Reviewerly/exHarmony huggingface dataset:
Self Hyper-parameter Tuning for Stream Classification Algorithms

The new 5G mobile communication system era brings a new set of communication devices that will appear on the market. These devices will generate data streams that require proper handling by machine algorithms. The processing of these data streams requires the design, development, and adaptation of appropriate machine learning algorithms. While stream processing algorithms include hyper-parameters for performance refinement, their tuning process is time-consuming and typically requires an expert to do the task. In this paper, we present an extension of the Self Parameter Tuning (SPT) optimization algorithm for data streams. We apply the Nelder-Mead algorithm to dynamically sized samples that converge to optimal settings in a double pass over data (during the exploration phase), using a relatively small number of data points. Additionally, the SPT automatically readjusts hyper-parameters when concept drift occurs. We did a set of experiments with well-known classification data sets and the results show that the proposed algorithm can outperform the results of previous hyper-parameter tuning efforts by human experts. The statistical results show that this extension is faster in terms of convergence and presents at least similar accuracy results when compared with the standard optimization techniques.
Relighting Images in the Wild with a Self-Supervised Siamese Auto-Encoder

We propose a self-supervised method for image relighting of single view images in the wild. The method is based on an auto-encoder which deconstructs an image into two separate encodings, relating to the scene illumination and content, respectively. In order to disentangle this embedding information without supervision, we exploit the assumption that some augmentation operations do not affect the image content and only affect the direction of the light. A novel loss function, called spherical harmonic loss, is introduced that forces the illumination embedding to convert to a spherical harmonic vector. We train our model on large-scale datasets such as Youtube 8M and CelebA. Our experiments show that our method can correctly estimate scene illumination and realistically re-light input images, without any supervision or a prior shape model. Compared to supervised methods, our approach has similar performance and avoids common lighting artifacts.
Person re-identification from appearance cues and deep Siamese features

Automated person re-identification in a multi-camera surveillance setup is very important for effective tracking and monitoring crowd movement. In this paper, we propose an efficient hierarchical re-identification approach in which color histogram-based comparison is employed to find the closest matches in the gallery set, and next deep feature-based comparison is carried out using the Siamese network. Reduction in search space after the first level of matching helps in improving the accuracy as well as efficiency of prediction by the Siamese network by eliminating dissimilar elements. A silhouette part-based feature extraction scheme is adopted in each level of hierarchy to preserve the relative locations of the different body parts and make the appearance descriptors more discriminating. The proposed approach has been evaluated on five public data sets and also a new data set captured in our laboratory. Results reveal that it outperforms most state-of-the-art approaches in terms of overall accuracy.
A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection

Breast cancer is reported as one of most common malignancy amongst women in the world. Early detection of this cancer is critical to clinical and epidemiologic for aiding in informing subsequent treatments. This study investigates automated breast cancer prediction using deep learning techniques. A new 19-layer deep convolutional neural network (CNN) model for detecting the benign breast tumors from malignant cancers was proposed and implemented. The experiments on BreaKHis dataset was conducted and K-fold Cross Validation technique are used for the model evaluation. The proposed 19-layer deep CNN based classifiers compared with conventional machine learning classifier, namely Support Vector Machine (SVM) and a state-of-the-art deep learning model, namely GoogLeNet in terms of Accuracy, Area under the Receiver Operating Characteristic (ROC) Curve (AUC), the Classification Mean Absolute Error (MAE), Mean Squared Error (MSE) metrics. The results demonstrate that the proposed new model outperformed the other classifiers. The proposed model achieved an accuracy, AUC, MAE and MSE of 84.5%, 85.7%, 0.082, and 0.043, respectively.
A Low-Complexity Quantum Principal Component Analysis Algorithm

In this article, we propose a low-complexity quantum principal component analysis (qPCA) algorithm. Similar to the state-of-the-art qPCA, it achieves dimension reduction by extracting principal components of the data matrix, rather than all components of the data matrix, to quantum registers, so that the samples of measurement required can be reduced considerably. Both our qPCA and Lin&#x2019;s qPCA are based on quantum singular-value thresholding (QSVT). The key of Lin&#x2019;s qPCA is to combine QSVT, and modified QSVT is to obtain the superposition of the principal components. The key of our algorithm, however, is to modify QSVT by replacing the rotation-controlled operation of QSVT with the controlled-<small>not</small> operation to obtain the superposition of the principal components. As a result, this small trick makes the circuit much simpler. Particularly, the proposed qPCA requires three phase estimations, while the state-of-the-art qPCA requires five phase estimations. Since the runtime of qPCA mainly comes from phase estimations, the proposed qPCA achieves a runtime of roughly 3/5 of that of the state of the art. We simulate the proposed qPCA on the IBM quantum computing platform, and the simulation result verifies that the proposed qPCA yields the expected quantum state.
Rethinking Assistive Technologies: Users, Environments, Digital Media, and App-Practices of Hearing

Abstract Against the backdrop of an aging world population increasingly affected by a diverse range of abilities and disabilities as well as the rise of ubiquitous computing and digital app cultures, this paper questions how mobile technologies mediate between heterogeneous environments and sensing beings. To approach the current technological manufacturing of the senses, two lines of thought are of importance: First, there is a need to critically reflect upon the concept of assistive technologies (AT) as artifacts providing tangible solutions for a specific disability. Second, the conventional distinction between user and environment requires a differentiated consideration. This contribution will first review James Gibson’s concept of “affordances” and modify this approach by introducing theories and methods of Science and Technology Studies (STS) and Actor-Network Theory (ANT). Then, we present two case studies where we explore the relations between recent “assistive” app technologies and human sensory perception. As hearing and seeing are key in this regard, we concentrate on two specific media technologies: ReSound LINX 2 , a hearing aid which allows for direct connect (via Bluetooth) with iPhone, iPad, or iPod Touch, and Camassia, an IOS app for sonic wayfinding for blind people. We emphasize the significance of dis-/abling practices for manufacturing novel forms of hearing and seeing and drawing on sources like promotional materials by manufacturers, ads, or user testimonials and reviews. Our analysis is interested in the reciprocal relationships between users and their socio-technical and media environments. By and large, this contribution will provide crucial insights into the contemporary entanglement of algorithm-driven technologies, daily practices, and sensing subjects: the production of techno-sensory arrangements.
A New Belief-Based Incomplete Pattern Unsupervised Classification Method

The clustering of incomplete patterns is a very challenging task because the estimations may negatively affect the distribution of real centers and thus cause uncertainty and imprecision in the results.To address this problem, a new beliefbased incomplete pattern unsupervised classification method (BPC) is proposed in this paper.Firstly, the complete patterns are grouped into a few clusters by a classical soft method like fuzzy c-means to obtain the corresponding reliable centers and thereby are partitioned into reliable patterns and unreliable ones by an optimization method.Secondly, a basic classifier trained by reliable patterns is employed to classifies unreliable patterns and the incomplete patterns edited by the neighbors.In this way, most of the edited incomplete patterns can be submitted to specific clusters.Finally, some ambiguous patterns will be carefully repartitioned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory.By doing this, a few patterns that are very difficult to classify between different specific clusters will be reasonably submitted to metacluster which can characterize the uncertainty and imprecision of the clusters due to missing values.The simulation results show that the BPC has the potential to deal with real datasets.
IncreACO: Incrementally Learned Automatic Check-out with Photorealistic Exemplar Augmentation

Automatic check-out (ACO) emerges as an integral component in recent self-service retailing stores, which aims at automatically detecting and counting the randomly placed products upon a check-out platform. Existing data-driven counting works still have difficulties in generalizing to real-world retail product counting scenarios, since (1) real check-out images are hard to collect or cover all products and their possible layouts, (2) rapid updating of the product list leads to frequent and tedious re-training of the counting models. To overcome these obstacles, we contribute a practical automatic check-out framework tailored to real-world retail product counting scenarios, consisting of a photorealistic exemplar augmentation to generate physically reliable and photorealistic check-out images from canonical exemplars scanned for each product and an incremental learning strategy to match the updating nature of the ACO system with much fewer training effort. Through comprehensive studies, we show that the proposed IncreACO serves as an effective framework on the recent Retail Product Checkout (RPC) dataset, where the proposed photorealistic exemplar augmentation remarkably improves the counting performance against the state-of-the-art methods (77.15% v.s. 72.83% in counting accuracy), whilst the proposed incremental learning framework consistently extends the counting performance to new categories.
Effect of in-app components, medium, and screen size of electronic textbooks on reading performance, behavior, and perception

Although many app-based textbooks are available for students, reading have not been thoroughly outlined. This study aimed to understand how changes from paper to electronic textbooks have affected the academic reading task, investigate student users’ perceptions of in-app components and screen sizes, and identify issues affecting in-app components and task requirements. A mixed factorial design experiment was employed. Results showed that there were no significant changes in comprehension and time spent reading between print text and the iPad. Yet, student highlighting, notetaking, and reading behavior and perception significantly changed based on condition. In addition, students struggled to use in-app components and found them frustrating especially when accounting for sentence splitting. The findings presented can assist in understanding the changes in student reading behavior, which can be used to improve interface design of future e-textbooks.
Early detection of breast malignancy using wavelet features and optimized classifier

Abstract Breast cancer considered to be a significant health issue among women. Early detection will ensure the treatment is easier and more successful. Recently, numerous methodologies have developed using medical imaging to investigate breast cancer. This research seeks to build a computer‐aided diagnostic (CAD) system to interpret mammograms. The first stage of CAD includes preprocessing, Fuzzy c means based segmentation applied to a localized area. In the second stage of the CAD method, the extraction of the feature is carried out using three distinct wavelet families with decomposition level at 4 and 6. The ANN, SVM, and ELM classifiers are used in the final stage to enable accurate classification. This article proposes ELM with the Grasshopper Optimization Algorithm (ELM‐GOA) to adjust the weight between the input and hidden layer to obtain maximum performance at the middle layer. This method adopts mammogram enhancement, optimum image segmentation, wavelet‐based feature extraction, and grasshopper optimization algorithm based ELM to ameliorating the accuracy and reducing the computational cost. The result shows that ELM‐GOA has precision and sensitivity of 100% and 98% respectively. The CAD system can identify tumors with 99.33 % accuracy.
What is bitcoin

Many want to know what bitcoin is and how it works. But bitcoin is as complex as it is controversial, and relatively few have the technical background to understand it. In this paper, I offer an accessible on-ramp for understanding bitcoin in the form of a model. My model reveals both what bitcoin is and how it works. More specifically, it reveals that bitcoin is a fictional substance in a massively coauthored story on a network that automates and distributes jobs normally entrusted to centralized publishing institutions. My model therefore falsifies a popular view according to which each bitcoin is a chunk of code.
Technology-Mediated Memory Impairment

The technology and tools that we develop have always been transformative, but the pace of change, particularly in the last few decades is undoubtedly altering humans in ways we don't understand. As researchers look to develop novel prosthetics and tools to enhance our memories and extend cognition, further consideration is needed to understand how technologies can help (or, indeed, hinder) our inherent abilities. In this chapter, we identify two distinct forms of cognitive risk associated with current and emerging technologies: memory inhibition and memory distortion. We describe how lifelogging, search engines, social media, satnavs and other developments are prompting us to retain less information for ourselves (inhibition), and present three specific examples of this phenomenon: the Google effect, photo-taking-impairment and alterations in spatial memory attributed to satnav use. We further consider cases in which technology actually increases the likelihood of errors in what and how we remember (distortion), including doctored evidence effects, creation of false memories for current or historical affairs ("fake news") and retrieval-induced forgetting. Finally, we provide an exploration of these cognitive vulnerabilities in the context of human memory augmentation, including the reporting of a mixed design experiment with 48 participants in which we demonstrate both retrieval-induced forgetting and false memory creation for real-world experiences.
Chatbot for Peer Support Realization based on Mutual Care

In this paper we build a chatbot as part of a stress management framework that aims to help professionals in the health care sector manage their occupational stress. Our framework employs chatbots and robots to conduct conversations with individuals in order to derive a measure of their stress using a Sense of Coherence (SOC) model and then provide support depending on the calculated SOC value. Our framework can deliver different types of support to individuals, in this paper we present the care-receiving support type which is based on helper theory. Experiments are carried out with a smartphone based chatbot which is developed using the LINE chatbot platform, allowing our chatbot to be accessed easily by either Android or Apple devices.
A hybrid method for biometric authentication-oriented face detection using autoregressive model with Bayes Backpropagation Neural Network

This paper proposes a novel method, which is coined as ARBBPNN, for biometric-oriented face detection, based on autoregressive model with Bayes backpropagation neural network (BBPNN). Firstly, the given input colour key face image is modelled to HSV and YCbCr models. A hybrid model, called HS-YCbCr, is formulated based on the HSV and YCbCr models. The submodel, H, is divided into various sliding windows of size, 3 × 3. The model parameters are estimated for the window using the BBPNN. Based on the model coefficients, autocorrelation coefficients (ACCs) are computed. An autocorrelation test tests the significance of the ACCs. If the ACC passes the test, then it is inferred that the small image region, viz. the window, represents the texture and it is treated as the texture feature. Otherwise, it is regarded as structure, which is treated as the shape feature. The texture and shape features are formulated as feature vectors (FV) separately, and they are combined into a single FV. This process is performed for all colour submodels. The FVs of the submodels are combined into a single holistic vector, which is treated as the FV of the key face image. The key FV has twenty feature elements. The similarity of the key and target face images is examined, based on the key and target FVs, by deploying multivariate parametric statistical tests. If the FVs of the key and target images pass the tests, then it is concluded that the key and target face images are the same; otherwise, they are regarded as different. The GT, FSW, Pointing'04, and BioID datasets are considered for the experiments. In addition to the above datasets, we have constructed a dataset with face images collected from Google, and many images captured through a digital camera. It is also subjected to the experiment. The obtained recognition results show that the proposed ARBBPNN method outperforms the existing methods.
The Rise of the Roboid

Abstract The aim of this paper is to analyze a new phenomenon that is emerging in the field of social robotics, which we name as “roboid”. The roboid is a robot that is still at the prototype stage but claims to be fully functioning. We argue that the roboid has been created to handle a new phase between the prototyping and the commercialization of robots. In this intermediate phase, a wide-spread promotional campaign is organized by robot producers, with the purpose of understanding the desires, needs, and suggestions of potential customers. We present the first case of a a highly human-like roboid named Sophia. We carried out the visual analysis of the content and the content-agnostic factors of a selection of 15 videos uploaded on YouTube on the social robot Sophia and a qualitative analysis of the textual component of these videos to investigate a selected part of the promotional campaign on Sophia. Furthermore, on the 23,810 comments that users have posted on these videos, we applied quantitative analysis to explore the observers’ opinions about Sophia. Results highlight that: (1) a powerful but potentially ridiculous narrative has been used to build the rhetoric of Sophia; (2) the comments on the videos are too poor to enable a co-construction of this social robot with the audiences (implicit intention of Hanson Robotics); (3) the introduction of the roboid might be a very good solution to reduce the uncertainties that may occur when a new robot moves directly from the laboratory to the market.
Efficient FPGA implementation and verification of difference expansion based reversible watermarking with improved time and resource utilization

This paper presents Xilinx System Generator (XSG) model design for realization of reversible watermarking algorithm using Difference Expansion (DE) approach in System-On-Chip (SoC) Field Programmable Gate Array (FPGA) environment. The reversible watermarking is verified by taking a (4 × 4) sized test image and is applicable for larger sizes of cover images. The outcomes of the result demonstrate that the proposed structural design allows combining MATLAB-Simulink and XSG during graphical user interface for image processing applications. The superiority of the algorithm is justified by using comparative analysis with some well-known methods in both software and hardware environments. The method provides effectively higher PSNR at higher embedding capacity. It is also found that the method requires less time and hardware resources with throughput of 13.516 Mb/s at operational frequency of 80 MHz for real time implementation using FPGA.
Token Economics in Real-Life: Cryptocurrency and Incentives Design for Insolar’s Blockchain Network

The study of how to set up cryptocurrency incentive mechanisms and to operationalize governance is token economics. Given the $250 billion market cap for cryptocurrencies, there is compelling need to investigate this topic. In this paper, we present facets of the token engineering process for a real-life 80-person Swiss blockchain startup, Insolar. We show how Insolar used systems modeling and simulation combined with cryptocurrency expertise to design a mechanism to incentivize enterprises and individual users to use their new MainNet public blockchain network. This study shows that subsidy pools that incentivize application developers to develop on the network does indeed have the desired positive effect on MainNet adoption. For a startup like Insolar whose success hinges upon how well their model incentivizes various stakeholders to participate on their MainNet network versus that of numerous alternatives, this token economics simulation analysis provides invaluable insights.
A novel FCNs‐ConvLSTM network for video salient object detection

Summary A video saliency detection model is proposed based on deep learning, which improves the existing fully convolutional network (FCN)‐based model by introducing a convolutional long short‐term memory (ConvLSTM) module. The ConvLSTM splits the input into two flows with two layers in each one. The two flows have different dilation rates that make them have different receptive fields, which enables the proposed model to perform better in depicting the contour of objects. The ConvLSTM module receive frames in order as input rather than unordered frames that FCN modules do, so the proposed model can learn both spatial and temporal information of video data. Considering the lack of manually labeled annotations in the dataset, augmentation technologies are used in training the model to expand the dataset, such as performing mirror transformation, introducing Gaussian noise and abandoning every other frame to simulate fast movement situation. The proposed FCNs‐ConvLSTM model is trained and evaluated on extensively used dataset, and the results demonstrate that it performs better on recall rate (0.52 to 0.64) with a similar level on precision rate (0.72) when threshold is 125 and it also gets an increase on maximum F‐measure (0.66 to 0.70), which indicates that the proposed model has better capacity in detecting moving object.