Reviewerly/exHarmony huggingface dataset:
Robotically Printed Seaweed as a Biomaterial within Architecture and Design

This research aims to develop and understand the impact of seaweed as a bio-based material within architecture and design.The research is influenced by current global challenges, outlined by the Sustainable Development Goals (SDG), such as carbon drawdown, the problem of material waste, and the need to create more sustainable manufacturing processes.Seaweed is an organic biomass that does not require land, fresh water or fertilisers to grow, and growing it can reduce the effects of global warming as it sequesters large amounts of carbon dioxide.In turn, it can be harvested and used for a range of products including food, biofuel, fertiliser and bioplastic.The research focuses on the development of an organic, water-based biocomposite material made from sodium alginate, a derivative of brown seaweed, combined with cellulose powder, vegetable glycerine, and kelp powder.A set of methodical experiments were conducted and studied, with the aim of creating a novel material which can adapt to its surrounding environment and can degrade naturally.By creating and fabricating using renewable resources, one can create novel materials that are carbon neutral and contribute to a natural resource cycle.Ultimately, the material decays and returns to the earth, for the purpose of remediating soils and replenishing growth.
FACCE: Marco para la evaluación de la carga cognitiva en la educación en línea

Analyzing the cognitive load involved in the online teaching and learning process is an important factor in ensuring more concrete educational results, mainly because it observes the interference that different formats of content presentation can cause in the acquisition of information by human memory. With this in mind, this article presents a Framework called FACCE, which is based on aspects arising from the convergence of four studies: the Cognitive Load Theory, the Cognitive Theory of Multimedia Learning, the principles of electronic learning and the Usability Heuristics of Interfaces and search instrumentalize the assessment of cognitive load in online education. This is a qualitative research that addresses the construction and validation of FACCE which was built based on the resources available in HTML5, CSS, Bootstrap Framework, JavaScript and PHP. For data collection in the validation phase of the tool, an adaptation of the SUS System Usability Scale (System Usability Scale) was used, which identifies the quality of the interface of computer systems. The results showed that the proposed Framework has interface quality and therefore can be widely used in the qualification of online teaching from the balance of the cognitive load in the presentation of contents.
Keterampilan Berbicara Bahasa Jawa Ragam Krama Menggunakan Media Fun Learning Mat

This study was conducted to describe the use of media fun learning mat in an effort to improve Javanese speaking skills, the variety of manners of fifth grade students is still low. Sources of data in this study were students of class V MI Muhammadiyah 10 Yanggong a total of 16 students. The data collection technique is done by interview, observation, test and documentation. The use of this media has a positive impact including; students are more active in learning activities, self-confidence increases, learning activities are more memorable and fun and students' oral test scores have increased with the percentage of classical completeness 87.50%. So it can be said that the use of fun learning mat media is able to improve the Javanese language speaking skills in a variety of manners, and is expected to be able to motivate students to practice it in everyday life.
Exploring the Factors in Student’s Retention of E-learning Mathematics: A Case of Grade 12 Senior High School Students at the University of Perpetual Help System-Pueblo de Panay Campus

The study aims to determine the effects of teacher’s factors, student attitude, and motivation on student’s ability to absorb, recall and maintain the learned concepts. This research measures the effects of the said factors on student’s mathematics retention. For this study, the data are collected from 101 STEM students of the University of Perpetual Help System-Pueblo de Panay Campus. The data analysis is done using Paired t-test, Cronbach’s Alpha and Pearson r. The results have revealed that teacher’s factor, students’ attitude and motivation have significant differences. Results indicate that the three factors have statistical effect and significant impact on student's ability to absorb, recall and maintain the learned concepts in Mathematics via e-learning platform. Based on the findings, teacher’s factor is one of the factors the school should give much focus on since teacher has the ability to motivate and influence the attitude and performance of the students and they are accountable for the performance for the students. Furthermore, the study finds out that e-learning does not reduce education. Retention rates for online students are much higher than for traditional because it makes every learning material accessible, thus increasing student’s productivity by finishing a course when physical limitations are removed.
A Joint Expedition into the Future of Soft Tech

This article is based on a series of participatory art & technology workshops where professional artists and designers, hobbyists, researchers, and tinkerers were invited to four online workshops investigating their relationship to current and future technologies through participatory speculation.Drawing from critical making, the workshops were focused on joint discovery through hands-on making as thinking where knowledge of techno-social systems is produced collectively.Our analysis of the workshop series shows that workshop participants did not necessarily feel part of a joint discovery and that we were challenged in creating the intended non-hierarchical structure throughout the workshops.However, the workshops did succeed in challenging participants' idea of hard and soft technology and thereby also their own skills.We saw a recognition of the need for dialogue around technology and what it is perceived to be.
Coded Feminisms in Indonesia

Citizen labs often take in the role of promoting equality and tech literacy in their community.These two aims are entangled when it comes to enabling free speech online and offline.The methods of Indonesian citizen labs were shaped by decades of state propaganda and extended colonialism.These citizen labs also have deep roots in Indonesia's history, which informs the way critical media practises are cultivated.Sharing tech skills, offering formats for critical thinking and creativity was performed with a specific form of care, one that protects participants through encoding.In this paper we want to focus on three generations of Indonesian pioneers, who set up feminist citizen labs struggling against hegemonic forces.In particular we are investigating the roots of feminist citizen labs in the grassroot organization Gerwani.In the scope of our arts-based research project interviews with survivors of this destroyed women movement and interviews with initiators and members of current collectives were translated into an animated documentary.Indonesia.
Racial Data in Identity Construction of ‘Intelligent Agents’: Examining Conversations with BINA48 and Mythiccbeing

Conversational Artificial Intelligence (AI) has been utilized to create interaction between humans and machines.As an artistic medium to mediate an artificial being, the training dataset for the conversational AI as well as the algorithmic model plays a significant role in constructing the identity of 'intelligent agents'.In this paper, two of the artworks: Conversation with BINA48 (2014) by Stephanie Dinkins and Mythiccbeing (2018) by Martine Syms are used as case studies to give a critical glance into the usage of racial data in AI identity construction.Through a close reading of Dinkins' performative interaction with a customized AI, BINA48 and a chatbot created by Syms, the focus of this research lies in locating the current discourse of constructing virtual identities by a comparative textual analysis of the conversations respectively between BINA48 and Dinkins, Teenie and the audience.
Intimacy in Design Processes – An Interim Reflection

This paper is an interim reflection upon an ongoing initiative of practitioners who joined together to form a lateral community, which approaches the intersections of technology, intimacy and the stigmatized through hands on Do-It-With-Others (DIWO) attitudes.Intimate Technology.Critical Making.Do-It Together Practices.
Modulating Matters of Computation, Modelling and Hyper-Separations

We engage in a conversation with critical ecofeminism, which proposed to transform the colonialism-racism-capitalism-patriarchalism induced environmental crisis by non-essentialist countering of oppressions and hyper-separations produced by human/nature dualism.We modulate the critical ecofeminist approach by countering a similar dualism, namely that of nature/technology.Furthermore, our theoretical balance-act has a praxis-oriented side: we believe that computation can be included in ecofeminist action.By providing alternative forms of engagement to instrumentalization, we trace pathways to different futures, countering the binary narratives of technology but also its moralizing of socio-cultural mediation.We take an intersectional approach to outcomes of computational modelling (simulations, visualisations, forecasts) and discuss the ecofeminist method of synthesis as a way to include different perspectives into computational processes.We work with two 'modulated models' that pay attention to assumptions, observations and thinking about urban commoning initiatives, and amateur knowledge of radio telecommunications.We aspire to provoke discussions about different modes of inclusion in communities and archives that are centred on shared, environment-friendly, solidarity oriented lifestyle and mutual care.Our approach engages with feminist arguments and inquiries into ways patriarchalism is embedded in our relationship to technoscience and engineering.We explore modes of resistance by proposing skilled and alternative uses of these techniques.
Quadratic and Higher-Order Unconstrained Binary Optimization of Railway Rescheduling for Quantum Computing

As consequences of disruptions in railway traffic affect passenger experience/satisfaction, appropriate rerouting and/or rescheduling is necessary. These problems are known to be NP-hard, given the numerous restrictions of traffic nature. With the recent advances in quantum technologies, quantum annealing has become an alternative method to solve such optimization problems. To use quantum annealing, the problem needs to be encoded in QUBO (quadratic unconstrained binary optimization) or HOBO (higher-order binary optimization) formulation that can be recast as a QUBO. This paper introduces QUBO and HOBO representations for rescheduling problems of railway traffic management; the latter is a new approach up to our knowledge. This new approach takes into account not only the single-track lines but also the double- and multi-track lines, as well as stations composed of tracks and switches. We consider the conditions of minimal headway between trains, minimal stay on stations, track occupation, and rolling stock circulation. Furthermore, a hybrid quantum-classical procedure is presented that includes rerouting. We demonstrate the proof of concept implementation on the D-Wave Quantum Processing Unit and D-Wave hybrid solver.
Tight Bound for the Number of Distinct Palindromes in a Tree

For an undirected tree with $n$ edges labelled by single letters, we consider its substrings, which are labels of the simple paths between pairs of nodes. We prove that there are $O(n^{1.5})$ different palindromic substrings. This solves an open problem of Brlek, Lafreni\`ere, and Proven\c{c}al (DLT 2015), who gave a matching lower-bound construction. Hence, we settle the tight bound of $\Theta(n^{1.5})$ for the maximum palindromic complexity of trees. For standard strings, i.e., for paths, the palindromic complexity is $n+1$. We also propose $O(n^{1.5} \log{n})$-time algorithm for reporting all distinct palindromes in an undirected tree with $n$ edges.
PERANCANGAN SISTEM REKAYASA LINGKUNGAN SMART GREENHOUSE MENGGUNAKAN FUZZY LOGIC CONTROLLER PADA TANAMAN CABAI

In Indonesia, chili can be planted in various areas, both in the fields or moor, sea coast, and mountains. However, chili productions is not good, it is only increasing little by little and is not proportional to the rapid market needs. Efforts are neede to encourage the advancement of technology so that chili cultivation is more efficient. Smart Greenhouse is one of technology that can be used to help humans produce their own chili plants. The concept of smart greenhouse is the integration of data into the internet that connected to a accessed database using an android smartphone application compatible with features such as reporting, updating feeds, and controlling. DHT 22 is used as a sensor for reading temperature and humidity in Greenhouse, Moisture Sensor is a sensor for reading soil moisture. Fuzzy Logic Controller is applied for controlling temperature, humidity and soil moisture in Greenhouse. From the testing that has been done, Fuzzy Logic Controller that is applied can reach the reference temperature at 27oC in 465 seconds, the humidity control can reach 80% in 150 seconds and the soil moisture control can reach 80% in 10 seconds.
Attendance and Performance Monitoring System Implementation

Abstract: Attendance Management is very important for every organization. Taking attendance of lot student is very time consuming process. It is difficult to analyze attendance of students how frequently one is skipping classes. And also there will be some proxy attendance of student in traditional system. The possible solution to this problem is to use an automatic attendance system which uses face recognition techniques. This system will mark attendance electronically and recorded attendance will be stored in a database. The preparation of a question paper for the Internal Assessment Exam can be automatically generated with the help of teachers according to RBT levels. The Internal Assessment is very important for students. This system will give individual student reports based on the performance of student in internal assessment, exactly in which type of RBT level the student need to improve themselves.
Indian Paper Currency Recognition with Audio Output System for Visually Impaired based on Image Processing using Transfer Learning

Abstract: We give an outline of camera-based computer vision technology in this paper. It can aid those who are not visually blessed in instantly identifying paper currency. For those who are blind or visually challenged, an effective paper money detection algorithm should have the following qualities: 1) Complete precision; and 2) adaptability in a range of circumstances in various environments and emergence. Many of the currency identification algorithms in use today are constrained to specific situations. We suggest a deep learning strategy focused on high accuracy in this project. This method works well for gathering more class-specific data and is better at handling partial closure and viewpoint changes. Evaluation of transfer learning also demonstrate its efficiency in coping with visual rotation, measures, and illumination changes.
Extract and Organize Information in Images with AI using IBM Services

Abstract: OCR is a short form of Optical character recognition or optical character reader. By the full form, we can understand it is something that can read content present in the image. Every image in the world contains any kind of object in it and some of them have characters that can be read by humans easily, programming a machine to read them can be called OCR. In machine learning, data mining is one of the major sections that cover the extraction of the data from the different platforms. OCR (Optical Character Recognition) is part of the data mining process that mainly deals with typed, handwritten, or printed documents. These documents hold the data mainly in the form of images. Extracting such data requires some optimised models which can detect and recognize the texts. Getting information from complex structured documents becomes difficult and hence they require some effective methodologies for information extraction. In this article, we will discuss OCR with IBM Watson Natural Language Understanding API, a deep learning-based tool for localizing and detecting the text in documents and images.
Image Captioning - A Deep Learning Approach

Abstract: Image captioning is a brand-new study area in the science of computer vision. The primary goal of picture captioning is to create a natural language description for the input image. In recent years, research on natural language processing and computer vision has become increasingly interested in the problem of automatically synthesising descriptive phrases for photos. Image captioning is a crucial task that demands both the ability to create precise and accurate description phrases as well as a semantic understanding of the images. Long Short Term Memory (LSTM) is used to precisely organise data using the available keywords to form meaningful sentences. The authors of this research propose a hybrid system based on multilayer Convolutional Neural Networks to create a lexicon for characterising the visuals. The convolutional neural network employs trained captions to deliver an accurate description after comparing the target image to a sizable dataset of training images. We demonstrate the effectiveness of our suggested methodology using the Flickr 8K datasets.
IOT Based Patient Health Monitoring System

Abstract: Patients are facing a problematic situation of unforeseen demise due to the specific reason of heart problems and attack which is because of non existence of good medical maintenance to patients at the needed time. This is for specially monitoring old age patients and informing doctors and loved ones. So, we are proposing an innovative project to dodge such sudden death rates by using Patient Health Monitoring that uses sensor technology and uses the internet to communicate to the doctor in case of problems. This system uses temperature, heartbeat,ECG, Saline-level sensor for tracking patient’s health. These sensors are connected to the Arduino-uno. The patient health is tracked with the controller which is in turn interfaced to an LCD display, bluetooth and WI-FI connection to send the data to the web-server (Thinkspeak).In case of any abrupt changes in patient heart-rate or body temperature alert is sent about the patient using IoT. This system also shows patients temperature ,saline level and heartbeat tracked live data with timestamps over the internetwork. Thus, a Patient health monitoring system based on IoT uses the internet to effectively monitor patient health and helps the user monitor themselves.
Network Intrusion Detection via Tri-Broad Learning System based on Spatial-temporal Granularity

Abstract Network intrusion detection system plays a crucial role in protecting the integrity and availability of sensitive assets. Network traffic data contains a large amount of time, space, and statistical information. Existing research lacks the utilization of spatial-temporal multi-granularity data features and the mutual support among different data features, thus making it difficult to specifically and accurately identify anomalies. Taking into account the distinctions among different granularities, we propose a framework called Tri-Broad Learning System (TBLS), which can learn and integrate the three granular features. In order to accurately explore the spatial-temporal connotation of the traffic information, a feature dataset containing three granularities is constructed according to the characteristics of time, space, and data content. In this way, we use broad learning basic units to extract abstract features of different granularities, and then express these features in different feature spaces to enhance them separately. We use the “He” instead of the original initialization method in BLS to initialize the weights of feature nodes and enhancement nodes to achieve better detection accuracy. We exhibit the merits of our proposed model on the UNSW-NB15, CIC-IDS-2017, and mixed traffic datasets. Experimental JOURNAL OF SUPERCOMPUTING results show that TBLS outperforms the typical BLS in terms of various evaluation metrics and time consumption. Compared with other machine learning methods, TBLS achieves better performance metrics.
Semantic uncertainty intervals for disentangled latent spaces

Meaningful uncertainty quantification in computer vision requires reasoning about semantic information -- say, the hair color of the person in a photo or the location of a car on the street. To this end, recent breakthroughs in generative modeling allow us to represent semantic information in disentangled latent spaces, but providing uncertainties on the semantic latent variables has remained challenging. In this work, we provide principled uncertainty intervals that are guaranteed to contain the true semantic factors for any underlying generative model. The method does the following: (1) it uses quantile regression to output a heuristic uncertainty interval for each element in the latent space (2) calibrates these uncertainties such that they contain the true value of the latent for a new, unseen input. The endpoints of these calibrated intervals can then be propagated through the generator to produce interpretable uncertainty visualizations for each semantic factor. This technique reliably communicates semantically meaningful, principled, and instance-adaptive uncertainty in inverse problems like image super-resolution and image completion.
Discover and Mitigate Unknown Biases with Debiasing Alternate Networks

Deep image classifiers have been found to learn biases from datasets. To mitigate the biases, most previous methods require labels of protected attributes (e.g., age, skin tone) as full-supervision, which has two limitations: 1) it is infeasible when the labels are unavailable; 2) they are incapable of mitigating unknown biases -- biases that humans do not preconceive. To resolve those problems, we propose Debiasing Alternate Networks (DebiAN), which comprises two networks -- a Discoverer and a Classifier. By training in an alternate manner, the discoverer tries to find multiple unknown biases of the classifier without any annotations of biases, and the classifier aims at unlearning the biases identified by the discoverer. While previous works evaluate debiasing results in terms of a single bias, we create Multi-Color MNIST dataset to better benchmark mitigation of multiple biases in a multi-bias setting, which not only reveals the problems in previous methods but also demonstrates the advantage of DebiAN in identifying and mitigating multiple biases simultaneously. We further conduct extensive experiments on real-world datasets, showing that the discoverer in DebiAN can identify unknown biases that may be hard to be found by humans. Regarding debiasing, DebiAN achieves strong bias mitigation performance.
Dot Product Bounds in Galois Rings

We consider a problem related to the Erdo\H{o}s Unit Distance Conjecture: How often can a single dot product configuration or a multiple dot product configuration occur over a Galoi Ring? We also find a bound on an inverse vector matrix multiplication problem.