Reviewerly/exHarmony huggingface dataset:
Meta-Heuristic Optimization Methods for Quaternion-Valued Neural Networks

In recent years, real-valued neural networks have demonstrated promising, and often striking, results across a broad range of domains. This has driven a surge of applications utilizing high-dimensional datasets. While many techniques exist to alleviate issues of high-dimensionality, they all induce a cost in terms of network size or computational runtime. This work examines the use of quaternions, a form of hypercomplex numbers, in neural networks. The constructed networks demonstrate the ability of quaternions to encode high-dimensional data in an efficient neural network structure, showing that hypercomplex neural networks reduce the number of total trainable parameters compared to their real-valued equivalents. Finally, this work introduces a novel training algorithm using a meta-heuristic approach that bypasses the need for analytic quaternion loss or activation functions. This algorithm allows for a broader range of activation functions over current quaternion networks and presents a proof-of-concept for future work.
AMF: Aggregated Mondrian Forests for Online Learning

Abstract Random forest (RF) is one of the algorithms of choice in many supervised learning applications, be it classification or regression. The appeal of such tree-ensemble methods comes from a combination of several characteristics: a remarkable accuracy in a variety of tasks, a small number of parameters to tune, robustness with respect to features scaling, a reasonable computational cost for training and prediction, and their suitability in high-dimensional settings. The most commonly used RF variants, however, are ‘offline’ algorithms, which require the availability of the whole dataset at once. In this paper, we introduce AMF, an online RF algorithm based on Mondrian Forests. Using a variant of the context tree weighting algorithm, we show that it is possible to efficiently perform an exact aggregation over all prunings of the trees; in particular, this enables to obtain a truly online parameter-free algorithm which is competitive with the optimal pruning of the Mondrian tree, and thus adaptive to the unknown regularity of the regression function. Numerical experiments show that AMF is competitive with respect to several strong baselines on a large number of datasets for multi-class classification.
STUDI PERBANDINGAN ANIMASI 360 DERAJAT BERTEMA SEJARAH

To identify general characteristics of historical-themed 360o animation, visual characteristics, and ways of delivering material in historical learning media, this study attempts to compare three historical-themed 360o animations, namely the game trailler Assassin's Creed Syndicate Jack the Ripper, Dinosaurs World 360 VR, and Dunkirk 'Save Every Breath'. This research method is by determine the benchmark focus, planning and research, data collection, implementation, recommendations, and analysis. In general, historical 360o animation uses 3D animation techniques. The flow used tends to be linear with narrative storytelling. The point of view used is first person. The camera movement used is a follow subject. Key Words: Animation 360 Degree, Learning Media, History
BlockChain (BC) Upending Customer Experience: Promoting a New Customer Relationship Management (CRM) Structure Using Blockchain Technology (BCT)

This study aims to explore the role of BC and its impact on CRM by suggesting an extended CRM on the basis of BC capabilities thru developing an analytic hierarchy planning-based framework to establish criteria weights developing a new self-assessment model to determine the most critical factors impacting the BC investment in CRM to enhance customer experience and to enable parties to work together in a trusted technology environment.&#x0D; An analytical hierarchical process (AHP) approach was utilized to prioritize and weigh the factors affecting the BC investment in modern CRM in the service industry based on the extant literature and its interpretation. This approach resulted in a ranking of 19 sub-factors based on experienced customer service professionals and technologists&amp;rsquo; evaluations. Findings revealed a significant insight into proposing a new generation of CRM based on BCT, focusing on using the powerful BC platform considering all factors influencing the BC investment in modern CRM from a business perspective. Understanding the new combination of BC and CRM can solve the challenges and dilemmas linked to the untrusted environment of handling CRM data in the information systems field. This study provides valuable information and critical analysis of BC regarding CRM integration. Directions for future research are also included.
Semantic Segmentation by Multi-Scale Feature Extraction Based on Grouped Dilated Convolution Module

Existing studies have shown that effective extraction of multi-scale information is a crucial factor directly related to the increase in performance of semantic segmentation. Accordingly, various methods for extracting multi-scale information have been developed. However, these methods face problems in that they require additional calculations and vast computing resources. To address these problems, this study proposes a grouped dilated convolution module that combines existing grouped convolutions and atrous spatial pyramid pooling techniques. The proposed method can learn multi-scale features more simply and effectively than existing methods. Because each convolution group has different dilations in the proposed model, they have receptive fields of different sizes and can learn features corresponding to these receptive fields. As a result, multi-scale context can be easily extracted. Moreover, optimal hyper-parameters are obtained from an in-depth analysis, and excellent segmentation performance is derived. To evaluate the proposed method, open databases of the Cambridge Driving Labeled Video Database (CamVid) and the Stanford Background Dataset (SBD) are utilized. The experimental results indicate that the proposed method shows a mean intersection over union of 73.15% based on the CamVid dataset and 72.81% based on the SBD, thereby exhibiting excellent performance compared to other state-of-the-art methods.
Agent-Oriented Goal Models in Developing Information Systems Supporting Physical Activity Among Adolescents: Literature Review and Expert Interviews

Background Information and communication technologies (ICTs) are becoming increasingly popular in supporting the fight against low physical activity (PA) levels among adolescents. However, several ICT solutions lack evidence-based content. Therefore, there is a need to identify important features that have the potential to efficiently and consistently support the PA of adolescents using ICT solutions. Objective This study aims to create evidence-based models of requirements for ICT solutions supporting PA by combining scientific evidence from literature and health experts. In addition, we test the suitability of agent-oriented goal models in this type of modeling process. Methods A literature search of PubMed, Web of Science, and Scopus databases was conducted to identify evidence-based functional, quality, and emotional goals that have previously been proven to be relevant in supporting PAs among youth using ICT solutions. The identified goals were presented in the form of goal models. These models were used to collaborate with health experts to receive their input on the topic and suggestions for improvement. The initial goal models were improved based on the feedback from the experts. Results The results indicated that agent-oriented goal modeling is a suitable method for merging information from the literature and experts. One strength of agent-oriented goal models is that they present emotional requirements together with quality and functional requirements. Another strength is the possibility of presenting results from a literature review in a systematic manner and using them thereafter in the communication process with stakeholders. Agent-oriented goal models that were created were easy to understand for health experts without previous experience in requirements engineering, which facilitates and supports collaboration with nontechnical stakeholders. Conclusions The proposed agent-oriented goal models effectively merged information from scientific literature and experts in the field and presented early functional, quality, and emotional requirements in a holistic and coherent manner. We believe that the created models have high potential to help requirements engineers and developers to provide more efficient ICT solutions that support PA among adolescents in the future.
Assessing Code Clone Harmfulness: Indicators, Factors, and Counter Measures

Code clones are identical or similar code in software projects. On one hand, developers clone code to achieve higher productivity and thus clones inherently exist; on the other hand, code clones demand extra effort to maintain the consistency between clone instances and may introduce bugs, and thus are often considered harmful for software maintenance and quality. We believe that not all code clones have the same level of harmfulness. A systematic way of assessing the harmfulness level of cloned code would facilitate informed decisions on how to deal with clones. We propose a model for clone harmfulness level assessment with four quantitative indicators that can be extracted from the evolution history of the clones. Specifically, we gather information, such as code clone changes and bug- fixes related to clone divergence and re-synchronization, to find objective evidence that a clone harms the software quality or brings potential risks even if no bugs are found. The assessment model consists of four harmfulness levels of clones determined by the four indicators. We also derive three harmfulness factors from the intrinsic properties of clones that potentially affect the harmfulness of clones. We conduct a large-scale empirical study with five open-source and three industry systems and find that 61.0-84.7% of the clones are not harmful in terms of consistent maintenance overhead. We find evidence in the evolution history that several factors, such as spread of clone instances, number of clone instances, and number of developers, have non-trivial correlation with clone harmfulness levels. We also propose six counter measures for clone harmfulness mitigation based on the observation of the harmfulness factors, and have collected useful feedback from industrial software architects and senior developers through an interview meeting.
Human Motion Prediction Using Manifold-Aware Wasserstein GAN

Human motion prediction aims to forecast future human poses given a prior pose sequence. The discontinuity of the predicted motion and the performance deterioration in long-term horizons are still the main challenges encountered in current literature. In this work, we tackle these issues by using a compact manifold-valued representation of human motion. Specifically, we model the temporal evolution of the 3D human poses as trajectory, what allows us to map human motions to single points on a sphere manifold. To learn these non-Euclidean representations, we build a manifold-aware Wasserstein generative adversarial model that captures the temporal and spatial dependencies of human motion through different losses. Extensive experiments show that our approach outperforms the state-of-the-art on CMU MoCap and Human 3.6M datasets. Our qualitative results show the smoothness of the predicted motions.
Remote hand: Hand-centered peripersonal space transfers to a disconnected hand avatar

The space surrounding our body is called peripersonal space (PPS). It has been reported that visuo-tactile facilitation occurs more strongly within PPS than outside PPS. Furthermore, previous research has revealed several methods by which PPS can be extended. The present study provides the first behavioral evidence of the transfer of PPS in a virtual environment by a novel technique. PPS representation was investigated using a remote-controlled hand avatar presented far from the body in a virtual environment. Participants showed strongest visuo-tactile facilitation at the far space around the remote hand and no facilitation at the near space around the real hand, suggesting that PPS transfers from near the body to the space around the hand avatar. The present results extend previous findings of the plasticity of PPS and demonstrate flexibility of PPS representation beyond the physical and anatomical limits of body representation.
Application of Basic Graph Theory in Autonomous Motion of Robots

Discrete mathematics covers the field of graph theory, which solves various problems in graphs using algorithms, such as coloring graphs. Part of graph theory is focused on algorithms that solve the passage through mazes and labyrinths. This paper presents a study conducted as part of a university course focused on graph theory. The course addressed the problem of high student failure in the mazes and labyrinths chapter. Students’ theoretical knowledge and practical skills in solving algorithms in the maze were low. Therefore, the use of educational robots and their involvement in the teaching of subjects in part focused on mazes and labyrinths. This study shows an easy passage through the individual areas of teaching the science, technology, engineering, and mathematics (STEM) concept. In this article, we describe the research survey and focus on the description and examples of teaching in a university course. Part of the work is the introduction of an easy transition from the theoretical solution of algorithms to their practical implementation on a real autonomous robot. The theoretical part of the course introduced the issues of graph theory and basic algorithms for solving the passage through the labyrinth. The contribution of this study is a change in the approach to teaching graph theory and a greater interconnection of individual areas of STEM to achieve better learning outcomes for science students.
An Improved SQL Injection Attack Detection Model Using Machine Learning Techniques

SQL Injection Attack (SQLIA) is a common cyberattack that target web application database. With the ever increasing and varying techniques to exploit web application SQLIA vulnerabilities, there is no a comprehensive method that can solve this kind of attacks. Therefore, these various of attack techniques required to establish many methods against in order to mitigate its threats. However, most of these methods have not yet been evaluated, where it is still just theories and require to implement and measure its performance and set its limitation. Moreover, most of the existing SQL injection countermeasures either used syntax-based detection methods or a list of predefined rules to detect the SQL injection, which is vulnerable in advance and sophisticated type of attacks because attackers create new ways to evade the detection utilizing their pre-knowledge. Although semantic-based features can improve the detection, up to our knowledge, no studies focused on extracting the semantic features from SQL stamens. This paper, investigates a designed model that can improve the efficacy of the SQL injection attack detection using machine learning techniques by extracting the semantic features that can effectively indicate the SQL injection attack. Also, a tenfold approach will be used to evaluate and validate the proposed detection model.
Zero-Shot Image Classification Based on a Learnable Deep Metric

The supervised model based on deep learning has made great achievements in the field of image classification after training with a large number of labeled samples. However, there are many categories without or only with a few labeled training samples in practice, and some categories even have no training samples at all. The proposed zero-shot learning greatly reduces the dependence on labeled training samples for image classification models. Nevertheless, there are limitations in learning the similarity of visual features and semantic features with a predefined fixed metric (e.g., as Euclidean distance), as well as the problem of semantic gap in the mapping process. To address these problems, a new zero-shot image classification method based on an end-to-end learnable deep metric is proposed in this paper. First, the common space embedding is adopted to map the visual features and semantic features into a common space. Second, an end-to-end learnable deep metric, that is, the relation network is utilized to learn the similarity of visual features and semantic features. Finally, the invisible images are classified, according to the similarity score. Extensive experiments are carried out on four datasets and the results indicate the effectiveness of the proposed method.
DRAS-CQSim: A reinforcement learning based framework for HPC cluster scheduling

For decades, system administrators have been striving to design and tune cluster scheduling policies to improve the performance of high performance computing (HPC) systems.However, the increasingly complex HPC systems combined with highly diverse workloads make such manual process challenging, time-consuming, and error-prone.We present a reinforcement learning based HPC scheduling framework named DRAS-CQSim to automatically learn optimal scheduling policy.DRAS-CQSim encapsulates simulation environments, agents, hyperparameter tuning options, and different reinforcement learning algorithms, which allows the system administrators to quickly obtain customized scheduling policies.
TheNorth @ HaSpeeDe 2: BERT-based Language Model Fine-tuning for Italian Hate Speech Detection

This report was written to describe the systems that were submitted by the team "TheNorth" for the HaSpeeDe 2 shared task organised within EVALITA 2020. To address the main task which is hate speech detection, we fine-tuned BERT-based models. We evaluated both multilingual and Italian language models trained with the data provided and additional data. We also studied the contributions of multitask learning considering both hate speech detection and stereotype detection tasks.
A Framework for Multi-Threshold Image Segmentation of Low Contrast Medical Images

Accurate medical images segmentation plays a vital role in contouring during diagnosis and treatment planning. To improve the segmentation accuracy in low contrast images, we propose a method by combining Hill entropy and fuzzy c-partition. Here, using membership function, an image is first transformed into fuzzy domain. Subsequently, the fuzzy Hill entropies are defined for foreground (object) and background. Next, the total fuzzy Hill entropy is maximized to compute the accurate threshold; this process is employed to calculate a proper parameter combination of membership function. This Hill entropy is then optimized to acquire an image threshold by Differential Evolution “DE” optimization algorithm. The key benefit of the presented approach is that it considers the information of background and object as well as interactions between them in threshold selection mechanism. The results and performance evaluations show the better accuracy of our technique over other existing approaches.
Parallel and Distributed Implementation of Sine Cosine Algorithm on Apache Spark Platform

The Sine Cosine Algorithm (SCA) has experienced wide spread use in solving optimization problems in many disciplines mainly due to its simplicity and efficiency. However, like many other meta-heuristics, SCA requires considerable amount of compute time when solving large size optimization problems. Therefore, in order to tackle such challenging problems efficiently, this work proposes Spark-SCA, a scalable and parallel implementation of SCA algorithm on Apache Spark distributed framework. Spark-SCA exploits Spark platform native support for iterative algorithms through in-memory computing to speed-up computations when handling large optimization problems. Both the design and implementation details of Spark-SCA are presented herein. The performance of Spark-SCA was compared to standard SCA on different benchmark functions with up to 1,000-dimension as well as three practical engineering design problems. Simulation experiments conducted on Amazon Web Services (AWS) public cloud demonstrated how Spark-SCA outperforms the standard version in terms of solution quality and run time as well as it competitiveness in exploring solution space of complex optimization problems.
QAConv: Question Answering on Informative Conversations

This paper introduces QAConv, 1 , a new question answering (QA) dataset that uses conversations as a knowledge source.We focus on informative conversations, including business emails, panel discussions, and work channels.Unlike open-domain and task-oriented dialogues, these conversations are usually long, complex, asynchronous, and involve strong domain knowledge.In total, we collect 34,608 QA pairs from 10,259 selected conversations with both human-written and machinegenerated questions.We use a question generator and a dialogue summarizer as auxiliary tools to collect and recommend questions.The dataset has two testing scenarios: chunk mode and full mode, depending on whether the grounded partial conversation is provided or retrieved.Experimental results show that stateof-the-art pretrained QA systems have limited zero-shot performance and tend to predict our questions as unanswerable.Our dataset provides a new training and evaluation testbed to facilitate QA on conversations research.
Dualformer: A Unified Bidirectional Sequence-to-Sequence Learning

This paper presents a new dual domain mapping based on a unified bidirectional sequence-to-sequence (seq2seq) learning. Traditionally, dual learning in domain mapping was constructed with intrinsic connection where the conditional generative models in two directions were mutually leveraged and combined. The additional feedback from the other generation direction was used to regularize sequential learning in original direction of domain mapping. Domain matching between source sequence and target sequence was accordingly improved. However, the reconstructions for knowledge in two domains were ignored. The dual information based on separate models in two training directions was not sufficiently discovered. To cope with this weakness, this study proposes a closed-loop seq2seq learning where domain mapping and domain knowledge are jointly learned. In particular, a new feature-level dual learning is incorporated to build a dualformer where feature integration and feature reconstruction are further performed to bridge dual tasks. Experiments demonstrate the merit of the proposed dualformer for machine translation based on the multi-objective seq2seq learning.