Reviewerly/exHarmony huggingface dataset:
Some Common Functions

This chapter covers some common functions in R. The first section discusses the options() function, which sets the default options for R. The second section describes the round(), signif(), and noquote() functions, which are used in formatting objects. The third section discusses the format(), print(), and plot() functions for displaying objects. The functions in the third section operate differently on different classes of objects, so they are generic functions. The fourth section goes over the eval(), attributes(), attr(), and summary() functions. The functions return evaluated expressions, the attributes of an object, a specific attribute of an object, and a summary of the contents of an object, respectively. The eval() and summary() functions are generic functions, but eval() has just one method. The fifth section introduces the deriv(), numericDeriv(), and integrate() functions. The functions take derivatives of or integrate numeric vectors. The parse() and deparse() functions are covered in the sixth section. The functions read and return text and attempt to interpret the text as R language. In the seventh section, we cover the anova(), coef(), effects(), residuals(), fitted(), vcov(), confint(), and predict() functions. The functions operate specifically on models and are generic functions.
Applying Service-Dominant Logic and Conversation Management Principles to Social Robotics for Autism Spectrum Disorder

Anthropomorphism in social robots amplifies the big five human personality traits (extraversion, conscientiousness, neuroticism, openness to experience, and agreeableness), consequently aiding with the social motivation needs of consumers (especially, individuals with Autism Spectrum Disorder or ASD). According to the social motivation theory of autism, consumers with ASD show deficits in orienting toward social stimuli, engaging with humans, and maintaining social relations. Robotic anthropomorphism has been found to be positively related to the big five human personality types and robot likeability in human-robot interaction (HRI) situations. This research focuses on the conversational approach of social robotics using service dominant approach. Conversational-based perspectives have been studied extensively in organizational and management literature, however, these perspectives have not been utilized in the context of social robots, HRI, and autism.
Towards Minimally-Intrusive Navigation in Densely-Populated Pedestrian Flow

Mobile robot navigation has achieved great success in last decades. However, traditional methods usually aim at minimizing trajectory length or time, the planned trajectories may hinder pedestrians from walking. To solve this problem, we propose two penalty terms called flow disturbance penalty (FDP) and individual disturbance penalty (IDP) which describe the disturbance of the vehicle in macro and micro levels, respectively. In order to conduct minimally-intrusive navigation, in the micro level navigation, we adopt sampling-based method to minimize not only distance to goal but also IDP. In the macro level navigation, A*-based algorithm has been adopted to minimize FDP while achieving the optimal trajectory length. In addition, we build a triangulation map which gives an abstract representation of the environment to improve real-time performance. Extensive experiments have been carried out to compare our method with the state-of-the-art methods. The results have validated that the proposed method not only can provide minimal disturbance, but also has better real-time performance.
Research of the Blended Teaching Model of College English Writing Based on Artificial Intelligence

The application and integration of artificial intelligence in English writing teaching is in the stage of research and innovation. In order to improve the quality of college English writing teaching, the article first explains the research background of the blended teaching model under the background of artificial intelligence, and then It discusses the application of mixed teaching mode in English writing teaching under the background of artificial intelligence from three aspects: before class, during class and after class. Introduction
Struggling for Leadership Authority: Critical Discourse Analysis on Agus Harimurti Yudhoyono's Political Speech

This study aims to determine the socio-cognitive dimensions of Agus Harimurti Yudhoyono's (AHY) political speech as chairman of the Democratic Party (DP). The data was taken from AHY's speech entitled Konferensi Pers Ketua Umum Partai Demokrat Agus Harimurti Yudhoyono which was broadcasted on Agus Yudhoyono's YouTube channel. The lingual data was taken by using free listening techniques through the stages of transcription, reading and observation, sorting, storing and printing, re-reading, and determining the research subject. Meanwhile, non-lingual data was collected by using the Internet archive documentation method. The data were analysed through extra lingual matching technique, hermeneutics, and phenomenology. The analysis was carried out through the stages of data reduction, data display, conclusion, and verification. The findings showed that AHY represents himself as a democratic, caring, vigilant, thorough, careful, and committed to party sovereignty leader. The social cognition aspect of AHY is dominated by his background as a former military officer and his status as party leader. The narrative built in his speech was influenced by the poor results of DP in the last 2 elections, the low electability of DP, and conflicts at the internal level of DP. The sociocognitive analysis showed that AHY constructs leadership authority by representing himself as a selective, strategic, and political leader. The three characters he builds are the strong influence of his social cognition. Through his speech, AHY strives to fight for his leadership authority, both as the general chairman of the DP, and for the wider community. Keywords: Leadership authority, critical discourse analysis, political speech, sociocognitive analysis, political speech.
System End-User Actions as a Threat to Information System Security

As universities migrate online due to the advent of Covid-19, there is a need for enhanced security in information systems in the institution of higher learning. Many opted to invest in technological approaches to mitigate cybersecurity threats; however, the most common types of cybersecurity breaches happen due to the human factor, well known as end-user error or actions. Thus, this study aimed to identify and explore possible end-user errors in academia and the resulting vulnerabilities and threats that could affect the integrity of the university's information system. The study further presented state-of-the-art humanoriented security threats countermeasures to compliment universities' cybersecurity plans. Countermeasures include well-tailored ICT policies, incident response procedures, and education to protect themselves from security events (disruption, distortion, and exploitation). Adopted is a mixedmethod research approach with a qualitative research design to guide the study. An open-ended questionnaire and semi-structured interviews were used as data collection tools. Findings showed that system end-user errors remain the biggest security threat to information systems security in institutions of higher learning. Indeed errors make information systems vulnerable to certain cybersecurity attacks and, when exploited, put legitimate users, institutional network, and its computers at risk of contracting viruses, worms, Trojan, and expose it to spam, phishing, e-mail fraud, and other modern security attacks such as DDoS, session hijacking, replay attack and many more. Understanding that technology has failed to fully protect systems, specific recommendations are provided for the institution of higher education to consider improving employee actions and minimizing security incidents in their eLearning platforms, post Covid-19.
Microaggregation Optimisation Through Random Cluster Shuffling

The Internet of Things facilitates the collection of large amounts of data: sensors, smartphones, and even home appliances, generate a data deluge about individuals, their context and the events in their daily life. Providers can analyse these data in order to extract patterns and increase knowledge about their services, either on their own or by transferring datasets to third parties. To mitigate the Big Brother effect, i.e. to preserve the individuals’ right to privacy, techniques in the scope of Statistical Disclosure Control (SDC) must be applied. Microaggregation, is one of the best-known methods in the SDC arena. However, its results are far from optimal. In this paper, we introduce Random Cluster Shuffling, a new post-processing method that aims at improving the results of microaggregation techniques. We describe the proposal and present some results that support the potential of our approach.
TPO: A Type System for the Architecture of Agent Societies

We present TPO, a type system designed to support the type-based modeling of the architectural structure of agent societies. The basic concepts of TPO are presented. The TPO-typing of the multiagent system model supported by the multiagent programming framework JaCaMo is presented to illustrate the scope of application of TPO.
A Classification Module for Automated Mosquito Surveillance Using Computer Vision

The necessity for an effective and automated mosquito inspection in subsurface drains and narrow water channels is highly inevitable in the current scenario of increasing mosquito-vectored diseases and human causalities associated with it. In this work, we put forward an automated vision-based mosquito classification module and report the design and implementation of two effective schemes for mosquito classification and identifies the most appropriate method for real-time implementation of the mosquito classification. The performances of both schemes are systematically analyzed. The performance comparison of classification of mosquitoes and a kind of morphologically similar fly using both schemes of identification are presented in this research as well.
Photon transport enhancement through a coupled-cavity QED system with dynamic modulation

We investigate photonic transport through fiber optical cavities under tunable fast modulation, which is induced by modulating atom-cavity coupling in a cavity quantum electrodynamics (CQED) system. The modulation bandwidth exceeds the timescales of all other system processes, such as cavity decay and atom-cavity coupling, and allows control of the dynamics of photonic transport through the cavity array. The transmission as a function of the modulation frequency clearly shows enhancement peaks in single cavity and two coupled-cavity cases. In particular, in the coupled-cavity case, the position of the enhancement peak is shifted due to the delocalized cavity field in the coupled system.
Fault Location Technique of Distribution Power Network Based on Traveling Wave Measurement

Distribution network is the most important part of power system, distribution network fault location technology can help power companies to find and eliminate faults quickly, can provide reliable and continuous power supply plays a very important role. Based on traveling wave detection principle, this paper puts forward the corresponding solution of distribution network fault location system, and gives different types of traveling wave fault location methods.
Virtual Trailblazing: Incorporating Electronic Journals into an Academic Library

The World Wide Web makes it possible for patrons to link easily to electronic journals but does not resolve problems of selection, acquisition, serials control, and cataloging. A pilot project undertaken at the University of Southern California Libraries involved creating a subject and title index to e-journals of interest to the USC faculty on the Web with links to the journals. Upon completion of the pilot, maintenance of the collection was assigned to the Serials Department in the University Library. It then became necessary to mainstream e-journal collection maintenance into the technical services operations of the library. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
Human Activity Recognition from Accelerometer Data with Convolutional Neural Networks

Smartphones are present in most people's daily lives. Sensors embedded in these devices open the possibility of monitoring users’ activities. The classification of the intricate data patterns collected through these sensors is a challenging task when considering hand-crafted features and pattern recognition algorithms. In this work, to face this challenge, we propose a convolutional neural network architecture along with two methods for transforming sensor data stream into images. The proposed model was evaluated using the UniMiB SHAR dataset. The best macro average accuracy obtained for classification of 17 types of activities, with fivefold-cross-validation-method, was 90.44%.
Software Development Practice: How Organisation Dynamics Inhibit the Utilization of Process Tools in Small Software Companies

Low-quality software products are synonymous with small software companies, yet thousands of process tools and methods are available, although unfortunately, they remain unutilized. The utilization of these process tools are said to be very challenging to small software companies with the difficulty of adaptability tagged to the context in which the companies operate since the process tools are not designed to take care of the operational context of the small software companies. This survey study was undertaken to empirically investigate the implication of the organizational dynamics on using process tools in SSCs. A total of 115 respondents from Tanzania, Namibia, and Ghana were reached through a structured questionnaire. Our findings reveal that the ad-hoc behaviors and attitude breed a culture that inhibits procedural and process-intensive practices synonymous with most software engineering process tools. This finding is interesting because this area has been underestimated while looking at software practice, yet it is significant in understanding practice in relation to software process tools, significantly how small software companies can be helped to adopt the different tools and also provide a mechanism to help the designers of process tools make tools that are usable in the context of the practice of SSCs.
Adaptable Focal Loss for Imbalanced Text Classification

In this paper, we study the problem of imbalanced text classification based on the pre-trained language models. We propose the Adaptable Focal Loss (AFL) method to solve this problem. Firstly, we use the word embeddings from the pre-trained models to construct the sentence level prior by the sum of the word embeddings in the sentence. Then, we extend the Focal Loss, which is widely used in the field of object detection, by replacing the task-special parameters with the scaled-softmax of the distance between the fine-tuned embeddings and the prior embeddings from the pre-trained models. By removing the task-special parameters in Focal Loss, not only can the parameters of arbitrary imbalanced proportion distribution be adjusted automatically according to the task, but also the sentences that are difficult to classify can be given a higher weight. Experimental results show that our methods can easily combine with the common classifier models and significantly improve their performances.
Examining Spontaneous Perspective Taking and Fluid Self-To-Data Relationships in Informal Open-Ended Data Exploration

Engaging learners with complex unfamiliar datasets is a known challenge in Data Science education. One promising phenomenon investigated in related work is perspective-taking. A first-person “actor” perspective can help facilitate group and individual sensemaking by mediating observations and actions taken by learners. Here we investigate how museum visitors made use of an actor perspective when exploring an open-ended, interactive data map museum exhibit. We use a mix of qualitative and quantitative empirical methods to explore how actor perspective-taking (APT) may mediate joint sensemaking around data visualizations. By applying interpretive coding to 54 conversations wherein APT naturalistically emerged, we identify 3 distinct self-to-data relationships constructed via APT: role-play, projection, and orientation. A further analysis explores how APT was embedded in joint sensemaking of the visualized data. Twelve APT-mediated sensemaking processes are identified; two (extrapolating and noticing absence) were used in conjunction with 33multiple APT self-to-data relationships, while the remaining ten (e.g., enacting, spatially characterizing, generalizing) were exclusively used with specific self-to-data APT relationships. We use these empirical findings to generate hypotheses about how APT and associated sensemaking processes may support Data Science learning goals.
Investors’ Information Acquisition and Firm Financing Decisions: Evidence from a Natural Experiment

Using the SEC’s 2016 Tick Size Pilot Program (TSPP) as a natural experiment, we investigate the effects of investors’ information acquisition on equity versus debt financing. We find a significant increase in firms’ preference for equity over debt issuance after the TSPP’s implementation. This increase is concentrated among firms that experience decreases in algorithmic trading and increases in information acquisition activities. We also find that the effect of the TSPP is more pronounced for firms with high information asymmetry prior to the program. Taken together, our results suggest that investors’ information acquisition has a causal effect on firms’ financing choices.
Comparative analysis between Traditional learning and Deep learning

In modern days, Artificial intelligence is gaining popularity due to use in smart technology. For making system more intelligent machine learning and deep learning approaches are performed well in research fields. Traditional learning is sub sphere of artificial intelligence and deep learning lies under the Traditional learning, these approaches are performed robustly in solving real life complex problems with more accuracy. This paper primarily focuses on comparative analysis among traditional learning and deep learning approaches. Considering comparative analysis following parameters have been taken under consideration amount of data, features, time, accuracy, debugging, expensive and decision path.
Intelligent assembly farm house design based on BIM and sensor technology

research-article Share on Intelligent assembly farm house design based on BIM and sensor technology Authors: Zheng Wu Tianjin University, China and Fujian University of Technology, China Tianjin University, China and Fujian University of Technology, ChinaView Profile , Qi Zhang Tianjin University, China Tianjin University, ChinaView Profile , Zhigang Wang Tianjin University, China Tianjin University, ChinaView Profile Authors Info & Claims ICASIT 2021: 2021 International Conference on Aviation Safety and Information TechnologyDecember 2021 Pages 935–939https://doi.org/10.1145/3510858.3511427Published:04 March 2022Publication History 0citation12DownloadsMetricsTotal Citations0Total Downloads12Last 12 Months12Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Social Recommendation via Graph Attentive Aggregation

Recommender systems play an important role in helping users discover items of interest from a large resource collection in various online services. Although deep graph neural network-based collaborative filtering methods have achieved promising performance in recommender systems, they are still some weaknesses. Firstly, existing graph neural network methods only take user-item interactions into account neglecting direct user-user interactions which can be obtained from social networks. Secondly, they treat the observed data uniformly without considering fine-grained differences in importance or relevance in the user-item interactions. In this paper, we propose a novel graph neural network social graph attentive aggregation (SGA) which is suitable for parallel training to boost efficiency which is the common bottleneck for neural network deployed machine learning models. This model obtains user-user collaborative information from social networks and utilizes self-attention mechanism to model the differentiation of importance in the user-item interactions. We conduct experiments on two real-world datasets and the results demonstrate that our method is effective and can be trained in parallel efficiently.
A Parallel Scanner for the Concurrent Execution of Lexical Analyzer Tasks on Multi-Core Machines using Dynamic Task Allocation Algorithm

Purpose: Massive multi-core architecture is rapidly becoming the standard in digital technology due to its high and parallel computational capability and performance benefits. To fully utilize the technological capabilities of multi-core systems, system software such as compilers should be re-engineered for parallel processing. Several important contributions have been made in the past to enhance the efficiency of the lexical analysis process by leveraging the innate parallel processing capability of multi-core computers. This trend of implementation shows that a parallel lexical analyzer tends to perform lexing tasks better than a conventional sequential lexical analyzer. This article discusses the way of making the tasks in parallel during the scanning of source program in the phase of lexical analysis. The objective of this study is to explore how to perform lexical analysis in parallel. On multi-core processors, multiple processes of the lexical analyzer program can run concurrently to scan multiple lines in the input stream in parallel for token detection. This is done by allocating tasks line-by-line to the core which is not engaged yet. Design/Methodology/Approach: Developing a theoretical and experimental approach for parallelizing the lexical scanning process on a multi-core system. Findings/Result: According to the theoretical and experimental results, the proposed methodology significantly outperforms the sequential approach in terms of tokenization. It considerably reduces the time required for lexical analysis during the compilation process. The result establishes unequivocally that the parallel lexical analyzer's performance should scale linearly with the number of cores. It is clearly observed that the speedup is expected to increase further if the number of CPU cores increases. This enhancement would speed up the compilation process even more. Originality/Value: A dynamic task allocation algorithm is developed for the concurrent execution of a lexical analyzer task on multi-core systems. Paper Type: Experimental Research.