Reviewerly/exHarmony huggingface dataset:
Roads in you

Roads in You is an interactive biometric-data artwork that allows participants to scan their veins and find the roads that match their vein lines. The vein data as one of the fascinating forms of biometric data contain uniquely complicated lines that resemble the roads and paths surrounding us. The roads resemble how our vein lines are interconnected and how the blood circulates in our bodies in various directions, at various speeds, and in different conditions. This artwork explores the line segmentation and the structure of veins and compares them to roads in the real world. The participants can also export the data and keep them as a personalized souvenir (3d printed sculptures) as part of the artistic experience. Through this project, users can explore the correlation between individuals and environments using the hidden patterns under the skin and vein recognition techniques and image processing. This project also has the potential to lead the way in the interpretation of complicated datasets while providing aesthetically beautiful and mesmerizing visualizations.
Two-Step Classification using Recasted Data for Low Resource Settings.

An NLP model’s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.
Context in Informational Bias Detection

Informational bias is bias conveyed through sentences or clauses that provide tangential, speculative or background information that can sway readers' opinions towards entities.By nature, informational bias is context-dependent, but previous work on informational bias detection has not explored the role of context beyond the sentence.In this paper, we explore four kinds of context for informational bias in English news articles: neighboring sentences, the full article, articles on the same event from other news publishers, and articles from the same domain (but potentially different events).We find that integrating event context improves classification performance over a very strong baseline.In addition, we perform the first error analysis of models on this task.We find that the best-performing context-inclusive model outperforms the baseline on longer sentences, and sentences from politically centrist articles.
MENINGKATKAN LAYANAN PRIMA DI ERA DISRUPTIF : TANTANGAN BAGI PUSTAKAWAN

Introduction. Service excellence is the main thing librarians should provide , and it is no exception with current technological developments. T his rapid technological development makes information becomes easy and fast to access. Therefore, librarians as knowledge organizer and information dissemination agents need to continually update their knowledge and competencies in order to provide quality services for library users. Purpose.  T his study aims to find out how best service should be provided by librarians to users in the midst of the current disruptive era of technology. Method.  Qualitative method by studying literature was used in this study . Results and discussion. The study shows that technology-based library services such as promotion through social media, VR-AR-based service, and android-based library applications are excellent forms of services librarians can provide to users. This should be balanced by constantly increasing librarian’s  competencies, for example, their proficiency in the use of information technology, knowledge management, management and leadership, and by enhancing their excellent communication skills and attitudes in giving services .
Multi-Request Scheduling and Collaborative Service Processing for DASH-Video Optimization in Cloud-Edge Network

With the explosive growth of video traffic, the traditional Cloud-based video streaming network has been difficult to meet the video services' requirements of low latency and high bandwidth. Therefore, Edge Computing with low latency and Dynamic Adaptive Streaming over HTTP (DASH) with dynamic adaptive changing video bitrate versions are utilized to optimize video streaming service. However, the edge server deployed next to the base station is not an exclusive tool for video streaming optimization, and its processing ability is limited. Therefore, in some request intensive scenarios such as shopping malls, schools and residential areas, when the server receives a large amount of video segment requests, it cannot process all video segment requests at the same time. The existing DASH-video service optimization methods with Edge Computing did not consider how to schedule requests which exceed the available processing ability of the edge server. Moreover, the collaboration of edge servers has not been made full use in the existing methods. In view of these challenges, we propose a multi-request scheduling and collaborative service processing method (MRSCP) for DASH-video in Cloud-Edge network. The cache placement and update, multi-request scheduling, and collaborative caching and transcoding are considered in our method. Extensive experiments are conducted to validate the efficiency and effectiveness of our method.
Predicting and Using Target Length in Neural Machine Translation.

Attention-based encoder-decoder models have achieved great success in neural machine translation tasks. However, the lengths of the target sequences are not explicitly predicted in these models. This work proposes length prediction as an auxiliary task and set up a sub-network to obtain the length information from the encoder. Experimental results show that the length prediction sub-network brings improvements over the strong baseline system and that the predicted length can be used as an alternative to length normalization during decoding.
Noise-tolerant quantum speedups in quantum annealing without fine tuning

Abstract Quantum annealing is a powerful alternative model of quantum computing, which can succeed in the presence of environmental noise even without error correction. However, despite great effort, no conclusive demonstration of a quantum speedup (relative to state of the art classical algorithms) has been shown for these systems, and rigorous theoretical proofs of a quantum advantage (such as the adiabatic formulation of Grover’s search problem) generally rely on exponential precision in at least some aspects of the system, an unphysical resource guaranteed to be scrambled by experimental uncertainties and random noise. In this work, we propose a new variant of quantum annealing, called RFQA, which can maintain a scalable quantum speedup in the face of noise and modest control precision. Specifically, we consider a modification of flux qubit-based quantum annealing which includes low-frequency oscillations in the directions of the transverse field terms as the system evolves. We show that this method produces a quantum speedup for finding ground states in the Grover problem and quantum random energy model, and thus should be widely applicable to other hard optimization problems which can be formulated as quantum spin glasses. Further, we explore three realistic noise channels and show that the speedup from RFQA is resilient to 1/ f -like local potential fluctuations and local heating from interaction with a sufficiently low temperature bath. Another noise channel, bath-assisted quantum cooling transitions, actually accelerates the algorithm and may outweigh the negative effects of the others. We also detail how RFQA may be implemented experimentally with current technology.
Sas enterprise guide 6.1:&#x0D; independent groups comparison for nominal data

Objective:to develop algorithm of independent groups comparison for nominal data of prospective non-randomized clinical trial AMIRI CABG (ClinicalTrials.gov Identifier: NCT03050489) using SAS Enterprise Guide 6.1.&#x0D; Materials and methods.Data collection was performed according to prospective non-randomized clinical trial AMIRI CABG in Pavlov First St. Petersburg State Medical University, Saint Petersburg, Russia between 2016-2019years with 336 patients. Patients were allocated into three groups of treatment. There is database which include following information: gender, myocardial infarction, stroke and postoperative bleeding. Comparison for nominal data (gender and incidence of myocardial infarction, stroke and bleeding) were calculated with SAS Enterprise Guide6.1 software with Chi-squared test and exact Fisher test.&#x0D; Results.There was developed algorithm of two independent groups comparison for nominal data.&#x0D; Conclusion.Presented algorithm of data analysis allows to compare independent groups for nominal data.
An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task.

This paper describes our submission to the WMT20 news translation shared task in English to Japanese direction. Our main approach is based on transferring knowledge of domain and linguistic characteristics by pre-training the encoder-decoder model with large amount of in-domain monolingual data through unsupervised and supervised prediction task. We then fine-tune the model with parallel data and in-domain synthetic data, generated with iterative back-translation. For additional gain, we generate final results with an ensemble model and re-rank them with averaged models and language models. Through these methods, we achieve +5.42 BLEU score compare to the baseline model.
Local path planning algorithm for autonomous vehicle based on multi-objective trajectory optimization in state lattice

The paper presents an algorithm for constructing a local path for a vehicle with nonholonomic kinematics of an automobile type. A local path is a sequence of transitions in the graph of possible maneuvers that minimizes a given cost function. The graph is constructed by duplicating along the global path pre-calculated in a curvilinear coordinate system set of kinematically feasible motion primitives. The use of pre-computed motion primitives significantly reduces the time of graph construction. The weight of each maneuver – the edge of the transition graph – is calculated as a weighted sum of costs based on several criteria. The specified cost function minimizes maneuvering and maintains a safe distance to static obstacles. The information about obstacles is extracted from an occupancy grid map. Dijkstra‘s algorithm is used to search a path in the weighted directed graph. The algorithm was tested on a dataset containing real road scenes. Each scene represents a given global path and a static environment model where a safe local path must be found. Local path search is performed in real-time. Experiments have shown that safe local paths have been found in all scenes where it was physically possible. At the same time, the obtained local paths were on average only on 1:3% longer than the given global paths which demonstrate the high applicability of the proposed algorithm.
ISLab System for SMM4H Shared Task 2020

In this paper, we described our systems for the first and second subtasks of Social Media Mining for Health Applications (SMM4H) shared task in 2020. The two subtasks are automatic classi-fication of medication mentions and adverse effect in tweets. Our systems for both subtasks are based on Robustly optimized BERT approach (RoBERTa) and our previous work at SMM4H’19. The best F1-scores achieved by our systems for subtask 1 and 2 were 0.7974 and 0.64 respec-tively, which outperformed the average F1-scores among all teams’ best runs by at least 0.13.
K-SNACS: Annotating Korean Adposition Semantics

While many languages use adpositions to encode semantic relationships between content words in a sentence (e.g., agentivity or temporality), the details of how adpositions work vary widely across languages with respect to both form and meaning. In this paper, we empirically adapt the SNACS framework (Schneider et al., 2018) to Korean, a language that is typologically distant from English—the language SNACS was based on. We apply the SNACS framework to annotate the highly popular novellaThe Little Prince with semantic supersense labels over allKorean postpositions. Thus, we introduce the first broad-coverage corpus annotated with Korean postposition semantics and provide a detailed analysis of the corpus with an apples-to-apples comparison between Korean and English annotations
Recalibrating Cost-per-Use: Implications of COUNTER Release 5 and Unsub

Calculating the cost-per-use of serials is a common practice in academic libraries, but the accuracy of these calculations requires accurate, comparable usage counts, and shortcomings of previous standards have made obtaining such counts difficult. Librarians may also want to exclude some uses, for example of open access articles, from their cost-per-use calculations, but these uses have often been difficult to identify. This column discusses two recent developments, the adoption of COUNTER Release 5 and the release of an online tool called Unsub, that will lead to improvements in usage counting and a significant readjustment of cost-per-use calculations for many libraries.
POSTECH-ETRI’s Submission to the WMT2020 APE Shared Task: Automatic Post-Editing with Cross-lingual Language Model

This paper describes POSTECH-ETRI’s submission to WMT2020 for the shared task on automatic post-editing (APE) for 2 language pairs: English-German (En-De) and English-Chinese (En-Zh). We propose APE systems based on a cross-lingual language model, which jointly adopts translation language modeling (TLM) and masked language modeling (MLM) training objectives in the pre-training stage; the APE models then utilize jointly learned language representations between the source language and the target language. In addition, we created 19 million new sythetic triplets as additional training data for our final ensemble model. According to experimental results on the WMT2020 APE development data set, our models showed an improvement over the baseline by TER of -3.58 and a BLEU score of +5.3 for the En-De subtask; and TER of -5.29 and a BLEU score of +7.32 for the En-Zh subtask.
Survey on Security Parameters of Operating System

OS2200 is a complete, integrated and high security operating system of clear path. The os2200 which resides in the core environment and provides security, high volume transaction, data integrity. OS2200 which also provide plugins to be embedded in IDE such as eclipse also provide compatible environment to run the applications. The operating system consist of different security levels to provide the high security system for critical applications.
Dynamic parameter choices in evolutionary computation

tutorial Share on Dynamic parameter choices in evolutionary computation Author: Carola Doerr CNRS and Sorbonne University, Paris, France CNRS and Sorbonne University, Paris, FranceView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 890–922https://doi.org/10.1145/3319619.3323372Published:13 July 2019Publication History 1citation117DownloadsMetricsTotal Citations1Total Downloads117Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Analysis of Indonesian Motorcycle Gang with Social Network Approach

Analysis of motorcycle gang networks in Indonesia was conducted to determine the dynamics of the motor gang network.This analysis is needed by the government in making appropriate and effective policies in overcoming social problems caused by the existence of this group.The purpose of this study is to detect and determine the community structure of motorcycle gang networks in Indonesia through the use of big data available on the internet, especially social media.This research also utilizes several approaches such as social and behavioral sciences, as well as the computer technology in understanding and finding solutions to problems that arise in society.This study uses a social network analysis method as an instrument that will reveal the social structure of motorcycle gangs with a network centrality approach and community detection.This research succeeded in finding the network structure pattern and network insight of motorcycle gangs by finding the most influential actors.The study also found 25 motorcycle gang groups with high-value network interactions and these groups had more than 2000 active members on social media.In the biker gang social network analysis, the most influential actor has 531 degrees with a weighted degree of 1557.
Integrating Deep Learning and Non-monotonic Logical Reasoning for Explainable Visual Question Answering

Deep learning algorithms represent the state of the art for many problems in robotics and AI. However, they require a large labeled dataset, are computationally expensive, and the learned models are difficult to understand. Our architecture draws inspiration from research in cognitive systems to address these limitations. In the context of answering explanatory questions about scenes and an underlying classification task, our architecture uses non-monotonic logical reasoning with incomplete commonsense domain knowledge, and the features extracted from input images, to answer the input queries. Features from images not processed by such reasoning are mapped to the desired answers using a learned deep network model. In addition, previously unknown state constraints of the domain are learned incrementally and used for subsequent reasoning. Experimental results show that in comparison with an “end to end” deep architecture, our architecture significantly improves accuracy and efficiency of decision making.
Improving the Usage of Subword-Based Units for Turkish Speech Recognition

Subword units are often utilized to achieve better performance in speech recognition because of the high number of observed words in agglutinative languages. In this study, the proper use of subword units is explored in recognition by a reconsideration of details such as silence modeling and positiondependent phones. A modified lexicon by finite-state transducers is implemented to represent the subword units correctly. Also, we experiment with different types of word boundary markers and achieve the best performance by adding a marker both to the left and right side of a subword unit. In our experiments on a Turkish broadcast news dataset, the subword models do outperform wordbased models and naive subword implementations. Results show that using proper subword units leads to a relative word error rate (WER) reductions, which is 2.4%, compared with the word level automatic speech recognition (ASR) system for Turkish.
Social-aware Web API Recommendation in IoT

The core idea of IoT is the connectivity of real-world devices to the Internet, which allows them to expose their functionalities in APIs ways, communicate to other entities, and flow their data over internet. With the massive growth of connected IoT devices, the number of APIs have also increased. Thus, led up to overload information problem, which is making APIs selection more and more difficult for devices owners and users. Therefore, this paper propose web APIs recommendation framework in IoT environment based on social relationships. The main purpose is providing a novel Recommendation method, which enable to discover APIs and provide relevant suggestion for users. The proposed hybrid algorithm is combined content-based filtering and collaborative filtering techniques to improve the accuracy of rating prediction. Finally, experiments are conducted to evaluate the performance of recommendation.
Overview of NTCIR-15 MART

MART (Micro-activity Retrieval Task) was a NTCIR-15 collaborative benchmarking pilot task. The NTCIR-15 MART pilot aimed to motivate the development of irst generation techniques for high-precision micro-activity detection and retrieval, to support the identiication and retrieval of activities that occur over short time-scales such as minutes, rather than the long-duration event segmentation tasks of the past work. Participating researchers developed and benchmarked approaches to retrieve micro-activities from rich time-aligned multi-modal sensor data. Groups were ranked in decreasing order of micro-activity retrieval accuracy using mAP (mean Average Precision). The dataset used for the task consisted of a detailed lifelog of activities gathered using a controlled protocolof real-world activities (e.g. using a computer, eating, daydreaming, etc). The data included a lifelog camera data stream, biosignal activity (EOG, HR), and computer interactions (mouse movements, screenshots, etc). This task presented a novel set of challenging micro-activity based topics.
FJWU participation for the WMT20 Biomedical Translation Task.

This paper reports system descriptions for FJWU-NRPU team for participation in the WMT20 Biomedical shared translation task. We focused our submission on exploring the effects of adding in-domain corpora extracted from various out-of-domain sources. Systems were built for French to English using in-domain corpora through fine tuning and selective data training. We further explored BERT based models specifically with focus on effect of domain adaptive subword units.
IceSum: An Icelandic Text Summarization Corpus

Automatic Text Summarization (ATS) is the task of generating concise and fluent summaries from one or more documents.In this paper, we present IceSum, the first Icelandic corpus annotated with human-generated summaries.IceSum consists of 1,000 online news articles and their extractive summaries.We train and evaluate several neural networkbased models on this dataset, comparing them against a selection of baseline methods.The best model obtains a ROUGE-2 recall score of 71.06, outperforming all baseline methods.Furthermore, we evaluate how the amount of training data affects the quality of the generated summaries.Our results show that while the corpus is sufficiently large to train a wellperforming model, there could still be significant gains from increasing the size of the training set.We release the corpus and the models with an open license.