Reviewerly/exHarmony huggingface dataset:
Pattern-based Fall Prediction using Hospital Clinical Notes

Falls are considered as the second most leading cause of lethal and non-lethal injuries among the aging population. The fall prediction enables to reduce the health care cost and the negative impacts related to fall such as loss of independence.The objective of this paper is to propose an approach to select discriminative patterns from clinical notes. The selected discriminative patterns are used as features to predict falls among elderly people based on their clinical data. A collection of 12,911 labeled clinical notes of patients aged ≥ 65 was selected from a medical database, named Medical Information Mart for Intensive Care (MIMICIII), to create the corpus. The classification algorithms, Logistic Regression, Support Vector Machine and Random Forest are used to evaluate the effectiveness of the selected discriminative patterns for predicting falls. The Support Vector Machine classification algorithm gave the best results. The prediction accuracy of using the proposed discriminative patterns is higher than the baseline approaches when predicting `Fall” using the clinical notes. The results of this research suggest that the proposed discriminative pattern mining approach was able to generate a set of interesting patterns that were able to distinguish between fall and not fall clinical notes.
Positive-Unlabeled Data Purification in the Wild for Object Detection

Deep learning based object detection approaches have achieved great progress with the benefit from large amount of labeled images. However, image annotation remains a laborious, time-consuming and error-prone process. To further improve the performance of detectors, we seek to exploit all available labeled data and excavate useful samples from massive unlabeled images in the wild, which is rarely discussed before. In this paper, we present a positive-unlabeled learning based scheme to expand training data by purifying valuable images from massive unlabeled ones, where the original training data are viewed as positive data and the unlabeled images in the wild are unlabeled data. To effectively utilized these purified data, we propose a self-distillation algorithm based on hint learning and ground truth bounded knowledge distillation. Experimental results verify that the proposed positive-unlabeled data purification can strengthen the original detector by mining the massive unlabeled data. In particular, our method boosts the mAP of FPN by +2.0% on COCO benchmark.
Rainfall forecasting using wavelet transform (case study: Rainfall period of West Seram District of Maluku Province-Indonesia)

West Seram District is an area of Maluku Province that has high rainfall intensity. Trend of erratic weather changes nowadays makes it important to know the information about rainfall intensity. To know this information, we need to know about rainfall period. This will give some contributions to forecast the rainfall later. Rainfall forecasting plays an important role in water resources management. It also can be used to control the unusual event related to rainfall. In this paper, we use Wavelet transform to analyze rainfall data of West Seram District of Maluku Province-Indonesia. The data that we use is data of monthly rainfall from 1991-2016 from Meteorology, Climatology, and Geophysics agency in Ambon-Maluku-Indonesia. We also show some simulation results using Matlab.
Learning Depth from Light Field via Deep Convolutional Neural Network

Depth estimation based on light field imaging is a new way of depth estimation. The abundant data of light field paves the way for deep learning methods to play a role. In recent years, deep convolutional neural network (CNN) has shown great advantages in extracting image texture features. Inspired by this, we design a deep CNN with EPI synthetic images as input for depth estimation, which is called EENet. Under the constrains of epipolar geometry, pixels corresponding to a common object point are distributed in a straight line in the epipolar plane image (EPI), and the EPI synthetic image is easier to extract features by convolution kernel. Our EENet has the structure characteristics of multi-stream inputs and skip connections. Specifically, the horizontal EPI synthetic image, the vertical EPI synthetic image and the central view image are first generated from the light field, and input into the three streams of EENet respectively. Next, the U-shaped neural network is designed to predict the depth information, that is, the convolution and pooling blocks are used to encode the features, while the deconvolution layer and convolution layer are combined to decode features and recover the depth information. Furthermore, we employ skip connections between the encoding layers and the decoding layers to fuse the shallow location features and deep semantic features. Our EENet is trained and tested on the light field benchmark, and has obtained good experimental results.
Using Linguistically Non-local Punjabi Queries to Search the Global Web

Most part of the web is based on English language. The information, one requires may be available in English-based Web sites that are returned by the search engines based on some keywords extracted from the search query. Although, web searching is now allowing non-English queries also, but when non-English queries are used to search the web, the search engine returns Web sites related to the language that is used for search query. These local language Web sites may not provide relevant information to the user. We can call these queries as linguistically local queries. This paper presents a procedure to search the global English web using linguistically non-local Punjabi language queries. The input queries are processed through various natural language processing techniques such as tokenization, stemming, translation, transliteration, etc. The processing extracts and generates web search keywords, which are then used to reformulate the web search query to get the globally relevant information. It can further be enhanced to enable multi-language web search to get relevant information across the web.
What’s in a Pattern: A Vocabulary to Articulate Group Routines and Practices

When people come together as part of a community, oriented towards a collective activity, over an extended period of time, they develop and maintain different routines for the way they are organized, delegate, and carry out their activities. These routines involve a mixture of artifacts and technologies, and are shaped by key common dimensions such as how regulated an activity is, or whether it must follow a particular order. The routines also becomes inherited by new members of the community who use and develop them in their activity. We propose that similar routines, taking place across different communities, with their technologies and dimensions can be expressed and understood as a pattern. We present four patterns as examples that highlight the ways in which communities carry out routines related to organizing and engaging in their joint activity and discuss how such ways of addressing patterns may support the design of new community technologies.
Optimal Normal Bases Over Finite Fields

In this paper, a method for constructing a near optimal normal basis for algebraic extensions of a finite field is described. In each extension, except for the squares of basis elements, the product of two distinct normal basis elements can be expressed as a linear combination of those two basis elements, with coefficients in a much smaller subfield.
Cryptanalysis of an encrypted database in SIGMOD '14

Encrypted database is an innovative technology proposed to solve the data confidentiality issue in cloud-based DB systems. It allows a data owner to encrypt its database before uploading it to the service provider; and it allows the service provider to execute SQL queries over the encrypted data. Most of existing encrypted databases (e.g., CryptDB in SOSP '11) do not support data interoperability: unable to process complex queries that require piping the output of one operation to another. To the best of our knowledge, SDB (SIGMOD '14) is the only encrypted database that achieves data interoperability. Unfortunately, we found SDB is not secure! In this paper, we revisit the security of SDB and propose a ciphertext-only attack named co-prime attack. It successfully attacks the common operations supported by SDB, including addition, comparison, sum, equi-join and group-by. We evaluate our attack in three real-world benchmarks. For columns that support addition and comparison , we recover 84.9% -- 99.9% plaintexts. For columns that support sum, equi-join and group-by , we recover 100% plaintexts. Besides, we provide potential countermeasures that can prevent the attacks against sum, equi-join, group-by and addition. It is still an open problem to prevent the attack against comparison.
Software Estimation’s Risk in Pakistan Software Industry

Software and IT industry in Pakistan has seen a dramatic growth and success in past few years and is expected to get doubled by 2020, according to a research. Software development life cycle comprises of multiple phases, activities and techniques that can lead to successful projects, and software evaluation is one of the vital and important parts of that. Software estimation can alone be the reason of product’s success factor or the product’s failure factor. To estimate the right cost, effort and resources is an art. But it is also very important to include the risks that may arise in the in a software project which can affect your estimates. In this paper, we highlight how the risks in Pakistan Software Industry can affect the estimates and how to mitigate them.
Research on Dynamic Path Planning Algorithm of UAV

The path planning of the unmanned aerial vehicle refers to the computation of the optimum or suboptimum executable flight path of the unmanned aerial vehicle from the starting position to the final position under the condition of meeting corresponding performance indexes and their dynamic constraints. For the purpose of adjusting the path of the unmanned aerial vehicle timely in the case of emergency during flight, the PSO-AFSA algorithm is proposed in this paper, in which the artificial fish swarm optimizing strategy is introduced to the particle swarm optimization, the behavioral pattern of particles is selected through the behavior evaluation strategy of the artificial fish swarm, and the global best of particles is continuously updated through the bulletin board. The simulation result indicates that the algorithm can overcome the prematurity phenomenon of the particle swarm effectively, the rate of convergence is higher, and the simplified and safe flight path of the unmanned aerial vehicle can be planned quickly in three-dimensional dynamic environment.
Panel Session-Co-creative Virtual Reality Content Development in Healthcare: Tackling The Content Availability Problem

Virtual Reality (VR), blanket term used for all the reality-virtuality spectrum, is exploding in proliferation, especially in healthcare. The core challenge of this explosion is the timely and cost-effective provision of bespoke content. Co-creative approaches can facilitate this endeavor by making the educators active participants in the development process reducing development overheads and democratizing healthcare digital content creation. This panel session aims at health educators, VR technologists, developers and technology enthusiasts. It will present, and actively engage its audience with co-creative methods and approaches, providing them with the gateway experience that could become useful for introducing such methods in their institutions and workflows. The panelists are cross-disciplined educational technologists/medical educators with extensive experience in the field of technology enhanced learning (TEL) who have implemented such methodologies in practice. The workshop will follow a mixed format. A brief “observations from the field” introduction to the topics of VR TEL and co-creation will be followed by an introduction to real world healthcare VR projects and resources. In these, realistic examples, the participants will engage in hands-on storyboarding and non-technical design and development tasks, in order to acclimate with the co-creative process and become able to explore it for their own use cases.
Research on Application of Convolutional Neural Network Algorithm

Abstract This paper proposes a method based on deep learning to improve the efficiency of children’s English learning. Firstly, the migration method is used to improve the accuracy of Resnet18 model. Then the accuracy of the model was further improved by adding channel attention mechanism. Finally, the model fine-tuning method is used to reduce the computational load of the model, so as to facilitate the transplantation to embedded devices and improve the efficiency of the classification model. By comparing with the classical classification model, it is found that the classification model proposed in this paper has higher accuracy and fewer parameters, so it is suitable for transplantation into the English teaching of children.
Notice of Violation of IEEE Publication Principles: An Improved Particle Swarm Optimization Method for Motion Planning of Multiple Robots

Notice of Violation of IEEE Publication Principles <br><br>“An Improved Particle Swarm Optimization Method for Motion Planning of Multiple Robots” by Zhanna Gabbassova <br> in the Proceedings of the IEEE Smart Information Systems and Technologies (SIST) April 2021 <br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br> This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <br><br> Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: <br><br> “An Improved Particle Swarm Optimization Method for Motion Planning of Multiple Robots” <br> by Ellips Masehian and Davoud Sedighizadeh <br> in Springer Tracts in Advanced Robotics, 83, 2013, pp. 175-188 <br><br> <br/> Multi robot motion planning is a challenging problem in the robotics field due to its complexity and high computational costs induced by the number of robots. In this paper, a new heuristic method is presented for solving this problem through a decentralized approach with global coordination. The method is based on a new improved variant of the Particle Swarm Optimization (PSO) metaheuristic, which serves as a global planner. Alternatively, for local planning and avoiding obstacles in narrow passages, the Probabilistic Roadmap Method (PRM) is employed. The global and local planners act sequentially until all robots reach their goals. The algorithm iteratively and simultaneously minimizes two main objectives, shortness and smoothness of the paths. The proposed algorithm is simulated and compared with the standard (basic) PSO, as well as the standard Probabilistic Roadmap methods. The experimental results show a meaningful advantage of the new method regarding computation-al time and path quality.
Universal interactive Gaussian quantization with side information

We consider universal quantization with side information for Gaussian observations, where the side information is a noisy version of the sender’s observation with an unknown noise variance. We propose a universally rate optimal and practical quantization scheme for all values of unknown noise variance. Our scheme is interactive, uses Polar lattices from prior work, and proceeds by checking in each round if a reliable estimate has been formed. In particular, our scheme is based on a structural decomposition of the underlying auxiliaries so that even when recovery fails in a round, the parties agree on a common "reference point" that is closer than the previous one.
A Novel Region Duplication Detection Algorithm Based on Hybrid Approach

The digital images from various sources are ubiquitous due to easy availability of high bandwidth Internet. Digital images are easy to tamper with good or bad intentions. Non-availability of pre-embedded information in digital images makes the tampering detection process more difficult in case of digital forensics. Thus, passive image tampering is difficult to detect. There are various algorithms available for detecting image tampering. However, these algorithms have some drawbacks, due to which all types of tampering cannot be detected. In this paper researchers intend to present the types of image tampering and its detection techniques with example based approach. This paper also illustrates insights into the various existing algorithms and tries to find out efficient algorithm out of them.
Pablo: Designing an Everyday Companion for Visualizing Auditory Memories

From a slow technology perspective, we designed Pablo to visualize auditory memories and explore the recalling processes. Pablo randomly records daily sound of users, visualizes the sound into an abstract image and prints it out with QR code of the sound file. In this manner, Pablo introduces time gaps for users to speculate and recall after they receive the picture card. We conducted a field trial with two participants and each with Pablo at home for two days. We found that Pablo encourages the users to reflect on their memories in different aspects. Through associating images with sound and memories, the users give new interpretations on images. They start to think and reflect due to the slowness of this sense-making process and interact with their embodied memories.
Secure Blockchain Smart Contracts for Efficient Logistics System

Blockchain and allied technologies have been investigated as panacea for the problems faced by supply chain and logistics industry. However, earlier literatures have focused on limited aspects of a typical supply chain such as monitoring assets, securing traceability widely neglecting data integrity, and data access. To overcome such drawbacks, the current paper proposes secure smart contracts based on the ERC20 interface in a permissioned blockchain with relevant processes and functions to obtain a holistic framework for securing the supply chain and logistic operations. The efficacy of the proposed framework was demonstrated in a case study. It was found that critical loopholes in a current supply chain can be overcome using the proposed framework. Additionally, several outlines for future research are outlined.
Mathematical Model of the Computer Attack Implementation Possibility by an Intruder

The article represents a substantiated mathematical model of the computer attack (CA) possibility by an intruder based on the theory of “defensible space” and the theory of cellular automata. A comparative analysis of mathematical approaches for describing the dynamics of the attacked IS and the cellular automata is carried out. The results of the analysis made it possible to draw a reasonable conclusion: the dynamics of the CA is similar to the dynamics of the cellular automata. Based on the analysis of the CA model, a reasonable list of quantitative indicators has been formed to determine the probability coefficient. The probabilistic coefficient is called the weighting factor of the transition (WFT) in the mathematical model of the CA implementation possibility. We show seven quantitative indicators. Two of them are the time required to develop protection method and the number of nodes potentially affected by CA; and (7) the total number of nodes. Other are coefficients. There are coefficient of availability / popularity of protection means against CA; coefficient of solution availability for CA monitoring; the coefficient of absence / presence of visible CA damage; the coefficient of the attack method popularity. Defines as active discussion (“explanation”) of the attack method on the DarkNet forums. For a specific attack method, the coefficient for all transitions is the same. The sufficiency of selected quantitative indicators list is confirmed by the results of the analysis of the Petya ransomware scenario on information system (IS) located on the territory of Ukraine.
A Proposed Solution to Road Traffic Accidents Based on Fuzzy Logic Control

Road Traffic accidents are one of the main problems causing death and fatal injuries in all countries. Several general suggestions were presented to the ministry of transport to try to overcome this problem. This paper discusses a specific approach based on fuzzy logic control to train semi-autonomous cars to prevent accidents from occurring. These types of cars equipped with many devices along with decision making algorithms based on learning will make cars more intelligent to take the right decision when no reaction of the driver is noticed.
Quality of professional training of IT specialists: &#x0D; strategy for interaction between employers and secondary vocational education

The article is devoted to the problem of the quality of vocational training of graduates of educational institutions of secondary vocational education in the field of information technology. The urgency of the problem is the fact that the popularity of secondary vocational education in the field of information technology is growing: every year a large number of middle-level specialists enter the labor market, who are required to be ready to work, adaptability to work's conditions, which minimizes the need to retrain them. There is a need to control the quality of education and the results of professional training of graduates. The article analyzes the documents regulating the educational process of secondary vocational education on the example of the specialty 09.02.07 Information systems and programming: the decree of the government of the Russian Federation On the approval of the state educational standard of secondary vocational education, the procedure for organizing educational activities in educational programs of secondary vocational education, order of the Ministry of Labor of Russia On the approval of the list of the 50 most in demand in the labor market, new and promising professions requiring secondary vocational education, federal state educational standard for the specialty 09.02.07 Information systems and programming, the order of the Ministry of Labor of Russia On the approval of the professional standard Programmer. The conclusion is made that educational institutions have the ability to ensure the quality of vocational training if they involve employers and specialists from the industry close to the educational program in the educational process. However, educational institutions should involve external, independent experts from the industry to organize the educational process and monitor its results.