Reviewerly/exHarmony huggingface dataset:
Knowledgeable Dialogue Reading Comprehension on Key Turns

Multi-choice machine reading comprehension (MRC) requires models to choose the correct answer from candidate options given a passage and a question. Our research focuses dialogue-based MRC, where the passages are multi-turn dialogues. It suffers from two challenges, the answer selection decision is made without support of latently helpful commonsense, and the multi-turn context may hide considerable irrelevant information. This work thus makes the first attempt to tackle those two challenges by extracting substantially important turns and utilizing external knowledge to enhance the representation of context. In this paper, the relevance of each turn to the question are calculated to choose key turns. Besides, terms related to the context and the question in a knowledge graph are extracted as external knowledge. The original context, question and external knowledge are encoded with the pre-trained language model, then the language representation and key turns are combined together with a will-designed mechanism to predict the answer. Experimental results on a DREAM dataset show that our proposed model achieves great improvements on baselines.
Tunable Subnetwork Splitting for Model-parallelism of Neural Network Training

Alternating minimization methods have recently been proposed as alternatives to the gradient descent for deep neural network optimization. Alternating minimization methods can typically decompose a deep neural network into layerwise subproblems, which can then be optimized in parallel. Despite the significant parallelism, alternating minimization methods are rarely explored in training deep neural networks because of the severe accuracy degradation. In this paper, we analyze the reason and propose to achieve a compelling trade-off between parallelism and accuracy by a reformulation called Tunable Subnetwork Splitting Method (TSSM), which can tune the decomposition granularity of deep neural networks. Two methods gradient splitting Alternating Direction Method of Multipliers (gsADMM) and gradient splitting Alternating Minimization (gsAM) are proposed to solve the TSSM formulation. Experiments on five benchmark datasets show that our proposed TSSM can achieve significant speedup without observable loss of training accuracy. The code has been released at https://github.com/xianggebenben/TSSM.
Sebuah Pola Cluster Geospatial Eksplorasi Kejahatan Narkoba di DKI Jakarta

This study is focused on building some visualizations of crimes that occur in the Jakarta area in general, and specifically on drug problems. As the largest city in Indonesia, Jakarta faces the highest number of crimes throughout Indonesia. But unfortunately, there is a lack of geospatial visualization about crime in Jakarta. The visualizations are presented as some cluster models. These models show which parts of Jakarta with a high level of crime, the biggest crimes in Jakarta, and the types of crimes that occur in Jakarta. The biggest crime in Jakarta is also explained with some additional information such as the type of crime, age, and distribution. Clustering is divided into three, which are high, medium, and low. The grouping model was built using Tableau with the K-means algorithm. The results of this study can be used for the Provincial Government of DKI Jakarta to make strategic plans to develop actions that can reduce crime rates in Jakarta.
Conceptualization of Hypersituation as Result of IoT in Education

With the emergence of new technologies and their use in different areas, new experiences emerge. In the context of the use of IoT in educational contexts, the potential of hypersituation has been considered by several authors as the greatest potential of these technologies for this field of study. However, despite several references to this fact, this term still lacks further conceptualization and the drawing of guidelines to achieve it. Thus, this paper aims to present an interpretation and definition of the term hypersituation indicating potentials, challenges, and ways to achieve it.
Implementation of an online emergency education programme at a public university in Mexico

&#x0D; &#x0D; &#x0D; &#x0D; The Universidad Autónoma de Nuevo León (UANL) is a large public university situated in Monterrey, Mexico. Most of the programmes, including a pharmacy related programme (Químico Farmacéutico Biólogo/Chemist Pharmacist Biologist), were running at the university until the pandemic. A lockdown was put in place where it was established that schools and non-essential jobs should be carried out from home. Since then, the UANL has started a training effort to migrate all current classes to online emergency educational schemes. Microsoft Teams was designated as the main platform. Students and faculty members were trained in its use and, after one month, classes were successfully restarted on this platform. The chosen platform was used to create virtual classrooms, problem-based learning was encouraged, and videos and discussion panels were used especially in place of pre-COVID-19 planned laboratory classes. The semester ended with good results, but faculty member training continues and the adaptation to a better organised online programme is now running. Options to try to compensate the lack of in person laboratory classes are still being explored.&#x0D; &#x0D; &#x0D; &#x0D;
Artificial intelligence in financial services – benefits and costs

Artificial intelligence and machine learning are changing the face of the financial sector. They modify the existing connections between entities operating on the financial market, open possibilities for completely new operational models and introduce new rules of competition. The potential of these technologies is therefore a great challenge for the economy and the financial system, which is why it is becoming increasingly important to consider the consequences of new technologies for financial stability and protection of the financial consumer. Proper understanding and regulation of data privacy and ethics issues will play a significant role. Moreover, since artificial intelligence and machine learning are increasingly important in the functioning of the financial system, they are also a new source of systemic risk. These new technologies will therefore force re-analysis of the principles and techniques of supervision and regulation, including financial regulation, in order to avoid possible distortions for both the national and global economy.JEL: A12, C63, D18, G20, G28, O33
Traces of History in Tere Liye’s Novels Towards the Development of Teaching Material for Historical Texts Teaching in High School

This study aimed at describing the historical traces in Tere Liye's novels.The research method used is descriptive qualitative in the form of historical traces in the novel by Tere Liye.The data of this research are in the form of discourse in four Tere Liye novels.The primary source of the data is the novel written by Tere Liye that are Rindu, Tentang Kamu, Ross dan Sunset, Anak Cahaya.The secondary sources of the data are theses, articles, and the internet.The data were collected through literature, attentive and note-taking techniques.The data were analyzed using structural genetic analysis of literature through a historical extrinsic point of view.The results of the research are historical traces found in four Tere Liye novels, Indonesian Communist Party (PKI) history in the novel Tentang Kamu and The Children of Light.In these two novels, the history of the Indonesian Communist Party {PKI} is proven by the historical fact that the PKI betrayal did occur in Indonesia in 1948 and 1965.Likewise, in the novel Rose dan Sunset, there are historical traces about the Bali bombings.Historical facts prove that there was a big explosion in 2002 in the Kuta area of Bali.The pilgrimage journey in the Dutch colonial era, contained in the novel Rindu.The takeover of the pilgrimage from kiyai by the Dutch by providing a steamboat that occurred in the 1900s became a historical fact that supported it.The results of this analysis can be developed as teaching materials, especially for historical story text material in high school.
Launch Overheads of Spark Applications on Standalone and Hadoop YARN Clusters

Spark is widely used as a distributed computing framework for in-memory parallel processing of iterative applications. The basic unit of execution in Spark is executor. Spark applications perform well for applications in which repeated iterations are done on data stored in resilient distributed datasets (RDD). The RDDs reside on executors hosted on exclusively allocated JVMs. Application jobs are split and distributed on executors running in parallel on the nodes of a cluster. High performance for Spark applications is achieved by increasing parallelism by scaling up the number of executors. Spark can be configured to run on native standalone clusters or Hadoop YARN clusters. The run time of Spark applications can be divided into launch time and execution time. Launching Spark applications incurs overheads depending on the clustering framework. The execution time depends on the hardware capabilities of the servers of the node and the clustering framework. The launch overhead is proportional to the number of executors. This paper presents the results of the study done to determine the application launch overheads of Spark applications on two commonly used clustering platforms used for deploying Spark applications, namely standalone and Hadoop YARN clusters. To minimize the dependency of I/O overheads, a CPU intensive job is used for evaluating the launch overheads. On both types of clusters, the overheads are determined experimentally. The results of this study help in deciding the number of executors to minimize the impact of the launch overheads on run time. Models are developed using regression methods that can be used to predict the launch overheads of Spark applications on both types of clusters.
Accelerating sequential Monte Carlo with surrogate likelihoods

Delayed-acceptance is a technique for reducing computational effort for Bayesian models with expensive likelihoods. Using a delayed-acceptance kernel for Markov chain Monte Carlo can reduce the number of expensive likelihoods evaluations required to approximate a posterior expectation. Delayed-acceptance uses a surrogate, or approximate, likelihood to avoid evaluation of the expensive likelihood when possible. Within the sequential Monte Carlo framework, we utilise the history of the sampler to adaptively tune the surrogate likelihood to yield better approximations of the expensive likelihood, and use a surrogate first annealing schedule to further increase computational efficiency. Moreover, we propose a framework for optimising computation time whilst avoiding particle degeneracy, which encapsulates existing strategies in the literature. Overall, we develop a novel algorithm for computationally efficient SMC with expensive likelihood functions. The method is applied to static Bayesian models, which we demonstrate on toy and real examples, code for which is available at this https URL.
Il linguaggio del trolling. Ingredienti semiotici, cause socioculturali ed effetti pragmatici

The article singles out and describes the main rhetorical ingredients of trolling through contrasting it with comparable discursive practices: provocation, joke, defensive anonymity, critical public discourse, controversy, and lie. The following elements are found to play a major role in the discursive construction of trolling: topic-insensitive provocation; time-boundless jest; sadistic hierarchy of sender and receiver; anonymity of both the troll and her or his audience; choral character of the ‘actant observer’ of trolling; construction of artificial contradictory semantics; disruption of argumentative logics; irrelevance of the relation between beliefs and expressions. Trolling profoundly disrupts the conversational ethics of the human civilization because it severs expression from content, signifier from signified, communication from intention.
Coarse-grain cluster analysis of tensors with application to climate biome identification

A tensor provides a concise way to codify the interdependence of complex data. Treating a tensor as a d-way array, each entry records the interaction between the different indices. Clustering provides a way to parse the complexity of the data into more readily understandable information. Clustering methods are heavily dependent on the algorithm of choice, as well as the chosen hyperparameters of the algorithm. However, their sensitivity to data scales is largely unknown.
Beyond Differential Privacy: Synthetic Micro-Data Generation with Deep Generative Neural Networks

Recent advances in generative modeling, based on large scale deep neural networks, provide a novel approach for sharing individual-level datasets (micro-data) without privacy concerns. Unlike differential privacy, which enforces a specific query mechanism on data to ensure privacy, generative models can accurately learn the statistical patterns of such micro-data and then be used to generate "synthetic data" that accurately reflects these statistical patterns, yet contain none of the original data itself, and thus can be safely shared for analysis and modeling without compromising privacy. The successful application of these techniques to various industries including healthcare, finance, and autonomous vehicles is promising and results in continued investment in research and development of generative models in both academia and industry.
Virtual Reality as a Remote Workspace Platform: Opportunities and Challenges

ABSTRACT Millions of people all around the world are participating in an unprecedented experiment of working from home due the Covid-19 pandemic. There is no doubt that Working from Home (WFH) brings many benefits to the workers, including flexibility of hours and less commute. The problems associated with this working style include distractions, and lack of appropriate space and work-life separation. Through surveying the literature in this paper, we show that virtual reality (VR) has the capability to amplify benefits associated with WFH and assist workers in tackling its challenges. We then discuss some of the challenges that need to be considered and tackled to effectively work in virtual environments at home. Keywords working from home, virtual reality, head-mounted displays, notifications, interruption ABOUT THE AUTHOR/S Nadia Fereydooni Georgia Institute of Technology Atlanta, USA nadia.fereydooni@gatech.edu Nadia Fereydooni is a Human-Centered Computing doctoral student at Georgia Institute of Technology. She earned her Bachelors degree in Computer Engineering from University of New Hampshire. Her research focuses on the use of Virtual Reality in automated vehicles to provide rich, immersive experiences while maintaining passengers’ trust and safety.. https://nadiafereydooni.weebly.com/ Bruce N. Walker Georgia Institute of Technology Atlanta, USA bruce.walker@psych.gatech.edu Bruce N. Walker is a Professor at Georgia Tech, in the Schools of Psychology and Interactive Computing. His Sonification Lab studies the human-computer interaction (HCI) issues in non-traditional interfaces, ranging from mobile devices, to cockpits and vehicle displays, to multimodal interfaces in education and in complex task environments. Particular research interests include sonification and auditory displays. New Future of Work 2020, August 3–5, 2020 © 2020 Copyright held by the owner/author(s).
HSMF-Net: Semantic Viewport Prediction for Immersive Telepresence and On-Demand 360-degree Video

The acceptance of immersive telepresence systems is impeded by the latency that is present when mediating the realistic feeling of presence in a remote environment to a local human user. A disagreement between the user's ego-motion and the visual response provokes the emergence of motion sickness. Viewport or head motion (HM) prediction techniques play a key role in compensating the noticeable delay between the user and the remote site. We present a deep learning-based viewport prediction paradigm that fuses past HM trajectories with scene semantics in a late-fusion manner. Real HM profiles are used to evaluate the proposed approach. A mean compensation rate as high as 99.99% is obtained, clearly outperforming the state-of-the-art. An on-demand 360-degree video streaming framework is presented to prove its general validity. The proposed approach increases the perceived video quality while requiring a significantly lower transmission rate.
GERAKAN LITERASI DIGITAL PELAJAR NAHDLATUL ULAMA

The development of the world of information and communication technology we cannot avoid, this creates a wide open information space. However, the negative impacts of information and communication technology should also be wary of us, such as spreading false news, radical understanding and information that is driven by the interests of groups. Therefore, the need to create human resources who have digital literacy understanding. This research uses qualitative approach and is a field research, while data obtained through interviews and pbservations in the field. Nahdlatul ulama students who are members of the PC IPNU and PC IPPNU Bandar Lampung City aware of it so as to carry out digital literacy movements through activities such as pesantren journalism, social media management, design schools, and online studies which is carried out systematically on an annual basis in order to realize human beings who are responsible for utilizing digital media so as to create information that is accurate, trustworthy, soothing, does not cause anxiety, and disunity.
Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data

We focus on the problem of unsupervised cell outlier detection and repair in mixed-type tabular data. Traditional methods are concerned only with detecting which rows in the dataset are outliers. However, identifying which cells are corrupted in a specific row is an important problem in practice, and the very first step towards repairing them. We introduce the Robust Variational Autoencoder (RVAE), a deep generative model that learns the joint distribution of the clean data while identifying the outlier cells, allowing their imputation (repair). RVAE explicitly learns the probability of each cell being an outlier, balancing different likelihood models in the row outlier score, making the method suitable for outlier detection in mixed-type datasets. We show experimentally that not only RVAE performs better than several state-of-the-art methods in cell outlier detection and repair for tabular data, but also that is robust against the initial hyper-parameter selection.
Translation of news reports related to COVID-19 of Japanese Linguistics based on page link mining

In the face of the current epidemic situation, news reports are facing the problem of higher accuracy. The speed and accuracy of public emergency news depends on the accuracy of web page links and tags clustering. An improved web page clustering method based on the combination of topic clustering and structure clustering is proposed in this paper. The algorithm takes the result of web page structure clustering as the weight factor. Combined with the web content clustering by K-means algorithm, the basic content that meets the conditions is selected. Through the improved translator of clustering algorithm, it is translated into Chinese and compared with the target content to analyze the similarity. It realized the translation aim of new crown virus epidemic related news report of Japanese Linguistics based on page link mining.
Improving android-based e-learning skills of Biology teachers in Enrekang Regency, South Sulawesi

The era of the industrial revolution 4.0 changed the way of looking at education. The changes made are not just a way of teaching, but are far more essential that is in the perspective on the concept of education itself. The competence of Biology teachers in Enrekang Regency in designing learning tools and media based on Information and Communication Technology (ICT) is still low. This community partnership program activity aimed to improve the ability of high school biology teachers to use android-based e-book creation software. In the implementation of this training activity, the following results were obtained: (1) Increasing the ability of MGMP teachers for Biology subject in Enrekang Regency in designing Android-based e-book learning media, (2) As the assessment, an overall average score of this program was 4.93 (categorized as good criteria). Based on the results of this community partnership program, it is hoped that the results can be a good input for the Enrekang Regency Education office in particular, education practitioners and the government in general. To sum up, the ability of teachers related to the application of technology and information in learning activities using various technology utilization through sorts of trainings still need hard efforts to increase.
Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling

Geometric deep learning is increasingly important thanks to the popularity of 3D sensors. Inspired by the recent advances in NLP domain, the self-attention transformer is introduced to consume the point clouds. We develop Point Attention Transformers (PATs), using a parameter-efficient Group Shuffle Attention (GSA) to replace the costly Multi-Head Attention. We demonstrate its ability to process size-varying inputs, and prove its permutation equivariance. Besides, prior work uses heuristics dependence on the input data (e.g., Furthest Point Sampling) to hierarchically select subsets of input points. Thereby, we for the first time propose an end-to-end learnable and task-agnostic sampling operation, named Gumbel Subset Sampling (GSS), to select a representative subset of input points. Equipped with Gumbel-Softmax, it produces a "soft" continuous subset in training phase, and a "hard" discrete subset in test phase. By selecting representative subsets in a hierarchical fashion, the networks learn a stronger representation of the input sets with lower computation cost. Experiments on classification and segmentation benchmarks show the effectiveness and efficiency of our methods. Furthermore, we propose a novel application, to process event camera stream as point clouds, and achieve a state-of-the-art performance on DVS128 Gesture Dataset.
ICT AS A KEY INSTRUMENT FOR A BALANCED SYSTEM OF PEDAGOGICAL EDUCATION

Aleksander Spivakovsky, Lyubov Petukhova, Anisimova Olena, Horlova Anastasiia, Vira Kotkova, Anastasiia Volianiuk. ICT as a key instrument for balanced system of pedagogical education. Proceedings of the 16th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume I: Main Conference, ICTERI 2020; Kharkiv; Ukraine; 6 October 2020 – 10 October 2020; Code 122555. Volume 2740, 2020, Pages 307-321.
A cloud computing scheduling and its evolutionary approaches

&lt;span&gt;Despite the increasing use of cloud computing technology because it offers unique features to serve its customers perfectly, exploiting the full potential is very difficult due to the many problems and challenges. Therefore, scheduling resources are one of these challenges. Researchers are still finding it difficult to determine which of the scheduling algorithms are appropriate and effective and that helps increases the performance of the system to accomplish these tasks. This paper provides a broad and detailed examination of resource scheduling algorithms in the environment of a cloud computing environment and highlights the advantages and disadvantages of some algorithms to help researchers in selecting the best algorithms to schedule a particular workload to get a satisfy a quality of service, guarantee good utilization of the cloud resources also minimizing the make-span.&lt;/span&gt;