Reviewerly/exHarmony huggingface dataset:
Our Symbiotic Life

We will explore four plausible futures and experiment on a speculative approach to investigate "wicked problems" [14]. Grounded in the field of design fiction, this work combines insights from the climate impact research community, technology meta trends, and plant science, all of which work as a basis for the exploration of relationships between humans, plants, and technology. Based on scenarios from the climate impact research community, we prototyped four plausible worlds and visualized exemplary scenes in the daily life of the plausible citizens. Sketching and low-fidelity prototyping supported the process of gaining knowledge and iterating on storytelling.
Grid quorum‐based spatial coverage in mobile wireless sensor networks using nature‐inspired firefly algorithm

Abstract Optimization and design of mobile wireless sensor networks (MWSNs) must assure adequate spatial coverage of the site. The spatial coverage optimization aims to enrich discoverability of MWSN by specifying mobile sensors geographical locations in order to maximize their coverage. In this paper, an enhanced metaheuristic algorithm called “firefly algorithm with crossover and detection phases” is introduced for optimizing the area coverage percentage of MWSN. The proposed algorithm is tested on many datasets with different criterions and compared with other algorithms including differential evolution, whale optimization algorithm, and flower pollination algorithm. The experimental results are analysed with one‐way ANOVA test. In addition, the proposed algorithm is compared with particle swarm optimization, and the results are analysed with Wilcoxon signed‐rank test. The overall analysis results prove the prosperity and efficient exploration of the proposed algorithm.
GPU road network graph contraction and SSSP query

PHAST is to date one of the fastest algorithms for performing single source shortest path (SSSP) queries on road-network graphs. PHAST operates on graphs produced in part using Geisberger's contraction hierarchy (CH) algorithm. Producing these graphs is time consuming, limiting PHAST's usefulness when graphs are not available in advance. CH iteratively assigns scores to nodes, contracts (removes) the highest-scoring node, and adds shortcut edges to preserve distances. Iteration stops when only one node remains. Scoring and contraction rely on a witness path search (WPS) of nearby nodes. Little work has been reported on parallel and especially GPU CH algorithms. This is perhaps due to issues such as the validity of simultaneous potentially overlapping searches, score staleness, and parallel graph updates.
General representational automata using deep neural networks

Unlabeled data representation constitutes a major challenge in data mining. Different unsupervised learning methods such as clustering and dimensionality reduction form the basis of data representations. The impact of attribute combinations and their interactions on data is less addressed by such models. A representation model supported with machine learning concepts can reveal more information about the nature of underlying data. We herein present a novel unsupervised minimum attribute instance selection (UMAIS) labeling algorithm that selects a categorical attribute as a class label, and a novel attribute-based powerset generation (APSG) algorithm for describing the formation of relevant attribute sets using correlation and powerset. Using these algorithms, we present a diagrammatic representation known as Representational Automata that depict the importance of interactions among correlated and non-correlated attributes present in an unlabeled dataset. We performed experiments using two large-scale datasets from the energy and financial domains and compared our approach with other standard classifiers. Our approach obtains a significantly better classification accuracy of 92.187% and 87.32% for the energy and financial datasets, respectively, compared to 74% and 82% of the linear classifier, respectively.
Embedding Knowledge Graphs for Semantics-aware Recommendations based on DBpedia

In this paper we present a semantics-aware recommendation strategy that uses graph embedding techniques to learn a vector space reresentation of the items to be recommended. Such a representation relies on the tripartite graph which connects users, items and entities gathered from DBpedia, thus it encodes both collaborative and content-based information. These embeddings are then used to feed with positive and negative examples (the items the user liked and those she did not like) a classification model, which is finally exploited to classify new items as interesting or not interesting for the target user. In the experimental evaluation we evaluate the effectiveness of our method on varying of different graph embedding techniques and on several topologies of the graph. Results show that the embeddings learnt by combining collaborative data points with the information gathered from DBpedia led to the best results and also beat several state-of-the-art techniques.
Efficient Data Augmentation Using Graph Imputation Neural Networks

Recently, data augmentation in the semi-supervised regime, where unlabeled data vastly outnumbers labeled data, has received a considerable attention. In this paper, we describe an efficient technique for this task, exploiting a recent framework we proposed for missing data imputation called graph imputation neural network (GINN). The key idea is to leverage both supervised and unsupervised data to build a graph of similarities between points in the dataset. Then, we augment the dataset by severely damaging a few of the nodes (up to 80% of their features), and reconstructing them using a variation of GINN. On several benchmark datasets, we show that our method can obtain significant improvements compared to a fully-supervised model, and we are able to augment the datasets up to a factor of $$10\times $$ . This points to the power of graph-based neural networks to represent structural affinities in the samples for tasks of data reconstruction and augmentation.
Implementation of Platform Controller and Process Modules of the Edge Computing for IoT Platform

Edge computing requires a flexible choice of data-processing and rapidly computation performed at the edge of networks. We proposed an edge computing platform with container-based virtualization technology. In the platform, data-processing instances are modularized and deployed to edge nodes suitable for user requirements with keeping the data-processing flows within wide area network. This paper reports the platform controller and the process modules implemented to realize the secure and flexible edge computing platform.
Sketching Cognition and Creativity

In this workshop, the focus will be on providing a forum for researchers in the various interdisciplinary fields to engage in and explore research outcomes and discussions on sketching interactions, specifically for interfaces that promote creativity and cognition. Attendees of the workshop will actively experience sessions related to the presentation, discussion, and sharing of discoveries and innovations that target or leverage sketching interactions for interfaces in domains that include the learning sciences, education, artificial, and engineering. The workshop will serve as a platform to encourage opportunities of new collaborations and inspire growth for leveraging sketching interactions towards creativity and cognition.
Quantum Singular Value Transformation & Its Algorithmic Applications

In this dissertation we study how efficiently computers can solve various problems, and how large speedups can be achieved compared to classical computers. In particular we develop a generic algorithmic framework that we call quantum singular value transformation, capable of working with exponentially large matrices, that can apply polynomial transformations to the singular values of a block of a unitary. We show how singular value transformation unifies a large number of prominent algorithms, and show several problems where it leads to new algorithms or improves earlier approaches. We develop an improved version of Jordan's algorithm for gradient computation that can speed up the training of variational optimization, and prove an essentially matching lower bound on gradient computation. We also show that a computer can very efficiently compute an approximate subgradient of a convex Lipschitz function. Combining this with some recent classical results we get improvements for black-box convex optimization problems. Then we take a new perspective on SDP-solvers, introducing several new techniques, and improve on all prior algorithms for SDP-solving. Finally we study the variable version of the Lovasz Local Lemma (LLL) and its generalization. We improve on the previous constructive results by designing an algorithm that works efficiently for non-commuting terms as well, assuming that the system is uniformly gapped. For the variable version of the classical LLL we find optimal bounds for the guaranteed-to-be-feasible probabilities on cyclic dependency graphs.
Implementation of Mind Mapping Technique to Improve The Student’s Writing Ability of English Descriptive Texts: A Mixed Method

The students' problem in writing was technique how to organize the structured and systematic ideas, besides how to write correctly and effectively appropriate to grammatical structure. This study aims to determine the learning outcomes of descriptive writing texts using mind mapping techniques and to find out students' perspectives on the use of these techniques in improving their writing skills. This study used mixed methods, action research and quantitative research. The total respondents were 25 students (from The State Islamic Institute of Lhokseumawe). Instruments used to collect data in this study were pre-test and post-test, and questionnaire to analyze the quantitative data and to investigate the process and the learning outcomes that were structured and systematized using Kemmis and Taggart methods. The results showed that there were increasing points of each student in cycle 1 and 2 from the pre-test and post-test scores. The mean score of students in cycle 1 for the pre-test was 48.8 and the test post was 62.8. Whereas, in cycle 2, the students' mean scores reached 77.4 (test post) from that of the pre-test (53.2).
Method for Identification of Waste in the Process of Software Development in Agile Teams Using Lean and Scrum

Waste in software development projects is defined as anything that consumes resources such as time, effort, room, and money without adding value to the customer. Methods and techniques to identify waste indicators, which are specific for each project, are applied to part of total interactions and the development phases; and spend analysts and developers’ time and effort. Therefore, this paper aims to define a method to identify waste within the software development process in Scrum teams, from data based on JIRA tool, which supports software development planning, management and controlling activities. According to the bibliographic review are defined: (i) indicators for types of waste according to Lean software development principles; (ii) JIRA’s attributes, mathematical operators, keywords, functions and reports related to such indicators. In the proposed method are defined requirements that establish a semantic relation between each indicator variables and formulas to the set of JIRA’S attributes, functions and keywords and, based on them, queries in JIRA Query Language are implemented to quantify the indicators. The method validation is performed using graphics that show queries results classified and grouped by project, indicator and type of waste, acquired from a software project base for a company in the Brazilian financial market. Through the quantitative analysis of results, it is possible to suggest a hypothesis for the occurrence of the types of observed wastes.
Exploring youth engagement in agricultural development: the case of farmers’ children in the Philippines as rice crop manager infomediaries

Purpose: This paper explores the intersections between community development and youth development in an initiative that mobilized farmers' children as Rice Crop Manager (RCM) infomediaries (information mediators). RCM is an ICT-enabled nutrient management application.Design/Methodology/Approach: The study engaged 30 farmers' children in high school level from the provinces of Pangasinan, Isabela, Camarines Sur, Iloilo, Bukidnon, and Davao del Norte from November 2016 to October 2017. They interacted with the research team (the authors) and the RCM-SMS platform that sends text messages regarding fertilizer recommendations. A staff member from the Agricultural Training Institute (ATI) also called the students for some follow-up questions. The research team conducted three rounds of interviews with the students concerning the messages and calls that they received and what they did with the information.Findings: This study finds that farmers' children can perform infomediary roles quite effectively. Academically excellent children and those involved in farm work performed best.Practical Implications: The study provides guidance on similar initiatives tapping young people in agricultural development.Theoretical Implications: This paper finds that while Community Youth Development (CYD) Theory provides a powerful lens in understanding community and youth development intersections, some identified outcomes may overlap and may not be very easy to observe. Hence, the identified outcomes may be revisited for clarity and to make them more all-encompassing.Originality/Value: The paper documents actual parent-child interaction when the latter is mobilized to serve as an infomediary to access nutrient management-related information on rice.
Optimization Based on Simulation of Ants Colony

Natural processes optimize life on earth for thousands of years, so people are inspired by many problem-solving techniques in nature. Metaheuristics inspired by natural processes and systems have become a very active field of research in recent years. One of the most popular methods is Ant Colony Optimization (ACO). In this paper is considered the application of Ant Colony Optimization in the case of the Traveling Salesman Problem (TSP). Different cases, with a different number of ants (population size) with a different number of iteration using software simulation, are considered. It is shown that Roulette Wheel Selection has some impact on the speed of the result. On the other hand, with more ants in each iteration, we get more constructed solutions, which increases the probability of finding a better solution.
A Reconfigurable 64-Dimension <i>K</i>-Means Clustering Accelerator With Adaptive Overflow Control

This brief presents a novel reconfigurable K-means clustering accelerator that is suitable for integration in both IoT and data center system. The high vector dimension reconfigurability and design cost reduction is achieved through vector-streaming and adaptive overflow control to adapt distance computation using as-needed precision (dynamic 16-bit fixed-point data format). A two-stage shift-bit counted comparator is proposed. It can determine most results through only turning on the shift-bit comparator (3-bit), reducing the power consumption by 7× compared to the direct full dynamic range comparison. Four vectors with two cluster centroids are processed simultaneously. Up to 8-dimension cluster vectors are stored in local buffer to reduce data exchange between the main memory and the processing engine. A prototype accelerator was implemented in TSMC 65 nm. The accelerator occupied 0.26 mm2 and can support up to 64-D vector clustering. It achieved 31.2M query vectors/s with 41-mW power consumption at 250-MHz clock (cluster number: 2, vector dimension: 64) and an energy efficiency of 0.41 TOPS/W at 30 MHz clock.
Finding Structure in Point Cloud Data with the Robust Isoperimetric Loss

We present a new regularization method to find structure in point clouds corrupted by outliers. The method organizes points into a graph structure, and uses isoperimetric inequalities to craft a loss function that is minimized alternatingly to identify outliers, and annihilate their effect. It can operate in the presence of large amounts of outliers, and inlier noise. The approach is applicable to both low-dimensional point clouds, such as those obtained from stereo or structured light, as well as high-dimensional ones.
Understanding Embodied State Using Speculative Artifacts

As the number of devices integrated into our lives skyrockets, we find ourselves inundated with opportunities for interaction. IoT promises to distribute computation and communication so pervasively that it becomes like background music for daily life, but the instruments often intrude in ways that subtract from an assistive, harmonious environment. We conducted anticipatory ethnography to enable people to imagine harmonious ecosystems that integrate speculative objects and their personal devices. Through activities we explored how participants might interact with future ecosystems within their daily routines. In this paper, we present a framework for embodied state as a central dimension from which to connect human motion and cognitive circumstance within the context of space and time. We find the need for substantial specialization in device ecosystems that bridge ambient and interactive spaces, and we present the notion of cognitive onloading for symmetrically explaining information distribution.
Tool for Map Creation and Map Interaction During Tabletop Game Sessions

Tabletop board games are a collaborative creative process that can benefit from new technical tools to augment game sessions. This demo is an interactive map creator made to create and display dungeon maps for people who play tabletop board games. It has an editing component for people to make maps, and a display component to view and interact with those maps. Users can engage with the demo on two levels, as a creator making dungeon maps, or as a player exploring them.
Forward Looking Huffman Coding

Huffman coding is known to be optimal, yet its dynamic version may yield smaller compressed files. The best known bound is that the number of bits used by dynamic Huffman coding in order to encode a message of n characters is at most larger by n bits than the number of bits required by static Huffman coding. In particular, dynamic Huffman coding can also generate a larger encoded file than the static variant, though in practice the file might often, but not always, be smaller. We propose here a new dynamic Huffman encoding approach, that provably always performs at least as good as static Huffman coding, and may be better than the standard dynamic Huffman coding for certain files. This is achieved by reversing the direction for the references of the encoded elements to those forming the model of the encoding, from pointing backwards to looking into the future.
A Survey Study on the Adoption and Implementation of Automation Projects in Federal University Libraries in the South-West of Nigeria

This article reports on a survey study on the adoption and implementation of automation projects in federal university libraries in the south-west of Nigeria (SWN). The objectives are to ascertain the implementation status of automation projects, discuss the accruable benefits, investigate the library management system software being used, determine the modules already activated and fully implemented and their web presence, highlight the challenges encountered, draw conclusions and recommendations on enhancing successful implementation of library automation projects in Nigeria. The method used for gathering data for the study is questionnaire-based survey, using google form template. The findings of the study are that all the six federal university libraries in the SWN have adopted and implemented automation; two out of them are using open source integrated library automation software (Koha), one library uses in-house built integrated library system (ILS), while the remaining three use different proprietary software for their automation projects; and all the ILSs being used for automation projects are interoperable. The conclusion from the study is that the effective performance of libraries lies in the ability to access research materials and other relevant information contents with remarkable ease. This becomes achievable when libraries adopt and implement automation in their respective domains.
Multi-objective evolutionary triclustering with constraints of time-series gene expression data

The rapid development of gene expression profiling experiments in recent years has created a great demand for triclustering, i.e., simultaneously clustering genes, conditions or samples and time points of the time-series gene expression data. Triclustering of the time-series gene expression data is of significant importance in biological engineering due to its great potential in identifying key genes in uncharted genome regions. In this paper, a new multi-objective constrained triclustering model is formulated to detect the key genes for time-series gene expression data, where a new objective based on the Wilcoxon sign-rank test is developed to measure the fluctuation of the gene expression values across different time points. A novel population decomposition based evolutionary multi-objective algorithm with customized three-point crossover and mutation operators is proposed for the formulated model. To validate the effectiveness of the proposed method, a series of experimental comparisons are first conducted on a set of artificial benchmark datasets, and then the proposed method is applied on real-life human gene engineering problems of detecting the key gene with similar functionality in biological processes. Experimental results, compared with three previous well-established triclustering algorithms, demonstrate the effectiveness of the proposed method. Furthermore, applications of the proposed triclustering method on biological and computer engineering problems are conducted.
lEarn

We aim to increase the profit of a given generator participating in a single-sided wholesale energy market. We model the market clearing mechanism and the behavior of other generators competing in the market. We utilize interesting structures in the data to classify generators and then build novel supervised competition models for each class of generators. We leverage these models to build an interactive system through which we discover better bidding strategies for the given generator using reinforcement learning (RL). We relax several assumptions made in existing works in order to make the problem more relevant to real life. Our MDP formulation enables us to tackle action space explosion in an efficient way. Further, our state formulation enables us to compute optimal actions across all time-steps of the day in parallel. We compare the performance of the proposed RL based bidding agent with the historical real world performance of a generator in a wholesale energy market.