Reviewerly/exHarmony huggingface dataset:
Information governance for the implementation of cloud computing

Information Governance has become gradually an important area due to the explosion of data in numerous formats at diverse levels of any organization. An Information Governance platform may have the capability to improve the data quality and may also support in leveraging strategic decision making. There is a growing trend to move the data from on-premise software to the new on-demand cloud-based solutions to support the growing needs of the business computing services which has the potential to lower the costs and mitigating the risks. Cloud computing is considered as a utility that is massively scalable and can be readily modified, the way we may control the temperature in the furnace using the thermostat which has the potential to save energy. There are many organizations that are investigating the Cloud to see if the application can meet their information Governance needs such as eDiscovery, compliance, law, security, risk management, records management, and business operations. Information Governance is a super-discipline that embraces components of several key fields as mentioned above (Robert F. Smallwood, 2014).
Neural Attention-Aware Hierarchical Topic Model.

Neural topic models (NTMs) apply deep neural networks to topic modelling. Despite their success, NTMs generally ignore two important aspects: (1) only document-level word count information is utilized for the training, while more fine-grained sentence-level information is ignored, and (2) external semantic knowledge regarding documents, sentences and words are not exploited for the training. To address these issues, we propose a variational autoencoder (VAE) NTM model that jointly reconstructs the sentence and document word counts using combinations of bag-of-words (BoW) topical embeddings and pre-trained semantic embeddings. The pre-trained embeddings are first transformed into a common latent topical space to align their semantics with the BoW embeddings. Our model also features hierarchical KL divergence to leverage embeddings of each document to regularize those of their sentences, paying more attention to semantically relevant sentences. Both quantitative and qualitative experiments have shown the efficacy of our model in 1) lowering the reconstruction errors at both the sentence and document levels, and 2) discovering more coherent topics from real-world datasets.
Correction: Vukovic et al. COVID-19 Pandemic: Is the Crypto Market a Safe Haven? The Impact of the First Wave. Sustainability 2021, 13, 8578

The authors would like to make the following corrections to the published paper [...]
Applying Suggestopedia Method In Teaching Tenses Simple Past To The Second Semester Students Of English Department At Cokroaminoto Palopo University

This research aims to find out the appropriate way in teaching simple past tense by applying Suggestopedia method and also to find out the students’ participation during the teaching simple past tense by applying Suggestopedia method to the second semester students of English Department of Cokroaminoto Palopo University. The subject of this research is 25 students of the second semester students of English Language Education Study Program, Faculty of Teacher Training and Education, Cokroaminoto Palopo University class B. The result of this research shows that teaching structure simple past tense by applying Suggestopedia method was one of the effective method that was able to enhance the students’ understanding well and easily. It was supported by the achievement of the ideal score in the second cycle. In this case, the mean score in the first cycle was only 66.28 while in the second cycle the mean score was very good 86.6. That mean, the mean score in the second cycle was higher than the mean score in the first cycle. It means that, the students had a good understanding in learning structure simple past tense by applying Suggestopedia method. Meanwhile, the result of observation, the student’s participation in the second cycle was more active 85% than in the first cycle 50%. That mean, the students had a good participation in learning structure simple past tense by applying Suggestopedia method. So, it could be concluded that the Application of Suggestopedia Method in Teaching Simple Past Tense effective in enhancing student’s understanding and also could overcome students’ psychological barriers of the second semester students of Cokroaminoto Palopo University. Thus, it could be clearly concluded that Suggestopedia method is able to enhance students’ understanding in learning structure simple past tense well and easily as fast and intensive as possible.
THE COMPARISON BETWEEN PARENTS’ EDUCATIONAL BACKGROUND WITH STUDENTS’ ABILITY IN MASTERING ENGLISH VOCABULARY AT SMA NEGERI 2 SIBORONGBORONG

The Comparison between Parents’ Educational Backgrounds with Students’ Ability in Mastering English Vocabulary AtSma Negeri 2 Siborongborong. This  study deal with the comparison of parents educational background influence to the students ability in getting lesson especially in mastering English vocabulary.And it is corroborate Jhon and Berman (989 : 18) say in their book : “Learning vocabulary is a very important part of learning English. If you make a grammar mistake, it maybe “wrong” but very often people will understand you anyway. But you do not know the exact words that you need; it is very frustrating for you and the person you are talking to, so, this journal talk The Comparison Between Parents’ EducationalBackground With Students’ Ability InMastering English Vocabulary AtSma Negeri 2 Siborongborong
Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions

Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, calling a taxi etc. In this paper, we present a data creation strategy that uses the pre-trained language model, GPT2, to simulate the interaction between crowd workers by creating a user bot and an agent bot. We train the simulators using a smaller percentage of actual crowd-generated conversations and their corresponding instructions. We demonstrate that by using the simulated data, we achieve significant improvements in low-resource settings on two publicly available datasets - MultiWOZ dataset and the Persona chat dataset.
Psychological and social adaptation at the workplace

The aim of the study is to optimize the adaptation process of nursing specialists of the city hospital. Results. The special literature on the methods of managing the adaptation process of the employees admitted to the organization, the existing system of labor adaptation of the nursing staff of the city hospital; the influence of mentoring as a method of professional adaptation of young specialists in new working conditions, influencing the results of their professional activities, has been established; the factors influencing the socio-psychological adaptation of new nursing specialists are analyzed; formulated practical recommendations for improving the system of labor adaptation of the nursing staff of the city hospital. Conclusion. The sisters of the leaders are directly responsible for the development and improvement of the professional thinking of employees. Their actions should be aimed at maintaining and improving the quality of professional skills necessary for successful work in the present and in the future. The hospital should introduce a new method of drawing up individual work adaptation programs that should meet the needs of each new employee. The path to professionalism goes through the awareness of the need, the importance of improving their knowledge and skills, gaining experience.
DOMAINS – An Ontology: Internal Qualities

In this chapter we introduce the concept of internal qualities of endurants, and cover the analysis and description of unique identifiers, mereologies and attributes of endurants.
Robust Object Detection in Colour Images Using a Multivariate Percentage Occupancy Hit-or-Miss Transform

Abstract The extension of Mathematical Morphology to colour and multivariate images is challenging due to the need to define a total ordering in the colour space. No one general way of ordering multivariate data exists and, therefore, there is no single, definitive way of performing morphological operations on colour images. In this paper, we propose an extension to mathematical morphology, based on reduced ordering, specifically the morphological Hit-or-Miss Transform which is used for object detection. The reduced ordering employed transforms multivariate observations to scalar comparisons allowing for an order to be derived and for both flat and non-flat structuring elements to be used. We also compare other definitions of the Hit-or-Miss Transform and test alternative colour ordering schemes presented in the literature. Our proposed method is shown to be intuitive and outperforms other approaches to multivariate Hit-or-Miss Transforms. Furthermore, methods of setting the parameters of the proposed Hit-or-Miss Transform are introduced in order to make the transform robust to noise and partial occlusion of objects and, finally, a set of design tools are presented in order to obtain optimal values for setting these parameters accordingly.
An Improved Algorithm of UAV 3D Path Planning

Path planning is the key of UAV autonomous flight. In order to improve the efficiency of UAV path planning in 3D complex environment, an improved path planning algorithm is proposed in this paper. First of all, in terms of map construction, aiming at the problem that sampling points cannot cover narrow free space in the traditional probabilistic roadmaps (PRM), this paper proposes a sampling point translation strategy to move out the sampling points within the obstacle threat. Then, in the aspect of path generation, this paper proposes an improved ant colony optimization (ACO), which improves the transition probability and pheromone update rule of traditional ACO. In order to improve the efficiency of the ACO, target guidance information is added to the calculation of transfer probability. Besides, collision threat cost and path length are added to the pheromone update rule to solve the problem that the traditional ACO can only optimize a single target. The simulation results show that the algorithm in this paper effectively makes up for the shortcomings of the traditional PRM and the traditional ACO. The path planning method in the 3D complex environment of UAV is feasible.
LibGuides: A LibGuide for ECE's: Books

Are you in the Early Childhood Education program at Algonquin College? Check out this guide for information to help you with your program!
APLIKASI MEDIA EDUKASI INTERAKTIF BERBASIS AUGMENTED REALITY

The Education model is widely applied using various technologies and tools that support it, either the most traditional or the modern ones such as books, articles, tutorials and internet CDs. In terms of reading education among preschoolers, children often have difficulty to understand the meaning of the word read to the object in question. Augmented Reality in educational media that is interactive aims to facilitate in understanding words those have been given. In this research, designed an application that allows users in this case pre-school students interact with interfaces to interpret the meaning of the writings provided in the form of props so that pre-school students can directly see the object in a real time. The application requires both internal and external webcam cameras as input source, then this app will detect marker by using tracking system on ARToolkit software, then the object will appear above the marker as directed.
Channel Modeling and Analysis for the Sensor Network Inside Tower Buildings

Symmetry-based channel digital twin is a great technology which can reproduce the communication channel of real scenes for performance evaluation of the wireless sensor network (WSN) inside tower buildings, based on the ray tracing (RT) method and machine learning (ML) theories, a cluster-based channel model is proposed in this paper. Meanwhile, an improved k-means method, which considers the weight of different dimensions in the multipath component distance (MCD) is presented for clustering, which has better clustering performance over the sparsity-based algorithm and traditional k-means algorithm. Moreover, the channel parameters such as cluster delay and cluster power are also investigated. On this basis, the communication performance of WSN, i.e., bit error rate (BER) and channel capacity are derived and analyzed. The simulation and analysis results show that the cluster model based on the RT method can get approximately equivalent channel impulse response (CIR), and the BER of proposed model is consistent with the simulated one. These results can provide reference for the node layout and optimization of WSN inside tower buildings.
$Q^2$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering.

Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability. Inspired by recent work on evaluating factual consistency in abstractive summarization, we propose an automatic evaluation metric for factual consistency in knowledge-grounded dialogue using automatic question generation and question answering. Our metric, denoted Q2, compares answer spans using natural language inference (NLI), instead of token-based matching as done in previous work. To foster proper evaluation, we curate a novel dataset of dialogue system outputs for the Wizard-of-Wikipedia dataset, manually annotated for factual consistency. We perform a thorough meta-evaluation of Q2 against other metrics using this dataset and two others, where it consistently shows higher correlation with human judgements.
Trust Management Using Blockchain In Iot Enabled Supply Chains

Recognizability and respectability are significant difficulties for the undeniably intricate inventory chains of the present world. In spite of the fact that blockchain innovation can possibly address these difficulties through giving a carefully designed review trail of store network occasions and information related with an item life-cycle, it doesn't take care of the trust issue related with the actual information. Notoriety frameworks are a viable way to deal with take care of this trust issue. Be that as it may, current standing frameworks are not fit to the blockchain based store network applications as they depend on restricted perceptions, they need granularity and robotization, and their overhead has not been investigated. In this work, we propose TrustChain, as a three-layered trust the board system which utilizes a consortium blockchain to follow cooperations among production network members and to progressively allot trust and notoriety scores dependent on these collaborations. The curiosity of Trustchain comes from: (a) the standing model that assesses the nature of items, and the reliability of elements dependent on different perceptions of store network occasions, (b) its help for notoriety scores that different between a production network member and items, empowering the task of item speciﬁc notorieties for a similar member, (c) the utilization of shrewd agreements for straightforward, efﬁcient, secure, and mechanized computation of notoriety scores, and (d) its negligible overhead as far as inactivity and throughput when contrasted with a basic blockchain based production network model..
The Speciality of the Appliance of Museum Pedagogy in the Art’s Lessons in Secondary School

The possibility of organisation the museum pedagogical activities in the musical art and art lessons are reviewed in the article by its authors. The important role of museums as dynamic, cultural and educational, as well as scientific impulse and focus in the educational process is implied. Also, the term «interactivity» in the training process is explained on the examples of different methods of museum pedagogy in the practice of the many educational institutions. The importance of the appliance of museum pedagogy is underlined, especially such approaches as comparison, localisation of the events, auditions, performance, commenting and reconstruction. The functions of these approaches are specified, that is, to develop the interdisciplinary art competency of students and to enrich their experience in analysis of musical compositions. As a consequence, the important role of museum pedagogy in musical art and art lessons as an interdisciplinary method is confirmed.
A Proposal of Digital Image Steganography and Forensics Based on the Structure of File Storage

Steganography is a technology for transmitting secret messages in public channel by embedding secret information into a digital carrier. It has a wide range of applications in the Internet security thus has attracted great interests. Among all kinds of digital carriers, digital image has outstanding performance. To the best of our knowledge, the researches on image steganography mainly focus on the use of the feature of digital image in the spatial or frequency domain, while there are few researches on hiding information through the file format structure. Taking JPEG image standard as an example, this paper analyzes the file format structure as well as the compression procedure and reports a novel proposal of image steganography and forensics. The proposal uses the redundant channel between any marker segments in the JPEG format structure to embed the secret information. The validity of the proposal has been proved by experiments, and further study has shown that this proposal has no limitation on the hiding capacity and has good practicability.
KOAS: Korean Text Offensiveness Analysis System

Warning: This manuscript contains a certain level of offensive expression. As communication through social media platforms has grown immensely, the increasing prevalence of offensive language online has become a critical problem. Notably in Korea, one of the countries with the highest Internet usage, automatic detection of offensive expressions has recently been brought to attention. However, morphological richness and complex syntax of Korean causes difficulties in neural model training. Furthermore, most of previous studies mainly focus on the detection of abusive language, disregarding implicit offensiveness and underestimating a different degree of intensity. To tackle these problems, we present KOAS, a system that fully exploits both contextual and linguistic features and estimates an offensiveness score for a text. We carefully designed KOAS with a multi-task learning framework and constructed a Korean dataset for offensive analysis from various domains. Refer for a detailed demonstration.
Joint abstractive and extractive method for long financial documentsummarization

In this paper we show the results of our participation in the FNS 2021 shared task. In our work we propose an end to end financial narrative summarization system that first selects salient sentences from the document and then paraphrases extracted sentences. This method generates an overall concise summary that maximises the ROUGE metric with the gold standard summary. The end to end system is developed using a hybrid extractive and abstractive architecture followed by joint training using policy-based reinforcement learning to bridge together the two networks. Empirically, we achieve better scores than the proposed baselines and toplines of FNS 2021 (LexRank, TextRank, MUSE topline and POLY baseline) and we were ranked 2nd in the shared task competition. Keywords: Summarization, Neural networks, Reinforcement learning, sequence to sequence learning; actor-critic methods; policy gradients.
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings

Language representations are known to carry stereotypical biases and, as a result, lead to biased predictions in downstream tasks. While existing methods are effective at mitigating biases by linear projection, such methods are too aggressive: they not only remove bias, but also erase valuable information from word embeddings. We develop new measures for evaluating specific information retention that demonstrate the tradeoff between bias removal and information retention. To address this challenge, we propose OSCaR (Orthogonal Subspace Correction and Rectification), a bias-mitigating method that focuses on disentangling biased associations between concepts instead of removing concepts wholesale. Our experiments on gender biases show that OSCaR is a well-balanced approach that ensures that semantic information is retained in the embeddings and bias is also effectively mitigated.
Decision Forests vs. Deep Networks: Conceptual Similarities and Empirical Differences at Small Sample Sizes

Deep networks and decision forests (such as random forests and gradientboosted trees) are the leading machine learning methods for structured andtabular data, respectively. Many papers have empirically compared large numbersof classifiers on one or two different domains (e.g., on 100 different tabulardata settings). However, a careful conceptual and empirical comparison of thesetwo strategies using the most contemporary best practices has yet to beperformed. Conceptually, we illustrate that both can be profitably viewed aspartition and vote schemes. Specifically, the representation space that theyboth learn is a partitioning of feature space into a union of convex polytopes.For inference, each decides on the basis of votes from the activated nodes.This formulation allows for a unified basic understanding of the relationshipbetween these methods. Empirically, we compare these two strategies on hundredsof tabular data settings, as well as several vision and auditory settings. Ourfocus is on datasets with at most 10,000 samples, which represent a largefraction of scientific and biomedical datasets. In general, we found forests toexcel at tabular and structured data (vision and audition) with small samplesizes, whereas deep nets performed better on structured data with larger samplesizes. This suggests that further gains in both scenarios may be realized viafurther combining aspects of forests and networks. We will continue revisingthis technical report in the coming months with updated results.
Waiving Windfalls: The Socio-Legal and Contextual Justification of a 'TRIPS Waiver' during the COVID-19 Pandemic

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Blockchain for Medicine and Healthcare

This chapter gives a brief overview of blockchain technology and its applications in medicine and healthcare. It firstly gives the introduction about the blockchain and after that we discuss about its different applications. There are different domains on to which blockchain technology can be applied. We mainly discuss the medicine and healthcare applications of blockchain as we move ahead in this chapter. Lastly we sum up the blockchain and its different applications.