Reviewerly/exHarmony huggingface dataset:
Difficulty Scaling in Proof of Work for Decentralized Problem Solving

We propose DIPS (Difficulty-based Incentives for Problem Solving), a simple modification of the Bitcoin proof-of-work algorithm that rewards blockchain miners for solving optimization problems of scientific interest. The result is a blockchain which redirects some of the computational resources invested in hash-based mining towards scientific computation, effectively reducing the amount of energy ‘wasted’ on mining. DIPS builds the solving incentive directly in the proof-of-work by providing a reduction in block hashing difficulty when optimization improvements are found. A key advantage of this scheme is that decentralization is not greatly compromised while maintaining a simple blockchain design. We study two incentivization schemes and provide simulation results showing that DIPS is able to reduce the amount of hash-power used in the network while generating solutions to optimization problems.
Pattern recognition based strategy to evaluate the stress field from dynamic photoelasticity experiments

For avoiding fails in loaded structures, adjust their geometry, removing material, or quantify residual stresses, photoelasticity studies often is limited by complex experiments, excessive computational procedures, expert supervision, narrow applications, and static focus. This paper proposes a pattern recognition-based strategy for evaluating the stress field from simplex dynamic experiments. Here, temporal color variations are processed to extract, select and classify stress magnitudes, isotropic points, and inconsistent information. This approach used synthetic photoelasticity videos from analytical stress models about disk and ring under diametric compression. Additional to improve limitations in conventional photoelasticity approaches, this strategy identifies isotropic and inconsistent points.
SHAP values for Explaining CNN-based Text Classification Models

Deep neural networks are increasingly used in natural language processing (NLP) models. However, the need to interpret and explain the results from complex algorithms are limiting their widespread adoption in regulated industries such as banking. There has been recent work on interpretability of machine learning algorithms with structured data. But there are only limited techniques for NLP applications where the problem is more challenging due to the size of the vocabulary, high-dimensional nature, and the need to consider textual coherence and language structure. This paper develops a methodology to compute SHAP values for local explainability of CNN-based text classification models. The approach is also extended to compute global scores to assess the importance of features. The results are illustrated on sentiment analysis of Amazon Electronic Review data.
Real-time dense 3D object reconstruction using RGB-D sensor

In this paper, we propose a new algorithm for dense 3D object reconstruction using a RGB-D sensor at high rate. In order to obtain a dense shape recovery of a 3D object, an efficient merging of the current and incoming point clouds obtained with the Iterative Closest Point is suggested. As a result, incoming frames are aligned to the dense 3D model. The accuracy of the proposed 3D object reconstruction algorithm on real data is compared to that of the estate-of-the-art reconstruction algorithms.
A Real-Time Data Delivery for Mobile Sinks Group on Mobile Cyber-Physical Systems

Mobile Cyber-Physical Systems (MCPS) have extended the application domains by exploiting the advantages of Cyber-Physical Systems (CPS) through the mobile devices. The cooperation of various mobile equipment and workers based on the MCPS further improved efficiency and productivity in the industry. To support this cooperation of groups of workers (hereafter referred to as the Mobile Sink Groups), data should be delivered to appropriate groups of workers in a timely manner. Traditionally, the data dissemination for MSG relies on flooding-based geocasting into the movable area of the group due to frequent movements of each group member. However, the flooding-based data dissemination could not be directly applied to real-time data delivery that demands the required time deadline and the end-to-end delivery distance, because the flooding could not define the end-to-end distance and progress to each member in a group. This paper proposes a real-time data delivery mechanism for supporting MSG in time-critical applications. In our mechanism, a ring-based modeling and data transfer scheme on a virtual grid in the ring for group mobility provides the end-to-end distance and the progress to forward real-time data to each member. Simulation results show our mechanism is superior to the existing ones in terms of real-time communication for MSG.
An Algorithm for Multi-Domain Website Classification

The web is the largest world-wide communication system of computers. The web has local, academic, commercial and government sites. As the types of websites increases in numbers, the cost and accuracy of manual classification became cumbersome and cannot satisfy the increasing internet service demands, thereby automated classification became important for better and more accurate search engine results. Therefore, this research has proposed an algorithm for classifying different websites automatically by using randomly collected textual data from the webpages. This research also contributed ten dictionaries covering different domains and used as training data in the classification process. Finally, the classification was carried out using the proposed and Naïve Bayes algorithms and found the proposed algorithm outperformed on the scale of accuracy by 1.25%. This research suggests that the proposed algorithm could be applied to any number of domains if the related dictionaries are available.
Technical aspects of publication in several languages – how to link digital object identifiers (DOIs)

We consider the problem of publishing in several languages and review related technological, bibliometric and ethical problems. The existing practices include a) using the same DOI assigned by the same publisher to all versions of the paper, b) using different DOI assigned by the same publisher to different versions of the paper, c) using different DOIs assigned by different publishers to different versions of the paper published in different journals. There are solutions for linking publications in several languages, such as Math-Net.Ru, but they only partially solve the outlined problems. In this paper, we analyse pros and cons of different options and propose the way of linking DOIs of the versions of the paper by using the relations mechanisms provided by Crossref. We describe how this mechanism can be used by international and Russian journals. The proposed solution allows linking the DOIs of the Russian and English versions of the same paper in the journals published by different publishers. If adopted widely, this solution would not only exclude ethical problems in publishing translations, but would also allow abstracting and indexing services to correctly count citations to articles published in several languages.
Alert Characterization by Non-expert Users in a Cybersecurity Virtual Environment: A Usability Study

Although cybersecurity is a domain where data analysis and training are considered of the highest importance, few virtual environments for cybersecurity are specifically developed, while they are used efficiently in other domains to tackle these issues. By taking into account cyber analysts’ practices and tasks, we have proposed the 3D Cyber Common Operational Picture model (3D CyberCOP), that aims at mediating analysts’ activities into a Collaborative Virtual Environment (CVE), in which users can perform alert analysis scenarios. In this article, we present a usability study we have performed with non-expert users. We have proposed three virtual environments (a graph-based, an office-based, and the coupling of the two previous ones) in which users should perform a simplified alert analysis scenario based on the WannaCry ransomware. In these environments, users must switch between three views (alert, cyber and physical ones) which all contain different kinds of data sources. These data have to be used to perform the investigations and to determine if alerts are due to malicious activities or if they are caused by false positives. We have had 30 users, with no prior knowledge in cybersecurity. They have performed very well at the cybersecurity task and they have managed to interact and navigate easily. SUS usability scores were above 70 for the three environments and users have shown a preference towards the coupled environment, which was considered more practical and useful.
Exploration on the Application of WeChat Official Accounts Platform in the Teaching Reform of Analytical Chemistry in Medical Universities

Objective: To construct a teaching mode based on WeChat Official Accounts Platform and to discuss its teaching effect in analytical chemistry courses in medical colleges and universities.Methods: The teaching mode based on WeChat Official Accounts Platform is used to teach the experimental subjects.After the course, the teaching effect is evaluated by questionnaire survey.Results: The experimental subjects are satisfied with the learning effect of the new teaching mode and have a good learning experience.Conclusion: The teaching mode and teaching form based on WeChat Official Accounts Platform are novel, which can improve students' interest in learning, increase their initiative in learning and advance their self-learning ability, and help to upgrade the teaching effect of analytical chemistry in medical universities.
Computer System Capability Hardware Course Group Construction Based on OBE

The cultivation of system ability has a great impetus to the construction of computer specialty courses. The cultivation of system-oriented capabilities is not only an urgent need for social development, but also a demand for computer courses in colleges and universities. The computer system's ability development enables students to be in an advantageous position in social competition. At present, the computer curriculum system lacks the connection between the courses. The teaching content lacks engineering approaches. The teaching method lacks comprehensive teaching. Based on this problem, this paper proposes a method for building a computer system capability hardware course group based on OBE. This method carries out teaching reform from three aspects: teaching content, teaching method and teaching effect evaluation.
An Optimized Deep Belief Network Model for Accurate Breast Cancer Classification

Breast cancer is the second most common cause of mortality in women. In the last decade, the rate of new diagnoses has increased significantly. Meanwhile, according to the World Health Organization, breast cancer can be treated effectively if it is detected in the early stages. Computer automated diagnosis systems have greatly helped researchers to learn the evolution and causes of breast cancer, as well as to monitor and detect the disease among women. However, there is a need for modern and accurate models to reduce the risk of breast cancer as well as for further improvements in diagnostic performance. In this paper, a deep belief network (DBN) model is proposed to achieve high accuracy in breast cancer classification. A particle swarm optimization algorithm is employed and integrated into the DBN to optimize the model’s parameters. The model is tested on two popular breast cancer datasets. Moreover, analysis of the datasets and improvements in data quality using data mining are shown, and an effective feature-selection method for a high-dimensional feature space is discussed. In experiments, the proposed method showed robust performance with high classification accuracy, matching, and even outperforming existing state-of-the-art algorithms.
Road-objects tracking for autonomous driving using lidar and radar fusion

Abstract In this paper, based on the fusion of Lidar and Radar measurement data, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. The lidar and radar devices are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is used for their data fusion. Lidars are accurate in determining objects’ positions but significantly less accurate on measuring their velocities. However, Radars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution. Therefore, the merits of both sensors are combined using the proposed fusion approach to provide both pose and velocity data for objects moving in roads precisely. The Grid-Based Density-Based Spatial Clustering of Applications with Noise (GB-DBSCAN) clustering algorithm is used to detect objects and estimate their centroids from the lidar and radar raw data. Then, the estimation of the object’s velocity as well as determining its corresponding geometrical shape is performed by the RANdom SAmple Consensus (RANSAC) algorithm. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. The performance of the UKF fusion is compared to that of the Extended Kalman Filter fusion (EKF) showing its superiority. Simulation studies have been carried out to evaluate the performance of the LR ODT for tracking bicycles, cars, and pedestrians.
Pengembangan Pedoman E-Modul Berorientasi Student Active Learning Sebagai Pendukung Pembelajaran di Sekolah Dasar

This study aims to (1) produce a valid e-module guidelines for active student learning in elementary schools that are valid; (2) produce eligibility guidelines for student active learning oriented e-modules in Elementary Schools, and (3) produce practical student learning oriented e-module guidelines. This research uses ADDIE model development research design which consists of 5 levels, namely analysis, design, development, application, and evaluation. The results showed that the results of the validation trial of teaching materials experts and material experts, the average score obtained was 83.95 with valid criteria. The results of user trials (teachers and students) then the average score obtained is 3.5 very well / very practical
Prediction of Disease Using Machine Learning and Deep Learning

Big data plays an important role in multiple industries, but it has a greater impact and is important to the rapidly growing healthcare industry. Big data plays a crucial role by providing a robust system wherein a better result in disease detection can be achieved. Initially, the predictions are made on the data available, but the lack of incomplete data leads to a reduction in the quality of accuracy. In addition to incomplete data the different characteristics of specific regional diseases, which vary with respect to the region can weaken the prediction more. In this paper, we compare machine learning algorithms and Convolutional neural networks (CNN) to show the increase in efficiency of predictions related to chronic disease outbreaks in communities. We implement and check the efficiency of the proposed models over real-life medical data. To avoid complications that arise due to missing data we use a latent factor model to reconstruct incomplete information. We perform predictions on a regional chronic disease lung infection. We propose a convolutional neural network-based multimodal disease prediction that performs predictions based on both structured as well as unstructured data.
A structure theorem for finite fields

We present a new structure theorem for finite fields of odd order that relates multiplicative and additive structure in an interesting way. This theorem has several applications, including an improved understanding of Dickson and Chebyshev polynomials and some formulas with a number-theoretic flavor. This paper is an abridged version of two math arXiv articles by the author [arXiv:1707.06870, arXiv:1707.06877].
Towards practical implementations of person re-identification from full video frames

With the major adoption of automation for cities security, person re-identification (Re-ID) has been extensively studied recently. In this paper, we argue that the current way of studying person re-identification, i.e. by trying to re-identify a person within already detected and pre-cropped images of people, is not sufficient to implement practical security applications, where the inputs to the system are the full frames of the video streams. To support this claim, we introduce the Full Frame Person Re-ID setting (FF-PRID) and define specific metrics to evaluate FF-PRID implementations. To improve robustness, we also formalize the hybrid human-machine collaboration framework, which is inherent to any Re-ID security applications. To demonstrate the importance of considering the FF-PRID setting, we build an experiment showing that combining a good people detection network with a good Re-ID model does not necessarily produce good results for the final application. This underlines a failure of the current formulation in assessing the quality of a Re-ID model and justifies the use of different metrics. We hope that this work will motivate the research community to consider the full problem in order to develop algorithms that are better suited to real-world scenarios.
Hybrid of Reinforcement and Imitation Learning for Human-Like Agents

Reinforcement learning methods achieve performance superior to humans in a wide range of complex tasks and uncertain environments. However, high performance is not the sole metric for practical use such as in a game AI or autonomous driving. A highly efficient agent performs greedily and selfishly, and is thus inconvenient for surrounding users, hence a demand for human-like agents. Imitation learning reproduces the behavior of a human expert and builds a human-like agent. However, its performance is limited to the expert's. In this study, we propose a training scheme to construct a human-like and efficient agent via mixing reinforcement and imitation learning for discrete and continuous action space problems. The proposed hybrid agent achieves a higher performance than a strict imitation learning agent and exhibits more human-like behavior, which is measured via a human sensitivity test.
Automatic Generation of Amharic Math Word Problem and Equation

Math word problem uses a real word story to present basic arithmetic operations using textual narration.It is used to develop student's comprehension skill in conjunction with the ability to generate a solution that agrees with the story given in the problem.To master math word problem solving, students need to be given fresh and enormous amount of problems, which normal textbooks as well as teachers fail to provide most of the time.To fill the gap, a few research works have been proposed on techniques to automatically generate math word problems and equations mainly for English speaking community.Amharic is a Semitic language spoken by more than hundred million Ethiopians and is a language of instruction in elementary schools in Ethiopia.And yet it belongs to one of a less resourced language in the field of linguistics and natural language processing (NLP).Hence, in this paper, a strategy for automatic generation of Amharic Math Word (AMW) problem and equation is proposed, which is a first attempt to introduce the use template based shallow NLP approach to generate math word problem for Amharic language as a step towards enabling comprehension and learning problem solving in mathematics for primary school students.The proposed novel technique accepts a sample AMW problem as user input to form a template.A template provides AMW problem with placeholders, type of problem and equation template.It is used as a pattern to generate semantically equivalent AMW problems with their equations.To validate the reality of the proposed approach, a prototype was developed and used as a testing platform.Experimental results have shown 93.84% overall efficiency on the core task of forming templates from a given corpus containing AMW problems collected from elementary school mathematics textbooks and other school worksheets.Human judges have also found generated AMW problem and equation as solvable as the textbook problems.
COVID-19 Pandemic Data Visualization with Moment about Midpoint: Exploratory and Expository Analyses

Aims: To visualize COVID-19 data using Exploratory Data Analysis (EDA) to tell the COVID-19 story expository.&#x0D; Study Design: The study uses EDA approach to visualize the COVID-19 data. The study uses secondary data collected from World Health Organization (WHO) in a panel form and partition the world using WHO regions. Moment about a midpoint and EDA are jointly used to analyze the data.&#x0D; Place and Duration of Study: Department of Mathematics &amp; Statistics, Statistical Laboratory, Lagos State Polytechnic and Federal Polytechnic, Ilaro. The data used covered all regions of the world from January 2020 to July 2020.&#x0D; Methodology: We included 198 countries (cross-sections) partitioned into 7 WHO regions over 7 months (190 days) time period, spanning 3000 datasets. The EDA and moment about a midpoint is used for the analysis. This is a purely descriptive and expository analysis to tell the story of the novel coronavirus disease (COVID-19).&#x0D; Results: The total sample points used for this analysis are 30,010, which can be taken as a big data and it is large enough to assume the central limit theorem. The results of the analysis showed that cumulative cases and deaths are increasing but at a slower rate. Some WHO region curves are already flattening.&#x0D; Conclusion: The study concluded that average number of new cases and new deaths will decrease in coming months but there will be increase in the cumulative cases and deaths but at a slower rate.
Risk Mitigation Strategies in Implementing Scrum Framework for Internet-Based IT Companies in Indonesia

To date many Information Technology Companies in Indonesia implement Scrum Framework which is one of the frameworks that uses Agile principles. The implementation of the Scrum Framework does not guarantee that these companies will be free from risk, since many risks will also appear during the implementation of the framework itself. Therefore, risk management strategies are needed to mitigate these risks. This research is a qualitative research to manage risk due to the implementation of Scrum Framework in software development of IT companies, especially in Indonesia. Interviews have been conducted to the Scrum stakeholders in three IT Companies to gather some initial data to be analyzed further using Qualitative Content Analysis Method and Risk Breakdown Structure. Our finding show that there is 17 Risk Registers and their Mitigation Strategies for the implementation of Scrum Framework in Indonesian IT Companies. Furthermore, it can be used as guidelines for decision making about whether to implement this framework in their core businesses.
Safety and Security: Managerial Tensions and Synergies

Abstract The relationship between organizational safety and security is a conceptual and practical challenge. This paper focuses on the management aspects of this challenge. Its argument is that we have yet to parse out the full range of contradictory and complementary requirements of these two as managerial missions. Considering the requirements for high reliability management can provide a clarifying lens for sorting out the contradictions and complementarities. Some overlapping requirements from a high reliability perspective actually argue for an integration of the two missions within one managerial framework with enhancements for “higher resolution” reliability.