Reviewerly/exHarmony huggingface dataset:
AcaChain

Blockchain has been a part of people's interest in the development of various areas such as digital currencies (e.g., Bitcoin), securities, healthcare, record-keeping and much more. Some of the key features of blockchain technology are immutability, decentralization, distributed ledger, consensus, and smart contract. In this paper, we explore how blockchain technology could possibly be utilized for the academic credential attestation in the education domain. This paper aims to re-engineer the academic credential attestation process to reduce both time and resource consumption and human involvement. It also aims to design a unique way for academic data collection to prevent fraud academic credentials. A blockchain-based system for academic credential attestation has been implemented.
Decision Support System for Detection of Skin Diseases in Smart Health development planning

Abstract Skin disease is a disease that is quite dangerous and is often found in tropical countries like Indonesia. Lack of knowledge about the types of skin diseases and do not know how to prevent them can cause a person suffering from acute skin disease. Detection of skin diseases is usually done by a dermatologist but actually the detection of skin diseases can be done alone or with the help of others by observing the diagnoses that arise. The expert system is a solution for using computers to help in the early detection of possible skin diseases. Dipen-ku is a expert system which designed to detect naive skin diseases with Bayes. System output is a type of skin disease which is a diagnosis of this type of disease. This expert system is a new innovation in innovation in drug materials that combines treatment technology and bigdata technology to support the design of smart city development.
Content-Consistent Generation of Realistic Eyes with Style

Accurately labeled real-world training data can be scarce, and hence recent works adapt, modify or generate images to boost target datasets. However, retaining relevant details from input data in the generated images is challenging and failure could be critical to the performance on the final task. In this work, we synthesize personspecific eye images that satisfy a given semantic segmentation mask (content), while following the style of a specified person from only a few reference images. We introduce two approaches, (a) one used to win the OpenEDS Synthetic Eye Generation Challenge at ICCV 2019, and (b) a principled approach to solving the problem involving simultaneous injection of style and content information at multiple scales. Our implementation is available at https://github.com/mcbuehler/Seg2Eye.
Burglar Alarm

This paper describes Burglar Alarm, an installation based on two patents formulated in the 1890s. They propose a curtain as an alarm system, suggesting, among others, a metal thread as a circuit conductor woven throughout the fabric - an early formulation of smart textile potentials. The installation shows the patents, material samples of the circuit solutions they propose, and a recreation of the main functionality: a curtain producing an alarm when opened unexpectedly. The installation aims at a critical and historical perspective on smart textiles, providing viewers with a tangible provocation to current narratives of innovation. It presents and performs design considerations of interactive systems from more than a hundred years ago, contextualizing smart and electronic textiles as a historical technology. The work is part of an ongoing material inquiry into metal threads, and their historic and present use in aesthetic and technical objects.
Edge Sensor Signal Processing Algorithms for Earthquake Early Detection

A new earthquake early warning algorithm is proposed in this paper. Intelligent sampling technique is used to expose signal information in a way to facilitate the inference of knowledge. The goal is to assess, by observing the first few seconds of P-wave, whether the incoming earthquake is destructive or not, and to generate an alert or eventually take action. Once the proposed method has been developed, performance results obtained using real seismic data from open-access databases are presented, thereby validating the effectiveness of the proposed method in estimating seismic magnitude. Since real-time and device interoperability are critical aspects in applications such as seismic detection, the suitability and compatibility of the proposed method with the IEEE1451 family of standards are demonstrated.
Modeling standard plenoptic camera by an equivalent camera array

Postprocessing of light fields enables us to extract more information from a scene compared to traditional cameras. Plenoptic cameras and camera arrays are two common methods for light field capture. It has been long recognized that the two devices are in some ways equivalent. Practically, both techniques have important constraints. Camera arrays are unable to provide high angular sampling, and the plenoptic camera can have a limited spatial sampling. In simulation, we can easily explore both constraints by simulating two-dimensional viewpoint images and combining them into a four-dimensional light field. We present a transformation for converting between equivalent plenoptic configurations and camera arrays when they capture pristine light fields produced in simulation. We use this approach to simulate light fields of simple scenes and validate our transformation by comparing the focus distance of a standard plenoptic camera and the equivalent camera array’s light field. We also show how some simple practical effects can be added to the pristine, synthetic light field via postprocessing and their effect on refocusing distance.
Scienscope

Many museums have begun to use mobile devices to provide interactive content to visitors. However, such devices can also easily distract visitors, drawing their attention away from the exhibits. In this pictorial, we show the steps in designing Scienscope-a hand-held mediation device that can be used as a stethoscope to explore additional information hidden within exhibits at close range. As Scienscope hovers on authentic objects, it provides digital content and audio-haptic feedback, reinforcing the visitors' exploratory behaviors. After interviewing museum experts and bodystorming the museum space, we followed an iterative design process using low-fi prototyping to explore the users' experience. Three application scenarios were devised to show how Scienscope can be used for different types of exhibits. We discussed how visitors respond to and explore hidden information about exhibits at close range, which can be helpful for museum curators and designers.
Comparative mixing for DSMGA-II

Dependency Structure Matrix Genetic Algorithm-II (DSMGA-II) is a recently proposed optimization method that builds the linkage model on the base of the Dependency Structure Matrix (DSM). This model is used during the Optimal Mixing (OM) operators, such as the Restricted Mixing (RM) and the Back Mixing (BM). DSMGA-II was shown to solve theoretical and real-world optimization problems effectively. In this paper, we show that the effectiveness of DSMGA-II and its improved version, namely Two-edge Dependency Structure Matrix Genetic Algorithm-II (DSMGA-IIe), is relatively low for NK-landscape problems. Thus, we propose the Comparative Mixing (CM) operator that extends the RM operator. The CM operator modifies the linkage information obtained from the DSM-based linkage model by comparing the receiver individual with a randomly selected member of the population. Such modification enables DSMGA-II to solve NK-landscape problems effectively and does not limit DSMGA-II performance on most problems for which it was already shown effective.
Lanczos-based fast blind deconvolution methods

The task of restoring an image that has been contaminated by blur and noise arises in many applications. When the blurring matrix (or equivalently, the point-spread function) is explicitly known, this task commonly is referred to as deconvolution. In many applications only an approximation of the blurring matrix is available. The restoration task then is referred to as blind deconvolution. This paper describes a family of blind deconvolution methods that allow a user to adjust the blurring matrix used in the computation to achieve an improved restoration. The methods are inexpensive to use; the major computational effort required for large-scale problems is the partial reduction of an available large symmetric approximate blurring matrix by a few steps of the symmetric Lanczos process. A real-time application to adaptive optics that requires fast blind deconvolution is described.
Human identification in health care systems using mobile edge computing

Abstract Mobile edge computing (MEC) is a new technology that provides an IT service environment and cloud‐computing resource at the edge of the mobile network, aiming to improve the user experience by reducing latency and delivering highly efficient services. A mobile health care system is a typical case of MEC. Recent advances in mobile health technology enable us to automatically record the states and behaviors of patients in hospitals by identifying their faces. Instead of using a centralized server or cloud to process these heavy loading analyses, MEC analyzes the patients' behavior locally in their rooms to enable quick response to their accidents and reduce the storage size of large‐scale patient information. By using MEC, only one frontal face for each patient is available in the gallery to reduce the local computational power. Although a patient's face can be captured from any other pose, doing so challenges the face‐identification algorithm. Several studies have been published to solve this problem, but most of them rely on pose estimation and landmark detection. In this paper, we propose a deeper convolutional neural network to extract the pose‐invariant face features and synthesize the virtual poses simultaneously, aiming to develop a landmark‐free and pose‐estimation‐free frontal‐face synthesis system. The deeper network was divided into multiple overlapped local networks, each one trained to synthesize a small pose change, such as one larger than 45° to 30° . The local networks were jointly trained to synthesize the frontal face from a nonfrontal pose in a progressive manner. By stacking multiple local networks, we were able to extract more robust pose‐invariant features, to generate multiple virtual poses before the frontal face was synthesized. The pose‐invariant features and virtual poses were incorporated to identify the face across poses. The proposed method was evaluated on the CAS‐PEAL dataset with 16 layers and fine‐tuned on the FERET dataset with 12 layers. The experimental results showed our network achieved impressive performance for pose‐unconstrained face recognition, which can be applied for human identification in healthcare systems using MEC.
Hetero-Dimensional Multitask Neuroevolution for Chaotic Time Series Prediction

Chaotic time series prediction has important research and application value, and neural network-based prediction methods have problems such as low accuracy and difficulty in determining the number of nodes in the hidden layer. In recent years, as a representative of evolutionary multitask optimization algorithms(EMTO), multifactorial evolutionary algorithms (MFEA) have achieved excellent results in Multitask Optimization (MTO) problems, but it does not work well in solving hetero-dimensional multitask problem. We analyzed this phenomenon and proposed the concept of harmful transfer. Then to solve these problems, in this paper, we propose the hetero-dimensional multitask neuroevolution algorithms (HD-MFEA neuroevolution) for chaotic time series prediction. First, we propose hetero-dimensional assortative mating algorithm and self-adaption elite replacement algorithm, so that the hetero-dimensional multitask evolutionary algorithm (HD-MFEA) can overcome the problems caused by harmful transfer on hetero-dimensional multitask problem. Second, in order to adapt to the hierarchical characteristics of neural networks, we propose the neural level segmental coding strategy. Third, we consider the optimization of neural networks with different hidden layer nodes as hetero-dimensional multitask problem and use the HD-MFEA neuroevolution to solve this problem. We verify the effectiveness of the HD-MFEA on six hetero-dimensional multitask benchmark problems. The Mackey-Glass, Lorenz and Sunspot time series are used to demonstrate the performance of the HD-MFEA neuroevolution. Experimental results have showed that the HD-MFEA neuroevolution performs well in predicting the chaotic time series problems.
Red blood cells and white blood cells detection by image processing

Abstract The common method of red and white blood cells identification and counting consider the manual processes on microscope which is arranged by the laboratory’s technician with their own experience. In this research, we will develop a computer program to detect and identify the proposed objects based on their pattern. The proposed objects are Red Blood Cells (RBCs), and White Blood Cells (WBCs). For blood cells identification and classification, an idea of Viola and Jones will be followed. Adaboost (adaptive boosting) method will be applied to increase the accuracy of the error of learning algorithm. The output of the proposed program shows that all the types of cells mentioned can be detected and classify effectively by showing the number and time spent of cells detected.
Refining high-frequencies for sharper super-resolution and deblurring

A sub-problem of paramount importance in super-resolution is the generation of an upsampled image (or frame) that is 'sharp'. In deblurring, the core problem itself is of removing the blur, and it is equivalent to the problem of generating a 'sharper' version of the given image. This sharpness in the generated image comes by accurately predicting the high-frequency details (commonly referred to as fine-details) such as object edges. Thus high-frequency prediction is a vital sub-problem in super-resolution and a core problem in deblurring. To generate a sharp upsampled or deblurred image, this paper proposes a multi-stage neural network architecture 'HFR-Net' that works on the principle of 'explicit refinement and fusion of high-frequency details'. To implement this principle, HFR-Net is trained with a novel 2-phase progressive–retrogressive training method. In addition to the training method, this paper also introduces dual motion warping with attention. It is a technique that is specifically designed to handle videos that have different rates of motion. Results obtained from extensive experiments on multiple super-resolution and deblurring datasets reveal that the proposed approach gives better results than the current state-of-the-art techniques.
InsTime

New technologies and practices are constantly transforming our interaction with computational systems. These transformations bring challenges to the Human-Computer Interaction (HCI) field, along with a constant need to better understand and describe how the design of interactive systems is changing. Currently, movements and theories such as speculative design and embodied cognition present unconventional perspectives and bring debate into the field. We investigate what kind of artifacts and attitude towards the design of interactive systems emerged in the InsTime project, in which 9 interactive installations were co-designed addressing the concept of deep time. Our discussion draws on a juxtaposition of backgrounds in philosophy of science, speculative design, and embodied cognition to analyze and characterize the empirical data from the InsTime project. As contributions, besides presenting the 9 interactive installations from InsTime and their co-design process, our discussion leads to a characterization of an attitude towards design that we named socioenactive design.
Soft Multi-Factor Authentication

The Classification of devices involved in authentication and classification of authentication systems by type and combination of protocols used are proposed. The system architecture for soft multi-factor authentication designed and simulated.
Searchable encryption scheme for multiple cloud storage using double‐layer blockchain

Summary With the emergence of cloud storage security, how to ensure the security of cloud storage has become a research focus. The typical searchable encryption model consists of a single cloud service provider and has some unsolvable drawbacks. In this article, a new searchable encryption scheme in multi‐cloud was proposed based on blockchain. We first define a system model in multiple clouds and combine multiple cloud service providers to store data through a consortium chain. Then we store the encrypted documents and indexes in The Interplanetary File System (IPFS) and store the hash value and IPFS address of the documents in the blockchain. Our scheme can provide outsourced encrypted data retrieval based on multiple keywords, as well as verification schemes for the integrity of retrieved files. The theoretical analysis and experiments on real‐world data show the security and high performance of our scheme.
Graph Based Mining of Code Change Patterns from Version Control Commits

Detailed knowledge of frequently recurring code changes can be beneficial for a variety of software engineering activities. For example, it is a key step to understand the process of software evolution, but is also necessary when developing more sophisticated code completion features predicting likely changes. Previous attempts on automatically finding such code change patterns were mainly based on frequent itemset mining, which essentially finds sets of edits occurring in close proximity. However, these approaches do not analyze the interplay among code elements, e.g., two code objects being named similarly, and thereby neglect great potential in identifying a number of meaningful patterns. We present a novel method for the automated mining of code change patterns from Git repositories that captures these context relations between individual edits. Our approach relies on a transformation of source code into a graph representation, while keeping relevant relations present. We then apply graph mining techniques to extract frequent subgraphs, which can be used for further analysis of development projects. We suggest multiple usage scenarios for the resulting pattern type. Additionally, we propose a transformation into complex event processing (CEP) rules which allows for easier application, especially for event-based auto-completion recommenders or similar tools. For evaluation, we mined seven open-source code repositories. We present 25 frequent change patterns occurring across these projects. We found these patterns to be meaningful, easy to interpret and mostly persistent across project borders. On average, a pattern from our set appeared in 45 percent of the analyzed code changes.
A Hybrid Recommender System Combing Singular Value Decomposition and Linear Mixed Model

We explain the basic idea of linear mixed model (LMM), including parameter estimation and model selection criteria. Moreover, the algorithm of singular value decomposition (SVD) is also mentioned. After introducing the related R packages to implement these two models, we compare the mean absolute errors of LMM and SVD when different numbers of historical ratings are given. Then a hybrid recommender system is developed to combine these two models together. Such system is proven to have higher accuracy than single LMM and SVD model. And it might have practical value in different fields in the future.
SoundWear

This study aims to clarify the effect of non-speech sound augmentation (i.e., everyday and instrumental sounds) on outdoor play for children, where has been lacking in empirical examination. In a within-subject observational study, sixteen children (ages 10-11) were divided into four equally sized groups and equipped with SoundWear, which is a wearable bracelet that allowed them to explore sounds, pick a desired sound, generate the sound with a swinging movement, and transfer the sound between multiple devices. Both the quantitative and qualitative results revealed that augmenting everyday sounds led to distinct play types with differences in physical, social, and imaginative behaviors, whereas instrumental sounds were naturally integrated into traditional games. Thus, sound augmentation with specific digital design features (e.g., transparent technology to provide new perspectives, margin for interpretation, and ownership through a sense of achievement) is significant for shaping distinctions in digitally enhanced play and requires considerable design attention.
Urgent point aware energy-efficient scheduling of tasks with hard deadline on virtualized cloud system

Cloud computing platform has emerged to be a promising computing paradigm of recent time. Various applications from different domains having rigid deadline constraints are deployed in the cloud system for their respective benefits. Energy-efficient execution of these applications, meeting their deadline constraints is a challenge. Most of the existing research on the energy-efficient scheduling of these applications in the cloud domain consider a linear relationship between the energy consumption and the resource utilization of the system, and they focus on maximizing the utilization of resources to reduce the active number of computing nodes to minimize energy consumption. In this paper, we first devise a power consumption model for the cloud system which considers both the static and dynamic components of it and assumes a nonlinear relationship with utilization. Then we introduce the concept of urgent points in case of tasks having deadline in the context of a heterogeneous cloud computing environment. Then we propose two energy-efficient scheduling approaches, named UPS and UPS-ES designed based on the urgent points of the tasks and two threshold values of the host utilization. Extensive simulation experiments are conducted both for synthetic tasksets and Google cloud tracelogs. The results are compared with a state of the art scheduling policy and found that our policies perform significantly better than them, with an energy reduction of up to 42% while the deadline constraints of all the tasks are met.
DogDog

Improvisation is embodied thought and expression. This paper outlines strategies and tactics to design expressive musical interfaces for improvisers. Some of these strategies are explored through a case study: a non-tactile hand-arm movement interface controlling a granular synthesizer (DogDog), based on high-level movement descriptors. The research through design and performance experience indicates that movement quality descriptors are inherently scalable from hand-arm movements to full body interaction, and that a textural approach to motion tracking fits well the morphing sonic masses generated through granular sound synthesis.