Reviewerly/exHarmony huggingface dataset:
Pressure of Digital Technologies and Students’ Creative Thinking in the Educational System

The article analyzes the phenomenon of digital pressure and the accompanying circumstances from the point of view of the need to develop various forms of sustainability and creative thinking on the part of students in the educational system. Digital pressure is examined as the result of a massive invasion of digital technologies in various spheres of life as manifested in various forms which contribute to the destabilization of systems, disrupting their work, causing tension, and changing ways of life. Special attention is paid to the ability of the instrumental-technological mind (in the form of the technologies used) to manipulate human consciousness and behavior, collecting and accumulating information about the human subject. Now personal and biometric data are an important resource that aid in modeling future states and possible personal choices. Each individual is studied separately and the mechanisms involved in social networks greatly simplify this process. In this context, such phenomena as computer addiction, “digital dementia”, a simplified perception of reality, a person’s subjectless existence of a person, as well as “new civilization” and “new caste” are analyzed. Higher education is viewed not only in its relation to social advancement but also as a “socially safe”, ensuring not only consistency in the profession but also life success and fulfilment.
Depth Estimation from a Single Omnidirectional Image using Domain Adaptation

Omnidirectional cameras are becoming popular in various applications owing to their ability to capture the full surrounding scene in real-time. However, depth estimation for an omnidirectional scene is more difficult than normal perspective images due to its different system properties and distortions. It is hard to use normal depth estimation methods such as stereo matching or RGB-D sensing. A deep-learning-based single-shot depth estimation approach can be a good solution, but it requires a large labelled dataset for training. The 3D60 dataset, the largest omnidirectional dataset with depth labels, is not applicable for general scene depth estimation because it covers very limited scenes. In order to overcome this limitation, we propose a depth estimation architecture for a single omnidirectional image using domain adaptation. The proposed architecture gets labelled source domain and unlabelled target domain data together as its input and estimated depth information of the target domain using the Generative Adversarial Networks (GAN) based method. The proposed architecture shows >10% higher accuracy in depth estimation than traditional encoder-decoder models with a limited labelled dataset.
On the number of q-ary quasi-perfect codes with covering radius 2

In this paper we present a family of q-ary nonlinear quasi-perfect codes with covering radius 2. The codes have length $$n = q^m$$ and size $$ M = q^{n - m - 1}$$ where q is a prime power, $$q \ge 3$$ , m is an integer, $$m \ge 2$$ . We prove that there are more than $$q^{q^{cn}}$$ nonequivalent such codes of length n, for all sufficiently large n and a constant $$c = \frac{1}{q} - \varepsilon $$ .
Depth Privileged Scene Recognition via Dual Attention Hallucination

RGB-D scene recognition has achieved promising performance because depth could provide complementary geometric information to RGB images. However, the inaccessibility of depth sensors severely limits RGB-D applications. In this paper, we focus on depth privileged setting, in which depth information is only available during training but not available during testing. Considering that the information obtained from RGB and depth images are complementary while attention is informative and transferable, our idea is using RGB input to hallucinate depth attention. We build our model upon modulated deformable convolutional layer and hallucinate dual attention: post-hoc importance weight and trainable spatial transformation. Specifically, we use modulation (resp., offset) learned from RGB to mimic Grad-CAM (resp., offset) learned from depth, to combine the strength of dual attention. We also design a weighted loss to avoid negative transfer according to the quality of depth attention. Extensive experiments on two benchmarks, i.e., SUN RGB-D and NYUDv2, demonstrate that our method outperforms the state-of-the-art methods for depth privileged scene recognition.
Deep Learning Radio Frequency Signal Classification with Hybrid Images

In recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. A DL approach is especially useful since it identifies the presence of a signal without needing full protocol information, and can also detect and/or classify non-communication waveforms, such as radar signals. This work focuses on the different pre-processing steps that can be used on the input training data, and tests the results on a fixed DL architecture. While previous works have mostly focused exclusively on either time-domain or frequency domain approaches, in this work a hybrid image is proposed that takes advantage of both time and frequency domain information, and tackles the classification as a Computer Vision problem. The initial results point out limitations to classical pre-processing approaches while also showing that it’s possible to build a classifier that can leverage the strengths of multiple signal representations.
Dynamic Fusion Network for Light Field Depth Estimation

Focus-based methods have shown promising results for the task of depth estimation in recent years. However, most existing focus-based depth estimation approaches depend on maximal sharpness of the focal stack. These methods ignore the spatial relationship between the focal slices. The problem of information loss caused by the out-of-focus areas in the focal stack poses challenges for this task. In this paper, we propose a dynamically multi-modal learning strategy which incorporates RGB data and the focal stack in our framework. Our goal is to deeply excavate the spatial correlation in the focal stack by designing the pyramid ConvGRU and dynamically fuse multi-modal information between RGB data and the focal stack in a adaptive way by designing the multi-modal dynamic fusion module. The success of our method is demonstrated by achieving the state-of-the-art performance on two light field datasets.
Which Intelligence for Human-Machine Dialogue Systems?

We present here two experiments carried out to improve the performances of an intelligent human-machine interface, called UKKO. The interface is destined to be implemented in humanoid social robots. From the standpoint of the automatic generation of outgoing messages, the way the interface works is not repetitive but creative. In other words, it does not reproduce talking points extracted from corpus but produces its own utterances by using rules. These rules exploit quality linguistic resources, i.e., formalized descriptions of lexicon that concern its morphological, syntactic and semantic features. The system’s quality of speaking depends on the completeness of descriptions inserted in its database. We discuss an experimentation that uses deep learning techniques to automatically retrieve syntactic and semantic properties of lexical units, which can be inserted into the database. An unsupervised method, based on the Word2Vec algorithm, is used for the semantic properties. The first results obtained are promising and show the interest of this approach for the experimentation. A supervised method, based on a sequential algorithm, is used for the syntactic properties. This method requires the transformation of the original data to match the training data that come from the UKKO system. The initial results achieved need to be improved. A line of research would be the addition of semantic descriptors to syntactic descriptors so as to make the approach achieve a better performance. This is consistent with the linguistic modeling underlying this project.
Neurosymbolic Map Generation with VQ-VAE and WFC

We introduce a hybrid neural + symbolic approach to map generation that combines neural discrete representation learning with symbolic constraint solving methods. In application to WarCraft II and Super Metroid map designs, we show how a vocabulary of directly manipulable latent tiles can be inferred from the raw pixels of design training data. Despite working with a very small tile vocabulary, our method is able to express a very large effective set of unique tiles at the level of pixel appearances. This work shows new ways of combining generative methods, resulting in directly controllable generators for domains that are primarily specified only by visual design examples.
Summarizing Long-Form Document with Rich Discourse Information

The development of existing extractive summarization models for long-form document summarization is hindered by two factors: 1) the computation of the summarization model will dramatically increase due to the sheer size of the input long document; 2) the discourse structural information in the long-form document has not been fully exploited. To address the two deficiencies, we propose HEROES, a novel extractive summarization model for summarizing long-form documents with rich discourse structural information. In particular, the HEROES model consists of two modules: 1) a content ranking module that ranks and selects salient sections and sentences to compose a short digest that empowers complex summarization models and serves as its input; 2) an extractive summarization module based on a heterogeneous graph with nodes from different discourse levels and elaborately designed edge connections to reflect the discourse hierarchy of the document and restrain the semantic drifts across section boundaries. Experimental results on benchmark datasets show that HEROES can achieve significantly better performance compared with various strong baselines.
Image emotion distribution learning based on enhanced fuzzy KNN algorithm with sparse learning

With the trend of people expressing opinions and emotions via images online, increasing attention has been paid to affective analysis of visual content. Traditional image affective analysis mainly focuses on single-label classification, but an image usually evokes multiple emotions. To this end, emotion distribution learning is proposed to describe emotions more explicitly. However, most current studies ignore the ambiguity included in emotions and the elusive correlations with complex visual features. Considering that emotions evoked by images are delivered through various visual features, and each feature in the image may have multiple emotion attributes, this paper develops a novel model that extracts multiple features and proposes an enhanced fuzzy k-nearest neighbor (EFKNN) to calculate the fuzzy emotional memberships. Specifically, the multiple visual features are converted into fuzzy emotional memberships of each feature belonging to emotion classes, which can be regarded as an intermediate representation to bridge the affective gap. Then, the fuzzy emotional memberships are fed into a fully connected neural network to learn the relationships between the fuzzy memberships and image emotion distributions. To obtain the fuzzy memberships of test images, a novel sparse learning method is introduced by learning the combination coefficients of test images and training images. Extensive experimental results on several datasets verify the superiority of our proposed approach for emotion distribution learning of images.
The Agoraphilic Navigation Algorithm under Dynamic Environment with a Moving Goal

This paper presents a research conducted in developing a new navigation algorithm to navigate robots under dynamically cluttered environments with a moving goal. There are only a few navigation algorithms capable of navigating robots under dynamic environments compared to static environments. The inability to track and reach a moving goal/target is a one of the common weakness of existing navigating algorithms operating in dynamic environments. The existing free space attraction (Agoraphilic) based navigation algorithms also suffer from this common problem. The proposed algorithm, in this paper was developed to overcome this issue. Agoraphilic Navigation Algorithm under Dynamic Environment, “ANADE” consists of eight main modules. These modules are iteratively used to create the robot's driving force which pulls the robot towards the moving goal. An obstacle tracking module is used to identify the time varying free spaces by tracking moving obstacles. Furthermore, a tracking system is also used to track the moving goal. The capacity of the ANADE was further strengthen by obstacle and goal path prediction modules. Future location prediction allowed the algorithm to make decision considering future environments around the robot. The proposed algorithm was tested under dynamic environment. These tests were focused on testing the behavior of the algorithm under the challenge of reaching a moving goal. Furthermore, the test results demonstrate that ANADE is successful in reaching a moving goal under an unknown dynamically cluttered environment.
Body Ownership of Anatomically Implausible Hands in Virtual Reality

Manipulating sensory and motor cues can cause an illusionary perception of ownership of a fake body part. Presumably, the illusion can work as long as the false body part's position and appearance are anatomically plausible. Here, we introduce an illusion that challenges past assumptions on body ownership. We used virtual reality to switch and mirror participants' views of their hands. When a participant moves their physical hand, they see the incongruent virtual hand moving. The result is an anatomically implausible configuration of the fake hand. Despite the hand switch, participants reported significant body ownership sensations over the virtual hands. In the first between-group experiment, we found that the strength of body ownership over the incongruent hands was similar to that of congruent hands. Whereas, in the second within-group experiment, anatomical incongruency significantly decreased body ownership. Still, participants reported significant body ownership sensations of the switched hands. Curiously, we found that perceived levels of agency mediate the effect of anatomical congruency on body ownership. These findings offer a fresh perspective on the relationship between anatomical plausibility and assumed body ownership. We propose that goal-directed and purposeful actions can override anatomical plausibility constraints and discuss this in the context of the immersive properties of virtual reality.
Safe Perception: On Relevance of Objects for Vehicle Safety

Automated vehicles are a great promise for the future of transportation systems. However, safety assurance is still a major roadblock for the mass deployment of such vehicles. This is a great challenge especially for the perception systems that have to handle a diversity of environmental conditions and road users. Up to now, perception systems treat the complete environment and all traffic participants in the same way, although only a subset of all objects has an influence on vehicle safety. To close this gap, we present in this work a comprehensive definition of safety-relevant objects, and for the most critical area around the vehicle, the safety-relevant area. Using these definitions, we demonstrate that a common object detection system is not able to detect all safety-relevant objects, which will make new, safer approaches necessary in future.
Adaptive histogram equalization of wavelet sub bands for the enhancement of contrast in aerial images

Remote sensing images, like satellite images, have played a key role in various sectors of human life. Enhancement of contrast is critical for improved color vision and reproduction. Discrete Wavelet Transform (DWT) and adaptive histogram equalization (AHE) are used in this work to propose a unique method. For images considered for remote sensing, the current methods employ dominant illumination level reasoning and adaptive brightness translation. Using the low-frequency illumination element in wavelets, this technique calculates luminance strength transfer functions and converts pixel intensity as per the transfer function. We use the log-average brightness to divide the sub band (LL) into low, medium, and high frequency layers after performing a discrete wavelet transform on the input images. The gamma adjustment function and knee transfer function are incorporated to adaptively estimate intensity transfer functions depending on the prevailing illumination within each layer. The inverse DWT is used to produce the improved picture after the intensity translation. To increase the image's overall quality, the suggested technique employs adaptive histogram equalization. The current method favours LL sub bands over other bands, necessitating boundary softening and image fusion. This may induce a drop in overall quality. AHE is used in the suggested technique to solve this problem. The suggested method outperforms current methodologies in terms of overall contrast and visibility of local features, according to the findings of the experiments. The suggested approach outperforms the current systems in terms of performance.
Understanding Job Seeker Funnel for Search and Discovery Personalization

The search and discovery process of a job-seeker towards realizing the dream opportunity can be very complex. Given the dynamic nature of job postings, churn-rate of skills, and gaps in intent matches, professionals often find it hard to discover the right opportunity. Most often, they need guidance on the right search queries to issue or next-steps in the job-seeking funnel to reach a target job-apply. In this work, we experiment with job-sessions dataset from LinkedIn, to understand and represent user's job-seeking behavior. In particular using action sequences unified from various search and discovery channels, we pre-train language models, e.g. BERT (Bidirectional Encoder Representations from Transformers) to model user's activities. We further fine-tune the BERT based contextual session embeddings towards predicting entities from target sessions, in an eXtreme Multi-Label (XML) classification setting. We hypothesize that XML fine-tuning task enables dense-representation, and predicted entities to be used in multiple downstream tasks of job-search query recommendation, job-search ranking, job recommendation retrieval, and job-notification expansion, as shown in experiments. We demonstrate significant improvements in accuracy across tasks leading to reduced time to reach a given job-apply, as well as increase in total job-applies in the system. We also share the learning from deploying these models in production. To the best of our knowledge, this is the first work to efficiently model cross-channel activities at scale using self-attention mechanisms, leading to statistically significant improvement in job-seeker experience.
Long-Term Trajectory Prediction of the Human Hand and Duration Estimation of the Human Action

In the frameworkof human-robot collaborative assembly, it is important to predict the long-term human hand trajectory for collision avoidance and to estimate the durations of the human actions for collaborative task planning. Many existing works predict long-term human trajectory by a preset time horizon, while in this letter, our prediction horizon depends on how far in the future we could predict the human's actions. To be more specific, we predict the human trajectory and estimate the durations for the human's current action and future actions. To address this problem, we propose a recognition-then-prediction framework. First, we present a hierarchical recognition algorithm to infer the human intentions for the current action and the future actions. Next, we propose to use the sigma-lognormal function to model and predict the human movement, and from this model we estimate the action durations. To accommodate different human behaviors, we also propose an online algorithm to adapt the movement model by using the observed trajectory, the human intentions and the scene layout. The effectiveness of the proposed framework is supported by experimental validations on the human trajectory data for conducting a computer assembly task.
The Game of Cycles

The Game of Cycles, introduced by Su in 2020, is played on a simple connected planar graph together with its bounded cells, and players take turns marking edges with arrows according to a sink-source rule that gives the game a topological flavor. The object of the game is to produce a cycle cell—a cell surrounded by arrows all cycling in one direction—or to make the last possible move. We analyze the two-player game for various classes of graphs and determine who has a winning strategy. We also establish a topological property of the game: that a board with every edge marked must have a cycle cell.
A Media Architecture Design Space: The MAB 2012–2018 Nominees

We suggest organizing a collection of media architecture instances around the design space they populate. We offer a process for identifying the constituent parts of the media architecture installations, together with a tool that supports the process, and the subsequent exploration of the design space. In the main part of this paper we use this process to construct the design space occupied by the 54 nominees for awards at the media architecture biennales in 2012, 2014, 2016, and 2018, and we address how the process of decomposing the collection of media architecture instances and constructing the design space is the first step of the knowledge acquisition process; this is followed by an analysis of the MAB 2012–2018 design space. The process starts with a basic quantitative analysis, followed by a closer investigation of two-dimensional subspaces. Before presenting our conclusion, we address the possibility of using the design space as a generative tool.
End-to-end LSTM based estimation of volcano event epicenter localization

Locating sources of volcano-seismic event is very relevant to monitor and comprehend volcanic processes. Ordinary estimation of source seismic events is based on phase picking. The most accurate procedure of phase selection is the visual inspection of the records by experts, who employ local characteristics for phase detection and comparison with observed signals from other stations. This activity is highly time demanding, which in turn is a strong motivation to automatize the epicenter estimation process. However, automatic phase picking in volcano signals is highly inaccurate because of the short distances between the event epicenters and the seismograph stations. In this paper, an end-to-end based LSTM (Long-Short Term Memory) scheme is proposed to address the problem of volcano event localization without any a priori model relating phase picking with localization estimation. LSTM was chosen due to its capability to capture the dynamics of time varying signals, and to remove or add information within the memory cell state and model long-term dependencies. A brief insight into LSTM is also discussed here to justify the use of this neural network. The results presented in this paper show that the LSTM based architecture provided a success rate, i.e., an error smaller than 1.0 km, equal to 48.5%, which in turn is dramatically superior to the one delivered by automatic phase picking. Moreover, the proposed end-to-end LSTM based method gave a success rate (18%) higher than CNN (Convolutional Neural Network). The results presented suggest that the approach proposed here for automatic volcano event epicenter localization can be applied to other geophysics problems.