Reviewerly/exHarmony huggingface dataset:
Design and Validation of t-MOOC for the Development of the Digital Competence of Non-University Teachers

MOOCs are configured as one of the technologies that have been gaining ground in the educational field as a new approach in virtual education. In the past few years, its presence in educational institutions has increased. In addition, the level of research and publications that revolve around these technological developments is increasing. In this sense, this research focuses on the design and validation of the structure, content and tasks of a t-MOOC for the development of the Digital Competence of non-university teachers based on the DigCompEdu Framework of the European Union. For this, a Delphi-type validation design is established using an expert coefficient that has the participation of 191 people. The results demonstrate the validity of the training proposal, as well as the uniformity of criteria of the experts. In this sense, the application and benefits of t-MOOCs as tools for competence development are discussed.
Simplified description of dynamics in neuromorphic resonant tunneling diodes

In this article, the standard theoretical model accounting for a double barrier quantum well resonant tunneling diode (RTD) connected to a direct current source of voltage is simplified by representing its current-voltage characteristic with an analytically approachable, anti-symmetric N-shaped function. The time and variables involved are also transformed to reduce the number of parameters in the model. Responses observed in previous, more physically accurate studies are reproduced, including slow-fast dynamics, excitability, and bistability, relevant for spiking signal processing. A simple expression for the refractory time of the excitable response is derived and shown to be in good agreement with numerical simulations. In particular, the refractory time is found to be directly proportional to the circuit's intrinsic inductance. The presence or absence of bistability in the dependence of the parameters is also discussed thoroughly. The results of this work can serve as a guideline in prospective endeavors to design and fabricate RTD-based neuromorphic circuits for power and time-efficient execution of neural network algorithms.
On one-weight and ACD codes in Zr2 x Zs4 x Zt8

In this paper, one-weight and additive complementary dual (ACD) codes in Zr2 x Zs4 x Zt 8 are studied. Firstly, it is shown that the image of an equidistant Z2Z4Z8-additive code is a binary equidistant code. Then, some properties of the structure and possible weights for one-weight Z2Z4Z8-additive codes are described. Finally, it is given the sufficient conditions for a Z2Z4Z8-additive code to be ACD.
Entanglement criterion via general symmetric informationally complete measurement

Abstract We propose entanglement criteria for multipartite systems via symmetric informationally complete measurement and general symmetric informationally complete measurement. We apply these criteria to detect entanglement of multipartite states, such as the convex of Bell states, entangled states mixed with white noise. It is shown that these criteria are stronger than some existing ones.
Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images

Quality assessment of stitched images is an important element of many virtual reality and remote sensing applications where the panoramic images may be used as a background as well as for navigation purposes. The quality of stitched images may be decreased by several factors, including geometric distortions, ghosting, blurring, and color distortions. Nevertheless, the specificity of such distortions is different than those typical for general-purpose image quality assessment. Therefore, the necessity of the development of new objective image quality metrics for such type of emerging applications becomes obvious. The method proposed in the paper is based on the combination of features used in some recently proposed metrics with the results of the local and global image entropy analysis. The results obtained applying the proposed combined metric have been verified using the ISIQA database, containing 264 stitched images of 26 scenes together with the respective subjective Mean Opinion Scores, leading to a significant increase of its correlation with subjective evaluation results.
Securing IoT Data Using Steganography: A Practical Implementation Approach

Adding network connectivity to any “thing” can certainly provide great value, but it also brings along potential cybersecurity risks. To fully benefit from the Internet of Things “IoT” system’s capabilities, the validity and accuracy of transmitted data should be ensured. Due to the constrained environment of IoT devices, practical security implementation presents a great challenge. In this paper, we present a noise-resilient, low-overhead, lightweight steganography solution adequate for use in the IoT environment. The accuracy of hidden data is tested against corruption using multiple modulations and coding schemes (MCSs). Additive white Gaussian noise (AWGN) is added to the modulated data to simulate the noisy channel as well as several wireless technologies such as cellular, WiFi, and vehicular communications that are used between communicating IoT devices. The presented scheme is capable of hiding a high payload in audio signals (e.g., speech and music) with a low bit error rate (BER), high undetectability, low complexity, and low perceptibility. The proposed algorithm is evaluated using well-established performance evaluation techniques and has been demonstrated to be a practical candidate for the mass deployment of IoT devices.
Cohort and Trajectory Analysis in Multi-Agent Support Systems for Cancer Survivors

In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead to a rapid diminution of their physical abilities, cognitive impairment, and reduced quality of life. This paper proposes a personalized support system for cancer survivors based on a cohort and trajectory analysis (CTA) module integrated within an agent-based personalized chatbot named EREBOTS. The CTA module relies on survival estimation models, machine learning, and deep learning techniques. It provides clinicians with supporting evidence for choosing a personalized treatment, while allowing patients to benefit from tailored suggestions adapted to their conditions and trajectories. The development of the CTA within the EREBOTS framework enables to effectively evaluate the significance of prognostic variables, detect patient's high-risk markers, and support treatment decisions.
A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers

With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to be crucial for distracted driving and the establishment of driver assistance systems is of great value. However, almost all research on the recognition of the driver's distracted actions using computer vision methods neglected the importance of temporal information for action recognition. This paper proposes a hybrid deep learning model for recognizing the actions of distracted drivers. Specifically, we used OpenPose to obtain skeleton information of the human body and then constructed the vector angle and modulus ratio of the human body structure as features to describe the driver's actions, thereby realizing the fusion of deep network features and artificial features, which improve the information density of spatial features. The K-means clustering algorithm was used to preselect the original frames, and the method of inter-frame comparison was used to obtain the final keyframe sequence by comparing the Euclidean distance between manually constructed vectors representing frames and the vector representing the cluster center. Finally, we constructed a two-layer long short-term memory neural network to obtain more effective spatiotemporal features, and one softmax layer to identify the distracted driver's action. The experimental results based on the collected dataset prove the effectiveness of this framework, and it can provide a theoretical basis for the establishment of vehicle distraction warning systems.
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data

Unsupervised Data Augmentation (UDA) is a semi-supervised technique that applies a consistency loss to penalize differences between a model's predictions on (a) observed (unlabeled) examples; and (b) corresponding 'noised' examples produced via data augmentation.While UDA has gained popularity for text classification, open questions linger over which of its components are important, and how to extend the method to sequence labeling tasks; this paper addresses these questions.Our main contribution is an empirical study of UDA to establish which components of the algorithm confer benefits in NLP.Notably, although prior work has emphasized use of clever augmentation techniques including back-translation, we find that enforcing consistency between predictions assigned to observed and randomly substituted words often yields comparable (or greater) benefits compared to these more complex perturbation models.Furthermore, we find that applying UDA's consistency loss affords meaningful gains without any unlabeled data at all, i.e., in a standard supervised setting.In short, UDA need not be unsupervised to realize much of its noted benefits, and does not require complex data augmentation to be effective.
ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision

Scientific literature analysis needs fine-grained named entity recognition (NER) to provide a wide range of information for scientific discovery.For example, chemistry research needs to study dozens to hundreds of distinct, fine-grained entity types, making consistent and accurate annotation difficult even for crowds of domain experts.On the other hand, domain-specific ontologies and knowledge bases (KBs) can be easily accessed, constructed, or integrated, which makes distant supervision realistic for fine-grained chemistry NER.In distant supervision, training labels are generated by matching mentions in a document with the concepts in the knowledge bases (KBs).However, this kind of KB-matching suffers from two major challenges: incomplete annotation and noisy annotation.We propose CHEMNER, an ontologyguided, distantly-supervised method for finegrained chemistry NER to tackle these challenges.It leverages the chemistry type ontology structure to generate distant labels with novel methods of flexible KB-matching and ontology-guided multi-type disambiguation.It significantly improves the distant label generation for the subsequent sequence labeling model training.We also provide an expertlabeled, chemistry NER dataset with 62 finegrained chemistry types (e.g., chemical compounds and chemical reactions).Experimental results show that CHEMNER is highly effective, outperforming substantially the stateof-the-art NER methods (with .25 absolute F1 score improvement).* * The first two authors contributed equally to this work and should be considered as joint first authors.With these chiral nucleophiles, Suzuki-Miyaura cross-coupling reactions were carried out with various aryl-and hetaryl chlorides in good to excellent yields. INORGANIC PHOSPHORUS COMPOUNDS COUPLING REACTIONS CHLORIDES CHLORIDESThe boronic acids are predominantly aryl with only four vinyl boronic acids being used in the library. OXOACIDS OXOACIDS REACTIVE_INTERMEDIATES, CHLORIDES (a) Incomplete AnnotationWith these chiral nucleophiles, Suzuki-Miyaura cross-coupling reactions were carried out with various aryl-and hetaryl chlorides in good to excellent yields. INORGANIC PHOSPHORUS COMPOUNDS COUPLING REACTIONS CHLORIDES CHLORIDESAlthough it was necessary to employ a stoichiometric quantity of palladium , it is noteworthy that the cross-coupling proceeded in the presence of a wide array of functional groups.COUPLING REACTIONS CATALYSTS
Coexisting Tetrahedrite–(Zn) and Sphalerite at the Teremki Gold-Ore Deposit (East Transbaikalia): Chemical Composition and Formation Conditions

Abstract Paragenetic associations of tetrahedrite-(Zn) and sphalerite are distinguished in the Teremki gold-ore deposit. The chemical composition of coexisting minerals of this association is determined. The Sb/(Sb + As) and Fe/(Fe + Zn) ratios in tetrahedrite-(Zn) vary from 0.66 to 0.97 and from 0.28 to 0.40, respectively. A negative correlation was established between Sb/(Sb + As) and Fe/(Fe + Zn) ratios. Contents of Fe in sphalerite change from 0.88 to 1.43 wt % (1.5–2.5 mol % FeS). Temperature and sulfur fugacity when precipitation of tetrahedrite-(Zn)–paragenesis were estimated: they range from 130 to 280°C and from 10 –13.2 to 10 –8.1 bars, respectively.
Marine predators' algorithm: Application in applied mechanics

In this paper we will demonstrate how Marine Predators Algorithm (MPA for short) can be used for solving certain optimization problems in applied mechanics. In the first part, biological fundamentals, as well as method explanation are given. Afterwards, MPA algorithm and its ' applicability is explained in detail. The pseudo code for this algorithm was written using Matlab R2019a software suite. This algorithm can be used for optimization o f engineering problems, such as: pressure vessel optimization, cantilever beam optimization, cone clutch optimization and speed reducer optimization. In the end, all the results for the fore mentioned problems, as well as a result comparison with other methods are shown.
Axiomatization of Some Basic and Modal Boolean Connexive Logics

Abstract Boolean connexive logic is an extension of Boolean logic that is closed under Modus Ponens and contains Aristotle’s and Boethius’ theses. According to these theses (i) a sentence cannot imply its negation and the negation of a sentence cannot imply the sentence; and (ii) if the antecedent implies the consequent, then the antecedent cannot imply the negation of the consequent and if the antecedent implies the negation of the consequent, then the antecedent cannot imply the consequent. Such a logic was first introduced by Jarmużek and Malinowski, by means of so-called relating semantics and tableau systems. Subsequently its modal extension was determined by means of the combination of possible-worlds semantics and relating semantics. In the following article we present axiomatic systems of some basic and modal Boolean connexive logics. Proofs of completeness will be carried out using canonical models defined with respect to maximal consistent sets.
Posetal Games: Efficiency, Existence, and Refinement of Equilibria in Games With Prioritized Metrics

Modern applications require robots to comply with multiple, often conflicting rules and to interact with the other agents. We present Posetal Games as a class of games in which each player expresses a preference over the outcomes via a partially ordered set of metrics. This allows one to combine hierarchical priorities of each player with the interactive nature of the environment. By contextualizing standard game theoretical notions, we provide two sufficient conditions on the preference of the players to prove existence of pure Nash Equilibria in finite action sets. Moreover, we define formal operations on the preference structures and link them to a refinement of the game solutions, showing how the set of equilibria can be systematically shrunk. The presented results are showcased in a driving game where autonomous vehicles select from a finite set of trajectories. The results demonstrate the interpretability of results in terms of minimum-rank-violation for each player.
Intelligent Sports Video Classification Based on Deep Neural Network (DNN) Algorithm and Transfer Learning

Traditional text annotation-based video retrieval is done by manually labeling videos with text, which is inefficient and highly subjective and generally cannot accurately describe the meaning of videos. Traditional content-based video retrieval uses convolutional neural networks to extract the underlying feature information of images to build indexes and achieves similarity retrieval of video feature vectors according to certain similarity measure algorithms. In this paper, by studying the characteristics of sports videos, we propose the histogram difference method based on using transfer learning and the four-step method based on block matching for mutation detection and fading detection of video shots, respectively. By adaptive thresholding, regions with large frame difference changes are marked as candidate regions for shots, and then the shot boundaries are determined by mutation detection algorithm. Combined with the characteristics of sports video, this paper proposes a key frame extraction method based on clustering and optical flow analysis, and experimental comparison with the traditional clustering method. In addition, this paper proposes a key frame extraction algorithm based on clustering and optical flow analysis for key frame extraction of sports video. The algorithm effectively removes the redundant frames, and the extracted key frames are more representative. Through extensive experiments, the keyword fuzzy finding algorithm based on improved deep neural network and ontology semantic expansion proposed in this paper shows a more desirable retrieval performance, and it is feasible to use this method for video underlying feature extraction, annotation, and keyword finding, and one of the outstanding features of the algorithm is that it can quickly and effectively retrieve the desired video in a large number of Internet video resources, reducing the false detection rate and leakage rate while improving the fidelity, which basically meets people’s daily needs.
Talk-to-Resolve: Combining scene understanding and spatial dialogue to resolve granular task ambiguity for a collocated robot

The utility of collocating robots largely depends on the easy and intuitive interaction mechanism with the human. If a robot accepts task instruction in natural language, first, it has to understand the user’s intention by decoding the instruction. However, while executing the task, the robot may face unforeseeable circumstances due to the variations in the observed scene and therefore requires further user intervention. In this article, we present a system called Talk-to-Resolve (TTR) that enables a robot to initiate a coherent dialogue exchange with the instructor by observing the scene visually to resolve the impasse. Through dialogue, it either finds a cue to move forward in the original plan, an acceptable alternative to the original plan, or affirmation to abort the task altogether. To realize the possible stalemate, we utilize the dense captions of the observed scene and the given instruction jointly to compute the robot’s next action. We evaluate our system based on a data set of initial instruction and situational scene pairs. Our system can identify the stalemate and resolve them with appropriate dialogue exchange with 82% accuracy. Additionally, a user study reveals that the questions from our systems are more natural (4.02 on average on a scale of 1 to 5) as compared to a state-of-the-art (3.08 on average).
Bitcoin adoption and beliefs in Canada

Abstract We develop a tractable model of Bitcoin adoption with network effects and social learning, which we then connect to unique data from the Bank of Canada's Bitcoin Omnibus Survey for the years 2017 and 2018. The model determines how the probability of Bitcoin adoption depends on: (i) network effects, (ii) individual learning effects and (iii) social learning effects. After accounting for the endogeneity of beliefs, we find that both network effects and individual learning effects have a positive and significant direct impact on Bitcoin adoption, whereas the role of social learning is to ameliorate the marginal effect of the network size on the likelihood of adoption. In particular, in 2017 and 2018, a one percentage point increase in the network size increased the probability of adoption by 0.45 and 0.32 percentage points, respectively. Similarly, a one percentage point increase in Bitcoin beliefs increased the probability of adoption by 0.43 and 0.72 percentage points. Our results suggest that network effects, individual learning and social learning were important drivers of Bitcoin adoption in 2017 and 2018 in Canada.
Tomography of time-dependent quantum Hamiltonians with machine learning

Interacting quantum Hamiltonians are fundamental to quantum computing. Data-based tomography of time-independent quantum Hamiltonians has been achieved, but an open challenge is to ascertain the structures of time-dependent quantum Hamiltonians using time series measurements taken locally from a small subset of the spins. Physically, the dynamical evolution of a spin system under time-dependent driving or perturbation is described by the Heisenberg equation of motion. Motivated by this basic fact, we articulate a physics-enhanced machine-learning framework whose core is Heisenberg neural networks. In particular, we develop a deep learning algorithm according to some physics-motivated loss function based on the Heisenberg equation, which ``forces'' the neural network to follow the quantum evolution of the spin variables. We demonstrate that, from local measurements, not only can the local Hamiltonian be recovered, but the Hamiltonian reflecting the interacting structure of the whole system can also be faithfully reconstructed. We test our Heisenberg neural machine on spin systems of a variety of structures. In the extreme case in which measurements are taken from only one spin, the achieved tomography fidelity values can reach about $90%$. The developed machine-learning framework is applicable to any time-dependent systems whose quantum dynamical evolution is governed by the Heisenberg equation of motion.
Computer Vision based Detection of Partially Occluded Faces

In today’s world, security has gained utmost significance in every walk of life. With the recent advancements in image and video analytics, emphasis has been towards developing enhanced surveillance systems which perform complex tasks that include automated security incident detection, tracking and analysis in real time. The primary objective of this paper is to automatically detect the presence of any masked or occluded face in real time. A robust technique based on pivotal facial points has been developed. The paper discusses in detail how the pivotal points are observed extracted are used in discovering masked faces in real time. Analysis of this algorithm’s performance on test data sets gives positive insights for further enhancements towards occluded face detection in real time surveillance.