Reviewerly/exHarmony huggingface dataset:
Rayuela

Rayuela is an interactive light installation created with the purpose of using art to draw attention to a serious issue plaguing our society: namely plastic usage and waste, and how its overuse and under recycling end up polluting the oceans and compromising the food chain by disintegrating into microplastics. To create an experience that could be playful enough to attract and engage people, while still subtly conveying a serious message, we chose the game of hopscotch as a backdrop for a playful experience, revealing at each step the journey a plastic bottle goes through during its lifecycle. Accordingly, we named the work Rayuela, which is the Spanish word for hopscotch. We built Rayuela as a modular system consisting of 11 light boxes, each telling a different fact about the plastic bottle's journey. Each time people jumped on a box, it turned the next one on, thus progressively revealing the story. As the work was designed for exhibition in a very large public festival lasting about a month, design considerations ranged form aesthetic and interactive aspects to robustness and reliability. In this paper, we describe the conceptual rationale for Rayuela, its design and building process, and the methods for automated data gathering we implemented. We then present the results of the field study, and discuss preliminary findings, as well as implications for future design.
Automatic ICD code assignment utilizing textual descriptions and hierarchical structure of ICD code

International Classification of Diseases (ICD) codes provides a hierarchy of diagnostic codes for classifying diseases, which plays an important role in healthcare. Medical coding which assigns a subset of ICD codes to a patient visit is a mandatory process that is crucial for patient care and billing. Manual coding is time-consuming, expensive, and error-prone. In this paper, we present a deep learning based network that utilizes the information of textual descriptions and hierarchical structure of ICD codes to make a code assignment automatically. On one hand, we use the machine comprehension framework to model the relationship between the diagnostic description and the textual descriptions, enabling the model put more attention on most relevant segments of diagnostic description for each possible codes. On the other hand, we exploit tree-LSTM architecture to model hierarchical structure, capturing the hierarchical relationship among codes and the semantics of each code. We demonstrate the effectiveness of the proposed methods on a Chinese dataset and an English dataset.
Peace

The world is promoting inclusion and diversity more than ever before. Many people have dual-identities that they alternate between and may often blend. In our design research we explore everyday objects and the role of technology to accommodate people's needs and personalities. Can furniture change its shape to reflect our dual-identities? Can our interior spaces reveal their hidden aesthetics when interacting with us? We designed a set of matching interactive furniture to unfold these narratives. Our Peace Table and Peace Painting change colour with proximity to reflect the dual identity of Western-Muslims. This pictorial describes our design concept and process with the aim of encouraging the HCI community to design for experiential artwork. Such interactivity can enrich and add new dimensions to the quality of living experience by merging technology into home decor in calm, ubiquitous and non-intrusive ways.
LSB Steganography and AES Encryption for Multiple PDF Documents

Security is one important issue regarding data and information today. Steganography along with cryptography is used to hide and secure information. It is already an established practice to put watermark and to secure a single document using encryption. However, for large information requiring multiple documents has not received any attention. Here a program which can do steganography to multiple PDF documents to increase its steganography capacity is proposed, it is named MPDFStego. One of the steganography technique chosen in this research is the Least Significant Bit (LSB). LSB technique can be applied to Portable Document Format (PDF) by making use of its Tj Operator. Secret text is encrypted using Advanced Encryption Standard with 128-bit key size in Cipher Block Chaining (CBC) mode before it is embedded into the PDF document(s). Recompression and decompression of the PDF document(s) are done outside MPDFStego, with the help of QPDF tool. Testing and evaluation show that the MPDFStego is capable of hiding the secret text into one or many PDF documents (s).
Cybersecurity Analysis to Determine the Impact on the Social area in Latin America and the Caribbean

This document presents an introduction to the events that occurred in Latin America and the Caribbean, where banks are compromised by cyber-attacks, the same case is also seen in Ecuador, where losses from these attacks would amount to $ 6 billion by 2021. Our goal is to provide an analysis that demonstrates the social impact that cybersecurity has in Latin America and the Caribbean and a way to measure cybersecurity to combat threats due to constant technological progress and the great privacy risks presented to the day with cyber systems. The deductive method was used, adopting and generating a set of activities to evaluate the Total Cybersecurity (TC) of the cyber systems and / or companies to have a control of the security that it has and the improvements that can be implemented to prevent or combat Possible cyber threats or vulnerabilities. It resulted in cyber-resilience activities and activities based on the main functions before, during and after a cyber-attack. These activities are evaluated by the frequency of use, allowing each activity to assign a value and in this way apply the formulas proposed and obtain the CT, achieving the percentage of cybersecurity that has been implemented and discovering the weaknesses of the systems that They must be strengthened. It was concluded that in certain countries of Latin America and the Caribbean they have very little interest in the development and expansion of cybersecurity in their lands, unlike the progress of cybersecurity in European countries.
Identifying Indonesian Local Languages on Spontaneous Speech Data

Local languages are the most widely used as communication media in the daily conversations of Indonesian people. Preserving those local languages is crucial, especially for maintaining language and cultural identities. However, the variety of local languages raises communication problems. One of initial solution is developing a spoken language identification system to recognize different languages. This study developed a system of spoken language identification from speech data for Indonesian local languages, including Javanese, Sundanese, Madurese, Minangkabau, and Musi. The dataset used in this study is spontaneous speech data collected from local radio broadcasts for each language. This spontaneous dataset contains a lot of noises. Therefore, the suitable feature extraction and classification methods are required for developing a robust language identification system. In this study, three features are combined to identify languages, namely acoustic features based on i-vector, phonotactic features based on parallel phonemes and the dynamic prosody feature. Those features are merged on the hidden layer of Deep Neural Network (DNN). The experimental results showed that the f1-score achieved by combining those features with DNN on speech data with 3 seconds, 10 seconds and 30 seconds duration are 87.85%, 93.46%, and 96.73% respectively.
Maneuver detection and tracking of a space target based on a joint filter model

Abstract Orbit maneuver of non‐cooperative space targets is difficult to detect, and thus greatly affects the accuracy of real‐time target tracking. To solve this problem, a joint filter‐based algorithm is proposed to detect and track targets with random pulse maneuvering. In the algorithm, the joint filter is constructed using two nonlinear filters. The first is an auxiliary filter that utilizes the kinematic model to predict the target motion and estimate the target movement according to the continuity of Keplerian elements. The other is a primary filter that uses an orbit dynamic model to predict the target motion and utilize the estimated results provided as feedback by the auxiliary filter as the constraints to re‐estimate the target orbit in real time. Moreover, simulations were conducted to detect and track non‐cooperative space targets with a series of random pulse maneuvers, and thus validate the efficiency of the new algorithm.
Comparing digital histology slides with multiple staining based on decoloring and dyeing technique

Information in histology slides are usually visualized by different staining techniques, each of them unveils specific chemical and biological substances within tissue samples. Correlations between different stains can be useful to predict how certain tissue slides may look like if they were stained by other staining techniques. This work investigates two stains including hematoxylin and eosin (H&E) and immunohistochemistry (IHC) in digital pathological slides. Four cases of surgical biopsies were used in this work. The specimens were subjected to two consecutive stains with a decoloring process based on ethanol and potassium permanganate in between. After each stain, slides were digitized and archived as results. Comparing the effects of the two staining pipelines, IHC slides after decoloring of H&E showed that the cell structure was clear, the positive IHC staining was accurate, the background of the slide was clean, there was no DAB residue, and tissue fragments were intact. However, the other pipeline where IHC was stained before H&E showed that the nuclear border was blurred. Eosin is lightly colored resulting in low contrast visualization of nucleoplasm, DAB is not completely decolored, and parts of tissue were fragmented. We conclude that, from the proposed staining and decoloring technique, tissue slides could be stained with IHC more effectively on decolored H&E slides than those stained with H&E after IHC. Utilizing digital section scanning technology, we can obtain pairs of tissue images stained differently while preserving the exact same tissue structure.
Determination of the relative inclination and the viewing angle of an interacting pair of galaxies using Convolutional Neural Networks

Constructing dynamical models for interacting pair of galaxies as constrained by their observed structure and kinematics crucially depends on the correct choice of the values of the relative inclination ($i$) between their galactic planes as well as the viewing angle ($\theta$), the angle between the line of sight and the normal to the plane of their orbital motion. We construct Deep Convolutional Neural Network (DCNN) models to determine the relative inclination ($i$) and the viewing angle ($\theta$) of interacting galaxy pairs, using N-body $+$ Smoothed Particle Hydrodynamics (SPH) simulation data from the GALMER database for training the same. In order to classify galaxy pairs based on their $i$ values only, we first construct DCNN models for a (a) 2-class ( $i$ = 0 $^{\circ}$, 45$^{\circ}$ ) and (b) 3-class ($i = 0^{\circ}, 45^{\circ} \text{ and } 90^{\circ}$) classification, obtaining $F_1$ scores of 99% and 98% respectively. Further, for a classification based on both $i$ and $\theta$ values, we develop a DCNN model for a 9-class classification ($(i,\theta) \sim (0^{\circ},15^{\circ}) ,(0^{\circ},45^{\circ}), (0^{\circ},90^{\circ}), (45^{\circ},15^{\circ}), (45^{\circ}, 45^{\circ}), (45^{\circ}, 90^{\circ}), (90^{\circ}, 15^{\circ}), (90^{\circ}, 45^{\circ}), (90^{\circ},90^{\circ})$), and the $F_1$ score was 97$\%$. Finally, we tested our 2-class model on real data of interacting galaxy pairs from the Sloan Digital Sky Survey (SDSS) DR15, and achieve an $F_1$ score of 78%. Our DCNN models could be further extended to determine additional parameters needed to model dynamics of interacting galaxy pairs, which is currently accomplished by trial and error method.
Multitask and Multimodal Neural Network Model for Interpretable Analysis of X-ray Images

The quality and interpretability of the state-of-the-art methods for automatic analysis of chest X-ray images is still not sufficient. We address this problem by presenting a model that combines the analysis of frontal chest X-ray scans with structured patient information contained within radiology records. The proposed model generates a short textual summary with essential information on the found pathologies along with their location and severity; and the 2D heatmaps localizing each pathology on the original X-ray images. We test the proposed model on the MIMIC-CXR dataset. It achieves the state-of-the-art performance for image labelling and captioning (78.5% of correctly generated sentences) and defeats other similar solutions that dismiss the additional patient data (by 5.2% of correctly generated sentences). We also propose an automatic approach to label mining that leverages multimodal data: the X-ray images, related textual reports, patients' age and sex.
Nighttime Haze Removal Using Bilateral Filtering and Adaptive Dark Channel Prior

The weather conditions of fog and haze make the images taken at night blurred, which seriously affects the image and video quality such as video surveillance. The existing daytime dehazing methods are not suitable for night hazy scenes, night-specific dehazing methods need to be developed. In this paper, by analyzing the nature of night hazy scenes with Retinex theory, we propose a bilateral filtering approach to estimate the locally variant atmospheric illumination, which can accurately obtain the local atmospheric light as well as preserve edges. To solve the problem that the dark channel of haze-free image in the light source area is not zero, an adaptive dark channel prior (ADCP) is proposed to estimate the transmission map. Based on the difference of image value between light source area and non-light source area, the transmission can be estimated adaptively to achieve better dehazing effect. The experimental results show that the subjective visual effect of the proposed algorithm outperform the dark channel-based method. The proposed method can effectively remove haze in the night, improve the image contrast and visibility, especially for the light areas.
Private routing and ride‐sharing using homomorphic encryption

A framework for private and secure communication and interaction between agents interacting in transportation services is developed. An agent, i.e. a user, can ask questions or submit queries regarding whether the other agents, i.e. drivers, use the desired road at specific times of the day in an encrypted fashion. The authors developed the framework using semi-homomorphic encryption (namely, the Paillier's encryption method) to enable the algebraic manipulation of plain data without the need for decryption using appropriate computations over the encrypted data. Strong privacy and security guarantees are proved for the agents. Subsequently, the semi-homomorphic encryption method is utilised to develop privacy-aware ride-sharing and routing algorithms without the need for disclosing the origin and destination of the user.
Modeling of Request Cloning in Cloud Server Systems using Processor Sharing

The interest for studying server systems subject to cloned requests has recently increased. In this paper we present a model that allows us to equivalently represent a system of servers with cloned requests, as a single server. The model is very general, and we show that no assumptions on either inter-arrival or service time distributions are required, allowing for, e.g., both heterogeneity and dependencies. Further, we show that the model holds for any queuing discipline. However, we focus our attention on Processor Sharing, as the discipline has not been studied before in this context. The key requirement that enables us to use the single server G/G/1 model is that the request clones have to receive synchronized service. We show examples of server systems fulfilling this requirement. We also use our G/G/1 model to co-design traditional load-balancing algorithms together with cloning strategies, providing well-performing and provably stable designs. Finally, we also relax the synchronized service requirement and study the effects of non-perfect synchronization. We derive bounds for how common imperfections that occur in practice, such as arrival and cancellation delays, affect the accuracy of our model. We empirically demonstrate that the bounds are tight for small imperfections, and that our co-design method for the popular Join-Shortest-Queue (JSQ) policy can be used even under relaxed synchronization assumptions with small loss in accuracy.
Nonlocal Band-Weighted Iterative Spectral Mixture Model for Hyperspectral Imagery Denoising

Although efficient hyperspectral image (HSI) denoising relies on complete and accurate description and modeling the spatial-spectral signal in HSI, the current approaches do not fully account for key characteristics of HSI, i.e., the mixed spectra effect, the spatial nonstationarity effect, and noise variance heterogeneity effect. To address this issue, this article presents a linear spectral mixture model with nonlocal means constraint (LSMM-NLMC), with the following advantages. First, LSMM-NLMC can effectively learn the signal in mixed pixels in HSI by estimating clean endmembers and abundances for image restoration. Second, LSMM-NLMC can efficiently address nonstationary spatial correlation effect by imposing NLMC on the latent scene signal. Last, LSMM-NLMC provides accurate noise characterization by accounting for noise variance heterogeneity effect using a band-dependent noise model and a band-weighted Mahalanobis distance for similarity measurement. A novel optimization method based on the expectation-maximization (EM) algorithm and the purified means approach is used to efficiently solve the resulting maximum a posterior (MAP) problem. The experiments on both simulated and real HSI data sets demonstrate that the visual quality and denoising accuracy are significantly improved by the proposed LSMM-NLMC compared with previous methods.
Constructing new differentially 4-uniform permutations from known ones

In this paper we study a new construction of differentially 4-uniform permutations from known ones and the inverse function. We focus on constructing methods of [20]. We split a finite field into its subfield and remainder, and, we choose known differentially 4-uniform permutations over the subfield and the inverse function over the entire field. As a result, we obtain two families of differentially 4-uniform permutations.
Cooperate Delegation of Computation for Rational Party Using Zero-Determinant Strategy Approach

Delegating calculation is an important approach to solve the correctness and reliability of task subcontracting and delegating calculation results in cloud computing environment. However, the dynamic, complexity and openness of cloud computing bring unprecedented risks to the security and reliability of computing tasks. To solve the problem the new approach is presented. Firstly, the game theory is introduced into the delegating calculation, and the single-client and multi-server rational delegating calculation game model is given. Secondly, the Zero-determinant Strategy scheme based on delegating calculation is constructed through implementing the single-client and single-server rational delegating calculation protocol and Zero-determinant Strategy, and the conditions of the existence of Nash equilibrium of Zero-determinant Strategy is analyzed. Then, through the iterative implementation of the Zero-determinant Strategy, the participants will cooperate with each other actively. Finally, the performance analysis results show that the entrusting party can regulate the betrayers in the computing party through Zero-determinant Strategy to ensure the interests of honest people in cloud computing.
Heuristic Evaluation and User Experience Redesign of 'Think &amp; Link' Learning Environment – A Case Study

The online learning environment 'think & link' facilitates the teaching-learning of software conceptual design for undergraduate computer engineers. The learning environment's design can influence learners' learning experience. In this paper, we report the process of heuristic evaluation to evaluate the learning experience provided by 'think & link'. Heuristic evaluation involves the expert(s) to evaluate a user interface based on usability criteria to identify usability problems. Findings from the heuristic evaluation indicated certain issues relating to user experience and resulted in a user experience redesign. The process of evaluation, usability problems identified and redesign is presented as a case study.
Intelligent Hospital Guidance System based on Multi-Round Conversation

Registering a wrong hospital department is common when patients use on-line registering systems. Currently, there are some systems in practice. However, patients are unable to choose the best department due to different names and authorities of hospitals. To help solve the problem, we build a symptom-disease-disciplinary knowledge graph to recommend appropriate departments for patients. We obtain real disease-disciplinary information based on the regional health platform electronic health records (EHRs). Besides, we synthesize the symptom-disease relationship between ICD codes and medical encyclopedia websites. To further help the system predict the diseases based on patients' complaints, we update the weights of diseases through patients' choices in multi-round conversations. Experimental results show that the accuracy of final prediction is up to 92%.
Beyond Attributes: High-Order Attribute Features for Zero-Shot Learning

In this paper, SeeNet with the high-order attribute features (SeeNet-HAF) is proposed to solve the challenging zero-shot learning (ZSL) task. The high-order attribute features aims to discover a more elaborate, discriminative high-order semantic vector for each class and can distill the correlation between the class attributes embedding into modeling. SeeNet-HAF consists of two branches. The upper stream is capable of dynamically localizing some discriminative object region, and then the high-order attribute supervision is incorporated to characterize the relationship between the class attributes. Meanwhile, the bottom stream discovers complementary object regions by erasing its discovered regions from the feature maps. In addition, we propose a fast hyperparameter search strategy. It takes both the breadth and precision of the search into account. Experiments on four standard benchmark datasets demonstrate the superiority of the SeeNet-HAF framework.
Agile-scrum Software Development Monitoring System

XYZ Mart is a business entity in the retail sector that has problems in the lack of information about the supply of goods, namely goods not classified based on the intensity of their sales. Then we need a system that can help the store to monitor the flow of goods movements directly when the transaction is carried out and the results can be reported to the user. The methodology is one of the things that is needed especially to help complete a software project in the construction of the XYZ Mart monitoring system, researchers apply the Agile Scrum method which is a flexible framework and always prioritizes the importance of team collaboration in the process. the implementation of the Agile Scrum method, various stages are carried out, starting from forming a team, determining the subject matter, planning the sprint, to executing the sprint which is the main activity in Scrum. Sprint execution, researchers use metrics to review activities during the sprint. The results of the system built using the Agile Scrum method were tested together with the XYZ Mart as the product owner using a User Acceptance Test and a system that was built successfully and received a good response by users assessed by the Likert Scale.