Reviewerly/exHarmony huggingface dataset:
Insights into tectonic reactivation and landscape development processes at the Paraná Basin border using integrated geomorphometric and radiometric analyses

In the Bom Jardim de Goiás region, the Transbrasiliano Lineament shows evidence of a fault reactivation along NE-SW structures (main direction) and secondary directions related to subsequent events. To study the structural framework and reactivation inputs toward tectonic events, distinctive features of the northern Paraná Basin border were analyzed by integrating gamma spectrometry and radar data, both supported by field research. The remote sensing database obtained by the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) sensor allowed detecting lineaments and morphometric derivatives. Besides, the developed morphogenetic process images attest to the importance of structural control on relief evolution while concentrating lineaments as a function of lithological and geomorphological domains. Airborne gamma-ray spectrometry data processing focused on Kd (Anomalous Potassium) and F-Parameter indices. In this context, the fuzzy logic and Index Overlay Method allowed multivalued integration that indicated hydrothermal products and weathering processes correlated with Kd and F-Parameter anomalies. Thus, the joint interpretation of lineaments, geomorphological domains, and Kd/F-Parameter anomalies allowed differentiating primary geological elements and products from surface dynamics, directly or indirectly related to tectonic reactivation processes.
HoloCities: A Shared Reality application for Collaborative Tourism

Abstract Communication is a key arena for shared-reality application: our mixed reality application is developed on Microsoft Hololens and it has been designed to provide new engaging ways to discover the city using augmented reality. Most augmented reality tourism applications isolate the user, therefore, this application has been made multiplayer and collaborative to encourage shared experiences and socialization. In this project two scenarios are described: In the first scenario there is a real guide that exposes to the group, each equipped with Head Mounted Display, the peculiarities of the monument visited. Each user share the 3D models manipulated by the guide that can be in the same room or it can be a remote guide leveraging on the 5G network low latency. Moreover, the guide could highlight and label to convey relevant information about the objects. While in the second scenario there is no real guide, so the application automatically recognizes the framed monument thanks to a visual search engine running on a 5G-ready infrastructure and shows shared information to the whole group of tourists. Moreover, it uses the huge amount of multimedia data stored in the archives of the Italian Public Broadcaster RAI. Thanks to the fact that the new HMDs are able to track the position and orientation of the user, we have created a menu capable of following the user while exploring the city. Moreover, the shared-interaction with virtual objects is performed by hands or vocal commands. Both scenarios allow users to visit a city in a new and intelligent way, promoting participation and collaboration with others and with the guide, unlike previous approaches that involve only one tourist using the information provided by the app.
Emotions Polarity of Tweets Based on Semantic Similarity and User Behavior Features

In social networks, people share their emotions and opinion towards specific subjects in the form of comments. These comments are positive, negative, or neutral based on their content. Therefore, people try to pick out the comment's content; this content reflects the behaviour and style of expressing their emotions. In recent years, researchers interested in sentiment analysis (SA) achieved results using different methods, the lexical features and semantic similarity. But most of them neglect parts of the content of comments, considering it has no importance such as URLs, Numbers and Marks. They use the value of semantic similarity between comments direct to identify emotions polarity or to find synonyms of lexical features. This paper introduces an implementation that uses a combination of user behaviour, semantic and lexical features together for finding polarity emotions of Tweets. This proposed method examines some of the neglected content of tweets as features and adapts the semantic similarity value to emotional polarity as a feature in addition to lexical features. The main objective of this paper is to improve the classification accuracy. The performance of the proposed method evaluated using Naive Bayes and SVM as two popular machine learning classifiers. The best-obtained result is 94% using Naive Bayes and Sentiment140 dataset.
Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics

In this article, we propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments, showing how robotic tasks can be formulated as a free-energy minimization problem.The proposed approach allows handling partially observable initial states and improves the robustness of classical BTs against unexpected contingencies while at the same time reducing the number of nodes in a tree.In this work, we specify the nominal behavior offline, through BTs.However, in contrast to previous approaches, we introduce a new type of leaf node to specify the desired state to be achieved rather than an action to execute.The decision of which action to execute to reach the desired state is performed online through active inference.This results in continual online planning and hierarchical deliberation.By doing so, an agent can follow a predefined offline plan while still keeping the ability to locally adapt and take autonomous decisions at runtime, respecting safety constraints.We provide proof of convergence and robustness analysis, and we validate our method in two different mobile manipulators performing similar tasks, both in a simulated and real retail environment.The results showed improved runtime adaptability with a fraction of the hand-coded nodes compared to classical BTs.
Affine symmetries and neural network identifiability

We address the following question of neural network identifiability: Suppose we are given a function f:Rm→Rn and a nonlinearity ρ. Can we specify the architecture, weights, and biases of all feed-forward neural networks with respect to ρ giving rise to f? Existing literature on the subject suggests that the answer should be yes, provided we are only concerned with finding networks that satisfy certain “genericity conditions”. Moreover, the identified networks are mutually related by symmetries of the nonlinearity. For instance, the tanh function is odd, and so flipping the signs of the incoming and outgoing weights of a neuron does not change the output map of the network. The results known hitherto, however, apply either to single-layer networks, or to networks satisfying specific structural assumptions (such as full connectivity), as well as to specific nonlinearities. In an effort to answer the identifiability question in greater generality, we consider arbitrary nonlinearities with potentially complicated affine symmetries, and we show that the symmetries can be used to find a rich set of networks giving rise to the same function f. The set obtained in this manner is, in fact, exhaustive (i.e., it contains all networks giving rise to f) unless there exists a network A “with no internal symmetries” giving rise to the identically zero function. This result can thus be interpreted as an analog of the rank-nullity theorem for linear operators. We furthermore exhibit a class of “tanh-type” nonlinearities (including the tanh function itself) for which such a network A does not exist, thereby solving the identifiability question for these nonlinearities in full generality and settling an open problem posed by Fefferman in [6]. Finally, we show that this class contains nonlinearities with arbitrarily complicated symmetries.
A Novel Approach to Predict the Real Time Sentimental Analysis by Naive Bayes &amp; RNN Algorithm during the COVID Pandemic in UAE

At present, each and every part of the globe are facing a COVID crisis, which is affecting an individual physically, mentally and on the other hand, it is affecting the nation economically. Also, the unemployment scenario will be at its peak in the upcoming years as reported by UNGA. To combat this scenario, all the country are working on fostering their fiber network and so the sectors apart from the manufacturing, will tend to work from their home and contribute to the economy. But there are many problems arising to implement this culture practically, since it affects the mindsets of the people who have to endure this transformation within a very short span. Hence, in this research work, it has been decided to focus on this current issue for which the usage of certain apps in UAE such as zoom, totok, botim for internet calling have been identified since this is the only way of connectivity with the outside world. To perform this analysis, the tweets from December to July have been collected by converting the image to text and analyzed using two algorithms such as Naive Bayes Classifier (NBC) and Recurrent Neural Networks (RNN). The sentimental analysis found that 630 tweets were positive and people in UAE feels secured, satisfied and internet calling is very useful for them in the prospect of work, education, etc. Only 48 tweets has negative impact because people feel little bit harder in sudden change of culture with in short period of time and 155 tweets has impact that both positive and negative were view and said to be natural. The study found that NB (84%) is more accurate, user friendly and takes less time than RNN (79%) to perform the analysis. Finally, the sentimental analysis reveals that people in UAE were accepting the new culture of internet calling and it is useful for them in the prospect of work and education.
Fast and Robust Distributed Learning in High Dimension

Could a gradient aggregation rule (GAR) for distributed machine learning be both robust and fast? This paper answers by the affirmative through Multi-Bulyan. Given n workers, f of which are arbitrary malicious (Byzantine) and m = n - f are not, we prove that Multi-Bulyan can ensure a strong form of Byzantine resilience, as well as an m/n slowdown, compared to averaging, the fastest (but non Byzantine resilient) rule for distributed machine learning. When m ≈ n (almost all workers are correct), Multi-Bulyan reaches the speed of averaging. We also prove that Multi-Bulyan's cost in local computation is O(d) (like averaging), an important feature for ML where d commonly reaches 109, while robust alternatives have at least quadratic cost in d. Our theoretical findings are complemented with an experimental evaluation which, in addition to supporting the linear O(d) complexity argument, conveys the fact that Multi-Bulyan's parallelisability further adds to its efficiency.
Data-driven decision model based on dynamical classifier selection

In the era of big data, large volumes of data have been accumulated in different fields. To help make decisions by using the accumulated data, this paper proposes a data-driven decision model based on dynamical classifier selection (DCS). Under the framework of multi-criteria decision making, historical data including individual assessments on criteria and overall assessments are collected and represented by interval-valued numbers. A set of base classifiers is selected and trained using historical data. For each new alternative, its similar historical alternatives are identified from historical data through using its prediction derived from each trained classifier. By using the predictive accuracy of each base classifier and the average similarity between the new alternative and its similar historical alternatives, a new DCS strategy is developed to select an appropriate classifier from base classifiers for the new alternative. The developed DCS strategy effectively avoids the subjective determination of the size of the local region in the field of DCS. Based on the similar historical alternatives determined by the selected base classifier, an optimization model is constructed to learn criterion weights from the individual assessments of the alternatives on criteria and the predicted overall assessments derived from the selected base classifier. Then, the explainable decisions are generated with the learned criterion weights. The proposed decision model is used to aid the diagnosis of thyroid nodules. Through its comparison with traditional decision model and three representative DCS methods, the advantage of the proposed decision model in balancing accuracy and interpretability is validated.
Generation of Compound Emotions Expressions with Emotion Generative Adversarial Networks (EmoGANs)

Facial expressions of human emotions play an essential role in gaining insights into human cognition. They are crucial for designing human-computer interaction models. Although human emotional states are not limited to basic emotions such as happiness, sadness, anger, fear, disgust, and surprise, most of the current researches are focusing on those basic emotions. In this study, we proposed a new methodology to create facial expressions of compound emotions that evolve from combining those of basic emotions. In our experiments, we train our proposed model, namely Emotion Generative Adversarial Network (EmoGANs), in both unsupervised and supervised manners to improve the quality of generated images. To demonstrate the efficiency of the proposed method, we use the Extended Cohn-Kanade Dataset (CK+) and Japanese Female Facial Expressions Dataset (JAFFE) as inputs and visualize the generated images from our proposed EmoGANs. In the experiment, our proposed methodology can manipulate basic facial expressions to create facial expressions of compound emotions.
VisExPreS: A Visual Interactive Toolkit for User-Driven Evaluations of Embeddings

Although popularly used in big-data analytics, dimensionality reduction is a complex, black-box technique whose outcome is difficult to interpret and evaluate. In recent years, a number of quantitative and visual methods have been proposed for analyzing low-dimensional embeddings. On the one hand, quantitative methods associate numeric identifiers to qualitative characteristics of these embeddings; and, on the other hand, visual techniques allow users to interactively explore these embeddings and make decisions. However, in the former case, users do not have control over the analysis, while in the latter case. assessment decisions are entirely dependent on the user's perception and expertise. In order to bridge the gap between the two, in this article, we present VisExPreS, a visual interactive toolkit that enables a user-driven assessment of low-dimensional embeddings. VisExPreS is based on three novel techniques namely PG-LAPS, PG-GAPS, and RepSubset, that generate interpretable explanations of the preserved local and global structures in embeddings. In the first two techniques, the VisExPreS system proactively guides users during every step of the analysis. We demonstrate the utility of VisExPreS in interpreting, analyzing, and evaluating embeddings from different dimensionality reduction algorithms using multiple case studies and an extensive user study.
Quantum coherence, coherent information and information gain in quantum measurement

A measurement is deemed successful, if one can maximize the information gain by the measurement apparatus.Here, we ask if quantum coherence of the system imposes a limitation on the information gain during quantum measurement.First, we argue that the information gain in a quantum measurement is nothing but the coherent information or the distinct quantum information that one can send from the system to apparatus.We prove that the maximum information gain from a pure state, using a mixed apparatus is upper bounded by the initial coherence of the system.Further, we illustrate the measurement scenario in the presence of environment.We argue that the information gain is upper bounded by the entropy exchange between the system and the apparatus.Also, to maximize the information gain, both the initial coherence of the apparatus, and the final entanglement between the system and apparatus should be maximum.Moreover, we find that for a fixed amount of coherence in the final apparatus state the more robust apparatus is, the more will be the information gain.
Adaptive Window Based Sampling on The Edge for Internet of Things Data Streams

The Internet of Things (IoT) generates a massive amount of data. To relieve the pressure placed on cloud and network services, sampling techniques have been applied to remove the redundant data. Thanks to the recently emerged Edge Computing (EC) paradigm, sampling on the edge rather than on the IoT end devices can be more intelligent. Most existing sampling algorithms, applicable to edge, do not consider the complex use cases such as predictive analytics. In this paper, we propose a real-time Adaptive Window Based Sampling (AWBS) algorithm to dynamically sample IoT time-series data on the edge. We have verified AWBS on an IoT machine health dataset from NASA using Local Outlier Factor, a well-known unsupervised abnormality detection method. The results were compared with two state-of-the-art sampling algorithms. The experimental results show that AWBS outperforms the other two algorithms by 1) effectively reducing the data to 6.91% and 2) accurately identifying the abnormal behaviour of the machine.
The Lengths of Projective Triply-Even Binary Codes

It is shown that there does not exist a binary projective triply-even code of length $59$. This settles the last open length for projective triply-even binary codes. Therefore, projective triply-even binary codes exist precisely for lengths $15$, $16$, $30$, $31$, $32$, $45$--$51$, and $\ge 60$.
Concatenated pieceable fault-tolerant scheme for universal quantum computation

As existing approaches to get around the restriction of the no-go theorem generally exhibit high ancillary qubit overhead costs, we propose a scheme for universal fault-tolerant quantum computation by using the pieceable fault-tolerant protocol and code switching techniques. In additional, by utilizing the construction of the pieceable fault-tolerant circuit, we adopt a decoding strategy based on a deep-neural-network algorithm to improve the error threshold of the non-Clifford logical gate circuit. We describe our universal construction in detail with a two-level nonuniform concatenated 25-qubit code and perform numerical simulations to analyze the depolarizing noise threshold of a given universal gate set with this code. The resources required to implement this universal gate set are also estimated to further demonstrate the efficiency of our scheme. We compare these results with the existing universal concatenation methods and conclude that our method outperforms them in terms of the lower bound of the error threshold and qubit resource overhead.
Trends in East Asian policies on digital surveillance tools during the COVID-19 pandemic

This article investigates how digital surveillance tools used by East Asian governments against COVID-19 affect privacy and personal data protection. It applies doctrinal legal analysis and case study to compare national regulations of these tools as well as their implementation in China, Hong Kong, Macau, Taiwan, Japan and South Korea. The approaches range considerably from total (China) to selective surveillance, which, however, seems overly excessive towards privacy of certain social groups, exacerbating social stratification and business disruptions in East Asia. The article argues that selective surveillance models vary across the region from voluntary selective (Japan) to compulsory selective surveillance (Hong Kong, Macau, Taiwan, South Korea) and differ in terms of privacy and related rights. Yet, the increased risks of data misuse and leakages in all the East Asian states and territories need effective legal mechanisms for privacy and data protection that pay sufficient attention to public scrutiny and independent regulators.
Information Foraging for Enhancing Implicit Feedback in Content-based Image Recommendation

User implicit feedback plays an important role in recommender systems. However, finding implicit features is a tedious task. This paper aims to identify users' preferences through implicit behavioural signals for image recommendation based on the Information Scent Model of Information Foraging Theory. In the first part, we hypothesise that the users' perception is improved with visual cues in the images as behavioural signals that provide users' information scent during information seeking. We designed a content-based image recommendation system to explore which image attributes (i.e., visual cues or bookmarks) help users find their desired image. We found that users prefer recommendations predicated by visual cues and therefore consider the visual cues as good information scent for their information seeking. In the second part, we investigated if visual cues in the images together with the images itself can be better perceived by the users than each of them on its own. We evaluated the information scent artifacts in image recommendation on the Pinterest image collection and the WikiArt dataset. We find our proposed image recommendation system supports the implicit signals through Information Foraging explanation of the information scent model.
Digital platform as a means of process optimization of integrating electric vehicles into electric power networks

Abstract In this paper, we consider the need to create an algorithmized digital platform for the development of electric charging networks of road transport and to optimize the load on the current power grid complex. In modern conditions, this platform can become an example of a mechanism for public-private partnerships and the development of environmentally friendly transport. The purpose of the platform is to provide users with up-to-date information about the state of the electric vehicle battery, reduce the cost of creating a network charging infrastructure for electric vehicle transport, automated resource accounting for power supply companies, and other services. The use of information technologies to combine the processes of economic relations in the field of energy sales services will attract funds for infrastructure development by increasing the load factor of generating capacities and unloading power centers. In the article, the authors propose a digital platform architecture using modern technologies such as IIoT, Vehicle to Grid (V2G), Machine learning, Big Data.
Hate speech as a structural phenomenon

Hate speech is more than just expression used to malign people based on their fixed identity characteristics. It is a tool deployed by those with various forms of power to maintain their social, political, or economic dominance. It puts its victims in a subordinate position, which makes equality all but impossible. Therefore, we must reconsider the near absolute protection afforded to hate speech under the First Amendment. Americans should utilize existing civil legal remedies to combat hate speech, which only serves to maintain systems of oppression such as white supremacy, gender inequity, and heteronormativity. By opening the door to civil penalties against the subordination caused by hate speech, we pave the way for those seeking equality to achieve it.