Reviewerly/exHarmony huggingface dataset:
Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach

The proliferation of massive open online courses (MOOCs) demands an effective way of course recommendation for jobs posted in recruitment websites, especially for the people who take MOOCs to find new jobs. Despite the advances of supervised ranking models, the lack of enough supervised signals prevents us from directly learning a supervised ranking model. This paper proposes a general automated weak supervision framework (AutoWeakS) via reinforcement learning to solve the problem. On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models. On the other hand, the framework enables automatically searching the optimal combination of these supervised and unsupervised models. Systematically, we evaluate the proposed model on several datasets of jobs from different recruitment websites and courses from a MOOCs platform. Experiments show that our model significantly outperforms the classical unsupervised, supervised and weak supervision baselines.
Model‐based clustering of time‐dependent categorical sequences with application to the analysis of major life event patterns

Abstract Clustering categorical sequences is a problem that arises in many fields. There is a few techniques available in this framework but none of them take into account the possible temporal character of transitions from one state to another. A mixture of Markov models is proposed, where transition probabilities are represented as functions of time. The corresponding expectation–maximization algorithm is discussed along with related computational challenges. The effectiveness of the proposed procedure is illustrated on the set of simulation studies, in which it outperforms four alternative approaches. The method is applied to major life event sequences from the British Household Panel Survey. As reflected by Bayesian Information Criterion, the proposed model demonstrates substantially better performance than its competitors. The analysis of obtained results and related transition probability plots reveals two groups of individuals: people with a conventional development of life course and those encountering some challenges.
Artificial Intelligence and Robotics Addressing COVID-19 Pandemic’s Challenges

There is a growing awareness that the unfolding Covid-19 pandemic will deeply change people's lives, while in the humanitarian system the gap between available resources and need is widening. Authors aim to investigate the ways new technologies can be effective in addressing global challenges. A session has been conducted at the United Nations conference HNPW 2020 where humanitarian experts have recognized the potential for Artificial intelligence (AI) and robotics to support response, decision-making, logistics and health services. In effect, one of the differences between Covid-19 and previous epidemics, consists in the massive deployment of technologies' applications for monitoring, surveillance, detection, prevention, and mitigation. Areas of concern have been identified in bias, accuracy, protection and use of data, citizens' privacy and legal gaps. Provided that such issues are addressed in every new project, authors propose to link AI and robotics with the triple nexus concept of the Humanitarian-Development-Peace (HDP) aiming to bridge the divide between humanitarian assistance, development agenda and peacebuilding.
Unmanned Powerboat Motion Terminal Control in an Environment with Moving Obstacles

The major point for consideration throughout this paper is controlling the motion of an unmanned powerboat in an obstructed environment with stationary and moving objects. It offers a procedure for the terminal control law development based on the powerboat programmed motion trajectory in a polynomial form and proposes position-trajectory-based control algorithms. A hybrid method based on virtual fields and unstable driving modes, taking into account powerboat speeds and obstacles, is used to plan motion trajectories for obstacle avoidance. There were experiments carried out to test the developed methods and algorithms meanwhile estimating the energy consumption for control, the length of the trajectory and the safety indicator for obstacle avoidance. The novelty of the proposed approach lies in the method used to develop a local movement trajectory in the field with obstacles and in the hybridization of trajectory scheduling methods. This approach allows us to achieve a given safe distance when avoiding obstacles and virtually eliminate the chances of an emergency collision. The presented results can be used in systems of boats autonomous motion control and allow safe stationary and dynamic obstacles avoidance.
Augmented Reality Techniques in Industrial Warehouse Logistics in Mining Industry

Abstract Visualization techniques for augmented reality (AR) and mixed reality (MR) are developed well enough to display any data, if need be. This paper deals with a new data visualization technique for efficient product storage and use. The solution proposed herein applies AR visualization. The paper describes an application that displays all necessary data on the screen using AR-markers, and has an option of making them markers complicated and adding three-dimensional models of an object.
Alarm Prediction in Cellular Base Stations Using Data-Driven Methods

The importance of cellular networks continuously increases as we assume ubiquitous connectivity in our daily lives. As a result, the underlying core telecom systems have very high reliability and availability requirements, that are sometimes hard to meet. This study presents a proactive approach that could aid satisfying these high requirements on reliability and availability by predicting future base station alarms. A data set containing 231 internal performance measures from cellular (4G) base stations is correlated with a data set containing base station alarms. Next, two experiments are used to investigate (i) the alarm prediction performance of six machine learning models, and (ii) how different predict-ahead times (ranging from 10 min to 48 hours) affect the predictive performance. A 10-fold cross validation evaluation approach and statistical analysis suggested that the Random Forest models showed best performance. Further, the results indicate the feasibility of predicting severe alarms one hour in advance with a precision of 0.812 (±0.022, 95 % CI), recall of 0.619 (±0.027) and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score of 0.702 (±0.022). A model interpretation package, ELI5, was used to identify the most influential features in order to gain model insight. Overall, the results are promising and indicate the potential of an early-warning system that enables a proactive means for achieving high reliability and availability requirements.
A similarity clustering-based deduplication strategy in cloud storage systems

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.
A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems

Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms (EAs) belong to nature-inspired algorithms (NIAs) and swarm intelligence (SI) paradigms. Differential evolutionary algorithm is one of the most popular and well-known EAs and has secured top ranks in most of the EA competitions in the special session of the IEEE Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty-nine large-scale bound-constrained benchmark functions. The suggested C-DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE-CEC benchmark functions are tackled efficiently in terms of proximity and diversity.
The Practice of Mixed Teaching Mode in College English Teaching Under the Mobile Internet Environment

With the rapid development of social economy, the development level of mobile Internet technology has been improved to a certain extent, and the rational use of mobile Internet technology in the teaching stage of colleges and universities has greatly enhanced the diversity of teaching, improved the original teaching quality, and cultivated students’ comprehensive quality capabilities provide more favorable conditions. The rational use of the mobile Internet environment in the college English blended teaching model can further reflect the true value of college English teaching. This article analyzes the effectiveness of college English blended teaching in the mobile Internet environment, and puts forward some suggestions for reference.
Analyzing developer contributions using artifact traceability graphs

In a software project, properly analyzing the contributions of developers could provide valuable insights for decision-makers. The contributions of a developer could be in many different forms such as committing and reviewing code, opening and resolving issues. Previous approaches mainly consider the commit-based contributions which provide an incomplete picture of developer contributions. Different from the traditional commit-based approaches for analyzing developer contributions, we aim to provide a more holistic approach to reflect the rich set of software development activities using artifact traceability graphs. For analyzing the developer contributions, we propose a novel categorization of developers (Jacks, Mavens and Connectors) in a software project. We introduce a set of algorithms on artifact traceability graphs to identify key developers, recommend replacements for leaving developers and evaluate knowledge distribution among developers. We evaluate our proposed algorithms on six open-source projects and demonstrate that the identified key developers match the top commenters up to 98%, recommended replacements are correct up to 91% and identified knowledge distribution labels are compatible 94% on average with the baseline approaches. The proposed algorithms using artifact traceability graphs for analyzing developer contributions could be used by software project decision-makers in several scenarios. (1) Identifying different types of key developers. (2) Finding a replacement developer in large teams. (3) Evaluating the overall knowledge distribution amongst developers to take early precautions.
Maritime Safety Education with Augmented Reality (AR) Technology (Case study: Introduction of Ship Safety Plan)

Abstract Augmented Reality is a technology that can combine 2-dimension or 3-dimension virtual objects to be added to the real environment and combine them so that they can create mixed realities and project them into real-time. Augmented Reality is a field in which virtual objects are integrated into a real environment in real-time. This technology-based on the android application is applied to facilitate education media and inspection for life-saving appliances on board. All life-saving appliances can be displayed alongside the information and the location of the object. Utilizing AR is possible to be used as educational media to get precise information relating to the locations, objects, and quantity. Therefore, by becoming visible, it is possible to increase the sense of realism and immersion of this technology.
Fuzzy inference systems based on multi-type features fusion for intra-hour solar irradiance forecasts

The cloud cover information in ground-based cloud (GBC) images is important to direct normal irradiance (DNI) prediction. In order to obtain better performance, the DNI forecasting model needs to incorporate the features from GBC images and numerical time series. In this paper, to resolve the issues of feature fusion with various types, three novel fuzzy inference systems (FIS) models are proposed for intra-hour DNI prediction. The features of the GBC image and the numerical time series are firstly fuzzified by clustering and grid partition, respectively. Then, the hybrid fuzzification and optimization method lead to the presented two hierarchical fuzzy inference systems (HFIS) models and an adaptive neuro-fuzzy inference system (ANFIS) model for DNI prediction. The performance of the proposed models is validated with the data from the National Renewable Energy Laboratory (NREL) from January 1, 2018 to December 31, 2018. Experiments demonstrate that the proposed models outperform the reference model, and the best model is capable of achieving 19.66% improvement over the reference model for 10-minute ahead DNI prediction.
How Explicit Risk Management is Being Integrated Into Agile Methods: Results From a Systematic Literature Mapping

Agile software development methods exploit software development characteristics to minimize risks. Although Agile practices tend to mitigate risks, they are often not sufficient to manage project risks in certain scenarios such as in highly regulated domains. In order to understand how explicit risk management is being integrated into Agile software development methods, a Systematic Literature Mapping (SLM) was conducted, which identified 18 studies. Results indicate that the most used Agile method for integrating explicit risk management practices is Scrum. Ten studies present evidence for all PMBOK risk management processes, with the risk identification being the only process covered by all studies. In general, the selected studies indicate that the results of integrating explicit risk management with Agile methods are positive, encompassing improved communication, improved product quality, increased risks visibility, cost reduction, improved team efficiency and time-to-market reduction. Based on the results, we claim that Agile methods are enablers of risk management practices. However, a comprehensive guide that allows for the incorporation of risk management practices into any Agile method still seems to be needed. The results presented in this paper can be used as a steppingstone achieving this.
Global virus lockdown and cybercrime rate trends: a routine activity approach

Abstract Cybercrime perpetuates as a major threat to the reputation and economy of a nation. There is consensus in the media and academic journals alike, that cybercrimes have incremented over the years. During the coronavirus pandemic in the year 2020, a global lockdown imposed by governments forced people to be homebound, leading to a subsequent surge in online users. This resulted in cybersecurity risk hitting close to home, with an upsurge in cybercrimes. This article attempts to make sense of the elevated cybercrime landscape. For the last two decades, criminological research have attempted to explore variants of technology-enabled crime and have examined theories to account for offending. This study proposes ‘Routine Activity Theory’ to explain the magnitude of cybercrimes during this unprecedented period that reshaped people’s routine lifestyles. Routine activity theory predicts that changes in opportunity situations (increase in online users), potentially escalates the convergence of motivated offenders and suitable targets in the absence of capable guardianship. Guardianship (security measures) is pivotal in this theory, to protect the targets (online users) from offenders (cyber threat actors). Furthermore, this paper offers practical security measures, in light of the specified cyber-attacks during this unprecedented period. If this theory is empirically validated, it offers opportunities to mitigate the extent of cybercrimes with an upsurge of online users, and more so for cybercrimes in general.
Data analysis of grid-connected solar setup and regression based predictive models

The paper presents analysis carried on a 3.6 KW grid-connected solar facility. Exploratory data analysis has been carried out using python libraries for the solar energy generation, load, energy exported and imported from the grid. The paper develops a SVM model and XGBoost model in order to predict energy exported back to the grid at the CARET facility in AMU. SVM and XGBoost are powerful machine learning tools useful for time series prediction. The paper further compares the mean squared errors of the two models and explores which model gives a better prediction.
THE EVALUATION OF STUDENTS’ PHYSICAL HEALTH BASED ON THE INTEGRATION OF FAMILY AND SCHOOL PHYSICAL EDUCATION

ABSTRACT In recent years, China has paid more and more attention to students’ physical health, but it is difficult for schools to provide scientific guarantee for students’ physical health evaluation. How to use scientific algorithm for accurate guidance has become the current hotspot. Based on this, this paper studies the evaluation model of students’ physical health based on the integration of home and school sports. Firstly, this paper analyzes the research status of physical health evaluation at home and outside, then optimizes and improves the deficiencies in the integration of home and school sports in the current research hotspot, then applies SVM algorithm to the physical health evaluation model. Finally, the experimental results show that the SVM algorithm can objectively evaluate the integration of home and school sports, and can optimize the evaluation strategy according to the differences of students in the process of physical exercise, and the accuracy of physical health evaluation can reach more than 97%.
SqueezeFacePoseNet: Lightweight Face Verification Across Different Poses for Mobile Platforms

Ubiquitous and real-time person authentication has become critical after the breakthrough of all kind of services provided via mobile devices. In this context, face technologies can provide reliable and robust user authentication, given the availability of cameras in these devices, as well as their widespread use in everyday applications. The rapid development of deep Convolutional Neural Networks (CNNs) has resulted in many accurate face verification architectures. However, their typical size (hundreds of megabytes) makes them infeasible to be incorporated in downloadable mobile applications where the entire file typically may not exceed 100 Mb. Accordingly, we address the challenge of developing a lightweight face recognition network of just a few megabytes that can operate with sufficient accuracy in comparison to much larger models. The network also should be able to operate under different poses, given the variability naturally observed in uncontrolled environments where mobile devices are typically used. In this paper, we adapt the lightweight SqueezeNet model, of just 4.4 MB, to effectively provide cross-pose face recognition. After trained on the MS-Celeb-1M and VGGFace2 databases, our model achieves an EER of 1.23% on the difficult frontal vs. profile comparison, and 0.54% on profile vs. profile images. Under less extreme variations involving frontal images in any of the enrolment/query images pair, EER is pushed down to <0.3%, and the FRR at FAR = 0.1% to less than 1%. This makes our light model suitable for face recognition where at least acquisition of the enrolment image can be controlled. At the cost of a slight degradation in performance, we also test an even lighter model (of just 2.5 MB) where regular convolutions are replaced with depth-wise separable convolutions.
Siamese Multiplicative LSTM for Semantic Text Similarity

Learning the Semantic Textual Similarity (STS) is a critical issue for many NLP tasks such as question answering, document summarization and etc.. In this paper, we combine the Multiplicative LSTM structure with a Siamese architecture which learn to project word embeddings of each sentence into a fixed-dimensional embedding space to represent this sentence. Then these sentence embeddings can be used to evaluate the STS task. We compare with several similar architectures and the proposed method has achieved better results and is competitive with the best state-of-the-art siamese neural network architecture.
The Analysis of the Internal Restructuring Method Efficiency Used For a More Compact Representation of the Encoded Data

A principally new approach to restructuring a data proposed - namely - an internal data restructuring, the essence of which is to identify patterns in the internal data structure of an information resource based on a quantitative sign. Here the quantitative characteristic, namely the characteristic of the number of series units (NSU), is the instrument used to carry out the data restructuring. The evaluation of the effectiveness of the developed method of internal restructuring from the standpoint of a more compact representation of the encoded data is carried out. A statistical approach based on the classical Huffman algorithm is used as a coding tool. The developed method of internal restructuring allows to solve an urgent scientific and applied problem associated with increasing efficiency of information resource data (IRD) entropy coding in terms of information presentation length reduction.