Reviewerly/exHarmony huggingface dataset:
An Automatic Abstractive Text Summarization System

Abstractive text summarization is one of the most interesting problems in the research field of Natural Language Processing. Recent advances in sequence to sequence model have made it possible to apply new approaches for abstractive text summarization and perform significantly. But most of the existing systems suffer from some drawbacks like word repetition, producing inaccurate or irrelevant information etc. In this work we propose a novel architecture incorporating advanced word embedding layer and topical feature with a pointer generator network to generate more topic oriented summaries in a logically sequenced way. Adding a word embedding layer with the model can capture semantic features of words in the input sequence more accurately. Also our proposed system with incorporated topical features ensures that the summaries focus on the most important parts of the source document. We applied our model to the CNN/Daily Mail dataset and outperformed the baseline model by all the ROUGE scores. DUJASE Vol. 6 (2) 39-48, 2021 (July)
A more-than-human right-to-repair

Whilst the recent introduction of the Right-to-Repair to European citizens is undoubtedly a step forward in tackling planned obsolescence, and the resultant deluge of electronic product waste -the efficacy of this new legislation is reliant on consumers availing themselves of this right.Given that repairing and maintaining devices will often require specialist knowledge and skills, it is difficult to assess how effective this right may prove to be in practice.To address this concern, we draw from the expanding infusion of datafication and Artificial Intelligence into everyday products and services via the Internet of Things to consider alternative futures whereby the Right-to-Repair is granted to the device itself.Building upon More-than-Human-Centred Design approaches, we explore the potential embodiment for such a perspective and present two Speculative Designs that concretise this consideration: the Toaster for Life and The Three Rights of AI Things.
Locked down with abusers: Designing for the dignity and autonomy of domestic violence survivors during the Covid-19 pandemic

The COVID-19 pandemic and national quarantine requirements exacerbated domestic violence, as survivors had to stay at home with their abusers with limited access to communication channels or resources for help.In this project, a team of lawyers, designers, and technologists collaborated to assist a domestic violence clinic at a law school to design services to help student attorneys and advocates to better connect with domestic violence survivors who sought legal help during the pandemic.The aim of this service was to support the domestic violence survivors with remote assistance, such as remote intake, a safe means of collecting information, and access to court forms to file abuse-prevention orders.This COVID-19 rapid response case study contributes to the emerging field of legal design by showcasing how the interdisciplinary collaboration of law, design, and technology can help enhance stakeholders' dignity and autonomy in the design of services.
Universal targeted attacks against mmWave-based human activity recognition system

Millimeter wave (mmWave)-based human activity recognition (HAR) systems have emerged in recent years due to their better privacy preservation and higher-resolution sensing. However, these systems are vulnerable to adversarial attacks. In this work, we propose a universal targeted attack method for mmWave-based HAR system. In particular, a universal perturbation is generated in advance which can be added to new-coming mmWave data to deceive the HAR system, causing it to output our desired label. We validate our proposed attack using a public mmWave dataset. We demonstrate the effectiveness of our proposed universal attack with a high attack success rate of over 95%.
The junk playground as agora: Designing spaces to re-invigorate democratic participation

In this paper, I suggest that there is an urgent need to design new spaces and possibilities for democratic participation.With inspiration from the tradition of "junk" and "adventure playgrounds", I argue that play design can contribute to expanding the participatory repertoire within deliberative democracy.Two design experiments are presented and discussed through the prism of self-determination theory and the findings point to a new understanding of democratic participation and the intrinsic, civic motivation that drives it.By shifting focus from the classical deliberative ideals of rational discourse towards sensorial, open-ended exploration and creation, new democratic possibilities emerge.Finally, it is argued that such opportunities can foster a sense of collective joy, which is seen as vital to healthy democratic societies.
An Optical Computing Chip for Executing Complex-valued Neural Network and Its On-chip Training

The optical implementation of neural networks is proposed to have advantages over electronic implementations with lower power consumption and higher computation speed. However, most optical neural networks (ONNs) utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many advantages of optical computing such as efficient complex-valued operations. Complex-valued neural networks are advantageous to their real-valued counterparts by offering rich representation space, fast convergence, and strong generalizations. We propose and demonstrate an ONN that implements truly complex-valued neural networks, achieving high accuracy and strong learning capability in many benchmark tasks [1]. On the other hand, efficiently training ONNs remains a formidable challenge, due to the difficulty in obtaining gradient information from a physical device. We propose an efficient on-chip training protocol for ONNs and demonstrate it by several practical tasks [2]. The protocol is gradient-free and physical agnostic, and is applicable for various types of chip structures, especially those that cannot be analytically decomposed and characterized. The protocol is robust to experimental perturbations like imperfect phase detection and photodetection noise. Our results present a promising avenue towards deep complex networks with smaller chip size, stronger performance, and flexible reconfiguration to realistic applications (e.g., facial recognition, natural language processing, and autonomous vehicles).
A Model for Understanding Mandatory System Implementation: A Literature Review

The system implementation within the company or organization is accompanied by the redesign of business processes and changes in organizational structure that are carried out to align system implementation with business strategies. In a mandatory environment,, the decision to adopt technology is determined by the management so that in the end employees will use the technology. However, there is a possibility that the employee may engage in destructive behavior so that failing to implement innovations or technology. Many studies have developed models that can be used to measure the success of mandatory system implementation and identified the factors that influence that success. The purpose of this literature review is to provide a comprehensive summary of research related to the success model of mandatory system implementation, provide insight into the critical success factors of mandatory system implementation, and identify research limitations in the literature to provide possible future. The research selected in this literature review is research from 2016 to 2021.
Analysis of Knowledge Graph Path Reasoning Based on Variational Reasoning

Knowledge graph (KG) reasoning improves the perception ability of graph structure features, improving model accuracy and enhancing model learning and reasoning capabilities. This paper proposes a new GraphDIVA model based on the variational reasoning divergent autoencoder (DIVA) model. The network structures and calculation processes of the models are analyzed. The GraphSAGE algorithm is introduced into the path reasoning module to solve the inability of the original model to perceive the features of the graph structure, which leads to a decline in the accuracy rate. Hence, GraphDIVA can achieve a higher accuracy rate with fewer learning iterations. The experiments show the efficiency and effectiveness of our model and proves that our method has a better effect on the accuracy rate and training difficulty than the baseline model on the FB15k-237 and NELL-995 benchmark datasets.
Enhancing Cloud Security with Hybrid Encryption

The world is witnessing a monumental shift from the electronics era to the digital era. Though the mobile industry is experiencing a fast growth, it is not yet fully developed in its capability, storage and it is not able to fully meet the demands of the user. Cloud computing is a frontier which aims to centralize the processing and storing capabilities. By integrating the mobile computing with cloud computing, very huge processing of the data could be done with the minimum efforts as it extends the performance standards to a greater height. Along with its efficiencies, it also poses a high degree of vulnerability as cloud computing is in the developing stages on the security front. Many cryptographic standards including Attribute Based Proxy Re-Encryption are used in this work to create a safe model that enables data privacy, data security, integration, and dynamic distribution with access control in a secure environment. Also, this research study has incorporated a homomorphic encryption algorithm on the ABE, which makes it almost impossible to decode the data by any third party. Compared to the existing model, our proposed solution is lightweight and easy to deploy because the dependency of third party is completely broken. Finally, the performance analysis has been presented both graphically and numerically by comparing the suitable parameters of the model.
A Practical App for Quickly Calculating the Number of People Using Machine Learning and Convolutional Neural Networks

Calculating the number of people is often necessary and repeated in real life. As the number of people increases, the calculation is time-consuming. Efficiently calculating the number of people is helpful to human life. In this article, we propose a valuable app to quickly calculate the number of people in a photo by a convolutional neural network (CNN). Initially, suspected face areas are segmented into micro-blocks. The segmented blocks are then confirmed through the CNN by rejecting the segmented micro-blocks without the human face to ensure the detection accuracy of the face area. The experimental results reveal that the proposed app can efficiently calculate the number of people. The world is now seriously threatened by the COVID-19 epidemic. The proposed app can help quickly calculate the number of people, avoid crowd gathering, and cause the risk of group infections.
Glitch Serendipity: Alternative Information Seeking that Leads to Discovery

When scientists and artists engage in exploratory research, they can reach new insight through unexpected information encounters. As both glitch and serendipity share the connection of discovery through the unexpected, I encourage pushing the limitations of interfaces, structures, and systems to bring about novelty. This experimental practice-based research engages with a new framework of alternative information seeking that opens up potential to discovery, known as glitch serendipity. I share the current status of my research along with a concrete example of how digital experiments led to a serendipitous demonstration of nonlinearity in image expression. Future work considers a holistic cognitive framework of surprise mediated by the values of unpredictability, unexpectedness, and unfamiliarity.
When is a Ring Road a ’Ring Road’? A Brief Perceptual Study

Abstract. The shapes and patterns of the road network of a topographic map provide important visual cues when interpreting the map and moving between scales in interactive environments. The ’city ring road’ is an example of a road structure we might use in the recognition and characterisation of a city. Our goal is the automatic identification (and preservation) of such structures through changing scales. In this preliminary study, we conducted an online survey and face to face interviews in order to obtain and prioritise the structural, topological and semantic properties that define ’ring road-ness’. We then created a practical ontology of ring roads, with a view to algorithm implementation that mirrors the human perception of ring roads.
PENGARUH MODEL PEMBELAJARAN READING QUESTIONING AND ANSWERING (RQA) BERBANTUAN APLIKASI KIPIN SCHOOL TERHADAP HASIL BELAJAR KOGNITIF SISWA KELAS VII SMP NEGERI 3 KOTA TERNATE

The teaching and learning process is an interaction between teachers and students through reciprocal communication that takes place in an effective situation to achieve learning objectives. Entering a decade of the 21st century, the development of information and communication technology in the era of the industrial revolution 4.0 has had a significant influence on learning that must adapt and use technology in the current era, many application-based learning technologies in the field of education can be utilized by teachers as media. or tools in learning to support the teaching and learning process. The aims of this research are to determine the effect of the Reading questioning and answering learning model assisted by the Kipin School application on cognitive learning outcomes. This research is a quasi-experimental research. The sample of this study consisted of two classes, namely class VIIc as the experimental class and class VIIa as the control class with a total of 57 students. The instruments used tests to measure cognitive learning outcomes. Data analysis used the SPSS-assisted normality and homogeneity test, while the hypothesis test used anacova test and regression test. The results showed that there was an effect of the Reading questioning and answering learning model assisted by the Kipin School application on learning outcomes. cognitive, namely with a significant value level of 0.001&gt; 0.05 .
Increased Protection Versus the Cost of Increased Protection: Victimization and the Use of Protective Measures Against Identity Theft

Most identity theft victims experience no personal monetary loss or other financial problems; rational choice theory suggests that this could lead people to not change their behavior, increasing their future risk of victimization. Consequently, the current study will investigate if and how the severity and incidence of identity theft affect individuals’ protective behavior using the 2016 Identity Theft Supplement (ITS) victimization survey. It uses ordinary least squares (OLS) regression to predict the number of protective measures practiced and multinomial logistic regression to predict self-reported motivation for use of protective measures. The results indicate that victims use more protective measures than nonvictims and that victimization has a greater impact if it occurs repeatedly and/or the victims personally lost money or experienced other financial problems. However, there is evidence of a threshold effect. The multinomial results indicate victims who lost money or experienced other financial problems are more likely to say they practice protective measures because of their victimization.
A model of interacting quantum neurons with a dynamic synapse

Abstract Motivated by recent advances in neuroscience, in this work, we explore the emergent behaviour of quantum systems with a dynamical biologically-inspired qubits interaction. We use a minimal model of two interacting qubits with an activity-dependent dynamic interplay as in classical dynamic synapses that induces the so-called synaptic depression, that is, synapses that present synaptic fatigue after heavy presynaptic stimulation. Our study shows that in absence of synaptic depression the two-qubits quantum system shows typical Rabi oscillations whose frequency decreases when synaptic depression is introduced, so one can trap excitations for a large period of time. This creates a population imbalance between the qubits even though the Hamiltonian is Hermitian. This imbalance can be sustained in time by introducing a small energy shift between the qubits. In addition, we report that long time entanglement between the two qubits raises naturally in the presence of synaptic depression. Moreover, we propose and analyse a plausible experimental setup of our two-qubits system which demonstrates that these results are robust and can be experimentally obtained in a laboratory.
Evaluation of the Performance of an Immersive System for Tele-education

Tele-education was already a solution for people who cannot attend lessons in person (such as inaccessibility in rural areas or illness issues). However, COVID has revealed problems in tele-education with current technology, causing adolescents and children to slow down their learning curves and experience problems of social distancing with their classmates. This paper presents a user study to validate an immersive communication system for tele-education purposes. This system streams in real time a class using 360-degree cameras, allowing remote students to explore the whole scene and improving the feeling of being in the classroom with their colleagues. Additionally, the prototype provides notifications to the remote students about events (such as a changes in the teacher’s presentation or classmates raising their hands) that occur outside their viewport to indicate in which direction they should move their heads to visualize them.
More Weather Tomorrow. Engaging Families with Data through a Personalised Weather Forecast

As data literacy skills are increasingly important in today’s society, scholars have been exploring strategies to engage people with data, for example through storytelling and familiar media such as video. In this paper, we present the design of a video-based data storytelling application that prompts children and their families to explore and interpret historical weather data through a personalised weather forecast. The application was displayed at a 2-month summer exhibition of a popular television channel. In a controlled comparative study, we investigated how the application triggered reflection, as well as emotional and narrative engagement of families at home and at the exhibition. We combined this approach with an in-the-wild study, in which we observed spontaneous interactions of visitors. Our findings indicate that data engagement is encouraged when family interactions occur, which may be facilitated by external environmental conditions and internal story design. Here, we uncover 5 design recommendations for data video storytellers.
A Novel Privacy Preservation and Quantification Methodology for Implementing Home-Care-Oriented Movement Analysis Systems

Human movement is generally evaluated through both observations and clinical assessment scales to identify the state and deterioration of a patient's motor control. Lately, technological systems for human motion analysis have been used in clinics to identify abnormal movement states, while they generally suffer from privacy challenges and concerns especially at home or in remote places. This paper presents a novel privacy preservation and quantification methodology that imitates the forgetting process of human memory to protect privacy in patient-centric healthcare. The privacy preservation principle of this methodology is to change the traditional data analytic routines into a distributed and disposable form (i.e., DnD) so as to naturally minimise the disclosure of patients' health data. To help judge the efficacy of DnD-based privacy preservation, the researchers further developed a risk-driven privacy quantification framework to supplement the existing privacy quantification techniques. To facilitate validating the methodology, this research also involves a home-care-oriented movement analysis system that comprises a single inertial measurement sensor and a mobile application. The system can acquire personal information, raw data of movements and indexes to evaluate the risk of falls and gait at homes. Moreover, the researchers conducted a technological appreciation survey of 16 health professionals to help understand the perception of this research. The survey obtains positive feedback regarding the movement analysis system and the proposed methodology as suitable for home-care scenarios.
Heart Rate Variability for Non-Intrusive Cybersickness Detection

Cybersickness involves all the adverse effects that can occur during a Virtual Reality (VR) immersion, which can compromise the quality of the user experience and limit the usability, functionality and duration of use of VR systems. Standardised protocols help detect stimuli that may cause cybersickness in multiple users but do not fully discriminate which specific users experience cybersickness. Of the biometric measures used to monitor cybersickness in an individual, Heart Rate Variability (HRV) is one of the most used in previous work. However, these only considered its temporal components and did not allow for rest periods between sessions, even though these can affect users’ immersion. Our analysis addresses these limitations in that changes in HRV can measure specific levels of discomfort or ”alertness” associated with the initial cybersickness stimulus induced in the 360 videos. Primarily, our empirical results show significant differences in the frequency components of HRV in response to cybersickness stimuli. These initial measurements can compete with standard subjective assessment protocols, especially for detecting whether a subject responds to a VR immersion with cybersickness symptoms.
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain

Federated Learning has been introduced as a new machine learning paradigm enhancing the use of local devices. At a server level, FL regularly aggregates models learned locally on distributed clients to obtain a more general model. In this way, no private data is sent over the network, and the communication cost is reduced. However, current solutions rely on the availability of large amounts of stored data at the client side in order to fine-tune the models sent by the server. Such setting is not realistic in mobile pervasive computing where data storage must be kept low and data characteristic (distribution) can change dramatically. To account for this variability, a solution is to use the data regularly collected by the client to progressively adapt the received model. But such naive approach exposes clients to the well-known problem of catastrophic forgetting. The purpose of this paper is to demonstrate this problem in the mobile human activity recognition context on smartphones.
Knowledge Modelling and Learning through Cognitive Networks

Knowledge modelling is a growing field at the fringe of computer science, psychology and network science [...]