Reviewerly/exHarmony huggingface dataset:
Detailed Feature Guided Generative Adversarial Pose Reconstruction Network

Face frontalization is a critical and difficult task on face pose reconstruction. Previous researches use simple posture information as guidance, such as pose coding and facial landmarks. To explore the guidance effect of profile faces, we propose detailed features that provide much detailed information. In this paper, a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> etailed Feature <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> uided Generative Adversarial <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> ose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> econstruction Network (DGPR) is proposed. Firstly, frontal pose coding and profile detailed features are fed into DGPR to generate detailed features of front face. Then, the second generator combines frontal detailed features and profile face to reconstruct front face. Besides, we propose a conditional enhancement loss to strengthen the guiding role of detailed features, and a smoothing loss to reduce edge sharpness in generated faces. Experimental results show that our method generates photorealistic front faces and outperforms state-of-the-art methods on M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FPA and CAS-PEAL. Specifically, DGPR improves the face recognition accuracy under pose angles of ±60°, ±75°, ±90° by 2%, 1%, and 6% respectively over the state-of-the-art methods on M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FPA, achieves the average rank-1 recognition rate to 99.95% and improves it by 0.05% on CAS-PEAL. These results demonstrate the effects of detailed features and corresponding modules.
STUDENTS’ LANGUAGE AWARENESS ON LINGUISTICS PROBLEMS DURING NEGOTIATION OF MEANING IN PAIR WORK DISCUSSION.

The objectives of this study are :1) to describe linguistics problems produced by the students during the negotiation of meaning in pair work discussion, and 2) to describe the students’ language awareness (willingness and unwillingness to correct the linguistics problems). This study applied descriptive quantitative design and the subjects are 3 pairs consisting of high-high (H-H) learner, high and low (H-L) learner and low- low (L-L) learner. All of them are the fourth semester students of English department of teachers training faculty of Lampung University in academic 2018/2019. Each pair was given a topic (chosing 5 personality traits that a teacher should have). Each pair discussed the topic and during the discussion, all their utterances were recorded, transcribed and were analyzed based on relevant theories. The results show that each pair produced linguistics problems, such as grammatical and vocabulary. Most of the learners, especially H-L and L-L are not aware of linguistics problems because they focus more on the message across. Eventhough some of the pairs are aware of linguistics problems and are willing to correct them, it does not guarantee that the input can be internalized by their pairs since they focus more on meaning during the interaction. Therefore, it is suggested further researchers investigate this phenomena.
"What's The Context?" : Long Context NLM Adaptation for ASR Rescoring in Conversational Agents.

Neural Language Models (NLM), when trained and evaluated with context spanning multiple utterances, have been shown to consistently outperform both conventional n-gram language models and NLMs that use limited context. In this paper, we investigate various techniques to incorporate turn based context history into both recurrent (LSTM) and Transformer-XL based NLMs. For recurrent based NLMs, we explore context carry over mechanism and feature based augmentation, where we incorporate other forms of contextual information such as bot response and system dialogue acts as classified by a Natural Language Understanding (NLU) model. To mitigate the sharp nearby, fuzzy far away problem with contextual NLM, we propose the use of attention layer over lexical metadata to improve feature based augmentation. Additionally, we adapt our contextual NLM towards user provided on-the-fly speech patterns by leveraging encodings from a large pre-trained masked language model and performing fusion with a Transformer-XL based NLM. We test our proposed models using N-best rescoring of ASR hypotheses of task-oriented dialogues and also evaluate on downstream NLU tasks such as intent classification and slot labeling. The best performing model shows a relative WER between 1.6% and 9.1% and a slot labeling F1 score improvement of 4% over non-contextual baselines.
Diagnostic Breast Cancer Image Data Classification using CNN

Breast cancer has been dangerous form of cancer. In this report, we use a convolutional neural network to scan and separate infected cells.In this we diagnose if its benign or malignant cancer bulk using computer assisted detection(CAD). The productivity of open CAD has always been inadequate. Here, we use a deep CNN-based content detection method.We create narrower and broader images of histology patches with cell and tumour attributes. CNN constitutes unorganized data specifically for image data which has been said to be thriving in the area of image recognition .We use highly interconnected layer first cnn, in which those layers are incorporated before the first convolutional layer, since CNN does not support data sets.&#x0D;
Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks

In continuing tasks, average-reward reinforcement learning may be a more appropriate problem formulation than the more common discounted reward formulation. As usual, learning an optimal policy in this setting typically requires a large amount of training experiences. Reward shaping is a common approach for incorporating domain knowledge into reinforcement learning in order to speed up convergence to an optimal policy. However, to the best of our knowledge, the theoretical properties of reward shaping have thus far only been established in the discounted setting. This paper presents the first reward shaping framework for average-reward learning and proves that, under standard assumptions, the optimal policy under the original reward function can be recovered. In order to avoid the need for manual construction of the shaping function, we introduce a method for utilizing domain knowledge expressed as a temporal logic formula. The formula is automatically translated to a shaping function that provides additional reward throughout the learning process. We evaluate the proposed method on three continuing tasks. In all cases, shaping speeds up the average-reward learning rate without any reduction in the performance of the learned policy compared to relevant baselines.
Supervised Learning for Streaming Data

In the last chapter, you learned some concept drift detection algorithms. This chapter focuses on supervised learning algorithms (for classification tasks and regression tasks) in a streaming data context. In supervised learning, there is a target or a response variable and there are predictor variables. The goal of the learner/model is to understand the relationship between the target and the predictor variables. This can then be used to make predictions on the target variable for future observations of the predictor variables [1]. (I assume that you are already familiar with the supervised learning techniques used in a batch setting.)
Chapter 3. Language Contact, Maintenance and Conflict: The Case of the Guarani Language in Brazil

The main objective of this study was to examine whether the indigenous language of Guarani, spoken in the state of Espirito Santo (ES) in Brazil, showed traces of shift to Portuguese or whether it remained intact. In this chapter, we examine which social factors contributed to the maintenance or loss of this language. We begin by describing the linguistic panorama in Brazil, focusing on Guarani. We discuss Guarani in contact with the majority/official language Portuguese, and review the relevant sociolinguistics and language contact literature. We then analyze interviews with Guarani from ES in relation to the Language Vitality and Endangerment Document (UNESCO 2003). The overall results of the study suggest that the Guarani maintain their language despite the contact with Portuguese and the challenges posed by it.
The TeaComposition Initiative: Unleashing the power of international collaboration to understand litter decomposition

Collected harmonized data on global litter decomposition are of great relevance for scientists, policymakers, and for education of the next generation of researchers and environmental managers. Here we describe the TeaComposition initiative, a global and open research collaborative network to study organic matter decomposition in a standardized way allowing comparison of decomposition rate and carbon turnover across global and regional gradients of ecosystems, climate, soils etc. The TeaComposition initiative today involves 570 terrestrial and 300 aquatic ecosystems from nine biomes worldwide.  Further, we describe how to get involved in the TeaComposition initiative by (a) implementing the standard protocol within your study site, (b) joining task forces in data analyses, syntheses and modelling efforts, (c) using collected data and samples for further analyses through joint projects, (d) using collected data for graduate seminars, and (e) strengthening synergies between biogeochemical research and a wide range of stakeholders. These collaborative efforts within/emerging from the TeaComposition initiative, thereby, will leverage our understanding  on litter decomposition at the global scale and strengthen global collaborations  essential for addressing grand scientific challenges in a rapidly changing world.
Sarcasm and Sentiment Detection in Arabic language A Hybrid Approach Combining Embeddings and Rule-based Features

This paper presents the ArabicProcessors team’s system designed for sarcasm (subtask 1) and sentiment (subtask 2) detection shared task. We created a hybrid system by combining rule-based features and both static and dynamic embeddings using transformers and deep learning. The system’s architecture is an ensemble of Naive bayes, MarBERT and Mazajak embedding. This process scored an F1-score of 51% on sarcasm and 71% for sentiment detection.
Geomorphological and Sedimentological Features of River Sadong, Sarawak, Malaysia

DOI: 10.17014/ijog.8.1.119-130 The effect of topography, climate, soil, and geology on River Sadong sediments is related to its geochemistry. Eighteen surface sediments and five core samples were collected in replicates from six sampling sites along River Sadong, Sarawak, Malaysia. The main aim of this study is to describe the geomorphology and sedimentological characteristics of the river sediments. This was achieved by the study of the processes that form the sediments and influence their physical and chemical characteristics. The sediments were analyzed for their sedimentological characteristics. The results indicated that the studied area is enriched with organic matter, and there are no sedimentary structures in its vicinity.
SENTIMENTAL ANALYSIS FOR MOVIE REVIEWS

Sentimental analysis is defined as the use of computational linguistics, natural language, text analysis and biometrics to recognise,extract,test and study useful attributes and their information. We considered two different datasets both pre-dominantly pertaining to IMDB as source. One of the considered datasets composed only textual content which was processed by removing unnecessary contents and distributed into two categories namely positive and negative. We further divided the data into training dataset and testing dataset. Using more relevant training algorithms such as logistic regression and decision tree algorithm, we had more relevant attribute which helped us in training our model to predict if a review is positive or negative.  When this analysis is linked with other attributes of any product of interest, we can accurately pin point or predict a product’s rating even before it sees broad day light. SENTIMENTAL ANALYSIS FOR MOVIE REVIEWS
G2Basy: A framework to improve the RNN language model and ease overfitting problem

Recurrent neural networks are efficient ways of training language models, and various RNN networks have been proposed to improve performance. However, with the increase of network scales, the overfitting problem becomes more urgent. In this paper, we propose a framework—G2Basy—to speed up the training process and ease the overfitting problem. Instead of using predefined hyperparameters, we devise a gradient increasing and decreasing technique that changes the parameters training batch size and input dropout simultaneously by a user-defined step size. Together with a pretrained word embedding initialization procedure and the introduction of different optimizers at different learning rates, our framework speeds up the training process dramatically and improves performance compared with a benchmark model of the same scale. For the word embedding initialization, we propose the concept of “artificial features” to describe the characteristics of the obtained word embeddings. We experiment on two of the most often used corpora—the Penn Treebank and WikiText-2 datasets—and both outperform the benchmark results and show potential towards further improvement. Furthermore, our framework shows better results with the larger and more complicated WikiText-2 corpus than with the Penn Treebank. Compared with other state-of-the-art results, we achieve comparable results with network scales hundreds of times smaller and within fewer training epochs.
Insuring Evolving Technology

The study of the interaction between law and technology is more critical today than ever before. Advancements in artificial intelligence, information communications, biological and chemical engineering, and space faring technologies, to name but a few examples, are forcing us to reexamine our traditional understanding of basic concepts in torts and insurance law.Yet, few insurance professionals and scholars will identify themselves as working in the field of law-and-technology. For many of them, technology is just another fact about the world, as Ryan Calo once put it, not one that merits special care.This short paper is an attempt to build a first-of-its-kind bridge between these two scholarly silos. Directed at an insurance audience, the paper attempts to draw attention to a body of law-and-technology scholarship that has so far gone mostly unnoticed by insurance professionals.The paper is built on the premise that insurance lawyers whose business model depends on the mitigation of losses from technological harm are not dramatically dissimilar from their law-and-technology counterparts. Both are fascinated by the same set of questions: if, when, and how, might private and public regulation mitigate losses resulting from technological risk. The paper draws key concepts from the law-and-technology literature to explore the effectiveness and utility of regulation in mitigating risks from emerging, evolving, and disruptive technologies. The paper further identifies different phases in technology's life cycle discussing the challenges that each of these phases introduces on the insurance market.Relying on cyber insurance as its primary case study, the paper concludes by applying these insights in an assessment of a recent state-wide regulation, the New York Cyber Insurance Risk Framework, the first of its kind in the country. The paper demonstrates the promise and pitfalls of this type of regulation.
Performance Analysis of Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization for solving the Travelling Salesman Problem

The Travelling salesman problem also popularly known as the TSP, which is the most classical combinatorial optimization problem. It is the most diligently read and an NP hard problem in the field of optimization. When the less number of cities is present, TSP is solved very easily but as the number of cities increases it gets more and more harder to figure out. This is due to a large amount of computation time is required. So in order to solve such large sized problems which contain millions of cities to traverse, various soft computing techniques can be used. In this paper, we discuss the use of different soft computing techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and etc. to solve TSP.
GOING PAPERLESS CONCEPT IMPLEMENTATION AT SENIOR HIGH SCHOOL IN BEKASI, INDONESIA

An important reason for the rise of the paperless learning environment is that society is moving towards a green direction. At the same time, the advancement of technology and how people’s consumption of scarce resources affects digital generation and the understanding of the environment in the future have attracted more and more attention. The objective of this study is to investigate how capable, and available the students are at implementing paperless classrooms with substitutes such as the digital mode in learning activities. This quantitative descriptive research uses 108 participants as a sample through a questionnaire to collect the data. The student from Computer Network Engineering major is considered as the most suitable and related to one of the variables, namely digital literacy, where students more often use technology as learning media and solutions for reducing paper usage. The data were analysed using SPSS v 22. The results of this study found that there are significant influences from Environmental Awareness, digital literacy, and habits as factors in students’ readiness to apply this concept. The coefficient of determination shows that the Environmental Awareness (X1), Digital Literacy (X2), and Habit (X3) effected the Student’s Readiness (Y) to Implement Paperless Concept of 41.3%. The rest, 58.7% will be explained by other factors that will not be discussed in this study.
An Exploratory Study on the Inter-Korean MICE Industry Exchange and Cooperation Model

Purpose: This study examined the MICE Industry as an exchange and cooperation object to break through the inter-Korean relations in the crisis phase. Design/methodology/approach: This study conducted exploratory research applying grounded theory, a qualitative research method that builds a theoretical model through interviews with experts. Findings: The MICE industry is a viable alternative to inter-Korean exchange and cooperation and would play an essential role in improving inter-Korean relations. Paradoxically, the sanctions against North Korea reveal the MICE industry’s discriminatory characteristics as a new paradigm for exchange and cooperation. Research limitations/implications: This study tried to build a theoretical exchange and cooperation model. Therefore, it is necessary to present specific strategies based on empirical and policy research in the future. Originality and Value: This study sought to approach inter-Korean exchange and cooperation, which had not been attempted before, from the perspective of the MICE industry.
Impact of ERM on Sustainability and Financial Performance of Enterprises in the Gulf Cooperation Council: Case Study of Oman

This study argues that enterprise risk management (ERM) practices have a significant impact on enterprise sustainability and performance. This study uses 150 observations from 104 enterprises in Oman. The results show that the enterprises which measure their business risk and then adopt different strategies to manage their risk can visualize their future position and can have better plans for improving their performance.Numerous studies examined the impact of enterprise risk management practices on financial performance; however, few studies have focused on the sustainability of the enterprises. Therefore, our study will contribute to the literature by examining the impact of enterprise risk management practices on enterprise sustainability. This study was conducted in Oman; which is a member country of the Gulf Cooperation Council (GCC). The GCC countries are different than the other western countries because of their economic conditions, cultural values, and market situations. This research study has provided many implications for the enterprises of Oman.
Understanding the Influence of ICT in Development: A Study of Uganda’s Water Sector

In Sub-Saharan Africa, numerous Information and Communication Technology (ICT) initiatives have been implemented to address information gaps, service delivery and enhance decision-making within multiple sectors. Drawing on concomitant perceptions that ICT for Development (ICT4D) and Information Systems in Developing Countries (ISDC) studies postulate ICTs amplifying development conditions; this paper examines the influence of a development-oriented intervention implemented by Uganda's Ministry of Water and Environment (MWE) to manage information challenges. The artefact called Water Supply Database (WSDB) is now positioned by MWE as the main information repository, and we describe the conceptual process of understanding and theorising its influence. This exploratory study adopts Classic Grounded Theory (CGT) to determine how stakeholders in the water sector perceive developmental influence of the WSDB. We present theoretical findings analysed from qualitative interviews to rationalize WSDB influence on water sector development and the implications of adopting CGT as a suitable methodology for theorisation in ICT4D.
English Grammar Discrimination Training Network Model and Search Filtering

The statistics-based method ignores the semantic constraints in the English grammar area branch training model and is unable to identify the orientation information effectively. This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training model and situation model and the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. At present, there are few research studies on bias filtering, and the method of thematic filtering is generally used, which has poor effect. This paper makes full use of the domain knowledge and adopts the semantic pattern analysis technology to establish a wealth of semantic analysis resources, including various dictionaries, rules, and weight representation, so as to effectively filter the inclined English grammar area branch training model. The introduction of semantic data sources solves the problem of data sparsity and cold start in the traditional collaborative filtering system. In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. This paper proposes a search and filter scheme based on the orientation of the training model in English grammar area, elaborates on the details, constructs a whole set of function structure from representation to weight, and gives the experimental results, which prove that the system has a good filtering effect and is fast. Compared with the traditional statistical methods, the results are satisfactory.