Reviewerly/exHarmony huggingface dataset:
Training an Artificial Neural Network Using Qubits as Artificial Neurons: A Quantum Computing Approach

Artificial neural networks (ANN) proved to be efficient in solving many problems for big data analytics using machine learning. The complex and non-linear features of the input data can be learned and generalized by ANN. In the big data era, enormous amounts of data arrive from multiple sources. A stage is expected to be reached where even supercomputers are likely inundated with the big data. Training an ANN in such a situation is a challenging task due to the size and dimension of the big data. Also, a large number of parameters are to be used and optimized in the network to learn the patterns and analyze such data. Quantum computing is emerging as a field that provides a solution to this problem as a quantum computer can represent data differently using qubits. Qubits on quantum computers can be used to detect the hidden patterns in data that are difficult for a classical computer to find. Hence, there exists a huge scope for application in the area of artificial neural networks. In this work, we primarily focused on training an artificial neural network using qubits as artificial neurons. The simulation results show that our quantum computing approach for ANN (QC ANN) is efficient when compared to classical ANN. The model with qubits as artificial neurons can learn the features of data using fewer parameters for a binary classification task. We demonstrate our experiment using a quantum simulator and optimization of the quantum parameters used in QC ANN is carried out on a classical computer.
Enhanced Certificateless Auditing Protocols for Cloud Data Management and Transformative Computation

Due the advent of new wireless communication, Internet of Things (IoT) and cloud computing, outsourcing the data processing and management to the cloud servers has become a typical transformative computing paradigm. In this context, a cloud auditing protocol is an effective way to check the data integrity for cloud data management. We present a cryptanalysis of two recently proposed certificateless public verifiable auditing protocols, the Zhang et al.’s protocol and the Kang et al.’s protocol. We argue that the tags in the Zhang et al.’s protocol and the semi-tags in the Kang et al.’s protocol can be forged, leading to insecure protocols. We propose improved versions of both protocols and analyze their security and efficiency. Our new protocols can be used as a very effective way for checking the integrity of outsourced cloud storage in the context of transformative computation.
User experience evaluation of e-report application using cognitive walkthrough (cw), heuristic evaluation (he) and user experience questionnaire (ueq)

Abstract E-Report is developed based on the web to make it easier in preparing students’ assessment reports in the form of rating reports, competency achievement reports, and ledgers. Even, many high schools/vocational schools in Bali have not implemented the E-Report. Implementation of cognitive walkthrough techniques, heuristic evaluation, and users’ experience questionnaire in this research aimed to find out the results of evaluating user experience from the aspects of effectiveness, efficiency, user satisfaction, and recommendations for improvement. Effectiveness and efficiency were calculated by Cognitive Walkthrough techniques (CW), user satisfaction by user experience questionnaire techniques (UEQ) and recommendations for improvement, obtained from experts who discuss aspects of usability with Heuristic Evaluation techniques and Cognitive Walkthrough data. Based on the results obtained in terms of users, E-Report has been ineffective and inefficient. While in terms of user satisfaction, respondents were satisfied with Applications E-Report. This is because the effectiveness of a task that has a very large error and failure in one of the features as well as the clarity of results that poor user satisfaction can affect processing time. Therefore, evaluation of user experience based on aspects of usability in the E-Report has not met the criteria for products with good usability. Improvements made on the start page, page values, page value results were done through a photo frame. Therefore, identifying the problems from these three sources can further optimize the usability and improve user experience on E-Report.
One Shot Logo Recognition Based on Siamese Neural Networks

This work presents an approach for one-shot logo recognition that relies on a Siamese neural network (SNN) embedded with a pre-trained model that is fine-tuned on a challenging logo dataset. Although the model is fine-tuned using logo images, the training and testing datasets do not have overlapped categories; meaning that, all the classes used for testing the one-shot recognition framework remain unseen during the fine-tuning process. The recognition process follows the standard SNN approach in which a pair of input images are encoded by each sister network. The encoded outputs for each image are afterwards compared using a trained metric and thresholded to define matches and mismatches. The proposed approach achieves an accuracy of 77.07% under the one-shot constraints in the QMUL-OpenLogo dataset. Code is available at https://github.com/cjvargasc/oneshot_siamese/.
Mobile Volumetric Video Streaming Enhanced by Super Resolution

Volumetric videos allow viewers to exercise 6-DoF (degrees of freedom) movement when watching them. Due to their true 3D nature, streaming volumetric videos is highly bandwidth demanding. In this work, we present to our knowledge a first volumetric video streaming system that leverages deep super resolution (SR) to boost the video quality on commodity mobile devices. We propose a series of judicious optimizations to make SR efficient on mobile devices.
Deep Discrete Attention Guided Hashing for Face Image Retrieval

Recently, face image hashing has been proposed in large-scale face image retrieval due to its storage and computational efficiency. However, owing to the large intra-identity variation (same identity with different poses, illuminations, and facial expressions) and the small inter-identity separability (different identities look similar) of face images, existing face image hashing methods have limited power to generate discriminative hash codes. In this work, we propose a deep hashing method specially designed for face image retrieval named deep Discrete Attention Guided Hashing (DAGH). In DAGH, the discriminative power of hash codes is enhanced by a well-designed discrete identity loss, where not only the separability of the learned hash codes for different identities is encouraged, but also the intra-identity variation of the hash codes for the same identities is compacted. Besides, to obtain the fine-grained face features, DAGH employs a multi-attention cascade network structure to highlight discriminative face features. Moreover, we introduce a discrete hash layer into the network, along with the proposed modified backpropagation algorithm, our model can be optimized under discrete constraint. Experiments on two widely used face image retrieval datasets demonstrate the inspiring performance of DAGH over the state-of-the-art face image hashing methods.
A novel image encryption algorithm using chaotic compressive sensing and nonlinear exponential function

This paper presents an optically secure image cryptosystem in the compressed-sensing (CS) domain by using a chaos driven nonlinear function. First, the original image is divided into non-overlapping blocks of uniform size, and then the discrete cosine transform (DCT) is used for obtaining sparse representation of these blocks. A logistic map-based circulant sensing measurement matrix is used to compress the sparse representations of the blocks. This partial cipher is further processed for encryption using a nonlinear function based on a dynamic invertible exponential function. The decorrelation of values is done by applying the Arnold 2-D map on the cipher. The seed values to the logistic equation and the parameters of the Arnold map are based on the original image, which makes the proposed algorithm robust in terms of plain text sensitivity. Not only does the algorithm achieve a considerable amount of security, but it also conceals the original image identity by the compression introduced, thereby reducing the storage and transmission cost. The decryption of the cipher is done using a smooth l0 approximation, giving high-quality reconstruction results. The extensive simulation results and performance analysis illustrates the excellent security and reconstruction results of the proposed scheme in comparison with the existing algorithms.
Smart City Applications on the Blockchain: Development of a Multi-layer Taxonomy

Blockchain Technology (BT) has become widely recognized beyond the financial sector. Various other fields of application for the ground-breaking innovation are discussed by researchers and practitioners alike. One such field is the smart city. Driven by startups, projects aimed at alleviating negative effects of urbanization build on the properties of BT to improve quality of life, administrative processes, and environmental sustainability. Yet, due to the entrepreneurial dynamics and abundant fields of application for BT in smart cities, an integrated and boundary-spanning analysis is lacking. This study aims at developing a multi-layer taxonomy that illustrates how BT is used in different smart city business models. For this purpose, we identified a sample of 80 startups which offer applications for smart cities and examined their business models. The paper explores business model configurations and technological characteristics of blockchain-based smart city applications. We identify BT startup archetypes in several domains: sharing economy, privacy and security, and internet of things (IoT). The paper will be useful for researchers, practitioners, and regulators interested in gaining novel insights about how startups leverage BT to create and capture value.
Comprehending 3D and 4D ontology-driven conceptual models: An empirical study

This paper presents an empirical study that investigates the extent to which the pragmatic quality of ontology-driven models is influenced by the choice of a particular ontology, given a certain understanding of that ontology. To this end, we analyzed previous research efforts and distilled three hypotheses based on different metaphysical characteristics. An experiment based on two foundational ontologies (UFO and BORO) involving 158 participants was then carried out, followed by a protocol analysis to gain further insights into the results of experiment. We then extracted five derivations from the results of the empirical study in order to summarize our findings. Overall, the results confirm that the choice of a foundational ontology can lead to significant differences in the interpretation and comprehension of the conceptual models produced. Moreover, the effect of applying a certain foundational ontology can cause considerable variations in the effort required to comprehend these models.
A Fall Detection and Emergency Notification System for Elderly

Elderly are the fastest growing segment of the world population. Most elders prefer to live independently in their own homes. This makes them more vulnerable to emergency incidents such as fall and unconsciousness. Out of them, falling is the most dominant cause leading to both fatal and non-fatal injuries among elders. Timely detection and notification of falls can minimise the adversity caused by the impact. This paper discusses a non-obtrusive vision-based fall detection system using a service robot with a Kinect sensor attached and an automatic fall notification system based on Q-Learning. The proposed system is capable of distinguishing a fall from an activity of daily living by analysing body joint velocities and orientation of selected limbs using the RGB and depth data generated from the Kinect Sensor. The system can also differentiate between three types of falls referred to as Prone Position, Crawl Position and Kneel Position in the paper. Upon fall detection, the system can automatically make successive phone calls to a list of emergency contacts by considering the contact person's probability of answering a call and level of being busy. Furthermore this system has the ability to reduce the confusion and anxiety raised within an elder during a fall by ensuring quick notification and thereby timely assistance. An experiment was conducted to observe the effectiveness of the proposed system and it was able to achieve an accuracy of 92.5% and a sensitivity of 95.45%. The other critical observations made have also been highlighted in the paper.
Urban Object Detection Kit: A System for Collection and Analysis of Street-Level Imagery

In this paper, we propose Urban Object Detection Kit, a system for the real-time collection and analysis of street-level imagery. The system is affordable and portable and allows local government agencies to receive actionable intelligence about the objects on the streets. This system can be attached to service vehicles, such as garbage trucks, parking scanners and maintenance cars, thus allowing for large-scale deployment. This will, in turn, result in street-level imagery captured at a high collection frequency, while covering a large geographical region. Unlike more traditional panoramic street-level imagery, the data collected by this system has a higher frequency, making it suitable for the highly dynamic nature of city streets. For example, the proposed system allows for real-time detection of urban objects and potential issues that require the attention of city services. It paves the way for easy deployment and testing of multimedia information retrieval algorithms in a dynamic real-world setting. We showcase the usefulness of object detection for identifying issues in public spaces that occur within a limited time span. Finally, we make the kit, as well as the data collected using it, openly available for the research community.
A Comparison of LSTM and XGBoost for Predicting Firemen Interventions

In several areas of the world such as France, fire brigades are facing a constant increase in the number of their commitments, some of the main reasons are related to the growth and aging of the population and others to global warming. This increase occurs principally in constant human and material resources, due to the financial crisis and the disengagement of the states. Therefore, forecasting the number of future interventions will have a great impact on optimizing the number and the type of on-call firefighters, making it possible to avoid too few firefighters available during peak load or an oversized guard during off-peak periods. These predictions are viable, given firefighters’ labor is conditioned by human activity in general, itself correlated to meteorological data, calendars, etc. This article aims to show that machine learning tools are mature enough at present to allow useful predictions considering rare events such as natural disasters. The tools chosen are XGBoost and LSTM, two of the best currently available approaches, in which the basic experts are decision trees and neurons. Thereby, it seemed appropriate to compare them to determine if they can forecast the firefighters’ response load and if so, if the results obtained are comparable. The entire process is detailed, from data collection to the predictions. The results obtained prove that such a quality prediction is entirely feasible and could still be improved by other techniques such as hyperparameter optimization.
Vulnerability Assessment and Detection of Makeup Presentation Attacks

The accuracy of face recognition systems can be negatively affected by facial cosmetics which have the ability to substantially alter the facial appearance. Recently, it was shown that makeup can also be abused to launch so-called makeup presentation attacks. In such attacks, an attacker might apply heavy makeup to achieve the facial appearance of a target subject for the purpose of impersonation.In this work, we assess the vulnerability of a widely used open-source face recognition system, i.e. ArcFace, to makeup presentation attacks using the publicly available Makeup Induced Face Spoofing (MIFS) and FRGCv2 databases. It is shown that the success rate of makeup presentation attacks in the MIFS database has negligible impact on the security of the face recognition system. Further, we employ image warping to simulate improved makeup presentation attacks which reveal a significantly higher success rate. Moreover, we propose a makeup attack detection scheme which compares face depth data with face depth reconstructions obtained from RGB images of potential makeup presentation attacks. Significant variations between the two sources of information indicate facial shape alterations induced by strong use of makeup, i.e. potential makeup presentation attacks. Conceptual experiments on the MIFS database confirm the soundness of the presented approach.
A Bayesian Inference Based Hybrid Recommender System

The large mass of various products/services accessible on the Internet has motivated the development of recommender systems to refine the selection of items aligned with users' expectations.Recommender systems have been developed to tackle the item targeting problem.They are crucial tools that quickly target items fitting users' needs, thus allowing them to easily identify the items that fit their tastes and preferences.Following state-of-the-art methods, a distinction is made between content-based recommender approaches and collaborative filtering-based recommender approaches.Collaborative filtering-based recommender approaches are the most widely adopted methods.They are divided into memory-based methods that show the advantage of their easy-understandability, and model-based methods that are data sparsity resilient and high-accurate.In this paper, we propose a hybrid model-based recommendation approach, a combination of a user-based approach and an item-based approach.Our method estimates the probability with which a user would rate an item.It performs a Bayesian inference of future end-user interests and shows the advantage of the easy-understandability of memory-based methods and the effectiveness of model-based methods.Experiments are conducted on real-world datasets and show that our method outperforms several state-of-the-art recommendation methods regarding the prediction accuracy and the recommendation quality.
Image Synthesis from Locally Related Texts

Text-to-image synthesis refers to generating photo-realistic images from text descriptions. Recent works focus on generating images with complex scenes and multiple objects. However, the text inputs to these models are the only captions that always describe the most apparent object or feature of the image and detailed information (e.g. visual attributes) for regions and objects are often missing. Quantitative evaluation of generation performances is still an unsolved problem, where traditional image classification- or retrieval-based metrics fail at evaluating complex images. To address these problems, we propose to generate images conditioned on locally-related texts, i.e., descriptions of local image regions or objects instead of the whole image. Specifically, questions and answers (QAs) are chosen as locally-related texts, which makes it possible to use VQA accuracy as a new evaluation metric. The intuition is simple: higher image quality and image-text consistency (both globally and locally) can help a VQA model answer questions more correctly. We purposed VQA-GAN model with three key modules: hierarchical QA encoder, QA-conditional GAN and external VQA loss. These modules help leverage the new inputs effectively. Thorough experiments on two public VQA datasets demonstrate the effectiveness of the model and the newly proposed metric.
State Machine Based Human-Bot Conversation Model and Services

Task-oriented virtual assistants (or simply chatbots) are in very high demand these days. They employ third-party APIs to serve end-users via natural language interactions. Chatbots are famed for their easy-to-use interface and gentle learning curve (it only requires one of humans' most innate ability, the use of natural language). Studies on human conversation patterns show, however, that day-to-day dialogues are of multi-turn and multi-intent nature, which pushes the need for chatbots that are more resilient and flexible to this style of conversations. In this paper, we propose the idea of leveraging Conversational State Machine to make it a core part of chatbots' conversation engine by formulating conversations as a sequence of states. Here, each state covers an intent and contains a nested state machine to help manage tasks associated to the conversation intent. Such enhanced conversation engine, together with a novel technique to spot implicit information from dialogues (by exploiting Dialog Acts), allows chatbots to manage tangled conversation situations where most existing chatbot technologies fail.
Protect Your Pacemaker: Blockchain based Authentication and Consented Authorization for Implanted Medical Devices

Existing implanted medical devices often do not use any form of authentication due to resource constraints, resulting in unauthorized access or programming of devices. In this paper, we introduce Medical Implant Consent (MedIC), a non-embedded, blockchain-based solution that provides authentication and authorization according to patient consent. We design protocols for doctors to obtain digital medical licenses and for patients to obtain and verify digital patient consent. We evaluate the performance and cost of our solution, using time measurements and memory footprint of relevant operations. We also enumerate our cryptographic operations and compute the energy with benchmarked measurements. Our results demonstrate a memory footprint of 4436 MB and timing delay of 200 ms to authenticate according to our patient's consent.
Implementing Microlearning in Nursing Homes: Implications for Policy and Practice in Person-Centered Dementia Care

The aim of this study was to develop and pilot a 52-week Microlearning curriculum on person-centered dementia care in nine nursing homes. The goal was to evaluate the usability and application of Microlearning as a tool to increase staff knowledge and improve perceptions of people with dementia, thus increasing their ability to deliver person-centered care and their job satisfaction. Findings indicate that participants enjoyed the flexible, 24/7 access to training and found that the immediacy of the format encouraged them to apply their learning directly to practice. Staff knowledge and attitudes toward people with dementia were positively impacted by the training, as was job satisfaction. Further research to test the efficacy of Microlearning compared to usual training is warranted, as are coordinated policy development efforts that can guide the implementation of best practices in the use of Microlearning as an innovative training modality in nursing homes.