Reviewerly/exHarmony huggingface dataset:
mLearning in Workplace Learning

Mobile technology opens up a range of possibilities to enhance authentic workplace learning (WPL). In order to better understand the ways in which mobile learning (mLearning) can enhance WPL and design productive learning activities, this chapter examines practical contexts and theoretical foundations of mLearning in general, and of WPL in particular. Initially, it discusses the notion and provides a brief historical introduction to mLearning. Then, it presents an overview of the affordances of mobile technology for enhancing learning and considers some of the challenges. Finally, the chapter discusses some of the main practical and theoretical ideas supporting the use of mobile technologies to expand WPL as a hybrid space. It argues that approaches for mLearning designed for WPL need to consider simultaneously three aspects: (1) the entanglement of learning, work and technology; (2) learner agency; and (3) productive ways of designing for learning in workplaces. It is important to provide teachers and others involved in WPL with reusable design ideas that can be flexibly adapted for fruitful mLearning in WPL.
English Vocabulary Achievement Through Telling Stories Using Pictures in Islamic Elementary School

This reserch focuses on English vocabulary achievement through telling stories Using pictures. The general problem of this research is How far is the English vocabulary achievement through telling stories using pictures of the fifth year student of MIMA KH Siddiq Jember?". In this research the quantitative data was analyzed by using descriptive statistics. Based on the description from data analysis, the result of students’ English vocabulary achievement through telling stories was 76, 6. After consulting to the table score classification levels, the score lied in the interval of 66 to 75. This meant that the English vocabulary achievement by telling stories using pictures of the fifth year students of MIMA KH Siddiq Jember is enough. Since, the result of this research was enough; it is possible supported by the student's activity in learning English, the teaching learning process and the school facilities.
Information Extraction for Additive Manufacturing Using News Data

Recognizing named entities like Person, Organization, Locations and Date are very useful for web mining. Named Entity Recognition (NER) is an emerging research area which aims to address problems such as Machine Translation, Question Answering Systems and Semantic Web Search. The study focuses on proposing a methodology based on the integration of an NER system and Text Analytics to provide information necessary for business in Additive Manufacturing. The study proposes a foundation of utilizing the Stanford NER system for tagging news data related to the keywords “Additive Manufacturing”. The objective is to first derive the organization names from news data. This information is useful to define the digital footprints of an organization in the Additive Manufacturing sector. The existence of an organization derived using the NER approach is validated by matching their names with companies listed on the Companies House portal. The organization names will be matched using a Fuzzy-based text matching algorithm. Further information on company profile, officers and key financial data is extracted to provide information about companies interested and working within the Additive Manufacturing sector. This data gives an insight into which companies have digital footprints in the Additive Manufacturing sector within the UK.
Users’ preference-degree considered diffusion for recommendation on bipartite networks

In recommender systems, ratings represent the degree of users’ preference. However, in the top-L recommendation, negative ratings are simply discarded or different ratings are regarded as the same. In order to evaluate the ability to recommend objects with high ratings, we put forward a metric called Average High-score Ratio (AHR) to compute the average ratio of ratings of recommended objects to the maximum score allowed by the system. The higher of AHR, the more objects with high ratings will be recommended to users. Computing AHR does not need to collect extra information except ratings, and it is a proper metric to measure user satisfaction. We also propose users’ preference-degree considered diffusion algorithm for recommendation, which distinguishes different ratings and is parameter-free. Compared with some classic and recent proposed methods, users’ preference-degree considered diffusion algorithm has the best performance on recommendation accuracy and user satisfaction, and it gets the second-best performance on Hamming distance, novelty, and coverage, only next to the Heat Conduction method. Our work gives exploration on designing recommendation algorithms with better user satisfaction.
How Complex Does Compliance Get?

Metrics have been applied in software engineering to manage the complexity of program code. This paper explores a new application area of the classic software engineering metrics to determine the complexity of compliance rules in business processes. Despite the critical voices noting the rather weak theoretical foundation, metrics provide effective measures for overlooking the concepts that may drive the complexity of a program. Their scope, scalability, and perceived ease of use do not diffuse these doubts, but provide ample reasons to believe that there is more to complexity analysis than numbers, and that a better methodological approach can help to reveal their true potential. Utilizing this potential would be of great importance, not only for establishing effective and efficient compliance management, but also for providing innovative solutions to digitalization trends and increasing data stacks. While some extant work has shown the applicability of software metrics for analyzing the complexity of process models, metrics have not been applied so far to manage the complexity of compliance rules. The approach presented in this paper provides an integrated view on the complexity of compliance rules that are modeled with conceptually different compliance languages. To this end, we review and discuss the literature on software metrics to derive the definitions needed to compute the complexity of compliance rules, and to refurbish the methodological foundation of software engineering metrics.
Choosing the Right Password Manager

This segment of the Sharpest Tool in the Shed column explores three password manager options to consider incorporating into your library. Many libraries and businesses still rely on an Excel spreadsheet to house the myriad log-ins and passwords that staff need to do their daily work. Choosing the right password manager can save staff time and enhance security. This segment draws on research and trial experiences of three popular password managers: 1Password, LastPass, and KeePass.
Dynamic Equivalent Research Based on Fuzzy C-means

With the expansion of power system scales and the establishment of AC/DC interconnecting system, it is very difficult to simulate transient characteristics of power grid accurately during the fault. The existing electromechanical transient simulation program which can only simulate the fundamental case of symmetric systems is unable to meet the requirements. As the calculation scale of electromagnetic transient simulation is too large, network dynamic equivalent is needed before the simulation. In this paper, the calculation method of fuzzy C-means clustering algorithm and the dynamic equivalent based on physical equivalent have been introduced firstly. Then, the dynamic equivalent calculation method based on fuzzy C-means clustering algorithm and its flow chart have been proposed. Finally, the fuzzy C-means clustering and dynamic equivalent calculation have been used for a case study. The results have showed that the dynamic equivalent method based on fuzzy C-means has a good practicability as the dynamic characteristics before and after the equivalent are consistent.
Applying Formal Logic Validation to Enhance Natural Language Understanding

Inconsistencies and ambiguities of annotation can cause vagueness in the results obtained by natural language understanding (NLU). The quality of the type systems used for annotation affects the quality of annotation. To achieve highly accepted sets of annotated documents, the Fleiss' kappa score has been widely used to observe the level of agreement from annotated results, submitted by different human annotators. The challenge is that the kappa score cannot be used to validate the type systems nor to identify any incorrect annotations. Thus, we proposed an application of formal logic for validating type systems and annotations against expert rules. Experiments have been done by using four different type systems and annotation sets created by an expert and three novices. Our proposed formal logic model was used to validate the novice type systems and annotations against the expert rules. The results show that the technique could help identifying inconsistencies between expert and novice annotations, by using a model checker. The number of detected inconsistencies impacts the level of achieved F1 score. Thus, the proposed formal logic technique could be used to guide novice annotators to develop accepted type systems. This will help to enhance the performance of the generated machine learning models used by the NLU.
A Mapping Language for IoT Device Descriptions

Component models for IoT devices regain popularity. As more and more devices must be semantically connected within IoT platforms, digital abstractions for these devices are needed. For this purpose, textual device descriptions which encapsulate device-specific characteristics are a suitable candidate. Such component descriptions formally describe a device's information model as well as the offered functionality in a standardized way. However, smart IoT platforms mainly solve user goals by composing various IoT devices in a suitable manner. Current IoT descriptions, such as Eclipse Vorto do not address this need at all. In this paper, we introduce a formal mapping language that allows to capture functional interaction semantics already during device integration time. Our evaluation shows that only few mapping elements are needed to define functional mappings between operations as well as to capture the underlying communication pattern.
Using Shallow Neural Network Fitting Technique to Improve Calibration Accuracy of Modeless Robots

This paper describes a technique for the position error estimations and compensations of the modeless robots and manipulators calibration process based on a shallow neural network fitting function method. Unlike traditional model-based robots calibrations, the modeless robots calibrations do not need to perform any modeling and identification processes. Only two processes, measurements and compensations, are necessary for this kind of robots calibrations. By using the shallow neural network fitting technique, the accuracy of the position error compensation can be greatly improved, which is confirmed by the simulation results given in this paper. Also the comparisons among the popular traditional interpolation methods, such as bilinear and fuzzy interpolations, and this shallow neural network technique, are made via simulation studies. The simulation results show that more accurate compensation result can be achieved using the shallow neural network fitting technique compared with the bilinear and fuzzy interpolation methods.
An Experimental Study of Hybrid Machine Learning Models for Extracting Named Entities

The paper describes two hybrid neural network models for named entity recognition (NER) in texts, as well as results of experiments with them. The first model, namely Bi-LSTM-CRF, is known and used for NER, while the other model named Gated-CNN- CRF is proposed in this work. It combines convolutional neural network (CNN), gated linear units, and conditional random fields (CRF). Both models were tested for NER on three different language datasets, for English, Russian, and Chinese. All resulted scores of precision, recall and F1-measure for both models are close to the state-of-the-art for NER, and for the English dataset CoNLL-2003, Gated-CNN-CRF model achieves 92.66 of F1-measure, outperforming the known result.
Robust object recognition in 3D scene by stereo vision image processing with the generalized Hough transform

Object recognition is an automated image processing application of great interest in areas ranging from defect inspection to robot vision. In this regard, the generalized Hough transform (GHT) is a well-established technique for the recognition of geometrical features out of binary images even corrupted by noise or when the target is partially occluded. In order to enhance the performance of the original algorithm in detecting a given geometrical feature out of a single image, we consider the transformation of an stereo pair of a 3D scene under the GHT, one of the images using the template we are looking for and the other using its corresponding according to the perspective transformation that relates the images of the stereo pair. Validation experiments using partially occluded targets in noisy environments are presented.
Transfusion of Extended Vigenere Table and ASCII Conversion for Encryption Contrivance

In the field of cryptography, to make cryptosystem more secure an evaluation of modulus operations on integral values followed by ASCII value generation on plain text characters have been deeply explored in this research paper. On the basis of this an extended algorithm has been proposed. In this, the Encryption technique consists of an extended combination of Vigenere and Caesar cipher which is the main key feature of this algorithm and then decryption of text along with ASCII algorithm and substitution methodology has been done. The Algorithm is initiated on the basis of inspection of various research papers, furthermore, reviews have been made for proving this system more reliable. In the proposed algorithm modified Vigenere table and ASCII values are taken into consideration for decreasing the steps to reduce complexity and making a more secure way of cryptography.
Cooperative Hierarchical Framework for Group Activity Recognition

Deep neural network algorithms have shown promising performance for many tasks in computer vision field. Several neural network-based methods have been proposed to recognize group activities from video sequences. However, there are still several challenges that are related to multiple groups with different activities within a scene. The strong correlation that exists among individual motion, groups and activities can be utilized to detect groups and recognize their concurrent activities. Motivated by these observations, we propose a unified deep learning framework for detecting multiple groups and recognizing their corresponding collective activity based on Long Short-Term Memory (LSTM) network. In this framework, we use a pre-trained convolutional neural network (CNN) to extract features from the frames and appearances of persons. An objective function has been proposed to learn the amount of pairwise interaction between persons. The obtained individual features are passed to a clustering algorithm to detect groups in the scene. Then, an LSTM based model is used to recognize group activities. Together with this, a scene level CNN followed by LSTM is used to extract and learn scene level feature. Finally, the activities from the group level and the scene context level are integrated to infer the collective activity. The proposed method is evaluated on the benchmark collective activity dataset and compared with several baselines. The experimental results show its competitive performance for the collective activity recognition task.
Engineered perception architecture for healthcare

Experiencing sensory-pleasing environments and recreating perceptions of past lived environments can benefit patients greatly in coping with irreversible conditions (e.g., dementia) or during healing after traumatic experiences. Such environments can evoke emotional responses that are associated with a patient's positive memories of a place, an activity, or people. A design concept of an intimate architectural space informed by knowledge of anticipatory systems was developed for the purpose of generating variable personalized environments. In particular, such environments are designed for patients, according to their individual demands and expectations. The design is guided by aesthetic data acquired through perceptual user studies. Such adaptive spaces can be delivered to a large number of users in healthcare in the form of Architecture-as-Service (AaS). The research presented expands the notion of AaS and is premised upon an IoT application.
Marcel Danesi, <i>The Semiotics of Emoji: The Rise of Visual Language in the Age of the Internet</i>

Abstract This article reviews The Semiotics of Emoji: The Rise of Visual Language in the Age of the Internet 978-1-4742-8198-0
Development of Students’ Security and Privacy Habits Scale

The cyber space offers many opportunities to general public but it has its dark side in terms of cyber crimes. Apart from technical security, the human factor plays an important role in safe guarding the security and privacy of systems. The end users especially students need to be aware of protective measures they can adopt to safe guard themselves through security awareness and trainings. The awareness programs should be comprehensive and be tailored according to the security and privacy awareness of the individuals. Therefore, the target individuals should be assessed in terms of their security and privacy habits and practices. The previous endeavors in this respect make use of questionnaire instruments that are specific to a particular type of individuals such as employees of an organizations or tap onto use of a specific device. This study presents development of an instrument that gauges the security and privacy habits/practices of end users specifically students.
Quantum Leadership: Transmuting Technology

In a past era, alchemists believed they could magically transmute lead into valuable gold. Science has progressed a substantial distance since then and for decades nuclear and particle physicists could change various materials into gold. When considering technology, leaders are faced with a comparable challenge: How does one leverage technology to create unique organizational value? To manage emerging technologies effectively to create organizational value, managers will need to lead the producers and practitioners of technology effectively. In the age of global interdependence, organizations must abandon old outdated perspectives.Technology is a force which drives itself. Organizations must adopt to emerging technology or risk being obsolete. Leveraging technology to create value involves more then circumferentially managing technology. To create value, leaders must encourage staff to transmute technology. The principles and practices of quantum leadership provide for this possibility. This chapter will irradiate why simply managing technology does not offer organizations the maximum value from technology. The reader will be introduced to the four core features of quantum leadership: duality, superposition, entanglement, and observation. With this groundwork, the principles and practices of this leadership perspective will be discussed in context of transmuting technology into unique organizational value. Which lens one uses to see which possibility becomes reality are exclusively in the eyes of the viewer. Using information systems technology, artificial intelligence (AI), and 5G technology as the exemplars, readers can decide whether to accept, reject, or suspend judgement on using quantum leadership as the perspective to transmute technology into valuable organizational gold.
An Improved Initialization Method using Firefly Movement and Light Intensity for Better Clustering Performance

K-Means and EM algorithms are the most well-known clustering algorithms because they are simple, easy to understand and implement. However, both algorithms are sensitive to initial seeds which are randomly selected leading to slow convergence and less reliable clustering results. In this paper, an improved initialization method adopted the concept of light intensity and firefly movement to search for better initial seeds. Numerical experiments were conducted to evaluate the performance of the Enhanced K-Means and EM using faculty performance evaluation ratings as the dataset. The experiments showed that the implementation of the improved initialization method before the clustering process resulted in a higher intra-cluster and lower inter-cluster similarity. Also, there are fifty-nine percent (59%) and sixty-three percent (63%) decrease in the runtime execution while there are forty-four percent (44%) and twenty-seven percent (27%) fewer number of iterations recorded in the implementation of the enhanced KMeans and EM algorithms respectively.
Research on Application of Data Mining Based on Improved APRIORI Algorithm in Enrollment Management in Colleges and Universities

The quality of higher education is the most important criterion to measure the level of higher education. The enrollment of colleges and universities is related to the survival and development of colleges and universities. It is one of the important links to improve the quality of talent training in Colleges and universities, which is one of the important links to improve the quality of talent training in Colleges and universities. Data Mining is a process of extracting potentially useful information and knowledge from a large, incomplete, noisy, fuzzy, and random data, also known as the Knowledge Discovery in Database (KDD), and APRIORI algorithm is a number of frequent item sets. According to the mining clustering algorithm, it is widely used in the field of data mining, but the algorithm also has some limitations. In the process of data mining, there may be some useless branches, which makes the algorithm inefficient and may result in error. The improvement of the APRIORI algorithm is applied to the college enrollment work, which can make the college admissions workers. No longer only rely on experience, it can be based on data, make the enrollment work more pertinent, more scientific and reasonable, so as to promote the improvement of the quality of talent training and enhance the overall competitiveness of colleges and universities.
Fast Single Image Haze Removal Scheme Using Self-Adjusting

At present the classical problem of visibility improvement is hot topic of research. An image formation optical model is presented where a clear day image has high contrast with respect to an image plagued with bad weather. A degraded daytime image has high intensity with minimum deviation among pixels in every channel. No reference digital image haze removal is a problem. The static haziness factor for all types of images cannot be applicable for effective haze removal. A minimum intensity channel of the three RGB channels is estimated as transmission of an image with a dynamic haziness factor to be a ratio of minimum to maximum pixel intensity of the hazy image. Adaptive contrast, extinction coefficient, the maximum visible distance of hazy images as well as dehazed images from each image are evaluated uniquely. The resulting high-quality haze free image with linear computational complexity O(n) is appropriate for real time applications. The effectiveness of the technique is validated by quantitative, and qualitative evaluations.