Reviewerly/exHarmony huggingface dataset:
Designing User-Centric Information Security Management Systems in Financial Services Organisations

This paper summarises a research and engagement project on the design of user-centric information security management systems (ISMS) in financial services organisations (bank and FinTech companies; FinServ). The study responded to a desire to explore the complex dynamics that characterize the relationship between information security and productivity for employees in FinServ organisations and their clients. Collected data allowed the researcher to conclude that the concept of information security/cybersecurity as an experience/journey for both employees and customers is a promising approach to build user-centric ISMS; design-led methods help with identifying the basic elements of user-centric ISMS; and language barriers may arise that require the construction of a shared information security lexicon with Fin Serv employees.
Information and Communication Problems of Innovative Activity in a Regional University

The paper reveals the features of innovation activity in the higher education system in Russia. The authors summarize the reforms planned in the Strategy of innovative development of the Russian Federation for the period up to 2020, analyze the current state and prospects for the deployment of innovation, and draft some proposals for university practice. The article analyzes the collective nature of the subject of innovation activity in a regional university, as well as the problems of interrelationships between its individual groups. It highlights information and communication problems in the course of organizing and implementing university innovation activities, in particular, in the process of forming innovation competencies. The problems of commercialization of the results of scientific activity in regional universities of the country are considered separately. Specific sociological data gathered by the authors is used to verify conclusions and propositions. The main conclusion: innovation activity in a regional university (especially in a classical university) is developing slowly and with great difficulties, in particular, due to numerous information and communication problems of interaction and the formation of a corporate innovation culture.
Encoding Sememe and Context Information into Sentence Representation for Implicit Sentiment Analysis

Sentiment analysis is one of the highly popular fields in natural language processing. Most existing researches pay more attention to the identification of explicit sentiments than that of the implicit sentiments. In this paper, we develop our analysis on implicit sentiment for Chinese text from the view of semantic information and technology. In order to enrich the semantic information of every single sentence, we propose a two-level semantic model to represent each sentence, namely, context information at the sentence level and sememe information of HowNet at the word level. On the other hand, from the view of technology, since CNN-LSTM model has achieved excellent performance on sentiment analysis, the CNN-LSTM is adopted to extract local information and long-term contextual information of the enriched sentence and the sentence representation is obtained. We conduct experiments on the SMP2019-ECISA dataset. The results show that our method outperforms other methods and realizes an improvement of 5.6% macro-average F1 score in implicit sentiment analysis.
Occasion-Level Classifiers vs. Event-Level Classifiers in Child Mandarin -The First Experiment

Time-adverbials in English are ambiguous between an occasion-counting reading and an event-counting reading. On the contrary, Mandarin has a rich system of event classifiers that can lexically disambiguate the two readings. The classifier hui counts the number of occasions on which the event of ringing the doorbell occurs and we call it occasion-level classifier. The classifier xia counts the number of the event of ringing the doorbell that takes place in one single occasion and thus is called event-level classifier. We adopted the Truth Value Judgment task and our experiment examined whether Mandarin-speaking children had mastered the basic meaning of verbal classifier hui and also whether they could distinguish between the two types of verbal classifiers. The result revealed that Mandarin-speaking 4–5-year-old children behaved in the same way as adults and could successfully distinguish between the two types of event classifiers.
KLSI Methods for Human Simultaneous Interpretation and Towards Building a Simultaneous Machine Translation System Reflecting the KLSI Methods

Simultaneous machine translation aims to maintain translation quality while minimizing the delay between reading input and incrementally producing the output. KL Simultaneous Interpreting (KLSI) is a set of methods developed to deliver non-revisable translation from English into Chinese with one second as the benchmark latency. To achieve this, it trains the human brain to stop thinking and execute commands instead. It has developed a range of formulaic techniques to be applied mechanically. This paper presents some of the key features and techniques of KLSI and explores its implications for machine simultaneous interpreting. The techniques include convergence, the concept of interpreting within three words heard at any given moment in time, co-texting, defaulting, and sequential translation techniques such as repeat, replace, reverse logic, and SAI (Skip, Add, Insert). Multiple English-to-Chinese examples and video recordings are listed in the paper to illustrate the KLSI features and techniques. In the second part of this paper, we describe computational methods related to KLMI techniques: wait-k policy with and without anticipation; word-based and phrase-based alignment and mapping; constrained context for neural machine translation. Commercial machine translation requires at least several gigabytes (or millions of words) of language pair documents for the training. It is not realistic to obtain enough parallel texts in English and Chinese reflecting KLSI techniques for training a neural machine translation system. However, a lot of parallel texts exist that do not reflect KLSI techniques. We propose to use a rule-based approach to modify available parallel texts using KLSI rules to generate a large enough KLSI-based corpus. The main contribution of this paper is to propose a novel rule-based approach―with the rules reflecting human interpretation traits—to revise training corpus to enable short latency and non-revisable machine translation.
The Socialized Operation and Resource Development of Opening University Stadiums and Gymnasiums––Taking Huaihua University as an Example

The opening of university stadiums and gymnasiums is an effective way to solve the contradiction that sports venue resources cannot meet the needs of national fitness. Taking Huaihua University as an example, firstly, this paper investigates the current situation of the opening of its stadiums and gymnasiums. Secondly, this paper also discusses the choice of management mode, and the contradictions and issues between teaching and opening, public welfare and profit-making, operation and safety. Finally, the paper puts forward the comprehensive socialized operation strategy, including adopting the modern enterprise management system for management; making clear the opening time, opening object and charging standard; entering the stadiums and gymnasiums with certificate to ensure safety; adopting the principle of controlling the number of people to ensure the utilization rate of sports facilities by its own students; and expanding the venue functions and developing the potential of venue resources. The results of the study can provide reference for the socialized operation of stadiums and gymnasiums opening in local colleges and universities in China.
Development of business processes of distance educational activities at higher educational institutions using the experience of foreign countries against the backdrop of pandemic (by the example of MSTUCA)

This article examines the problem of distance educational activities at higher educational institutions against the backdrop of pandemic. The article compares the work of foreign HEIs and the results of their activities. Particular attention is paid to distance education at Russian HEIs, including the Moscow State Technical University of Civil Aviation. The characteristic features of business processes of distance educational activities at the university using applied programs are determined and described. This problem has been little studied and requires further research.
Applying Adaptive Intelligent Tutoring Techniques to Physical Fitness Training Programs

Adaptive Training Protocols (ATP) is a collection of algorithms and software to apply principals of intelligent tutoring to physical fitness training. To obtain norming data for ATP, we examined exercise performance from 34 participants under an adaptive workout regimen lasting 13 weeks. The goal of the regimen was to train to pass the performance criteria of the US Marine Corps Initial Strength Test (IST; a 1.5-mile run, sits-ups, pull-ups, and push-ups). The weekly regimen comprised an IST, an interval workout, and a maximum workout. Adaptation was accomplished via two algorithms: maximum-day reps were double those accomplished on the prior IST and maximum-day and interval-day runs were performed at specified rates of perceived exertion. Starting capabilities for run, sit-ups, and push-ups negatively correlated with progression rates; participants who exhibited lower performance at the start of the study made steeper gains in performance. Individual logistic curve fitting found decelerating, inflecting, and accelerating progression profiles. Participants showed considerable variation in their profiles both across individuals in each exercise and within individuals across exercises. Progression profiles can be used to forecast the performance that a person can attain in a given timeframe under a given training regimen. This knowledge can be used to adapt the workout to provide more time to reach a goal if needed or to focus on exercises that are in jeopardy of not achieving the goal in time. ATP will help the Marine Corps plan for when intended recruits may be physically ready to ship out to boot camp.
Matching of sprint performance videos focusing on the motion periodicity and the speed linearity

We present a method to analyze the sprinter's form in 100 m sprint races. To achieve good time records, sprinters should know the problem on their sprint form, for example, by analyzing the difference between good and bad forms. The proposed method captures two performance videos at different points (e.g., 50 and 90 m) in one 100 m sprint race, and matches them frame‐by‐frame to compare the two sprint forms. The method calculates the sprint motion similarity from body keypoints, and then matches video frames focusing on the periodicity of sprint motion and the linearity of sprint speed. We conducted experiments with performance video pairs for six sprinters, and confirmed that the proposed method could match them accurately, which showed the effectiveness and the usefulness of the method. Also, the coach of our track‐and‐field team could find some improvements of the sprinters by referring to the outputs of the method. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Learning to Generate Cost-to-Go Functions for Efficient Motion Planning

Traditional motion planning can be computationally burdensome for practical robots, due to extensive collision checking and considerable iterative propagation of cost values. We present a novel neural network architecture which can directly generate the cost-to-go (c2g) function for a given configuration space and a goal configuration. The output of the network is a continuous function whose gradient in configuration space can be directly used to generate trajectories in motion planning without the need for protracted iterations or extensive collision checking. This higher order function (i.e. a function generating another function) representation lies at the core of our motion planning architecture, c2g-HOF, which can take a workspace as input, and generate the cost-to-go function over the configuration space map (C-map). Simulation results for 2D and 3D environments show that c2g-HOF can be orders of magnitude faster at execution time than methods which explore the configuration space during execution. We also present an implementation of c2g-HOF which generates trajectories for robot manipulators directly from an overhead image of the workspace.
Accelerometer-Based Estimation of the User Interest While Viewing Content on Smartphones Considering Viewing Conditions

With the abundant availability of digital content, people have been facing difficulty finding what they prefer. Several recommendation systems have been developed, but conventional ones are primarily based on purchase and browsing histories, which do not reflect user evaluation. This study focuses on analyzing invisible and feeble physiological tremors to reflect user evaluation. One factor affecting physiological tremors is said to be psychological change. In our earlier study, the acceleration data while reading comic books on a smartphone were collected using the 3D accelerometer of a smartphone. Consequently, it was suggested that the more interesting a story becomes, the less the people move. In this study, we attempted to relatively evaluate the acceleration data while reading and estimate the user interest with no limit on postures. The acceleration data of six subjects while not reading and while reading comic books on a smartphone were collected using the 3D accelerometer of a smartphone. They read comic books in both sitting and standing postures. As a result of performing fast Fourier transforms, the data while reading comic books obtained the bigger peak amplitude than the data while not reading. In addition, the amplitude integral value of interesting pages became smaller than that of less interesting pages. On an average, the rate was 84.9%. These results suggested that viewing content on a smartphone affected the amplitude of physiological tremors, and people moved less while reading interesting pages.
On To A Centralized Sponsorship of Blockchain Based IoT Operational Infrastructure

Abstract With the rapidly developed IT technology, large-scale networks can be designed to link many heterogeneous devices into the Internet. The majority of current infrastructures are clustered, easier to maintain, but cannot efficiently provide immutable and verifiable service between multiple parties. For large-scale infrastructures like decentralization, reliability, trackability, and immutability, Blockchain technology offers several desirable functions. This article introduces an infrastructure based on blockchain that facilitates unchanging and controllable services. However, the need for storage capacity presents a threat to resource-controlled architecture when adding blockchain technologies to the infrastructure. The paper attempts to tackle the storage dilemma with a centralized storage side chain. In fact, the proposed architecture has a distributed storage structure where most of the blockchain is kept at the cloud, although these current blocks are kept in the real network integration node. The framework architecture allows the local, blockchain and server infrastructures to be conveniently merged to create a distributed blockchain storage between two connectors, the database and the blockchain. The extended version blockchain connection builds block of the network info, and addresses network/cloud overlay sync problems in blockchain. In a practical case of professional IoT, we also have a test case to demonstrate the reliability of the general architecture processing blockchain.
A Citation Recommendation System Using Deep Reinforcement Learning

Recommender systems have seen tremendous growth in the last few years due to the emergence of web services like YouTube, Netflix, and Amazon, etc. An excessive amount of data is being utilized to give proper recommendations to the users. The number of research articles getting published every day is increasing exponentially and thus an efficient model is required to provide accurate and relevant recommendations to the research scholars. The proposed Deep Reinforcement Recommender for Citations (DRRC) model uses reinforcement learning to train the available citation network to achieve the most relevant recommendations. The proposed DRRC model outperforms the state-of-the-art models.
Warped Input Gaussian Processes for Time Series Forecasting

Time series forecasting plays a vital role in system monitoring and novelty detection. However, commonly used forecasting methods are not suited for handling non-stationarity, while existing methods for forecasting in non-stationary time series are often complex to implement and involve expensive computations. We introduce a Gaussian process-based model for handling of non-stationarity. The warping is achieved non-parametrically, through imposing a prior on the relative change of distance between subsequent observation inputs. The model allows the use of general gradient optimization algorithms for training and incurs only a small computational overhead on training and prediction. The model finds its applications in forecasting in non-stationary time series with either gradually varying volatility, presence of change points, or a combination thereof. We implement the model in a probabilistic programming framework, evaluate on synthetic and real-world time series data comparing against both broadly used baselines and known state-of-the-art approaches and show that the model exhibits state-of-the-art forecasting performance at a lower implementation and computation cost, enabling efficient applications in diverse fields of system monitoring and novelty detection..
FLCARA: Frog Life Cycle Augmented Reality Game-Based Learning Application

The increased ubiquity of technology in everyday life, as well as the value placed on technology skills, has resulted in increased use of technology in the classroom. STEM education has become a focal point in modern K-12 classrooms, with educators seeking ways to create technology-rich learning spaces. We propose that pedagogical design around Augmented Reality (AR) technology can be used to provide students with novel learning opportunities that take advantage of tangible printed materials and mobile technology that is readily available at home. As a learning resource, augmented reality (AR) has shown its versatility at all levels of education (C. H. Chen et al., 2015, T. Bratitsis et al., 2017, A. M. Amaia et al., 2016). In this paper, we present FLCARA; the Frog Life Cycle Augmented Reality Application, which presents the frog life cycle stages to students as a complete three-dimensional model placed in front of them. Using printed cards representing the various stages of the frog’s life cycle, the students organize the cards to reflect the correct order of the cycle and then use the application to check if the order is correct. The application’s design can be mapped easily onto other subjects, providing teachers with additional pedagogical tools to utilize in the classroom. The results of our initial study on the usability of the application are quite promising and reflect positively on this application’s usability for the intended educational purpose. Overall, 90% of our participants agree that the application is effective; they were satisfied with the application’s educational content (mean = 4.56, st dev = 0.76).
A Systematic Review of Energy Consumption and SLA Violation Conscious Adaptive Threshold based Virtual Machine Migration

The upsurge in request of computational power with rapid progress in the Cloud, Fog and edge-technologies has escalated the need of electrical vitality with expanded outflow of harmful gases like CO2.Dynamic integration of cloud technology, the relocation of hubs and hosts amid run-time and the increase in asset availability has increased the issue. To tackle this issue, there are numerous strategies and proposed models which trim down the energy exploitation, vitality consumption, service level agreement (SLA) infringement and restrict the count of virtual machines migration etc. In this paper, considering the need of the hour, algorithms sentient about the energy consumption and SLA violations in conjunction with minimum migration time policy are considered. A state-of-art-analysis of few proposed algorithm and changes done to the adaptive threshold based VM migration techniques is conducted. It is observed that on the basis of CPU utilization threshold value and migration parameters, the hosts and nodes to be migrated are determined. Few algorithms also considered RAM available with the hosts and network Bandwidth for enhanced performance and load stabilizing. In addition, a comparative analysis of the existing algorithms is demonstrated to elucidate the best fit algorithm based on energy consumed per migration and percentage SLA violation.
Flipped Materials of Wetland Texts for English Teaching at Faculty of Teacher Training and Education, Lambung Mangkurat University

Although smartphones were not prohibited in the campus environment in Era 4.0, smartphones were still limited in their use as a means of communication and connection. Supposedly, the use of this smartphone must of course be observed wisely and maximized by various related parties, for example by making it a medium for learning. The challenge faced in maximizing the use of smartphones for learning is the limitations in processing and or managing existing material. With the various backgrounds and reasons outlined above, the study tries to develop Flipped Materials for English Courses at the Faculty of Teacher Training and Education, Lambung Mangkurat University. This research uses research and development type for Flipped Material on Wetlands in digital form which is developed and tested for its feasibility in a limited scope. For this research, the study took samples with consideration of feasibility in reaching the subjects. Sampling is done by using the Cluster Random Technique because the characteristics of the population used as the research subject are considered to be the same, namely students of ULM semester I.Although smartphones were not prohibited in the campus environment in Era 4.0, smartphones were still limited in their use as a means of communication and connection. Supposedly, the use of this smartphone must of course be observed wisely and maximized by various related parties, for example by making it a medium for learning. The challenge faced in maximizing the use of smartphones for learning is the limitations in processing and or managing existing material. With the various backgrounds and reasons outlined above, the study tries to develop Flipped Materials for English Courses at the Faculty of Teacher Training and Education, Lambung Mangkurat University. This research uses research and development type for Flipped Material on Wetlands in digital form which is developed and tested for its feasibility in a limited scope. For this research, the study took samples with consideration of feasibility in reaching the subjects. Sampling is done by using the Cluster Random Technique because the characteristics of the population used as the research subject are considered to be the same, namely students of ULM semester I.
Top-level Scene Information Extraction from Eye-level View Images

People tracking and trajectory prediction generate more research interest recently given the advancement of autonomous devices. To obtain accurate results, the behaviour modelling needs to take into consideration the interactions people have with the environment. This paper proposes a method to extract scene information from 2D eye-level images which can then be passed to a trajectory prediction system. It combines a bird-eye view perspective transformation with a segmentation technique to identify global obstacles and possible walking areas.