Reviewerly/exHarmony huggingface dataset:
War on Drugs: The Readability and Comprehensibility of Illegal Drug Awareness Campaign Brochures

Information communicated and produced by the government is essentially fundamental as the government is set and deemed as a reliable and a credible source of information. This study assessed the readability, comprehensibility, and document design of two brochures on prohibited drugs awareness, which were produced and distributed by the Dangerous Drugs Board (DDB), a government agency that is responsible in instituting policies about the use of illegal drugs in the Philippines. Using both qualitative and quantitative methods, a total number of 120 participants comprised of students, those employed, the unemployed, and drug patients from a rehabilitation center answered two 10-item multiple choice questionnaires and Likert scales to assess and evaluate the comprehensibility and document design of the two brochures. Using the Coh-Metrix tool to analyze the readability of the two brochures, results showed that the two existing brochures were not that readable with the huge discrepancy of the readability levels of the docments against the 9-10 normative values of the reading tool utilized. The results of the participants’ scores which were categorized as instructional or assisted level exhibited that the two brochures were not that comprehensible. Lastly, it was found that the designs of the documents were poor as some violations like small font size, pixelated pictures, and non-observance of color schemes were present. This paper argues that government agencies should produce information brochures that are patterned to the standards of Plain Language Movement in order to help the lay people grasp important information such as drug awareness.
An estimation model based on solar geometry parameters for solar power production

Renewable energy sources are can play an important role both in fulfilling the increasing demands for energy and in reduction of the emissions of the greenhouse gases and other air pollutants. For these reasons their energy yield increases dynamically. Renewable energy sources are available in different ratios in different regions of the continents. Every regions has its own features what result in different utilisation methods. This paper presents a solar energy potential estimating model what can be used at any locations over the Earth. Examinations were carried out on a remote controlled system composed of 64 solar panels in the Renewable Energy Park Research Center in Debrecen, Hungary. Results revealed that the agreement between the model and the values in our database is over 90% thus using the model the production of the solar panel systems could be predicted taking into account specific local conditions as well.
Leveraging Discourse Rewards for Document-Level Neural Machine Translation

Document-level machine translation focuses on the translation of entire documents from a source to a target language.It is widely regarded as a challenging task since the translation of the individual sentences in the document needs to retain aspects of the discourse at document level.However, document-level translation models are usually not trained to explicitly ensure discourse quality.Therefore, in this paper we propose a training approach that explicitly optimizes two established discourse metrics, lexical cohesion (LC) and coherence (COH), by using a reinforcement learning objective.Experiments over four different language pairs and three translation domains have shown that our training approach has been able to achieve more cohesive and coherent document translations than other competitive approaches, yet without compromising the faithfulness to the reference translation.In the case of the Zh-En language pair, our method has achieved an improvement of 2.46 percentage points (pp) in LC and 1.17 pp in COH over the runner-up, while at the same time improving 0.63 pp in BLEU score and 0.47 pp in F BERT .
Entanglement properties of GHZ and W superposition state and its decayed states*

We use the generalized Wootters formula, the positive partial transpose (PPT) criterion and the matched entanglement witness, to detect entanglement of three-qubit GHZ and W superposition state and its decayed states. It shows that the results of the generalized Wootters formula in the part near the W state are tight. In the other parts, the PPT criterion is superior to the generalized Wootters formula. Furthermore, we investigate the relationship between entanglement and coherence.
What is the NIST Framework?

Abstract As global adoption continues, Steven Cockcroft MSc MBCS MCQI CQP explains the origins and rise in popularity of the NIST Cybersecurity Framework, its relevance and implications for organisations and individuals alike.
Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation

Scarcity of parallel sentence-pairs poses a significant hurdle for training high-quality Neural Machine Translation (NMT) models in bilingually low-resource scenarios.A standard approach is transfer learning, which involves taking a model trained on a high-resource language-pair and fine-tuning it on the data of the low-resource MT condition of interest.However, it is not clear generally which high-resource language-pair offers the best transfer learning for the target MT setting.Furthermore, different transferred models may have complementary semantic and/or syntactic strengths, hence using only one model may be sub-optimal.In this paper, we tackle this problem using knowledge distillation, where we propose to distill the knowledge of ensemble of teacher models to a single student model.As the quality of these teacher models varies, we propose an effective adaptive knowledge distillation approach to dynamically adjust the contribution of the teacher models during the distillation process.Experiments on transferring from a collection of six language pairs from IWSLT to five low-resource language-pairs from TED Talks demonstrate the effectiveness of our approach, achieving up to +0.9 BLEU score improvement compared to strong baselines.
Mark-Evaluate: Assessing Language Generation using Population Estimation Methods

We propose a family of metrics to assess language generation derived from population estimation methods widely used in ecology.More specifically, we use mark-recapture and maximumlikelihood methods that have been applied over the past several decades to estimate the size of closed populations in the wild.We propose three novel metrics: ME Petersen and ME CAPTURE , which retrieve a single-valued assessment, and ME Schnabel which returns a double-valued metric to assess the evaluation set in terms of quality and diversity, separately.In synthetic experiments, our family of methods is sensitive to drops in quality and diversity.Moreover, our methods show a higher correlation to human evaluation than existing metrics on several challenging tasks, namely unconditional language generation, machine translation, and text summarization.
The Effect of Applying Analytical Procedures on Understanding Business Environment in Light of Using Accounting Information Systems in Auditing

Since the entire world is moving with a steady acceleration to invest the technology in various fields and professions, exploring the effectiveness of these technologies in achieving the desired goals became an imperative need. Accordingly, the strengths and weaknesses that surround these systems could be identified. In this vein, the current study aimed to explore the effect of using client's accounting information systems by auditors on the effectiveness of analytical procedures in understanding business environment of entities, assessing the going concern of entities, and estimating potential misstatements in financial reports. To carry out the current study quantitative approach was adopted. Thus, a survey questionnaire was designed and distributed to 164 qualified and unqualified person who work in auditing profession in Jordan. In order to test hypotheses, a multivariate regression analysis tests were performed. The results showed that using client's accounting information systems by auditors has a significant positive effect on the relationship between analytical procedures and understanding business environment of entities, and estimating potential misstatements in financial reports. Conversely, the results showed that using client's accounting information systems by auditors has a significant negative effect on the relationship between analytical procedures and assessing the going concern of entities. The results imply that, auditors should benefit from using clients' accounting information systems in enhancing their understanding of business environment and estimating potential misstatements in financial reports. On the other hand, they should not depend extensively on these systems in assessing the going concern of these entities.
Word Segmentation for Classical Chinese Buddhist Literature

With the growth of digital humanities, information technologies take on more important roles in humanities research, including the study of religion. To analyze text for further processing, many text analysis tools treat a word as a unit. However, in Chinese, there are no word boundary markers. Word segmentation is required for processing Chinese texts. Although several word segmentation tools are available for modern Chinese, there is still no practical word segmentation tool for Classical Chinese, especially for Classical Chinese Buddhist literature. In this paper, we adopt unsupervised and supervised learning techniques to build Classical Chinese word segmentation approaches for processing Buddhist literature. Normalized variation of branching entropy (nVBE) is adopted for unsupervised word segmentation. Conditional random fields (CRF) are used to generate supervised models for Classical Chinese word segmentation. The performance of our word segmentation approach achieves an F-score of up to 0.9396. The experimental results show that our proposed method is effective for correctly segmenting most Classical Chinese sentences in Buddhist literature. Our word segmentation method can be a fundamental tool for further text analysis and processing research, such as word embedding, syntactic parsing, and semantic labeling.
A Singular Value Perspective on Model Robustness

Convolutional Neural Networks (CNNs) have made significant progress on several computer vision benchmarks, but are fraught with numerous non-human biases such as vulnerability to adversarial samples. Their lack of explainability makes identification and rectification of these biases difficult, and understanding their generalization behavior remains an open problem. In this work we explore the relationship between the generalization behavior of CNNs and the Singular Value Decomposition (SVD) of images. We show that naturally trained and adversarially robust CNNs exploit highly different features for the same dataset. We demonstrate that these features can be disentangled by SVD for ImageNet and CIFAR-10 trained networks. Finally, we propose Rank Integrated Gradients (RIG), the first rank-based feature attribution method to understand the dependence of CNNs on image rank.
Face Recognition using Local Binary Patterns

Face recognition is one of the topics from which it is difficult to prepare a complete computational model. Unlike other detection's these are a high-level task, and a variety of methods have been proposed. On the other hand, face recognition is needed as a suitable data source for various applications that lead to the recognition of individuals. In this article, we will examine this issue and consider a method based on Local Binary Patterns and provide algorithms for it. The results analyzed in this method show its efficiency.
SWAGex at SemEval-2020 Task 4: Commonsense Explanation as Next Event Prediction

We describe the system submitted by the SWAGex team to the SemEval-2020 Commonsense Validation and Explanation Task.We used multiple methods on the pre-trained language model BERT (Devlin et al., 2018) for tasks that require the system to recognize sentences against commonsense and justify the reasoning behind this decision.Our best performing model is BERT trained on SWAG fine-tuned for the task.We investigate the ability to transfer commonsense knowledge from SWAG to SemEval-2020 by training a model for the Explanation task with Next Event Prediction data.
C2TOP: A Cloud Competition-based Truthful Online Pricing Mechanism in Secondary Markets

Flexibility and the ability of cost management have made on-demand price scheduling as one of the most favourable choices of cloud service users. This mechanism is not appealing for cloud service providers at the same level because it cannot provide the required information to anticipate and provide future market demands. Instead, cloud service providers are more inclined to use Futures contracts. However, these methods do not provide the flexibility and cost management that on-demand methods provide for service users. Cloud secondary markets emerged to meet the needs of both sides of the market: 1) cloud service providers, 2)cloud service users, where brokers and reseller buyers act as intermediaries to provide the demanded services for service users. There is high competition among service providers to attract service users and among service users to find appropriate services, and also there are high fluctuations in environmental parameters, such as price and the volume of supply and demand. These are the reasons that designing an appropriate pricing algorithm and resource allocation mechanism is of great importance. In this paper, we propose a competition-based price scheduling mechanism that considers the inherent competition and fluctuation attributes of such environments. The provided experimental results show that our mechanism is superior to other methods, such as fixed and history-based pricing mechanisms, in terms of utility and resource allocation efficiency.
Ranking Top Similar Documents for User Query Based on Normalized Vector Cosine Similarity Model

As the technology is developing information in each fields like literature, technology, science, medicine etc., also increasing in high pace. To extract related document in huge collection of documents based on user query in digital world is an interesting problem. Documents similarity Technique used in many applications like text categorization, plagiarism discernment, document clustering, information retrieval, machine translation and question answering system. Many algorithms have been developed for this purpose that take a document or input query and match it with the document databases. This paper proposes novel approach to vectorize each document and query with normalized TF-IDF method and applying Cosine Similarity function to extract top 3 documents based on user query.
Reinventing ourselves: new and emerging roles of academic librarians in Canadian research-intensive universities

The academic library profession is being redefined by the shifting research and scholarly landscape, the transformation in higher education, and advances in technology. A survey of librarians working in Canada’s research-intensive universities was conducted to explore new and emerging roles. This study focuses on librarians’ activities in: Research Support, Teaching and Learning, Digital Scholarship, User Experience, and Scholarly Communication. It addresses the scope and nature of the new roles, the skills required to provide new services, and the confidence librarians have in their abilities to perform the new roles. It also reports on librarians’ job satisfaction and their perceived impact on the academic enterprise.
Comparative Study of an Augmented Reality Software Development Kit Suitable for Forensic Medicine Education

In the process of creating an Augmented Reality (AR) system, there are various Augmented Reality Software Development Kits (AR SDK) available for development. Each AR SDK has different features. Therefore, a study has to be performed about which AR SDK is appropriate for implementation. This paper focuses on an AR system for Forensic Medicine education in Thailand, where drowning is one of the main causes of death. With the aim of a comparative study and selection, the AR SDK type suitable for use in creating an AR system for teaching Forensic Medicine is selected. The traditional teaching and learning method was studied. There were general requirements of SDKs which are recognition of objects in both 2D and 3D, recognition stability of immovable and movable markers, and supporting platforms both Android and iOS. It was found that Vuforia is an AR SDK suitable for use in Forensic Medicine education. Studying and comparing the various Vuforia feature types resulted in the image target and model target as appropriate methods for Forensic Medicine education in the case of investigating drowning.
Robo-Advisory: The Rise of the Investment Machines

Robo-advisors are an innovation in the financial market to provide automatized investment advice. We start this introductory chapter with a brief overview about the history of robo-advice. It started about ten years ago in the US and has spread over the globe since. The market volume has risen significantly, and the rise of both assets-under-management and number of clients is predicted to grow even further. We also throw a glance at the performance, which robo-advisors are generating, and find that robo-advice is by and large neither out- nor underperforming. In the next future, we assume that robo-advice may be combined with human elements, resulting in hybrid models which combine the best of two worlds.
On Modality Bias in the TVQA Dataset

TVQA is a large scale video question answering (video-QA) dataset based on popular TV shows. The questions were specifically designed to require vision and language understanding to answer. In this work, we demonstrate an inherent bias in the dataset towards the textual subtitle modality. We infer said bias both directly and indirectly, notably finding that models trained with subtitles learn, on-average, to suppress video feature contribution. Our results demonstrate that models trained on only the visual information can answer ~45% of the questions, while using only the subtitles achieves ~68%. We find that a bilinear pooling based joint representation of modalities damages model performance by 9% implying a reliance on modality specific information. We also show that TVQA fails to benefit from the RUBi modality bias reduction technique popularised in VQA. By simply improving text processing using BERT embeddings with the simple model first proposed for TVQA, we achieve state-of-the-art results (72.13%) compared to the highly complex STAGE model (70.50%). We recommend a multimodal evaluation framework that can highlight biases in models and isolate visual and textual reliant subsets of data. Using this framework we propose subsets of TVQA that respond exclusively to either or both modalities in order to facilitate multimodal modelling as TVQA originally intended.
Psychological Well-Being of Students in Inclusive Higher Education in Arts: The Experience of Structural Analysis

The article focuses on the study of the features of psychological well-being in healthy students and students with disabilities. It was assumed that self-assessment of psychological well-being in students with disabilities and healthy students differs in structure and content. The study involved 167 students, 98 with normative development and 67 with various types of disabilities (hearing, vision impairments etc.).The survey was conducted on the basis of Russian State Specialized Academy of Arts (RSSAA) with students of all faculties (theater, music, fine arts).We used the following techniques: the life satisfaction scale and the subjective happiness scale. It is shown that the perception of psychological well-being among students of the inclusive university is more differentiated. The assessment of psychological well-being is based on three parameters: ‘life satisfaction in general’; ‘life optimism, self- realization — satisfaction with the past’; ‘assessing oneself as happier than one’s peers’. Self-assessment of psychological well-being is based on the time dynamics of life’s journey: a retrospective view of one’s life and its perception from the point of view of future prospects.
Campus Intelligence Mental Health Searching System Based on Face Recognition Technology

The phenomenon of teenage campus suicide has become the focus of attention of parents, schools and the society. The causes behind it are extremely complicated, and the root cause is psychological and spiritual problems. However, one's negative psychology is often hidden, and it is difficult to detect and effectively intervene before the tragedy. How to effectively identify students with suicidal tendencies in order to prevent tragedies has aroused extensive research and discussion among the government, academia and the public. Through investigation and research, it is found that the current popular computer cutting-edge technologies such as artificial intelligence and computer vision can be well used for human emotion recognition and behavior prediction, and put into use in schools as a mental health auxiliary diagnosis and treatment system, thus effectively reducing the suicide rate on campus. The scenario assumes that machine learning can be used to deduce the risk of psychological problems in human samples by analyzing the frequency of negative emotions in facial expressions. Based on this, this paper proposes an effective solution for campus suicide prediction, and designs a set of auxiliary diagnosis and treatment system based on campus monitoring network system for suicide behavior prediction and student mental health analysis. Through preliminary experimental analysis and verification, the suicide psychological auxiliary diagnosis and treatment system has achieved good results in face recognition success rate, emotion recognition success rate and behavior prediction success rate. With the input of more experimental data and the increase of self-training time, the prediction system will perform better.
Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics

Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other.To perform a systematic comparative analysis, we evaluate the mapping between meaning representations from different frameworks using two complementary methods: (i) a rule-based converter, and (ii) a supervised delexicalized parser that parses to one framework using only information from the other as features.We apply these methods to convert the STREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a graph-structured full-sentence meaning representation).Both methods yield surprisingly accurate target representations, close to fully supervised UCCA parser quality-indicating that UCCA annotations are partially redundant with STREUSLE annotations.Despite this substantial convergence between frameworks, we find several important areas of divergence.
TextLearner at SemEval-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text

This paper describes the emphasis selection system of the team TextLearner for SemEval 2020 Task 10: Emphasis Selection For Written Text in Visual Media.The system aims to learn the emphasis selection distribution using contextual representations extracted from pre-trained language models and a two-staged ranking model.The experimental results demonstrate the strong contextual representation power of the recent advanced transformer-based language model RoBERTa, which can be exploited using a simple but effective architecture on top.