Reviewerly/exHarmony huggingface dataset:
A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysis

We present a new dataset with annotated eye movements. The dataset consists of over 800,000 gaze points recorded during a car ride in the real world and in the simulator. In total, the eye movements of 19 subjects were annotated. In this dataset, there are several data sources including the eyelid closure, the pupil center, the optical vector, and a vector into the pupil center starting from the center of the eye corners. These different data sources are analyzed and evaluated individually as well as in combination with respect to their suitability for eye movement classification. These results will help developers of real-time systems and algorithms to find the best data sources for their application. Also, new algorithms can be trained and evaluated on this data set. Link to code and dataset https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FA%20Multimodal%20Eye%20Movement%20Dataset%20and%20...&mode=list
A Full-Process Optimization-Based Background Subtraction for Moving Object Detection on General-Purpose Embedded Devices

Real-time computer vision tasks are emerging in consumer electronics with lightweight computing performance, which are an exquisite design art to balance the computational efficiency and accuracy. In this paper, we present the embedded background subtraction (EBGS) - an optimization algorithm for the entire process to increase computational efficiency and detection accuracy simultaneously. EBGS exploits a simple and efficient Additive Increase Multiplicative Decrease (AIMD) filter to improve the foreground detection accuracy without spending too much time. Moreover, the design combination between the contracted codebook background subtraction (BGS) model and a random model update is proposed to reduce the time consumption. Experiments demonstrate that EBGS can decrease the computing overhead for the three parts of BGS process simultaneously and achieve real-time performance and satisfactory detection accuracy under challenging environments.
Spatial Component Analysis to Improve Mineral Estimation Using Sentinel-2 Band Ratio: Application to a Greek Bauxite Residue

Remote sensing can be fruitfully used in the characterization of metals within stockpiles and tailings, produced from mining activities. Satellite information, in the form of band ratio, can act as an auxiliary variable, with a certain correlation with the ground primary data. In the presence of this auxiliary variable, modeled with nested structures, the spatial components without correlation can be filtered out, so that the useful correlation with ground data grows. This paper investigates the possibility to substitute in a co-kriging system, the whole band ratio information, with only the correlated components. The method has been applied over a bauxite residues case study and presents three estimation alternatives: ordinary kriging, co-kriging, component co-kriging. Results have shown how using the most correlated component reduces the estimation variance and improves the estimation results. In general terms, when a good correlation with ground samples exists, co-kriging of the satellite band-ratio Component improves the reconstruction of mineral grade distribution, thus affecting the selectivity. On the other hand, the use of the components approach exalts the distance variability.
Blockchain Adoption for Trusted Medical Records in Healthcare 4.0 Applications: A Survey

Healthcare 4.0 allows monitoring of electronic health record (EHR) at distributed locations, through wireless infrastructures like Bluetooth, ZigBee, near-field communication (NFC), and GPRS. Thus, the private EHR data can be tampered by malicious entities that affect updates through different stakeholders like patients, doctors, laboratory technicians, and insurance agencies. Hence, there must be a notion of trust among aforementioned stakeholders. Moreover, the accessed volume of data is humongous; thus, to ensure security and trust, blockchain (BC)-based solutions can handle timestamped volumetric data as chronological ledger. Motivated from the same, the paper presents a systematic survey of BC applications in Healthcare 4.0 ecosystems. The contribution of the paper is to conduct a systematic survey of BC adoption in Healthcare 4.0. The survey identifies tools and technologies to support BC-based healthcare applications and addresses open challenges for future research of integrating BC to secure EHR in Healthcare 4.0 ecosystem.
Neural Networks for Mortality Prediction: Ready for Prime Time?*

1 Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO 2 Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO 3 Data Science to Patient Value (D2V) Initiative, University of Colorado School of Medicine, Aurora, CO 4 Department of Bioengineering, College of Engineering, Design, and Computing, Aurora, CO *See also p. 519. Dr. Bennett’s institution received funding from the National Institutes of Health (NIH) and he received support for article research from the NIH. Dr. Albers received support for article research from the NIH (NIH/NLM 5R01LM012734). Dr. Russell has disclosed that he does not have any potential conflicts of interest.
Stitching interferometric characterization of geometric errors of hemispherical shells

Geometric errors including the surface error and thickness variation of a hemispherical shell are determinant of the system performance, e.g. the frequency splitting of a hemispherical resonator gyroscope. Therefore, complete characterization at 0.1 µm level of accuracy is required which is difficult with current roundness measuring instruments. This paper presents an interferometric method for axially eccentric hemispherical shells. The outer surface and inner surface are measured individually by simply translating the shell along its anchor axis. The two measurements are registered after correction of pixel misalignment and mapping distortion through ray tracing. Thickness variation is then calculated from their difference. A multi-axis platform is designed to scan multiple subapertures covering the hemispherical shell. All subapertures are stitched together with misalignment corrected, according to the minimized inconsistency in the overlapping region of neighboring subapertures. The full aperture map is thus obtained for both the surface error and thickness variation. Polar harmonic representation is suggested by applying annular Zernike polynomial fitting. The method is experimentally demonstrated and validated with roundness cross test.
Visually smooth multi-UAV formation transformation

Unmanned airborne vehicles (UAVs) are useful in both military and civilian operations. In this paper, we consider a recreational scenario, i.e., multi-UAV formation transformation show. A visually smooth transformation needs to enforce the following three requirements at the same time: (1) visually pleasing contour morphing - for any intermediate frame, the agents form a meaningful shape and align with the contour, (2) uniform placement - for any intermediate frame, the agents are (isotropically) evenly spaced, and (3) smooth trajectories - the trajectory of each agent is as rigid/smooth as possible and completely collision free. First, we use the technique of 2-Wasserstein distance based interpolation to generate a sequence of intermediate shape contours. Second, we consider the spatio-temporal motion of all the agents altogether, and integrate the uniformity requirement and the spatial coherence into one objective function. Finally, the optimal formation transformation plan can be inferred by collaborative optimization. Extensive experimental results show that our algorithm outperforms the existing algorithms in terms of visual smoothness of transformation, boundary alignment, uniformity of agents, and rigidity of trajectories. Furthermore, our algorithm is able to cope with some challenging scenarios including (1) source/target shapes with multiple connected components, (2) source/target shapes with different typology structures, and (3) existence of obstacles. Therefore, it has a great potential in the real multi-UAV light show. We created an animation to demonstrate how our algorithm works; See the demo at https://1drv.ms/v/s!AheMg5fKdtdugVL0aNFfEt_deTbT?e=le5poN .
CCLoc—An Improved Interferometric Seismic Event Location Algorithm Applied to Induced Seismicity

Abstract The amount of recorded seismic event data is rapidly growing, and manual processing by trained human experts to infer hypocenter, source parameters, and moment tensor solutions is therefore no longer feasible. Automated procedures are required to process data efficiently and include quality-control measures that allow for outlier detection. We present a modular cross-correlation location (CCLoc) algorithm for induced seismicity that uses cross correlations of either raw seismograms or characteristic functions derived from them followed by a reverse migration procedure. The novelty of this approach is the inclusion of cross pairs of P and S arrivals and the inclusion of autocorrelations, both of which add a distance constraint to the hypocenter estimation. The algorithm is modular in the sense that preprocessing can be tailored to specific data or task. Nine months of seismic data from an underground hard-rock tin mine are processed in a fully automated mode using a machine-learning approach for seismic phase arrival detection and using the estimated arrival functions as input for CCLoc. Making use of the average cross-correlation value as a quality constraint, CCLoc can successfully infer source information on 92% of previously manually processed data. The accuracy of automatic processing is demonstrated by comparing hypocenter, source parameter, and moment tensor solutions between the two datasets. The algorithm will potentially aid the analysis of induced or other seismicity and is particularly well suited to use in the case of large numbers of seismic sensors recording many events.
Understanding the data-sharing debate in the context of Aotearoa/New Zealand: a narrative review on the perspectives of funders, publishers/journals, researchers, participants and Māori collectives

This review outlines current debates about the sharing of research data, with a focus on relevance for Aotearoa/New Zealand. Recent years have seen increasingly frequent calls for public sharing of data from funders and publishers/journals in particular. Past research has suggested that researchers tend to agree that any detriments of data-sharing are outweighed by benefits for transparency and progress. We summarise trends across past research into perspectives of funders, publishers/journals, and researchers on data-sharing before raising three considerations. Firstly, past research on data-sharing has tended to overlook the potential implications of data-sharing for participants. We review the small body of research on participant perspectives. This research has conceptualised participants as a homogenous group without theorising how participants make sense of data-sharing. Secondly, perspectives on data-sharing vary depending on the methodology being applied, and we raise some specific considerations when data-sharing is proposed in long-term longitudinal research such as the Dunedin Study. Thirdly, Indigenous perspectives on data-sharing must be central to all research into data-sharing with any of the stakeholder groups, and we review existing research on data sovereignty in relation to data-sharing in Aotearoa/New Zealand and globally. We conclude by summarising a series of tensions between stakeholders in the data-sharing debate.
Exploring the Shift from Physical to Cybercrime at the Onset of the COVID-19 Pandemic

The novel coronavirus has made an impact on virtually every aspect of our lives. The current study utilizes secondary data to identify patterns and trends related to shifting crime from the physical to the cyber domain. With millions, if not billions, people staying at home, attackers now look for new ways to commit crimes. Our findings indicate that while a lot of crimes such as robbery, assault, rape, and murder have declined at the beginning of the pandemic, we are also witnessing a rise in cybercrime, vehicle theft, and domestic violence. The current study looks specifically at phishing and what new trends are observed due to COVID-19. The current work is grounded in routine activity theory and demonstrates its relevance to both the physical and cyberspace. The implications of our work can be used by scholars who want to continue researching this new phenomenon. Practitioners can utilize our findings to look for ways to improve the corporate security posture by protecting the employees and customers working from home. Developing new phishing training and awareness programs should be focused around possible scenarios involving COVID-19. Our study suggests victims are more likely to fall prey to those during times of fear and uncertainty like the current pandemic.
Markov Decision Processes with Incomplete Information and Semiuniform Feller Transition Probabilities

This paper deals with control of partially observable discrete-time stochastic systems. It introduces and studies Markov Decision Processes with Incomplete Information and with semiuniform Feller transition probabilities. The important feature of these models is that their classic reduction to Completely Observable Markov Decision Processes with belief states preserves semiuniform Feller continuity of transition probabilities. Under mild assumptions on cost functions, optimal policies exist, optimality equations hold, and value iterations converge to optimal values for these models. In particular, for Partially Observable Markov Decision Processes the results of this paper imply new and generalize several known sufficient conditions on transition and observation probabilities for weak continuity of transition probabilities for Markov Decision Processes with belief states, the existence of optimal policies, validity of optimality equations defining optimal policies, and convergence of value iterations to optimal values.
SGXPecial

Intel SGX is a hardware-based trusted execution technology that partitions an application into trusted and untrusted parts. The trusted part, known as an enclave, executes within an encrypted memory environment, preventing the host application and the OS from being able to access its memory. The enclave, however, has the ability to access the host's memory. When considering malicious code running in an enclave, the strong memory isolation and encryption properties offered may aid the stealthiness of malware, since malware detection tools cannot inspect the enclave. The enclave and the host communicate over bi-directional interfaces that the Intel SGX SDK generates.
Improving Sequential Recommendation with Attribute-Augmented Graph Neural Networks

Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with. In this paper, we propose an attribute-augmented graph neural network model named Murzim. Murzim takes as input the graphs constructed from the user-item interaction sequences and corresponding item attribute sequences. By combining the GNNs with node aggregation and an attention network, Murzim can capture user preference patterns, generate embeddings for user-item interaction sequences, and then generate recommendations through next-item prediction. We conduct extensive experiments on multiple datasets. Experimental results show that Murzim outperforms several state-of-the-art methods in terms of recall and MRR, which illustrates that Murzim can make use of item attribute information to produce better recommendations. At present, Murzim has been deployed in MX Player, one of India's largest streaming platforms, and is recommending videos for tens of thousands of users.
Secure and Privacy-Preserved Data Collection for IoT Wireless Sensors

The captured data from smart devices via Internet of Things (IoT) wireless sensors are vulnerable to numerous online and offline attacks and unauthorized accesses, hence, some digital signature and encryption solutions have been designed to ensure public verifiability, data integrity, and confidentiality. However, there are still some issues to be addressed. For example, the data source is revealed to the public due to the public verifiability of digital signatures, in which authentication is transferrable. Moreover, computation of these data can only be done after decryption, restricting outsourced computation, such as a computing facility from a cloud. The best approach of private computation, which supports outsourced computation, is based on homomorphic encryption. However, significant computational overhead is a concern. To deal with these issues, in this article, we propose an efficient and provably secure scheme based on designated-verifier proofs, deniable authentication and homomorphic encryption for secure and lightweight data collection, batch verification, and data analysis in the privacy-preserved IoT wireless sensors applications. The main contribution of our work is the privacy-preserved IoT wireless sensors system along with a novel deniable authenticated homomorphic encryption scheme that can securely aggregate data from IoT wireless sensors for secure outsourced applications. To prove the security and efficiency of our proposed scheme, we provide formal security analysis and performance comparisons for IoT wireless sensors.
A Resource Constrained Neural Network for the Design of Embedded Human Posture Recognition Systems

A custom HW design of a Fully Convolutional Neural Network (FCN) is presented in this paper to implement an embeddable Human Posture Recognition (HPR) system capable of very high accuracy both for laying and sitting posture recognition. The FCN exploits a new base-2 quantization scheme for weight and binarized activations to meet the optimal trade-off between low power dissipation, a very reduced set of instantiated physical resources and state-of-the-art accuracy to classify human postures. By using a limited number of pressure sensors only, the optimized HW implementation allows keeping the computation close to the data sources according to the edge computing paradigm and enables the design of embedded HP systems. The FCN can be simply reconfigured to be used for laying and sitting posture recognition. Tested on a public dataset for in-bed posture classification, the proposed FCN obtains a mean accuracy value of 96.77% to recognize 17 different postures, while a small custom dataset has been used for training and testing for sitting posture recognition, where the FCN achieves 98.88% accuracy to recognize eight positions. The FCN has been prototyped on a Xilinx Artix 7 FPGA where it exhibits a dynamic power dissipation lower than 11 mW and 7 mW for laying and sitting posture recognition, respectively, and a maximum operation frequency of 47.64 MHz and 26.6 MHz, corresponding to an Output Data Rate (ODR) of the sensors of 16.50 kHz and 9.13 kHz, respectively. Furthermore, synthesis results with a CMOS 130 nm technology have been reported, to give an estimation about the possibility of an in-sensor circuital implementation.
On emotionalisation of public domains

Emotionalisation of the pandemic crisisWhile editing this collection of articles exploring the geographies and styles of emotionalisation of public domains, we observed a veritable eruption of emotionalised public discourse in real-time: the COVID-19 pandemic.On some level this was only natural, since the pandemic generates sickness, pain, suffering, the primordial fear of death, and a general sense of existential uncertainty.Without minimising the authentic experiences of suffering and loss, we suggest that the emotionalisation of COVID-19 exemplifies key sociocultural and political processes unfolding in the contemporary world.The pandemic became an emotional event because every event considered significant today is, above all, an emotional one.Every private or collective occurrence is examined through its emotionality -its potential for exciting and moving people.Events are evaluated in terms of the emotional condition of the feeling subject, whether an individual or collective.The emotionality of a memory or experience is the measure of its value, its authenticity, its truth.Accordingly, tracing the discourse of COVID-19 that constitutes the pandemic as an emotional, mental event reveals how the pandemic -like most collective events of our time -has undergone substantial 'emotionalisation'.As we describe below in elaborating the notion of 'emotionalisation', we refer not only to the legitimisation, intensification and emphasis on emotional discourse in collective spheres of life, both formal and informal (Woodward, 2009;Holmes, 2010; Sieben and Wettergren, 2010), but also to a deep, tacit or overt intertwining of this emotionality with 'psychologisation' (De Vos, 2013;Madsen, 2018).In the emotionalisation processes that we discuss in this special issue, emotions are anchored in the concepts and logic of the global therapeutic habitus, the discourse
The influence of conversational role on phonetic alignment toward voice-AI and human interlocutors

Two studies investigated the influence of conversational role on phonetic imitation toward human and voice-AI interlocutors. In a Word List Task, the giver instructed the receiver on which of two lists to place a word; this dialogue task is similar to simple spoken interactions users have with voice-AI systems. In a Map Task, participants completed a fill-in-the-blank worksheet with the interlocutors, a more complex interactive task. Participants completed the task twice with both interlocutors, once as giver-of-information and once as receiver-of-information. Phonetic alignment was assessed through similarity rating, analysed using mixed effects logistic regressions. In the Word List Task, participants aligned to a greater extent toward the human interlocutor only. In the Map Task, participants as giver only aligned more toward the human interlocutor. Results indicate that phonetic alignment is mediated by the type of interlocutor and that the influence of conversational role varies across tasks and interlocutors.
Diacritics Restoration using BERT with Analysis on Czech language

We propose a new architecture for diacritics restoration based on contextualized embeddings, namely BERT, and we evaluate it on 12 languages with diacritics. Furthermore, we conduct a detailed error analysis on Czech, a morphologically rich language with a high level of diacritization. Notably, we manually annotate all mispredictions, showing that roughly 44% of them are actually not errors, but either plausible variants (19%), or the system corrections of erroneous data (25%). Finally, we categorize the real errors in detail. We release the code at https://github.com/ufal/bert-diacritics-restoration.