Reviewerly/exHarmony huggingface dataset:
Detection of Unconsciousness in Falls Using Thermal Vision Sensors

Unattended falls, where the injured person may be unconscious, are among the most frequent causes of death worldwide among the elderly population. Several fall detection methods using devices, such as wearable sensors and cameras, have been deployed in Smart Homes to detect and minimize the response time against these types of accidents. Some related works have proposed to trigger an alarm when a fall is detected. However, there is a lack of considering the state in which the person has been left after the fall. This work proposes a fall detection system using low-resolution thermal cameras that detects falls and classifies the falls depending on the state of conscience of the user. Thus, once detected in a risky state, a voice assistant routine starts to measure the degree of conscience of the person and make an emergency call only when necessary. The system is deployed in the Smart Home of the University of Almeria and validated with one user, triggering the alarm in all falls that lead to an unconscious state.
Metasurface Smart Glass for Object Recognition

We demonstrate a novel approach to realize optical neural networks by condensing the layered computations into a singlet of metasurface device which provides task-specific transformations of signals for recognizing the identity of an optical object.
A Systematic Review on Teaching Parallel Programming

This work aimed to perform a systematic review of the literature on teaching parallel programming using low-cost clusters, identifying the main programming languages, hardware platforms and software tools used in teaching-learning this type of programming. The research results showed that the most used clusters in the teaching of parallel programming were assembled from multicore machines (Cluster Beowulf) and by single board computers (SBC), in addition to multicore machines with graphics acceleration cards (GPUs). Regarding the use of programming languages, software tools and parallelism libraries used in teaching parallel programming, it is observed that most of the researched works mentioned the use of C, C++, and JAVA programming languages, and as parallelism libraries the use of MPI, OpenMP, CUDA and Apache Hadoop. Furthermore, the tests on the clusters were carried out through the implementation of parallelized generic algorithms and, in some cases, using algorithms that involve matrix operations.
Single image dehazing algorithm based on generative adversarial network

This paper proposes a kind of generative adversarial network which is used to remove the haze for single image. In this paper, the generator uses U-Net as the backbone, and in order to effectively fuse the feature of different scales between the non-adjacent layers of the generator, a dense linking module which based on back-projection is used in the generator. In this paper, a kind of enhancement strategy which based on boosting strategy is used to improve the effectiveness of skip connection between the encoder and the decoder in the generator model. In order to evaluate the effect of haze removing, the proposed model is trained on the RESIDE and evaluated on the SOTS. The experiment proves that our method has advantages in both qualitative comparison and quantitative assessment.
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.
Image Recognition Based on the Depth-Wise Separable Convolution and Softpool

For the purpose of enabling the garbage classification to work accurately and efficiently, the image recognition method based on improved Inception-ResNet-V2 network is studied, and four types of daily domestic wastes are classified and identified. In the proposed network, the connection structure in the primary inception module is improved to achieve a dense connection, Softpool is applied to replace the traditional Maxpool pooling method, fine-grained feature information is retained, more intensive feature activations are enlarged, and the Depth-wise separable convolution is used to replace the common convolution method. The improved network not only reduces the quantity of calculation and expedites the training speed for the network, but also captures more image features fully, thereby the recognition accuracy is improved further. Compared with the ResNet50, AlexNet, and YOLOv5 network model, the results show that the recognition accuracy of the network model proposed in this paper comes up to 96.8%, which is 5% higher than that of the YOLOv5 network. The performance of the improved network is significantly enhanced comparing with the traditional network. It is proved that the algorithm is eligible to be successfully applied to the problem of garbage classification, and it greatly weakens the difficulty of municipal garbage recovery.
Adjustment of ESP curriculum to the needs of digital university environment

The article examines the issues of updating ESP curriculum used in the educational process of the university. The need for updating is due to the process of digital transformation of society in general and digitalization of education in particular. The author describes the competencies (and corresponding knowledge, skills and abilities) that ESP curriculum focuses on and also provides the review of digital tools, the practical use of which in pedagogical practice will help to achieve the educational goal.
On-Chip Quantum Autoencoder for Teleportation of High-Dimensional Quantum States

Currently most quantum teleportation experiments are based on qubits. Here, we demonstrate a quantum autoencoder assisted teleportation for high-dimensional quantum states. Our method of training the autoencoder allows us to take a finite sample of those states, learn how to compress them to qubits with nearly unit fidelity. High fidelity is achieved between the input qutrit and the qutrit recovered from the teleported qubit.
Fuzzy Controller Design via Higher Order Derivatives of Lyapunov Function for Takagi-Sugeno Fuzzy System

This paper is concerned with the problem of designing fuzzy controller for Takagi-Sugeno fuzzy models. Many results and improvements regarding stabilization issues have been proposed for Takagi-Sugeno fuzzy system. In the literature, stabilization conditions for Takagi-Sugeno fuzzy system has been improved and its stabilization area has been expanded. However, there is still room to reduce the conservativeness for design condition. In this paper, a new fuzzy controller via a Lyapunov function with double integral of the membership functions is proposed. Our proposed stabilization conditions are derived based on higher order derivatives of the Lyapunov function, which reduces the existing conservative design condition. Finally, an illustrative example is given to show the effectiveness of our control design method by comparing with the previous work.
Value Added: A Case Study of Research Impact Services

What are the ways in which library-provided bibliometric services add value to the research enterprise? Tasked with developing a program to assess and improve research impact services at Indiana University Bloomington, the open scholarship librarian launched a research impact offering in fall 2019, with positive results. Research impact services help faculty see the full significance of their work, equip them with tools to demonstrate and advocate for the value of their scholarship, and often provide unexpected insights. Bibliometric analysis can also benefit library units, particularly in supporting administrative decision-making in interactions with publishers and in seeing publishing trends to make collection management decisions. Service development in this area has the potential to deepen library engagement with the campus community. It might also promote collaboration between librarians in functional roles and those who serve as subject specialists.
Disparity-based Stereo Image Compression with Aligned Cross-View Priors

With the wide application of stereo images in various fields, the research on stereo image compression (SIC) attracts extensive attention from academia and industry. The core of SIC is to fully explore the mutual information between the left and right images and reduce redundancy between views as much as possible. In this paper, we propose DispSIC, an end-to-end trainable deep neural network, in which we jointly train a stereo matching model to assist in the image compression task. Based on the stereo matching results (i.e. disparity), the right image can be easily warped to the left view, and only the residuals between the left and right views are encoded for the left image. A three-branch auto-encoder architecture is adopted in DispSIC, which encodes the right image, the disparity map and the residuals respectively. During training, the whole network can learn how to adaptively allocate bitrates to these three parts, achieving better rate-distortion performance at the cost of a lower disparity map bitrates. Moreover, we propose a conditional entropy model with aligned cross-view priors for SIC, which takes the warped latents of the right image as priors to improve the accuracy of the probability estimation for the left image. Experimental results demonstrate that our proposed method achieves superior performance compared to other existing SIC methods on the KITTI and InStereo2K datasets both quantitatively and qualitatively.
Locality preserving binary face representations using auto‐encoders

Abstract Crypto-biometric schemes, such as fuzzy commitment, require binary sources. A novel approach to binarising biometric data using Deep Neural Networks applied to facial biometric data is introduced. The binary representations are evaluated on the MOBIO and the Labelled Faces in the Wild databases, where their biometric recognition performance and entropy are measured. The proposed binary embeddings give a state-of-the-art performance on both databases with almost negligible degradation compared to the baseline. The representations' length can be controlled. Using a pretrained convolutional neural network and training the model on a cleaned version of the MS-celeb-1M database, binary representations of length 4096 bits and 3300 bits of entropy are obtained. The extracted representations have high entropy and are long enough to be used in crypto-biometric systems, such as fuzzy commitment. Furthermore, the proposed approach is data-driven and constitutes a locality preserving hashing that can be leveraged for data clustering and similarity searches. As a use case of the binary representations, a cancellable system is created based on the binary embeddings using a shuffling transformation with a randomisation key as a second factor.
SMMP: A Stable-Membership-Based Auto-Tuning Multi-Peak Clustering Algorithm

Since most existing single-prototype clustering algorithms are unsuitable for complex-shaped clusters, many multi-prototype clustering algorithms have been proposed. Nevertheless, the automatic estimation of the number of clusters and the detection of complex shapes are still challenging, and to solve such problems usually relies on user-specified parameters and may be prohibitively time-consuming. Herein, a stable-membership-based auto-tuning multi-peak clustering algorithm (SMMP) is proposed, which can achieve fast, automatic, and effective multi-prototype clustering without iteration. A dynamic association-transfer method is designed to learn the representativeness of points to sub-cluster centers during the generation of sub-clusters by applying the density peak clustering technique. According to the learned representativeness, a border-link-based connectivity measure is used to achieve high-fidelity similarity evaluation of sub-clusters. Meanwhile, based on the assumption that a reasonable clustering should have a relatively stable membership state upon the change of clustering thresholds, SMMP can automatically identify the number of sub-clusters and clusters, respectively. Also, SMMP is designed for large datasets. Experimental results on both synthetic and real datasets demonstrated the effectiveness of SMMP.
Transforming eyeglass rim into touch panel using piezoelectric sensors

The traditional interaction method for smart eyewear is by touching a control panel located at the temple front of the eyeglass. This method can be unnatural since the control panel and the display are not within the same plane. In this paper, we propose a new and natural interaction technology for smart eyewear that allows users to interact with the rim of the eyeglass without adding additional hardware to the rim. This design is based on an observation that a finger touch would slightly alter the channel frequency response (CFR) of the eyeglass. We use one pair of piezoelectric (PZT) transducers to measure the CFR, and we recognize the tiny CFR changes by analyzing the complex representation of the CFR. The system detects five touch locations using a deep learning classifier. We recruit ten subjects to evaluate the system and the result shows that the system can recognize the five touch locations with an F1 score of 0.91.
Resident Willingness to Participate in Digital Contact Tracing in a COVID-19 Hotspot: Findings From a Detroit Panel Study

Digital surveillance tools and health informatics show promise in counteracting diseases but have limited uptake. A notable illustration of the limits of such tools is the general failure of digital contact tracing in the United States in response to COVID-19.We investigated the associations between individual characteristics and the willingness to use app-based contact tracing in Detroit, a majority-minority city that experienced multiple waves of COVID-19 outbreaks and deaths since the start of the pandemic. The aim of this study was to examine variations among residents in the willingness to download a contact tracing app on their phones to provide public health officials with information about close COVID-19 contact during summer 2020.To examine residents' willingness to participate in digital contact tracing, we analyzed data from 2 waves of the Detroit Metro Area Communities Study, a population-based survey of Detroit, Michigan residents. The data captured 1873 responses from 991 Detroit residents collected in June and July 2020. We estimated a series of multilevel logit models to gain insights into differences in the willingness to participate in digital contact tracing across a variety of individual attributes, including race/ethnicity, degree of trust in the government, and level of education, as well as interactions among these variables.Our results reflected widespread reluctance to participate in digital contact tracing in response to COVID-19, as less than half (826/1873, 44.1%) of the respondents said they would be willing to participate in app-based contact tracing. Compared to White respondents, Black (odds ratio [OR] 0.45, 95% CI 0.23-0.86) and Latino (OR 0.32, 95% CI 0.11-0.99) respondents were significantly less willing to participate in digital contact tracing. Trust in the government was positively associated with the willingness to participate in digital contact tracing (OR 1.17, 95% CI 1.07-1.27), but this effect was the strongest for White residents (OR 2.14, 95% CI 1.55-2.93). We found similarly divergent patterns of the effects of education by race. While there were no significant differences among noncollege-educated residents, White college-educated residents showed greater willingness to use app-based contact tracing (OR 6.12, 95% CI 1.86-20.15) and Black college-educated residents showed less willingness (OR 0.46, 95% CI 0.26-0.81).Trust in the government and education contribute to Detroit residents' wariness of digital contact tracing, reflecting concerns about surveillance that cut across race but likely arise from different sources. These findings point to the importance of a culturally informed understanding of health hesitancy for future efforts hoping to leverage digital contact tracing. Though contact tracing technologies have the potential to advance public health, unequal uptake may exacerbate disparate impacts of health crises.
Trust-Based Permissioned Blockchain Network for Identification and Authentication of Internet of Smart Devices: An E-Commerce Prospective

The Internet of Things refers to billions of devices around us connected to the wireless internet. These IoT devices are memory-constrained devices that can collect and transfer data over the network without human assistance. Recently, IoT is materialized in retail commerce, transforming from recognition service to post-purchase engagement service. IoT examples in retail commerce are smart refrigerators, smart speakers, smart washing machines, smart automobiles, and automatic re-purchase of groceries using RFID tags. Despite the rise, one of the significant inconveniences slowing rapid adaption is the “security” of these devices, which are vulnerable to various attacks. One such attack is Distributed Denial-of-Service (DDoS) attacks targeting offline or online sensitive data. Hence, a lightweight cryptographic mechanism needs to establish secure communication among IoT devices. This paper presents the solution to secure communication among IoT devices using a permissioned blockchain network. Specifically, in this work, we proposed a mechanism for identifying and authenticating the smart devices using the Elliptic-curve cryptography (ECC) protocol. This proposed work uses permissioned blockchain infrastructure, which acts as a source of trust that aids the authentication process using ECC cryptosystem. In addition, lightweight Physical Unclonable Function (PUF) technology is also used to securely store the device’s keys. Using this technology, the private keys need not be stored anywhere, but it is generated on the fly from the trusted zone whenever the private key is required.
Verification of Privacy Protection Reliability through Mobile Forensic Approach Regarding iOS-Based Instant Messenger

With the diffusion of mobile devices and Internet hyperconnectivity technology, all daily living records of individuals are being recorded on mobile devices in real time. However, from the user’s point of view, the reliability of privacy protection, that is, whether the user’s data on the mobile device completely disappears when it is deleted, is critical. This is because, for the sustainability of social growth, it is necessary to control the digitalization and technology that heightens the risks of the future society. Therefore, this study aims to check the traces of the SQLite database to see if instant messenger messages deleted by the user can be recovered. When the SQLite database record is deleted, if the database shrink function or other application-level deletion does not work, it is possible to recover the deleted record. We chose two iOS-based instant messengers, WhatsApp and WeChat, and analyzed the SQLite DB file and Table Schema where messages are stored. As a result of the experiment in this study, it was verified that the area where the deleted message was stored in the SQLite DB file was overwritten with 0 × 00 or updated with a NULL value, making it impossible to recover the deleted message. This process operates at the app level, and user data is safely protected.
Integrating AIS and contemporary technologies

In this chapter, we explore four contemporary technologies – data analytics, artificial intelligence, robotic process automation and blockchain – and their integration with accounting information systems (AIS). Common themes arising include greater efficiency within organisations, leading to better decision making; improved risk assessment, enabling new business growth; more effective processes and controls, resulting in better analysis; improved accuracy of task completion, facilitating more effective control and audit procedures. There is however a recognition that the integration of these technologies will require a change in the skillset of accountants, organisational changes as a result of becoming more data driven and logistical challenges including technical compatibility with other systems and cost. With the increased level of data involved in each technology, concerns over data security and compliance with regulation arise. These technologies have, and will continue to have, a significant impact on AIS. It is a fast-paced environment, and regulators, professional bodies, organisations and individual accountants would be well advised to engage critically with these developments for future success.
Towards a better identification of Bitcoin actors by supervised learning

Bitcoin is the most widely used crypto-currency, and one of the most studied.Thanks to the open nature of the Blockchain, transaction records are freely accessible and can be analyzed by anyone.The first step in most analytics work is to group anonymous addresses into a set of addresses, called aggregates, that are meant to correspond to unique actors.In this paper, we propose a new method to discover more accurate address aggregates using supervised learning.We introduce a way to create a labeled training set based on reliable heuristics and external information, and propose two methods.The first automatically finds address aggregates from a set of transactions.The second improves an address aggregate of a target actor.We empirically validate our results on large-scale datasets.A striking result of our analysis is that training a model to recognize the change addresses of a particular actor is more efficient than using a larger dataset that does not target that particular actor.In doing so, we clearly show the feasibility and interest of supervised machine learning to identify Bitcoin actors.