Reviewerly/exHarmony huggingface dataset:
Human-Robot Assembly Task with Holographic Projections for Inexperienced Operators

In the age of flexible and fast-evolving industrial scenarios, the operator is often required to learn and perform different tasks during the same working shift. Making the collaboration between a human and a robot easier becomes then crucial. In this paper, we propose an exploratory study to analyze the advantages of using a mixed-reality interface to support the operator during the learning phase of a new task in collaboration with a robot. To highlight the benefits of the proposed solution, we compared the use of the holographic instructions against the more traditional textual guidelines. The results showed that the proposed mixed-reality interface reduces the variability of the cycle-time and of the operator's learning rate. From a subjective perspective, the holographic learning method turned out to be more intuitive and effective than the textual one.
Monte-Carlo Graph Search: the Value of Merging Similar States

We consider the problem of planning in a Markov Decision Process (MDP) with a generative model and limited computational budget. Despite the underlying MDP transitions having a graph structure, the popular Monte-Carlo Tree Search algorithms such as UCT rely on a tree structure to represent their value estimates. That is, they do not identify together two similar states reached via different trajectories and represented in separate branches of the tree. In this work, we propose a graph-based planning algorithm, which takes into account this state similarity. In our analysis, we provide a regret bound that depends on a novel problem-dependent measure of difficulty, which improves on the original tree-based bound in MDPs where the trajectories overlap, and recovers it otherwise. Then, we show that this methodology can be adapted to existing planning algorithms that deal with stochastic systems. Finally, numerical simulations illustrate the benefits of our approach.
How tight is your language? A semantic typology based on Mutual Information

Languages differ in the degree of semantic flexibility of their syntactic roles.For example, English and Indonesian are considered more flexible with regard to the semantics of subjects, whereas German and Japanese are less flexible.In Hawkins' classification, more flexible languages are said to have a loose fit, and less flexible ones are those that have a tight fit.This classification has been based on manual inspection of example sentences.The present paper proposes a new, quantitative approach to deriving the measures of looseness and tightness from corpora.We use corpora of online news from the Leipzig Corpora Collection in thirty typologically and genealogically diverse languages and parse them syntactically with the help of the Universal Dependencies annotation software.Next, we compute Mutual Information scores for each language using the matrices of lexical lemmas and four syntactic dependencies (intransitive subjects, transitive subject, objects and obliques).The new approach allows us not only to reproduce the results of previous investigations, but also to extend the typology to new languages.We also demonstrate that verb-final languages tend to have a tighter relationship between lexemes and syntactic roles, which helps language users to recognize thematic roles early during comprehension. Theoretical background and aims of the paperThis paper proposes a quantitative bottom-up corpus-based approach to cross-linguistic comparison, determining how tightly or loosely different lexemes can be mapped on basic syntactic roles.The idea goes back to Hawkins (1986Hawkins ( : 121-127, 1995; see also Müller-Gotama 1994), who coined the terms 'tight-fit' and 'loose-fit' languages.The former have unique surface forms that map onto more constrained meanings, whereas the latter have more vague forms with less constrained meanings.For instance, Present-Day English has fewer semantic restrictions on the subject and object than Old English, German or Russian.Consider several examples below.(1) a.Locative: This tent sleeps four.b.Temporal: 2020 witnessed a spread of the highly infectious coronavirus disease.c.Instrument: 10 Euros will buy you a meal.d.Source: The roof leaks water.While these sentences are perfectly acceptable in English, their German or Russian equivalents would be unacceptable or strange.This means that subjects in English are less semantically restricted than subjects in German and Russian (see also Plank 1984).Tightness and looseness have several components.Semantic flexibility of arguments is only one of them.Other features of tight languages include formal case marking, avoidance of raisings and long WH-movements and lower reliance on context in interpretation.Languages can change their degree of tightness.English is a well-known example of shifting from tight to loose (Hawkins 1986).As the case was lost, the zero-marked NPs in Middle English became more dependent on the verb for theta-role assignment.This is why the rigid SVO order emerged, which
Optimal Transport with Dimensionality Reduction for Domain Adaptation

Domain adaptation manages to learn a robust classifier for target domain, using the source domain, but they often follow different distributions. To bridge distribution shift between the two domains, most of previous works aim to align their feature distributions through feature transformation, of which optimal transport for domain adaptation has attract researchers’ interest, as it can exploit the local information of the two domains in the process of mapping the source instances to the target ones by minimizing Wasserstein distance between their feature distributions. However, it may weaken the feature discriminability of source domain, thus degrade domain adaptation performance. To address this problem, this paper proposes a two-stage feature-based adaptation approach, referred to as optimal transport with dimensionality reduction (OTDR). In the first stage, we apply the dimensionality reduction with intradomain variant maximization but source intraclass compactness minimization, to separate data samples as much as possible and enhance the feature discriminability of the source domain. In the second stage, we leverage optimal transport-based technique to preserve the local information of the two domains. Notably, the desirable properties in the first stage can mitigate the degradation of feature discriminability of the source domain in the second stage. Extensive experiments on several cross-domain image datasets validate that OTDR is superior to its competitors in classification accuracy.
On the Worst-Case Side-Channel Security of ECC Point Randomization in Embedded Devices

Point randomization is an important countermeasure to protect Elliptic Curve Cryptography (ECC) implementations against side-channel attacks. In this paper, we revisit its worst-case security in front of advanced side-channel adversaries taking advantage of analytical techniques in order to exploit all the leakage samples of an implementation. Our main contributions in this respect are the following: first, we show that due to the nature of the attacks against the point randomization (which can be viewed as Simple Power Analyses), the gain of using analytical techniques over simpler divide-and-conquer attacks is limited. Second, we take advantage of this observation to evaluate the theoretical noise levels necessary for the point randomization to provide strong security guarantees and compare different elliptic curve coordinates systems. Then, we turn this simulated analysis into actual experiments and show that reasonable security levels can be achieved by implementations even on low-cost (e.g. 8-bit) embedded devices. Finally, we are able to bound the security on 32-bit devices against worst-case adversaries.
The need for a harmonized speech dataset for Alzheimer’s disease biomarker development

This commentary is the product of a concerted effort to understand the needs, barriers, and gaps in the field of speech and language biomarkers for Alzheimer’s disease (AD). It distills interviews, surveys, and extensive correspondence with global leaders in the areas of dementia research, clinical trials, linguistics, and data analytics into an idealized clinical-study design for the harmonized collection of voice recordings. The ultimate goal of the effort is to democratize the ongoing speech and language analytics efforts by making such rich datasets available to the wider research ecosystem.
Acoustical analysis of additively manufactured snare drums

Additive manufacturing (3D printing) has seen tremendous growth over the past decade and is finding many fields of application, including in musical acoustics. Klapel Instruments has recently developed multiple snare drum prototypes using additive manufacturing production for the shell and rims of the drum. For the past two years, two pairs of undergraduates at the University of Hartford have worked with faculty to assess the acoustical properties of these drums, specifically focused on determining any acoustic differences caused by the new manufacturing technique and materials. Several traditionally manufactured drums along with the additively manufactured drums were tested structurally, performed on, audio recorded, and subjectively assessed. Modal, spectral, and subjective analysis along with a comparison between the drums created using additive manufacturing and the traditionally manufactured drums will be presented.
Occlusion Guided Scene Flow Estimation on 3D Point Clouds

3D scene flow estimation is a vital tool in perceiving our environment given depth or range sensors. Unlike optical flow, the data is usually sparse and in most cases partially occluded in between two temporal samplings. Here we propose a new scene flow architecture called OGSF-Net which tightly couples the learning for both flow and occlusions between frames. Their coupled symbiosis results in a more accurate prediction of flow in space. Unlike a traditional multi-action network, our unified approach is fused throughout the network, boosting performances for both occlusion detection and flow estimation. Our architecture is the first to gauge the occlusion in 3D scene flow estimation on point clouds. In key datasets such as Flyingthings3D and KITTI, we achieve the state-of-the-art results.
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution

Brain tumor segmentation is a critical task for patient's disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly with deep supervision and stochastic weight averaging, on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. Two independent ensembles of models from two different training pipelines were trained, and each produced a brain tumor segmentation map. These two labelmaps per patient were then merged, taking into account the performance of each ensemble for specific tumor subregions. Our performance on the online validation dataset with test time augmentation were as follows: Dice of 0.81, 0.91 and 0.85; Hausdorff (95%) of 20.6, 4,3, 5.7 mm for the enhancing tumor, whole tumor and tumor core, respectively. Similarly, our solution achieved a Dice of 0.79, 0.89 and 0.84, as well as Hausdorff (95%) of 20.4, 6.7 and 19.5mm on the final test dataset, ranking us among the top ten teams. More complicated training schemes and neural network architectures were investigated without significant performance gain at the cost of greatly increased training time. Overall, our approach yielded good and balanced performance for each tumor subregion. Our solution is open sourced at this https URL.
The Research of Teaching Quality Evaluation Model Based on the Principal Component Analysis and Learning Vector Quantization

To further improve the accuracy of teaching quality evaluation, a teaching quality evaluation model based on the combination of principal component analysis (PCA) and learning vector quantization (LVQ) is proposed. The teaching quality evaluation system is established using analytic hierarchy process (AHP), and then the characteristic information of the initial evaluation index system is extracted using principal component analysis. The characteristic information after dimensionality reduction is input to the LVQ neural network, and the network model is trained and tested for generalization ability. The experimental results show that the PCA-LVQ network model is simpler in structure, stronger in learning ability, faster in convergence speed, higher in evaluation accuracy and generalization ability than the single LVQ and BP neural network.
Image Classification Using Convolutional Neural Networks with Different Convolution Operations

In this paper, three types of convolution operations in convolutional neural networks (CNNs) are studied including regular convolution, separable convolution and group convolution. For regular convolution case, the modified VGG-19 is used to construct the deep networks. For separable convolution case, the MobileNet is applied to build deep model. For group convolution, the VGG-like plain network is used to construct the model. The experimental results of image classification on the CIFAR-10 and Flower102 datasets are used to evaluate the performance of CNNs and to demonstrate which convolution operation is a better choice according to accuracy and complexity.
[Retraction] A study on the professionalism of planning experts in the fields of culture, art and education: Focused on job analysis

“Arts and Cultural Education Promotion Act” comes with institutionalization of arts and cultural education in Korea, and tends to mean facilitating occupational professionalism in historical perspectives. In particular, the nature of vocational identity of professional educators in arts and cultural education is the main focus of analysis and debate for human resource development. Accordingly, this study emphasizes the diversity of arts and cultural education practices and reveals that the vocational training of arts and cultural education experts should be based on various curriculum and programs which take an interdisciplinary approach. In this respect, excessive simplification of vocational identity of the experts may exert negative effect on the diversity and excellence of arts and cultural education. To meet the needs of the arts and cultural education, curriculum and training program for university level should be newly developed or revised through an interdisciplinary approach with the job analysis of practicing professionals.
CAPNA—The Comprehensive Assessment Procedure for Natural Argumentation

The principal aim of this book is to set out a way of fully analysing the semantic content of arguments. That, however, is certain to be a difficult and laborious process. I have stated more than once that the fallacies of expression are among the subtlest, and occur most insidiously at the highest level of argumentation.
ACE-Net: Fine-Level Face Alignment through Anchors and Contours Estimation

We propose a novel facial Anchors and Contours Estimation framework, ACE-Net, for fine-level face alignment tasks. ACE-Net predicts facial anchors and contours that are richer than traditional facial landmarks while overcoming ambiguities and inconsistencies in their definitions. We introduce a weakly supervised loss enabling ACE-Net to learn from existing facial landmarks datasets without the need for reannotation. Instead, synthetic data, from which GT contours can be easily obtained, is used during training to bridge the density gap between landmarks and true facial contours. We evaluate the face alignment accuracy of ACE-Net with respect to the HELEN dataset which has 194 annotated facial landmarks, while it is trained with only 68 or 36 landmarks from the 300-W dataset. We show that ACE-Net generated contours are better than contours interpolated straight from the 68 GT landmarks and ACE-Net also outperforms models trained only with full supervision from GT landmarks-based contours.
Supporting the information transmission via social media of local sightseeing volunteer guides

In recent years, local tourism has been gaining traction as one of the measures for regional revitalization in Japan. One of the organizations that support these are the sightseeing volunteer guide organizations. These organizations have various kinds of information that are helpful for local tourism and active information transmission is desirable. On the other hand, many organizations face the issue of the information transmission as their aging volunteer guides find it difficult to handle information devices. This study therefore aims to provide support for continuous information transmission by local sightseeing volunteer guides via social media, and attempts to clarify the transmission of sightseeing information that matches the characteristics of social media through proof-of-concept demonstrations with the Historical “CULTURAL PATH” Guide Volunteers in Nagoya City.
Healthcare Management: Prospects and Risks of Using Blockchain Technology

Blockchain technology is a fundamental discovery that plays an indispensable part in today's professional world.this technology has even entered many industries and businesses these days.Even the health sector has not been spared from this trend.Blockchain is slowly preparing to enter the medical industry.Over the past five years, global technology giants such as IBM have devoted considerable resources to conduct comprehensive studies on the Chinese bloc.The results of these studies result in greater knowledge of the applications of this technology in the field of health and wellness.The results of a survey show that more than half of health executives are keen to see some form of blockchain technology enter the medical system in the future.The study grants an overview of appearing blockchain-healthcare technologies and relevant applications, highlighting studies in this area, and demonstrates the blockchain technology's potentials in enhancing the healthcare industry.
Quantum Light and Coherent States in Conducting Media

We present a simple description of classical and quantum light propagating through homogeneous conducting linear media.With the choice of Coulomb gauge, we demonstrate that this description can be performed in terms of a damped harmonic oscillator which is governed by the Caldirola-Kanai Hamiltonian.By using the dynamical invariant method and the Fock states representation we solve the time-dependent Schrödinger equation associated with this Hamiltonian and write its solutions in terms of a special solution of the Milne-Pinney equation.We also construct coherent states for the quantized light and show that they are equivalent to the well-known squeezed states.Finally, we evaluate some important properties of the quantized light such as expectation values of the amplitude and momentum of each mode, their variances and the respective uncertainty principle.
Voice signal analysis techniques for cognitive decline (stress) assessment

Abstract Speech signals may provide important information for measuring and modelling human behaviour, especially for assessing mental health as well as estimating the emotional state of a person. Speaker’s physiological and/or physical state may be thus identified by detecting the cognitive decline (CD) or stress levels using signal analysis of voice. This preliminary study presents a survey on methods introduced for CD and stress voice detection in humans. It is shown that increase in signal’s fundamental frequency (f 0 ) as well as its frequency formants, are the most common effects of CD and stress. Additional voice parameters could be used to identify normal from CD and normal from stressed voice as well as the cognitive state of the elderly. The present study poses the initiation of a project for the development of a mobile application for the automated voice detection and analysis on the fly, which will aid in the detection of early signs of CD and stress. Further investigation with application on a large number of voice samples is required for validating the methods.
Different responses of groundwater level changes through hydrogeological characteristics due to M5.4 Pohang earthquake

&amp;lt;p&amp;gt;Earthquake of magnitude M5.4 the second largest recorded earthquake occurred in Pohang, South Korea at 05:29:32 (UTC time) on November 15, 2017. The M5.4 event and hundreds of aftershocks produced extreme impacts across the area to date along with human and property damages. The distance between the epicenter of the M5.4 Pohang earthquake and the groundwater observation well is about 43 km for KJ-well and about 76 km for YS-well. Records from these two monitoring wells showed groundwater level changes occurred in 2017-11-15 05:30 (UTC time), about 30 seconds after the earthquake. In KJ-well, 8.0 cm of groundwater level change was observed, and in YS-well, about 30.0 cm of groundwater level change. The changes in groundwater level appeared to be a spike-like pattern that rises immediately due to the compressive action of the aquifer as the seismic waves pass through and then return to its original state. Interestingly, the groundwater level changes in YS-well was observed to be approximately three times greater than KJ-well although YS-well is approximately twice as far from the epicenter as KJ-well. The factors causing these different changes were compared and analyzed for the geometry, hydraulic properties, and geological characteristics of the well locations&amp;lt;/p&amp;gt;
Sample-Optimal and Efficient Learning of Tree Ising models

We show that $n$-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance $\epsilon$ from an optimal $O(n \ln n/\epsilon^2)$ samples, where $O(\cdot)$ hides an absolute constant which, importantly, does not depend on the model being learned - neither its tree nor the magnitude of its edge strengths, on which we place no assumptions. Our guarantees hold, in fact, for the celebrated Chow-Liu [1968] algorithm, using the plug-in estimator for estimating mutual information. While this (or any other) algorithm may fail to identify the structure of the underlying model correctly from a finite sample, we show that it will still learn a tree-structured model that is $\epsilon$-close to the true one in total variation distance, a guarantee called "proper learning." Our guarantees do not follow from known results for the Chow-Liu algorithm and the ensuing literature on learning graphical models, including a recent renaissance of algorithms on this learning challenge, which only yield asymptotic consistency results, or sample-inefficient and/or time-inefficient algorithms, unless further assumptions are placed on the graphical model, such as bounds on the "strengths" of the model's edges/hyperedges. While we establish guarantees for a widely known and simple algorithm, the analysis that this algorithm succeeds and is sample-optimal is quite complex, requiring a hierarchical classification of the edges into layers with different reconstruction guarantees, depending on their strength, combined with delicate uses of the subadditivity of the squared Hellinger distance over graphical models to control the error accumulation.