Reviewerly/exHarmony huggingface dataset:
Cryptocurrencies and the emergence of blockocracy

Blockocracies are a coherent, distinctive and novel organizational form bound by a collective ledger and a cryptocurrency. We frame our analysis of blockocracies against Weber's enduring description of bureaucracy, identifying those features of Weberian bureaucracies that are present, absent or marginalized in blockocracies. In contrast to bureaucracy's monocratic authority structure, authority in blockocracies is centered on four distinct layers. In each layer, there is governance of the code and governance by the code, and in the latter we distinguish between endogenous and exogenous rules. We also compare the "blockocrat" with Weber's depiction of the bureaucrat.
A survey of Type-2 fuzzy logic controller design using nature inspired optimization

In this paper, we are presenting a survey of research works dealing with Type-2 fuzzy logic controllers designed using optimization algorithms inspired on natural phenomena. Also, in this review, we analyze the most popular optimization methods used to find the important parameters on Type-1 and Type-2 fuzzy logic controllers to improve on previously obtained results. To this end have included a summary of the results obtained from the web of science database to observe the recent trend of using optimization methods in the area of optimal type-2 fuzzy logic control design. Also, we have made a comparison among countries of the network of researchers using optimization methods to analyze the distribution and impact of the papers.
Big Data Analysis of Media Reports Related to COVID-19

COVID-19 is lasting longer than expected, which has a huge impact on the economy and on personal life. Each country has a different response method, and the damage scale is also distinct. This study aims to find out how COVID-19-related news was handled in the domestic media to seek ways to minimize the pandemic. The paper focuses on the number of news features by period and by disaster and analyzes related words based on big data. The results of the analysis are as follows. First, in the initial response phase, keywords to identify accurate sources of actual broadcast contents, fake news, social networking service (SNS), etc. were also ranked in the top 20. Second, in the active response phase, when the number of confirmed persons and the government’s countermeasures were announced, more than 100 COVID-19-related articles were issued, and the related words increased rapidly from the initial response stage. Therefore, the fact that COVID-19 has been expressed as a keyword indicates that our society is watching with great interest in the government’s response to the disease.
Online Sparse BLSTM Models for Resource Usage Prediction in Cloud Datacentres

Real time resource usage prediction is an important part of resource provisioning in a cloud data centre. As cloud workloads vary dynamically, effective resource provisioning requires prediction of future resource usage trends. The problem is highly complicated because of highly time varying nature of cloud resource workloads. Training the future resource usage prediction models once, using a fixed set of observations is not sufficient to capture the variability in cloud workloads. In this work, we propose to use gradient descent (GD) and Levenberg-Marquardt (LM) adaptation algorithms for dynamic adaptation of resource utilization prediction models. We also propose a novel sparse framework for fast online adaptation of resource usage prediction models. We propose to analyze different algorithms such as ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> regularization, ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> regularization, optimal brain damage (OBD), optimal brain surgeon (OBS) for introducing sparsity. The proposed sparse framework for online adaptation of multivariate resource usage prediction models is validated for CPU usage prediction in the Google cluster trace and PlanetLab workload trace. A comparative analysis of different sparse frameworks shows that OBD-based LM adaptation algorithm performs better than other frameworks for online multivariate resource usage prediction in a cloud.
Digital Twin Modeling of Smart Cities

Smart cities utilize the Big Data and IoT to provide better life for citizens. Since, they are the most complicated human artifact, the adoption of such technologies become a complex task, requiring continuous data collection, aggregation and analysis. In order to transform city problems into concrete actions a systematic approach aimed at digital transition needs to be followed. There are huge efforts to build city information models for encoding city objects, their relations and supporting the decision-making. This requires a common knowledge base, supported by rich vocabularies and ontologies that are capable to handle the information diversity and overload. In this paper a methodological framework and an upper-level ontology for building digital city models are presented. The process of digital city modelling follows the concept of digital twin by providing a data-driven decision making. The proposed upper-level ontology aims to overcome city modeling problems due to data silos and lack of semantic interoperability.
Quasi-probability information in a coupled two-qubit system interacting non-linearly with a coherent cavity under intrinsic decoherence

We explore the phase space quantum effects, quantum coherence and non-classicality, for two coupled identical qubits with intrinsic decoherence. The two qubits are in a nonlinear interaction with a quantum field via an intensity-dependent coupling. We investigate the non-classicality via the Wigner functions. We also study the phase space information and the quantum coherence via the Q-function, Wehrl density, and Wehrl entropy. It is found that the robustness of the non-classicality for the superposition of coherent states, is highly sensitive to the coupling constants. The phase space quantum information and the matter-light quantum coherence can be controlled by the two-qubit coupling, initial cavity-field and the intrinsic decoherence.
Virtual Reality for Immersive Learning in Art History

Virtual reality (VR) applications using consumer grade headsets have now become popular for immersive learning. VR represents a key technology for showing to students various contents the way it would be presented in its real representation. In addition to the 3 dimensional effect, it includes an immersive dimension that can transport the user into an environment that is specific to the learning content. While teaching material using VR can be useful, such an offer must satisfy the learning objectives of the instructor and the specific needs of the course and the students. In this paper, we propose a VR museum for paintings designed for instructors in Art History. A key contribution is the definition of the painting characteristics and the questionnaires associated with the paintings in the JSON open-standard file format, facilitating the modification of the application without prior programming knowledge. Such a file can be easily edited and it contains only the key information needed by the Art History instructors. The system has been developed with Unity and tested with a Valve Index. The workload and system usability is assessed by 25 participants through a NASA-TLX and System Usability evaluation. The results support the conclusion that the system has a high usability, suggesting its potential deployment in classes of Art History to better engage students.
A Partially Binarized Hybrid Neural Network System for Low-Power and Resource Constrained Human Activity Recognition

A custom Human Activity Recognition system is presented based on the resource-constrained Hardware (HW) implementation of a new partially binarized Hybrid Neural Network. The system processes data in real-time from a single tri-axial accelerometer, and is able to classify between 5 different human activities with an accuracy of 97.5% when the Output Data Rate of the sensor is set to 25 Hz. The new Hybrid Neural Network (HNN) has binary weights (i.e. constrained to +1 or -1) but uses non-binarized activations for some layers. This, in conjunction with a custom pre-processing module, achieves much higher accuracy than Binarized Neural Network. During preprocessing, the measurements are made independent from the spatial orientation of the sensor by exploiting a reference frame transformation. A prototype has been realized in a Xilinx Artix 7 FPGA, and synthesis results have been obtained with TSMC CMOS 65 nm LP HVT and 90 nm standard cells. Best result shows a power consumption of 6.3 μW and an area occupation of 0.2 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> when real-time operations are set, enabling in this way, the possibility to integrate the entire HW accelerator in the auxiliary circuitry that normally equips inertial Micro Electro-Mechanical Systems (MEMS).
BITS: Blockchain based Intelligent Transportation System with Outlier Detection for Smart City

With the rise of smart cities, transportation systems are getting smarter every day. An Intelligent Transportation System (ITS) should be secure, autonomous, capable of discerning safeness levels at the roads, and provide services to improve human experience. To reach the gold standard, the ITS faces several issues such as centralization, trust, and data integrity. The Transportation System and the data generated from the vehicles can be intercepted, manipulated and corrupted with coordinated attacks. Moreover, every system might have bad actors who want to manipulate the system or data to his or her favor by exploiting the system. In order to guarantee data integrity, immutability, and availability for the ITS, we propose Blockchain based architecture with outlier detection to prevent malicious activity by the vehicles while preserving integrity in sharing information. The Outlier Detection is designed to reside before the consensus process, to identify and prevent participation of malicious vehicles in consensus process or block mining. In our proposed Blockchain based Intelligent Transportation system with Outlier Detection for Smart City (BITS), we used machine learning to detect the anomaly in the data. The proposed model can be used in various applications of ITS such as traffic monitoring, criminal activity profiling, accident detection and reporting, etc.
‘Doublecheck: a sensory confirmation is required to own a robotic hand, sending a command to feel in charge of it’

Over a lifetime of experience, the representation of the body is built upon congruent integration of multiple elements constituting the sensorimotor loop. To investigate its robustness against the rupture of congruency between senses and with motor command, we selectively manipulated in healthy subjects the binds between sight, proprioception, and efferent motor command. Two experiments based on the Moving Hand Illusion were designed employing Tendon Vibration Illusion to modulate proprioception and generate illusory altered feedback of movement. In Experiment A, visuomotor congruency was modulated by introducing adelay between complex multifingered movements performed by arobotic hand and real movement of each participant's hand. In the presence of the motor command, visuomotor congruency enhanced ownership, agency, and skin conductance, while proprioceptive-motor congruency was not effective, confirming the prevalence of vision upon proprioception. In Experiment B, the impact of visuo-proprioceptive congruency was tested in the absence of motor command because the robotic hand moved autonomously. Intersensory congruency compensated for the absence of motor command only for ownership. Skin conductance in Exp Band Proprioceptive Drift in both experiments did not change. Results suggest that ownership and agency are independently processed, and presence of the efferent component modulates sensory feedbacks salience. The brain seems to require the integration of at least two streams of congruent information. Bodily awareness can be generated from sensory information alone, but to feel in charge of the body, senses must be double-checked with the prediction generated from efference copy, which is treated as an additional sensory modality.
Neural Serendipity Recommendation

Recommender systems have been playing an important role in providing personalized information to users. However, there is always a trade-off between accuracy and novelty in recommender systems. Usually, many users are suffering from redundant or inaccurate recommendation results. To this end, in this article, we put efforts into exploring the hidden knowledge of observed ratings to alleviate this recommendation dilemma. Specifically, we utilize some basic concepts to define a concept, Serendipity , which is characterized by high-satisfaction and low-initial-interest. Based on this concept, we propose a two-phase recommendation problem which aims to strike a balance between accuracy and novelty achieved by serendipity prediction and personalized recommendation. Along this line, a Neural Serendipity Recommendation (NSR) method is first developed by combining Muti-Layer Percetron and Matrix Factorization for serendipity prediction. Then, a weighted candidate filtering method is designed for personalized recommendation. Finally, extensive experiments on real-world data demonstrate that NSR can achieve a superior serendipity by a 12% improvement in average while maintaining stable accuracy compared with state-of-the-art methods.
SCENS: Simultaneous Contrast Enhancement and Noise Suppression for Low-Light Images

Imaging in low-light conditions often suffers from degradations, such as low visibility, low contrast, and noticeable noise, which significantly reduces the performance of various vision-based applications. While various methods are proposed to enhance image contrast, inevitable noise is also amplified notably. Consequently, it is highly desired to take both contrast enhancement and noise suppression into consideration simultaneously. In this article, we propose a novel and unified framework SCENS to simultaneously enhance contrast and suppress noise for low-light images. An observed low-light image is decomposed into illumination, reflectance, and noise components. More specifically, the illumination is estimated using the second-order total generalized variation to preserve the spatial smoothness and the overall structure. In contrast, the piecewise continuity and fine detail of reflectance are maintained by minimizing the residual of gradients between the reflectance and the scene. Experimental results demonstrate the effectiveness of the proposed SCENS on contrast enhancement and noise mitigation. In addition, both subjective and objective comparisons with state-of-the-art algorithms indicate the superiority of the proposed method.
Exploring the unsupervised classification of seismic events of Cotopaxi volcano

This paper explores the use of six different clustering-based methods to classify long-period and volcano-tectonic seismic events and to find possible overlapping signals of non-volcanic origin that could occur at the same time or immediately after the occurrence of volcano-seismic events. According to the explored classifiers space, the BIRCH method with k = 2 was chosen as the best model in the classification of both pure seismic events, reaching a weighted balanced accuracy and accuracy scores of 0.81 and 0.88, respectively. The accuracy result represents a satisfactory and competitive classification performance when compared to the state of the art methods. Besides, the spectral-clustering method with k = 3 was able to classify seismic events with and without overlapped signals of non-volcanic origin, attaining a weighted balanced accuracy score of 0.51. This result was at least 0.18 units higher than the other classifiers. Additionally, the obtained true positive rates of 0.94 corroborated the excellent performance of this classifier to detect seismic events with overlapping. According to the obtained results, it is possible to state that the proposed clustering-based exploration was effective in providing competitive models for both the classification of uncontaminated seismic events as well as for the detection of seismic events with overlapped signals.
Periocular Recognition in the Wild With Generalized Label Smoothing Regularization

Periocular biometric covering the immediate vicinity of human eye is a synergistic alternative to face particularly when the face is masked or occluded. Most present work for periocular recognition in the wild are mainly convolutional neural networks learned based on cross-entropy loss. However, periocular images only capture the least salient face features, and thus suffering from severe intra-class compactness and inter-class dispersion issues for discriminative deep feature learning. Recently, label smoothing regularization (LSR) is discerned capable of diminishing the intra-class variation by minimizing the Kullback-Liebler divergence of a uniform distribution and a network prediction distribution. In this letter, we extend LSR to that of Generalized LSR (GLSR) by learning a pre-task network prediction, in place of the predefined uniform distribution. Extensive experiments on four periocular in the wild datasets disclose that the GSLR-trained networks prevail over the LSR-based counterpart and other most recent the state of the arts. This is supported by our empirical analyses that the embedding periocular features rendered by GLSR results in better class-wise cluster separation than the conventional LSR.
Empirical evaluation of multi-task learning in deep neural networks for natural language processing

Multi-task learning (MTL) aims at boosting the overall performance of each individual task by leveraging useful information contained in multiple-related tasks. It has shown great success in natural language processing (NLP). Currently, a number of MTL architectures and learning mechanisms have been proposed for various NLP tasks, including exploring linguistic hierarchies, orthogonality constraints, adversarial learning, gate mechanism, and label embedding. However, there is no systematic exploration and comparison of different MTL architectures and learning mechanisms for their strong performance in-depth. In this paper, we conduct a thorough examination of five typical MTL methods with deep learning architectures for a broad range of representative NLP tasks. Our primary goal is to understand the merits and demerits of existing MTL methods in NLP tasks, thus devising new hybrid architectures intended to combine their strengths. Following the empirical evaluation, we offer our insights and conclusions regarding the MTL methods we have considered.
Identifying Child Users via Touchscreen Interactions

With the proliferation of smart devices, children can be easily exposed to violent or adult-only content on the Internet. Without any precaution, the premature and unsupervised use of smart devices can be harmful to both children and their parents. Thus, it is critical to employ parent patrol mechanisms such that children are restricted to child-friendly content only. A successful parent patrol strategy has to be user friendly and privacy aware. The apps that require explicit actions from parents are not effective because a parent may forget to enable them, and the ones that use built-in cameras or microphones to detect child users may impose privacy violations. In this article, we propose iCare, a system that can identify child users automatically and seamlessly when users operate smartphones. In particular, iCare investigates the intrinsic differences of screen-touch patterns between child and adult users from the aspect of physiological maturity. We discover that one’s touch behaviors are related to his or her age. Thus, iCare records the touch behaviors and extracts hand geometry, finger dexterity, and hand stability features that capture the age information. We conduct experiments on 100 people including 62 children (3 to 17 years old) and 38 adults (18 to 59 years old). Results show that iCare can achieve 96.6% accuracy for child identification using only a single swipe on the screen, and the accuracy becomes 98.3% with three consecutive swipes.
The Quest for Secure and Privacy-preserving Cloud-based Industrial Cooperation

Industrial cooperation promises to leverage the huge amounts of data generated by and collected in industrial deployments to realize valuable improvements such as increases in product quality and profit margins. Cloud computing with its adjustable resources is a prime candidate to serve as the technical foundation for industrial cooperation. However, cloud computing further exaggerates existing security and privacy concerns of industrial companies, leading them to refrain from participating in cloud-based industrial cooperation. To overcome these concerns and thus allow companies to benefit from its advantages, we identify and discuss different aspects of secure and privacy-preserving cloud-based industrial cooperation, ranging from securing industrial devices and networks to secure storage and processing of industrial data in the cloud. By discussing already usable and emerging technical approaches as well as identifying open research challenges, we contribute to realizing the vision of secure and privacy-preserving industrial cooperation.
Utilization of Data Mining Techniques in National Food Security during the Covid-19 Pandemic in Indonesia

The aim of the research is to map clusters on Indonesia's national food security during the Covid-19 Pandemic. Where food security is a condition for the fulfillment of food for the state up to individuals, which is reflected in the availability of sufficient food, both in quantity and quality, safe, diverse, nutritious, equitable, and affordable and does not conflict with the religion, beliefs and culture of the community, to be able to live a healthy, active, and productive life in a sustainable manner. The data source used is secondary data from the Central Statistics Agency (abbreviated BPS). The data consists of monthly per capita expenditure in urban and rural areas by province and group of goods (rupiah) consisting of 33 data records (2011-2018). The group of goods used is expenditures used for food costs. The data mining method used is k-medoids which are part of the clustering. Cluster mapping uses 2 labels namely C1: labels with high food security and C2: labels with low food security. The results of the k-medoids method calculation concluded that 19 provinces were in C1 cluster and 14 provinces were in C2 cluster. From these results it is stated that 42% of Indonesia still has low food security as evidenced by the fulfillment of higher food needs than non-food. These regions are West Sumatra, Riau, Bangka Belitung Islands, Riau islands, DKI Jakarta, West Java, Banten, Bali, Central Kalimantan, South Borneo, East Kalimantan, North Sulawesi, West Papua and Papua.