Reviewerly/exHarmony huggingface dataset:
Eye and head movement recordings using smartphone: measurements of accuracy and precision

Smartphones have shown promise in the measurement of eye movements. We have shown that results of a common clinical test recorded using a smartphone are comparable with measurements from clinical video-oculography goggles. The smartphone uses ARKit’s capability to acquire eye and head movement position without the need of performing a calibration as in most eye tracking devices. Here we measure the accuracy and precision of the eye and head recordings using our application. We enrolled healthy volunteers and asked them to direct their eyes, their head, or both to targets on a wall at known eccentricities while recording their head and eye movements with our smartphone application. We measured the accuracy as the error between the eye or head movement measurement and the location of each target and the precision as the standard deviation of the eye or head position for each of the target positions. The accuracy of head recordings (16% error) was overall better than the accuracy of eye recordings (28% error). We also found that the accuracy for horizontal eye movements (26% error) was better than for vertical (30% error). Precision was also better for head movement (0.9 deg) recordings than eye movement recordings (1.5 deg) and variability tended to increase with eccentricity. Our results provide basic metrics demonstrating the utility of our application in quantitative assessment of head and eye movements. It may be advisable to include a calibration recording together with any planned recording to improve the accuracy.
Launching the Front Matter Roadmap

In a blog post last week I talked about what I am currently working on, namely a) helping to make it easier (and safer) to run the InvenioRDM digital repository software in Docker container infrastructure, and b) working on converting the bolognese metadata conversion Ruby gem to Python to enhance InvenioRDM functionality.
Artificial intelligence, inventorship and the myth of the inventing machine: Can a process be an inventor?

Institutional and academic debates have intensified regarding the recent efforts to claim inventorship of AI-related patent applications, as has notably been seen in the known cases of Thaler v Comptroller (‘DABUS’) that have been examined in various jurisdictions. The pertinent question that has emerged is whether artificial intelligence systems can independently produce patentable subject matter. What has to be looked at, first, is the preliminary question of what the claim of producing inventions ‘autonomously’ can possibly mean under a technological perspective – an essential stage in the debate that is usually bypassed in legal commentary. Once such a technological explanation has been provided, a legal question can reasonably arise as to whether an AI process, such as software, may make a contribution that rewards a patent. AI inventions are legally approached and analysed as processes and as to their relationship with their direct products. Thus, where a process (AI) ‘creates’ or ‘makes’ a product, the focus is reasonably put on if and to what extent disclosing the product can provide a contribution separate to that which has already been provided by the process that created it. It is stressed that the current push for AI-generated products bypasses this key question which is essential in assessing the invention.
Multidimensional evaluation of teaching strategies adopted in the COVID-19 pandemic

Abstract This paper proposes a multidimensional social open model to evaluate the teaching strategies adopted during the COVID-19 pandemic by assessing the decisions made by teachers by a group of teachers acting as evaluators. Based on the analysis of previous studies on teaching, this study aims to propose a formal model for the evaluation of teaching strategies in four dimensions: sustainability, usability, accessibility, and creativity. The use of information technologies to measure teaching strategies can bring decisive advantages. This work has been inspired by social rating systems of social networks to propose a measurement system in which a potential large number of evaluators with different levels assess the strategies. In addition, the proposal also includes the evaluation of the evaluators' own work, assigning confidence levels that are based on their experience but also on their evaluations. In this way, we have a social measurement system, in the sense that participation is open to a large number of evaluators. A large community of teacher evaluators will increase the objectivity of the measurement. The outcome of the system will be a characterization of the teaching strategies that will allow to decide in the future which ones should be adopted according to the needs of each one.
Evaluation of neural networks defenses and attacks using NDCG and reciprocal rank metrics

The problem of attacks on neural networks through input modification (i.e., adversarial examples) has attracted much attention recently. Being relatively easy to generate and hard to detect, these attacks pose a security breach that many suggested defenses try to mitigate. However, the evaluation of the effect of attacks and defenses commonly relies on traditional classification metrics, without adequate adaptation to adversarial scenarios. Most of these metrics are accuracy-based, and therefore may have a limited scope and low distinctive power. Other metrics do not consider the unique characteristics of neural networks functionality, or measure the effect of the attacks indirectly (e.g., through the complexity of their generation). In this paper, we present two metrics which are specifically designed to measure the effect of attacks, or the recovery effect of defenses, on the output of neural networks in multiclass classification tasks. Inspired by the normalized discounted cumulative gain and the reciprocal rank metrics used in information retrieval literature, we treat the neural network predictions as ranked lists of results. Using additional information about the probability of the rank enabled us to define novel metrics that are suited to the task at hand. We evaluate our metrics using various attacks and defenses on a pretrained VGG19 model and the ImageNet dataset. Compared to the common classification metrics, our proposed metrics demonstrate superior informativeness and distinctiveness.
Automatic modulation recognition via aligned signals and key features

Deep learning-based classification algorithms have been used for automatic modulation recognition (AMR). However, most methods only focus on end-to-end mapping and neglect the classic key features. In this paper, signals are enforced with key classification features to propose a novel deep learning AMR model by learning the shared latent space of the aligned signals and key features (LLAF); this increases the generalizability of the model and ensures the physical plausibility of the results. To obtain adequate signal representations, an encoder–decoder architecture is proposed to learn the shared latent space, and the architecture is trained to approximate prior label distributions for precise signal classification. Simulation results verify the high recognition accuracy of the proposed LLAF model under different signal-to-noise ratios (SNRs).
Greenwashing in the Bitcoin Industry

The Bitcoin Dilemma shows that increased minerminer efficiency will merely further fuel the miner arms race and will result in more miners sold, and profits for IntelIntel, BitmainBitmain and CanaanCanaan Creative, but with no commensurate decrease in electricity consumption miner industry-wide. Indeed, if the advertised cost of miners fall per kilowatt of power capacity, as Intel promises, electricity consumption may actually rise.
Trends of Blockchain

As an emerging technology, blockchain is developing rapidly, and has gained much achievements. However, it’s still full of uncertainty. In this chapter, the characteristics and trends in industrialization, technology, application and industrial ecology aspects of blockchain are analyzed, to provide some insights for foreseeing blockchain development in the future.
I Know How You Move: Explicit Motion Estimation for Human Action Recognition

Enabled by hierarchical convolutions and nonlinear mappings, recent action recognition studies have continuously boosted performance with spatiotemporal modelling. In general, motion clues are essential in video-oriented tasks, while existing approaches aggregate the spatial and temporal signatures via specially designed modules in the middle or output stages. To highlight the privilege provided by temporal motions, in this paper, we propose a simple but effective <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MOTion Estimator</b> (MOTE) to generate the motion patterns from every single frame, avoiding complex dense-frame input. In particular, MOTE follows an encoder-decoder structure, which takes the short-term motion features generated by the pretrained dense-frame network as the learning target. The spatial information of a single frame is utilized to estimate the instantaneous motion appearance. It can support the expression of vulnerable regions, such as the ‘hand’ in ‘waving hands’, which would otherwise be suppressed in the feature maps as the ‘hand’ suffers from motion blur. The training process of MOTE is independent of the action recognition system. Therefore, the trained MOTE can be transplanted to the input-end of existing action recognition methods to provide instantaneous motion estimation as feature enhancement according to practical requirements. Our experiments performed on Something-Something V1, V2, Kinetics-400, and Diving48 verify the effectiveness of the proposed method.
Pedagogical discourse in higher professional education of the future

Scientific discourse in the pedagogical sphere implies coverage of the activity of all subjects of the educational space. Scientific exploration aims to coordinate the positions of education applicants, teachers, and representatives of the administrative cluster possible in the organization of a comprehensive pedagogical discourse. The task of the article is to develop effective theoretical-methodological and practically oriented guidelines, which will become a reference point for the construction of higher professional education of the future. The methodological arsenal for conducting this type of research served as general scientific, educational-pedagogical, and philosophical methods. The key issues shaping the agenda of pedagogical discourse on the prospects of education are the definition of short-term pedagogical innovations and long-term strategies for the development of higher professional education. A promising area of research is the correlation between the potential of the educational environment and the potential of socio-cultural space. Thus, pedagogical discourse is called to implement synergetic models of educational development. Despite the conservatism of education, the present and, even more so, the future of this sphere requires innovative transformations. Scientific-technological and humanitarian-informational progress allows updating education and forecasting its development based on further transformations. Higher vocational education is in dire need of constant updating of the pedagogical discourse because the abilities and skills acquired (acquired) by future professionals immediately turn into indicators of professionalism, which is a determining factor in the competitive environment of the modern world. Flexibility, mobility, multidisciplinarity, openness, accessibility, efficiency - are the attitudes that guide the construction of future education perspectives and meet the principles of dynamism in this sphere of social activity.
Phonetic-acoustic database of highly accented Russian speech

In the present work, there is a description for the corpus of highly accented Russian speech. The principles for designing pronunciation dictionary are formulated. And the peculiarities for building on its base sets of allophones, detailed enough for practical use in the field of synthesis of Russian speech and developing speech systems that are resistant to the variety of accents that exist in mountain enclaves. This corpus may be used not only as a method to fix and to monitor peculiarities of the modern Russian. It may also be a useful tool to research problems of perceptive adaptation in oral communication and processes of language convergence and interference of phonetic systems in contact between native speakers of different languages. The proposed approach allows the corpus to be quickly enlarged without significant loss of usability, since the audio data is readily available.
EvAC3D: From Event-Based Apparent Contours to 3D Models via Continuous Visual Hulls

3D reconstruction from multiple views is a successful computer vision field with multiple deployments in applications. State of the art is based on traditional RGB frames that enable optimization of photo-consistency cross views. In this paper, we study the problem of 3D reconstruction from event-cameras, motivated by the advantages of event-based cameras in terms of low power and latency as well as by the biological evidence that eyes in nature capture the same data and still perceive well 3D shape. The foundation of our hypothesis that 3D reconstruction is feasible using events lies in the information contained in the occluding contours and in the continuous scene acquisition with events. We propose Apparent Contour Events (ACE), a novel event-based representation that defines the geometry of the apparent contour of an object. We represent ACE by a spatially and temporally continuous implicit function defined in the event x-y-t space. Furthermore, we design a novel continuous Voxel Carving algorithm enabled by the high temporal resolution of the Apparent Contour Events. To evaluate the performance of the method, we collect MOEC-3D, a 3D event dataset of a set of common real-world objects. We demonstrate the ability of EvAC3D to reconstruct high-fidelity mesh surfaces from real event sequences while allowing the refinement of the 3D reconstruction for each individual event.
The “metaverse society”: Beyond the discourse intrinsic potential and transformative impact

&lt;p&gt;With the full-scale development of the new digital revolution, with mobile Internet, application ecology and holographic Internet as keywords, and the leapfrog development of media technologies such as virtual reality, immersive environments, blockchain and open source, as well as the gradual emergence of user-produced content and interesting communities as mature online cultural production mechanisms and main organizational methods, the metaverse has “emerged”. The metaverse is now historically positioned as a new stage embedded in the entire sequence of Internet and digital development, and has been given social significance and future imagination beyond the level of discourse. As can be seen from its inherent technologies, mechanisms and value assumptions, the metaverse, as a utopian social vision and social practice still carrying the Silicon Valley discourse, presents a potential and transformative impact to be stimulated in three areas: production and creation, cognition and experience, and community and identity. However, this potential and transformative impact is constrained by the material world, and metaverse societies thus show inextricable connections to the real world.&lt;/p&gt;
The Kossakowski Matrix and Strict Positivity of Markovian Quantum Dynamics

We investigate the relationship between strict positivity of the Kossakowski matrix, irreducibility and positivity improvement properties of Markovian quantum dynamics. We show that for a Gaussian quantum dynamical semigroup strict positivity of the Kossakowski matrix implies irreducibility and, with an additional technical assumption, that the support of any initial state is the whole space for any positive time.
Online Book Recommendation System Based on Optimized Collaborative Filtering Using Ant Colony Optimization

Today, the amount of information on the Internet is growing rapidly, and people need some tools to find and access the right information. One of these tools is called a recommender system. Recommender systems help you navigate quickly and get the information you need. This paper proposes a fast and intuitive book recommendation system to help readers find the right book to read next. The overall architecture is presented with its detailed description. We used a collaborative filtering method. The idea behind the Ant Colony Algorithm is based on ant colonies, as suggested by its name. It leverages the idea that the more pheromones an ant releases, the more significant the goal is-in this case, the target being the location that is the shortest. Through the use of ant colony optimization, we aim to lower the effort needed for collaborative filtering to make good predictions, which in turn improves the algorithm's prediction accuracy. The results show that the Euclidean-based distance matrix approach can optimize EMBEDDING_SIZE well with the best value 0.3927 so that it has a lower loss value after optimization than before optimization. This means that optimization using Ant Colony Optimization is quite efficient, flexible, and beneficial, especially for Collaborative Filtering.
“SECURE LANE”: EXPLORING DOTA 2 PLAYERS’ EXPERIENCE ON THEIR INCIDENTAL VOCABULARY LEARNING

&lt;p&gt;Vocabulary is the key of language where people need to process words to understand their meaning of words. Learning vocabulary is not merely can be obtained from classroom interaction. It can be done outside classroom with the aid of various media, one of which is video games developed in English. Learning English from activities outside the classroom like playing video games can gain incidental vocabulary which has relation with repetition of the interaction from the learner with target language which is English. DoTA 2 is a popular video game among students since it not only provides a pleasurable experience for players but also allows them to engage with one another via the use of English. This survey study aimed at figure out the relation between player intensity of playing DoTA 2 and their vocabulary size. The data was gained from 100 students of a state university in Indonesia who played Dota 2 by distributing a questionnaire adopted from Cabraja (2016) to get the players’ view and Schmitt’s Vocabulary Levels Test (VLT) to know players’ vocabulary level. The findings showed that participants who plays DoTA 2 more regularly got a higher score on the Vocabulary Level Test score than participants who played Dota 2 less regularly. According to their experience, participants with higher score feel more confident when communicate with other player using English. It shows that playing the video game, DoTA 2 in this case, contributes a significant impact on players'/students' accidental vocabulary acquisition as well as giving a less daunting experience when learning English.&lt;/p&gt;
Optimization Heuristics for Cost-Efficient Long-Term Cloud Portfolio Allocations

Today’s cloud infrastructure landscape offers a broad range of services to operate software applications. The myriad of options, however, has also brought along a new layer of complexity. When it comes to procuring cloud computing resources, consumers can purchase their virtual machines from different providers on different marketspaces to form so called cloud portfolios: a bundle of virtual machines whereby the virtual machines have different technical characteristics and pricing mechanisms. Thus, selecting virtual machines for a given set of applications such that the allocations are cost-efficient is a non-trivial task. In this paper we propose a formal specification of the cloud portfolio management problem that takes an application-driven approach and incorporates the nuances of the commonly encountered reserved, on-demand and spot market types. We present two distinct cost optimization heuristics for this stochastic temporal bin packing problem, one taking a naive first fit strategy, while the other is built on the concepts of genetic algorithms. The results of the evaluation show that the former optimization approach significantly outperforms the latter.
LiDAR Point Cloud Image Interpolation via Separable Convolution

In recent years, the point cloud data generated by LiDAR has played an important role in 3D object detection, point cloud registration, 3D map splicing and other fields. However, the frame rate of a typical LiDAR sensor is limited by hardware performance so that it can't solve the problems of some scenes that require high frame rate, such as object tracking. In this paper, we propose a method of LiDAR point cloud image frame interpolation, which can be used to solve the problem of low frame rate of LiDAR. Given two consecutive point cloud images, LiDAR point cloud image interpolation aims to generate intermediate frame between them. Firstly, the point clouds are projected into 2D images, then the kernels of the Convolutional Neural Network output and the two consecutive point cloud images are used to realize the interpolation process of the intermediate frame. In order to improve the interpolation effect, we take into account the feature distribution of LiDAR point cloud images varies with space, the attention mechanism is introduced in the network model to effectively extract the features of the LiDAR point cloud images, At the same time, set the separable convolution kernels of the network output to a rectangle to meet the large aspect ratio of the LiDAR point cloud images. Both quantitative and qualitative experiments on the KITTI Dataset show that our method performs better than other mainstream methods.
Blockchain Application Status and Ecology

In order to give a general view of the status of blockchain applications, an application-centered viewpoint is applied to the research of advancement both worldwide and in China. To begin, the policy aspects and application progresses are investigated, with the conclusion that governments are increasingly focusing on blockchain technology and application development. A Blockchain application ecosystem model is proposed, which includes three layers: environmental, organizational and information system. The evolution of blockchain infrastructures, including EBSI, Diem, BSN, and other projects is examined, and an infrastructure ecosystem model is provided based on current trends. In terms of blockchain technology ecology, five prominent blockchain open source communities worldwide and in China are outlined, and the characteristics of global blockchain open source communities are discussed.