Reviewerly/exHarmony huggingface dataset:
Protocol for a reproducible experimental survey on biomedical sentence similarity

Measuring semantic similarity between sentences is a significant task in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and biomedical text mining. For this reason, the proposal of sentence similarity methods for the biomedical domain has attracted a lot of attention in recent years. However, most sentence similarity methods and experimental results reported in the biomedical domain cannot be reproduced for multiple reasons as follows: the copying of previous results without confirmation, the lack of source code and data to replicate both methods and experiments, and the lack of a detailed definition of the experimental setup, among others. As a consequence of this reproducibility gap, the state of the problem can be neither elucidated nor new lines of research be soundly set. On the other hand, there are other significant gaps in the literature on biomedical sentence similarity as follows: (1) the evaluation of several unexplored sentence similarity methods which deserve to be studied; (2) the evaluation of an unexplored benchmark on biomedical sentence similarity, called Corpus-Transcriptional-Regulation (CTR); (3) a study on the impact of the pre-processing stage and Named Entity Recognition (NER) tools on the performance of the sentence similarity methods; and finally, (4) the lack of software and data resources for the reproducibility of methods and experiments in this line of research. Identified these open problems, this registered report introduces a detailed experimental setup, together with a categorization of the literature, to develop the largest, updated, and for the first time, reproducible experimental survey on biomedical sentence similarity. Our aforementioned experimental survey will be based on our own software replication and the evaluation of all methods being studied on the same software platform, which will be specially developed for this work, and it will become the first publicly available software library for biomedical sentence similarity. Finally, we will provide a very detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results.
Designing Technologies for Playful Interspecies Communication

This one-day workshop examined how we might use technologies to support design for playful interspecies communication and considered some of the potential implications. We explored aspects of playful technology and reflected on what opportunities computers can provide for facilitating communication between species. The workshop's focal activity was the co-creation of some imaginary systems designed for specific multi-species scenarios. Through our activities, we aim to question and pave the way for designing technology that promotes interspecies communication, drawing input not only from ACI practitioners but also from those of the broader HCI and animal science community, who are often stakeholders in facilitating, expanding, and/or redefining playful technology.
Novel Algorithm for Image Classification Using Cross Deep Learning Technique

Abstract The basic concepts of training and the model structure of deep belief networks (DBNs) in deep analysis are studied to apply image recognition in the area of deep learning. Random propound is provided with the parameter in the fine-tuning stage and the randomly hidden layer eliminated to maintain unchanged weights. The results show that the layered DBN training system reduces training problems and training times significantly. In the small sample, the deep faith network has improved significantly after introducing the down sample and random dropdown and effectively alleviates the over-fitting phenomenon. Design a new Deep Learning Image Recognition and Classification Algorithm. Novel Algorithm for Image Classification Using Cross Deep Learning Technique.
An Efficient Blind Signature Scheme Based on SM2 Signature Algorithm

The Chinese government releases the SM2 digital signature algorithm as one part of the Chinese public key crypto standard, and now it has become an international standard algorithm. To protect the privacy of messages, we propose an efficient blind signature scheme based on the SM2 signature algorithm in this paper. We prove that our scheme can satisfy blindness and EUF-CMA (existential unforgeability under chosen message attacks). We implement our scheme using MIRACL Cryptographic SDK, and propose a variant blind signature scheme. Security analysis and experimental evaluation demonstrate that our proposed scheme is practical for real-world applications.
A Topology-Aware Performance Prediction Model for Distributed Deep Learning on GPU Clusters

Today, multi-GPU training has become a common practice for deep learning workloads. The performance of a training job could be affected significantly by both the GPU connectivity in the system topology and the computation-communication pattern of the job. This highlights the necessity of the awareness of jobs’ performance characteristics for cluster schedulers to improve both job and cluster efficiency.In this paper, we propose an online resource-performance model for deep learning training jobs on GPU clusters. This model can estimate the training speed as a function of any given resource setting (i.e., the number and locality of GPUs) for a specific job. The model is based on systematic modeling of the system topology and the communication patterns of individual jobs with online fitting on a sample set of profiled performance data. Experiments show that our performance model achieves 94% prediction accuracy on average (up to 99.9%). Additionally, a large-scale simulation on a real production trace demonstrates that our model helps a typical scheduling algorithm decrease average job completion time by 3.4x and makespan by 1.7x.
Stochastic Optimal Control via Hilbert Space Embeddings of Distributions

Kernel embeddings of distributions have recently gained significant attention in the machine learning community as a data-driven technique for representing probability distributions. Broadly, these techniques enable efficient computation of expectations by representing integral operators as elements in a reproducing kernel Hilbert space. We apply these techniques to the area of stochastic optimal control theory and present a method to compute approximately optimal policies for stochastic systems with arbitrary disturbances. Our approach reduces the optimization problem to a linear program, which can easily be solved via the Lagrangian dual, without resorting to gradient-based optimization algorithms. We focus on discrete- time dynamic programming, and demonstrate our proposed approach on a linear regulation problem, and on a nonlinear target tracking problem. This approach is broadly applicable to a wide variety of optimal control problems, and provides a means of working with stochastic systems in a data-driven setting.
Tracing Successful Online Teaching During Covid-19 Pandemic

Abstract The world of education dismisses students (studying at home) to reduce the spread of the coronavirus. In such conditions, teachers are challenged to apply all their abilities and expertise so that they can continue to teach students by transferring knowledge, pedagogic abilities, and their belief in successful online learning. This study aims to explore how lecturers can conduct teaching in the midst of the Covid-19 pandemic and what they use as a medium to conduct successful online teaching. In addition, researchers also want to know how lecturers can adjust the online teaching transition. The design of this research is qualitative research using deep interview tests to explore online learning that suits current needs. The research was conducted at the department of Office Administration Education Sebelas Maret University with the head of the study program as the key informant. The data validity test used source triangulation and method triangulation. The sampling technique was purposive snowball sampling, data collection techniques used observation, interviews and documentation, and data analysis techniques used interactive data reduction, data presentation, and conclusion drawing. The results show a good connection communication network of all students and lecturers is a major aspect that must be considered in lectures even though using simultaneously online social media, e.g. You Tube, Google Classroom, Google Meet, and WhatsApp. The Covid-19 pandemic is considered a challenge and an opportunity to conduct research in the field of information and communication technology (ICT) especially on digital classroom management. The outcomes of research that can be directly utilized by the user are the output targets research whose activities are manifested in the form of community service. Community Service Program (KKN) is a tangible form of student involvement in community service.
Normalized Maximal Margin Loss for Open-Set Image Classification

This work aims to address the image classification problem under open-set protocol: classes in test set do not appear in the training set.Intuitively, convolutional Neural Network (CNN) with softmax loss is a straight-forward solution.However, the unknown class (is not predefined in the training set) makes the boundaries of intra-class and inter-class more blurred, which brings more challenges for image classification.Although some softmax variants, such as center loss, CosFace loss etc., focus on learning discriminative features by minimizing the intra-class distance, they do not explicitly maximize inter-class distance, which is more important for open-set problem verified by our experiments.Besides, even though deep metric learning, such as with the contrastive loss and the triplet loss, can learn discriminative features of intra-class and inter-class, it needs a time-consuming image sampling process during training.In this paper, we propose a novel normalized maximal margin (NMM) loss for open-set image classification, which not only explicitly minimizes intra-class distance and maximizes inter-class distance, but also defines their margins.Specially, after analyzing the advantage of angular space that the softmax loss normalized by the feature and weights through geometric interpretation, we make NMM work in angular space.Then, the validity of NMM for discriminative features learning is demonstrated from the view of geometric interpretation as well.After that, we innovatively determine the upper bound of inter-class margin by theoretical analysis.Finally, extensive experiments are conducted on popular datasets: CIFAR-100 (object recognition), ImageNet (image classification), LFW (face recognition) and MSMT17 (person re-identification) to verify the effectiveness of NMM.The experimental results show that NMM achieves very competitive performance.
SCN: Dilated silhouette convolutional network for video action recognition

Human action is a spatio-temporal motion sequence where strong inter-dependencies between the spatial geometry and temporal dynamics of motion exist. However, in existing literature for human action recognition from a video, there is a lack of synergy in investigating spatial geometry and temporal dynamics in a joint representation and embedding space. In this paper, we propose a dilated Silhouette Convolutional Network (SCN) for action recognition from a monocular video. We model the spatial geometric information of the moving human subject using silhouette boundary curves extracted from each frame of the motion video. The silhouette curves are stacked to form a 3D curve volume along the time axis and resampled to a 3D point cloud as a unified spatio-temporal representation of the video action. With the dilated silhouette convolution, the SCN is able to learn co-occurrence features from low-level geometric shape boundaries and their temporal dynamics jointly, and construct a unified convolutional embedding space, where the spatial and temporal properties are integrated effectively. The geometry-based SCN significantly improves the discrimination of learned features from the shape motions. Experiment results on the JHMDB, HMDB, and UCF101 datasets demonstrate the effectiveness and superiority of our proposed representation and deep learning method.
Keeping Our Feet Dry: Impediments to Foreign Interference in Elections in the Netherlands

In recent years, and particularly after the 2016 American presidential election and the 2016 Brexit referendum, considerable concern has emerged concerning foreign interference in elections. This concern has also been felt in the Netherlands. This article examines this concern. To date, no evidence of foreign interference or “fake news” has been found. Although there is no guarantee that there will be no interference in the future, there are aspects of the electoral system that provide impediments to manipulation. New legislation concerning campaign finance is being implemented to provide stronger safeguards. Dutch voters have trust in the electoral system but nevertheless show concerns for the possibilities of foreign interference.
Design and Verification of Privacy Patterns for Business Process Models

Business process models can involve numerous operational activities for collecting, processing, and exchanging personal user data. Such processes may involve activities that are executed over one or more cloud-based platforms. With an increase in the use of enterprise business processes, the right to data privacy has become a key challenge for developers of process models deployed over such cloud platforms. Design of privacy patterns that are compliant with modern data privacy regulations remains a challenge with increasing adoption of such approaches. One such legislation is the General Data Protection Regulation (GDPR) aiming to protect European citizens from privacy violations, especially for data processing activities hosted within Europe or involving data of European citizens. Blockchain and smart contract technologies have been identified as promising approaches for supporting compliance checking and trust in business processes that utilize a distributed set of activities. Blockchains enable verification of GDPR obligations in an automatic way without the need of a trusted third party. This chapter describes how smart contracts can be used to meet GDPR compliance verification using a number of privacy patterns for business process models. We also describe how a transition system-based automated tool can be used to support such verification. We conclude with a discussion of integrating automated compliance checking (especially for personal user data) and the potential impact this has on the overall execution performance of business processes.
Why Two Y-Axes (Y2Y): A Case Study for Visual Correlation with Dual Axes

Many researchers and authors recommend against dual axis charts, yet the feature is common in financial timeseries analysis. We provide a case study where dual axis timeseries charts are are necessary for fine grain correlation analysis otherwise not observable using single axis charts, normalization charts, scatterplots nor statistical correlation analysis. Further, we show dual axis charts occur in 19% of financial timeseries charts indicating common use.
LEARNING CONDITION, LINGUISTIC COMPLEXITY, AND FIRST LANGUAGE TRANSFER IN SEMIARTIFICIAL LANGUAGE LEARNING

Abstract This study explored the interaction between learning conditions, linguistic complexity, and first language (L1) syntactic transfer in semiartificial grammar learning by conceptually replicating and extending Tagarelli et al. (2016). We changed the L1 background, elicited production data during debriefing, and added a binary mixed-effects logistic regression analysis to compare variability at learner and item levels with group-level variation on exposure condition, linguistic complexity, and their interaction. Our results replicated those of the original study regarding the comparative efficacy of explicit instruction; however, we also found a condition × complexity interaction absent in the original study. Debriefing sentence-production data suggest that the changed L1-L2 typological distance may have leveled off the advantage of explicit instruction in the learning of the complex V2-VF structure. Finally, our mixed-effects modeling analysis revealed that variability at learner and item levels accounted for a larger proportion of the variance of the outcomes than all the predictors combined.
Image Colorization Algorithm based on Self-Attention Network

In the task of colorizing gray image, it has been a challenging problem that the reconstructed color images have blur boundaries. In order to solve this problem, a novel method based on self-attention network is proposed in this work. In order to improve the color effect and increase the color contrast. An end-to-end deep learning model is constructed by using efficient convolution combination and self-attention network to extract image features, to learn the spatial dependence of features and the internal correlation between channels. In this way, the reconstructed performance can be improved with a better color effect and contrast. Using the pixel-wise color loss and the generative adversarial networks loss, the network parameters are optimized continuously to guide the generation of high-quality images. Compared with other state-of-the-art algorithms, the proposed method can result in color images with more clear boundary detail and more natural coloring effect than other approaches.
Improving Hidden Markov Model for very low resource languages: An analysis for Assamese parts of speech tagging

Recent advances in research in the field of natural language processing have facilitated many new applications. Natural Language Processing basically involves automating the synthesis and generation of natural languages. However, most of the developments have happened only for a few dominant languages spoken widely like English, Chinese, etc. Some of the developments are also observed for dominant Indian languages like "Hindi", "Bengali" Tamil, etc. However, research into most of the other languages spoken in the world is at a very primitive stage. This paper aims to highlight issues related to one such language for which many resources are not available in a computable format. We report our initial work on Assamese with limited resources. Assamese is the official language of Assam, India and it is also the most spoken Indian language in North East India. We report results of a few experiments carried with a very low amount of training, as resources available for Assamese are not adequate. Part of speech tagging is fundamental to any NLP application. Though some amount of research has been conducted for automatic parts of speech tagging, they have used large amount of data that are not available in public domain. We have modified the viterbi algorithm used for Hiden Markov Model and applied it for automatic parts of speech tagging for Assamese. We have also applied certain language characteristics that can be used for improving accuracy. The key is to use a training dataset as small as possible so that the dependency on training data is limited. We conclude the paper with a brief discussion on the scope of Assamese POS tagging in the future.
Assessment of Machine Learning Models for Classification of Movement Patterns During a Weight-Shifting Exergame

In exercise gaming (exergaming), reward systems are typically based on rules/templates from joint movement patterns. These rules or templates need broad ranges in definitions of correct movement patterns to accommodate varying body shapes and sizes. This can lead to inaccurate rewards and, thus, inefficient exercise, which can be detrimental to progress. If exergames are to be used in serious settings like rehabilitation, accurate rewards for correctly performed movements are crucial. This article aims to investigate the level of accuracy machine learning/deep learning models can achieve in classification of correct repetitions naturally elicited from a weight-shifting exergame. Twelve healthy elderly (10F, age 70.4 SD 11.4) are recruited. Movements are captured using a marker-based 3-D motion-capture system. Random forest (RF), support vector machine, k-nearest neighbors, and multilayer perceptron (MLP) are the employed models, trained and tested on whole body movement patterns and on subsets of joints. MLP and RF reached the highest recall and F1-score, respectively, when using combined data from joint subsets. MLP recall range are 91% to 94%, and RF F1-score range 79% to 80%. MLP and RF also reached the highest recall and F1-score in each joint subset, respectively. Here, MLP ranged from 93% to 97% recall, while RF ranged from 73% to 80% F1-score. Recall results, show that >9 out of 10 repetitions are classified correctly, indicating that MLP/RF can be used to identify correctly performed repetitions of a weight-shifting exercise when using full-body data and when using joint subset data.
Reciprocal guidance approach for suspicious collision point identification in a multi-agent unmanned aerial vehicle scenario

In a multi-agent unmanned aerial vehicle scenario, identification of the suspicious collision point is a crucial part of the collusion avoidance algorithm. This paper presents a novel suspicious collision identification approach, which is based on the assumption that the scenario participants intentionally guide themselves towards mutual interception. This reciprocal guidance approach enables the development of a closed-form solution for the expected miss distance between the agents, thus enabling the implementation of the approach in real-time systems. The algorithm incorporates agents' maneuvering capability restrictions, thus basing its decision on feasible maneuvers. Simulations demonstrated the ability of the reciprocal guidance approach to identify collisions in advance and exemplified its robustness to circular-path engagements and variable-speed agents.
An Optimized Extreme Learning Machine Algorithm for Improving Software Maintainability Prediction

In recent years, software maintainability has become a critical attribute in software engineering to determine software quality. Hence, predicting this maintainability in an accurate and timely manner is a fundamental requirement for effective management during the software maintenance phase. This has led the software developers to pay more attention to those modules that need high maintenance. The current study proposes an Optimized Extreme Learning Machine (OELM) algorithm for Software Maintainability Prediction (SMP) using three open-source datasets, viz.: Abdera, Ivy, & Rave. Since all these datasets are initially imbalanced, a Random Over Sampling technique is also used for re-sampling to avoid any problem encountered due to the imbalanced distribution of datasets. The predictive performance is analyzed based on the three performance evaluation measures, i.e., Accuracy, F1-Score, & Area under the ROC Curve. The results support the effective utilization of the proposed OELM algorithm in SMP. The OELM algorithm's performance is also compared with four different Machine Learning (ML) algorithms, namely AdaBoost, Bagged CART, Flexible Discriminant Analysis, and Penalized Multinomial Regression. This comparison further supports the effectiveness of the OELM algorithm in predicting maintainability. The OELM algorithm performs 7.82%, 8.54%, and 22.98% better for Abdera, Ivy, and Rave datasets, respectively, than the other four ML algorithms taken together concerning Accuracy.