. We demonstrate experimentally that using events provides a more in-depth, comprehensive understanding of the text. We showcase how MASS, along with Event representations, can be used for summarization. We couple our Event-Network-Encoders(ENE) with MASS based document level encoders(MASSEnc) by attention augmenting MASSEnc with ENE. We devise an innovative fine-tuning schedule to train our Pretrained-Encoders and Pretrained-Decoders with ENE efficiently. We show empirically that our model performs best compared to state-of-the-art baseline methods for abstractive and extractive summarization. Our model provides 0.85 point improvement from 43.331 to 44.179 for abstractive summarization and 0.63 point improvement from 43.85 to 44.48 for extractive summarization on ROUGE-1 over the existing best performing models.
Stablecoin Billionaires - A Descriptive Analysis of the Ethereum-based Stablecoin Ecosystem

While Ethereum, the second largest public blockchain after Bitcoin, was experiencing a story of success over the past few years, Stablecoins grew to an integral component within the Ethereum-based decentralized finance (DeFi) ecosystem. Various issuers of cryptographically-secured digital money emerged and in particular Tether, the most widespread dollar-pegged cryptocurrency, received much attention in recent time. Using empirical data broadcasted by seven different Stablecoins and their Smart Contracts, I statistically examine both Ethereum's Stablecoin landscape as a whole and the individual token ecosystems. By incorporating qualitative information obtained by analyzing the contracts' Solidity code, I discovered extreme concentrations of power within the token contracts of centralized Stablecoins, while decentralized tokens such as DAI lacked structural power imbalances. Analyzing the costs, empirical data revealed that transfers of Stablecoins that are primarily used in decentralized environments, were on average up to eight times more expensive than those carried out with `centralized' tokens, which could be traced back to an efficiency trade-off in favor of decentralization. Furthermore, I found that emission- and destruction policies have a decisive impact on basic statistical measures such as the mean transfer amount or its standard deviation, both of which depend heavily on the applied distribution mechanism of a token. In an attempt to identify `High Rollers' within the analyzed ecosystem, the exchanges Huobi and Binance were exposed as Stablecoin billionaires, who together control 20 % of Ethereum's Stablecoin supply. Finally a very unequal distribution of wealth in favour of crypto-exchanges and DeFi applications was revealed.
Estimation of Air Temperature under Cloudy Conditions Using Satellite-Based Cloud Products

This letter presents a novel method for instantaneous air temperature under cloudy conditions ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{a,\text {cloudy}}$ </tex-math></inline-formula> ) estimation using satellite-derived cloud top temperature (CTT), cloud top height (CTH), and Global Forecast System (GFS) forecasts. The radiosonde profiles were used to analyze the relationship between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{a,\text {cloudy}}$ </tex-math></inline-formula> and CTT, CTH. The results showed that it is feasible to estimate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{a,\text {cloudy}}$ </tex-math></inline-formula> using CTT and CTH, especially for low and middle cloud conditions. Linear and neural network (NN)-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{a,\text {cloudy}}$ </tex-math></inline-formula> estimation models were constructed and validated using the Visible Infrared Imaging Radiometer Suite (VIIRS) CTT, CTH, and GFS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{\text {air}}$ </tex-math></inline-formula> for summer 2017 and 2018. The NN model performs better than the linear model, and GFS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{\text {air}}$ </tex-math></inline-formula> can obviously improve the accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{a,\text {cloudy}}$ </tex-math></inline-formula> estimation. The correlation coefficient (R), root-mean-square error (RMSE), and bias of the NN model with GFS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{\text {air}}$ </tex-math></inline-formula> were 0.953, 1.950 °C, and −0.030 °C, respectively. The estimation model performed better under low and warm clouds than high and cold cloud conditions.
The Current Situation of Medical Information Retrieval Teaching under the Background of MOOC

Abstract The full name of MOOC is the massive open online course. The platform system of medical information retrieval and utilization resource base based on MOOC can realize online learning, examination, information release and other functions. It helps to improve students’ information literacy, reduce teachers’ workload, improve teaching efficiency and promote teaching reform.
Conceptual framework of intelligent decision support based on user digital life traces and ontology-based user categorisation

Abstract The paper presents conceptual framework and information model of intelligent decision support based on traces of user digital lives and ontology-based user categorisation. The conceptual framework defines components that provide information revealed from the traces of user digital lives, generalize this information, and make ontology inference. The information model defines information flows between the components of the conceptual framework. The novelties of this research are grouping users with common preferences and decision making behaviours based on the user digital traces, and context-sensitive ontology-based categorization of users into the user groups.
Story Creation Algorithm Using Q- Learning in a 2D Action RPG Video Game

In this paper, we try to solve the problem of making video games with multiple storylines. To do this, we developed an algorithm capable of generating variations in the game's story through altering the behaviors of the characters. For this task, we use Q-Learning. Our results indicate that this algorithm may be viable to be used in video games for commercial purposes.