Reviewerly/exHarmony huggingface dataset:
Tectonic geomorphology assessment of neotectonics in the north of Damghan Region, Alborz Mountain Belt, Northern Iran

In mountain ranges, recent and active tectonics can be viewed as the main factor contributing to rock uplift, their present-day topography being the result of the competition between tectonic and erosional processes. The region coincides with three faults which show signs of activity during the late Quaternary period: The Astaneh, North Damghan, and Damghan faults, which collectively make up the SW section of the left‐lateral Shahrud fault system. In this study we have analyzed morphotectonic and neotectonic indices to evaluate tectonic activity of the study area.Tectonic geomorphological indices that are used in this study are drainage basin based (drainage basin asymmetry factor (Af), Hypsometric integral (Hi)) and mountain front based (mountain-front sinuosity (Smf), valley floor width – valley height ratio (Vf)) which can be useful tools for studying active tectonics. The study area includes pop-up structures that are formed during inverse tectonic activities of Alborz mountain belt. Strike-slip faults and flower structures suggest transpressional tectonic regime at eastern part of Alborz.
Attention-based Sequence to Sequence Learning for Machine Translation of Low Resourced Indic Languages – A case of Sanskrit to Hindi

Deep Learning techniques are powerful in mimicking humans in a particular set of problems. They have achieved a remarkable performance in complex learning tasks. Deep learning inspired Neural Machine Translation (NMT) is a proficient technique that outperforms traditional machine translation. Performing machine-aided translation on Indic languages has always been a challenging task considering their rich and diverse grammar. The neural machine translation has shown quality results compared to the traditional machine translation approaches. The fully automatic machine translation becomes problematic when it comes to low-resourced languages, especially with Sanskrit. This paper presents attention mechanism based neural machine translation by selectively focusing on a particular part of language sentences during translation. The work shows the construction of Sanskrit to Hindi bilingual parallel corpus with nearly 10K samples and having 178,000 tokens. The neural translation model equipped with an attention mechanism has been trained on Sanskrit to Hindi parallel corpus. The approach has shown the significance of attention mechanisms to overcome long-term dependencies, primarily associated with low resources Indic languages. The paper shows the attention plots on testing data to demonstrate the alignment between source and translated words. For the evaluation of the translated sentences, manual score based human evaluation and automatic evaluation metric based techniques have been adopted. The attention mechanism based neural translation has achieved 88% accuracy in human evaluation and a BLEU score of 0.92 on Sanskrit to Hindi translation.
Automated Truncation of Differential Trails and Trail Clustering in ARX

We propose a tool for automated truncation of differential trails in ciphers using modular addition, bitwise rotation, and XOR (ARX). The tool takes as input a differential trail and produces as output a set of truncated differential trails. The set represents all possible truncations of the input trail according to certain predefined rules. A linear-time algorithm for the exact computation of the differential probability of a truncated trail that follows the truncation rules is proposed. We further describe a method to merge the set of truncated trails into a compact set of non-overlapping truncated trails with associated probability and we demonstrate the application of the tool on block cipher Speck64. We have also investigated the effect of clustering of differential trails around a fixed input trail. The best cluster that we have found for 15 rounds has probability $$2^{-55.03}$$ (consisting of 389 unique output differences) which allows us to build a distinguisher using 128 times less data than the one based on just the single best trail, which has probability $$2^{-62}$$ . Moreover, we show examples for Speck64 where a cluster of trails around a suboptimal (in terms of probability) input trail results in higher overall probability compared to a cluster obtained around the best differential trail.
Analysis of Bitcoin's Impact on the Efficiency of a Diversified Portfolio for Brazilian Investors

Purpose – This paper aims to investigate if the inclusion of Bitcoin among the assets available to retail investors in the Brazilian market has an impact on the efficient frontier and, therefore, on the optimal choices of investors. Design/methodology/approach – This study calculates the efficient frontier with and without the inclusion of Bitcoin and estimates optimal portfolios for different criteria and time intervals. The sample period runs from 07/01/2013 to 06/30/2018 and the daily closing values of the selected assets/indices were used. Findings – This study finds evidence that the inclusion of Bitcoin among the investment alternatives would cause a statistically significant positive displacement and an expansion of the efficient frontier of the Brazilian retail market. This would result in a significant increase in the return on the tangency portfolio. In addition to improving the indicators of optimization of the risk-return binomial, the cryptoasset would be included in many optimal portfolios in the 2013Q3-2018Q2 period. Originality/value – The results obtained show that, as reported for more developed markets, Bitcoin has caused an expansion of the efficient frontier of the Brazilian retail market.
Multipartite mixed maximally entangled states: mixed states with entanglement 1

We present a full definition of mixed maximally entangled (MME) states for multipartite systems, generalizing their existing definition for bipartite systems by using multipartite Schmidt decomposition and deriving a set of necessary and sufficient conditions for their existence. Additionally, we give worked examples and provide a large collection of tabulated multipartite MME states in a variety of systems. MME states are a special kind of maximally entangled mixed state (MEMS) for which every pure decomposition state in all decompositions is maximally entangled. Thus, MME states have entanglement 1 by all valid unit-normalized entanglement measures, whereas general MEMS can have entanglement less than 1. Multipartite MME states likely have important applications such as remote state preparation and also set critical performance goals for entanglement measures.
TreeNet: A lightweight One-Shot Aggregation Convolutional Network

The architecture of deep convolutional networks (CNNs) has evolved for years, becoming more accurate and faster. However, it is still challenging to design reasonable network structures that aim at obtaining the best accuracy under a limited computational budget. In this paper, we propose a Tree block, named after its appearance, which extends the One-Shot Aggregation (OSA) module while being more lightweight and flexible. Specifically, the Tree block replaces each of the $3\times3$ Conv layers in OSA into a stack of shallow residual block (SRB) and $1\times1$ Conv layer. The $1\times1$ Conv layer is responsible for dimension increasing and the SRB is fed into the next step. By doing this, when aggregating the same number of subsequent feature maps, the Tree block has a deeper network structure while having less model complexity. In addition, residual connection and efficient channel attention(ECA) is added to the Tree block to further improve the performance of the network. Based on the Tree block, we build efficient backbone models calling TreeNets. TreeNet has a similar network architecture to ResNet, making it flexible to replace ResNet in various computer vision frameworks. We comprehensively evaluate TreeNet on common-used benchmarks, including ImageNet-1k for classification, MS COCO for object detection, and instance segmentation. Experimental results demonstrate that TreeNet is more efficient and performs favorably against the current state-of-the-art backbone methods.
Authentication of Educational Practices in Online Learning

The aim of online learning is to help forward the individualization of educational practices and self-determination of a student. From the technological point of view, it is supposed to reduce the costs on the teacher’s presence in educational process (it is not a teacher but a plat-form which holds the knowledge) and to increase investments in the student’s presence (a student completes the given knowledge with his own senses, motives, values and creativity). An important phase of educational process is rating. According to the trend of “high technolo-gies” (technologies that minimize the human presence in management) we may say that there is also a trend to increase the level of rating. In online learning environment we rate not only the results and actions of a student, but also the level of his abilities or competence and the means of rating authenticity. The article discusses the data received on using the “Self-attestation practice” online course. It is a means of final assessment for students who complete the BA in psychology. By the way of variance analysis, we verified the hypothesis that the high level of abstract thinking, high coefficient of categorization, high level of creativity, field independence and reflexivity on cognitive styles are favor for intellectual productivity in scientific, research and creative areas. A measured set of level characteristics for intellectual products is transformed into a com-plex of observed language qualities. The high level of abstract thinking is demonstrated in speech coherence, in discourse consistency. The high coefficient of categorization is demon-strated in the ability to interpret texts into different languages and scales. High creativity level can be seen in making preference for difficult, challenging creative tasks. Field independence and reflexivity is demonstrated in the ability to make an individual educational track. These characteristics are markers of successful study, including online education. They allow us esti-mating each student’s involvement into professional studies and defining the levels of intellectual success, predicting the model of individual learning environment (individual learning track).
Intelligent software web agents: A gap analysis

Semantic web technologies have shown their effectiveness, especially when it comes to knowledge representation, reasoning, and data integration. However, the original semantic web vision, whereby machine readable web data could be automatically actioned upon by intelligent software web agents, has yet to be realised. In order to better understand the existing technological opportunities and challenges, in this paper we examine the status quo in terms of intelligent software web agents, guided by research with respect to requirements and architectural components, coming from the agents community. We use the identified requirements to both further elaborate on the semantic web agent motivating use case scenario, and to summarise different perspectives on the requirements from the semantic web agent literature. We subsequently propose a hybrid semantic web agent architecture, and use the various components and subcomponents in order to provide a focused discussion in relation to existing semantic web standards and community activities. Finally, we highlight open research opportunities and challenges and take a broader perspective of the research by discussing the potential for intelligent software web agents as an enabling technology for emerging domains, such as digital assistants, cloud computing, and the internet of things.
Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models

Code-switching (CS) is common in daily conversations where more than one language is used within a sentence. The difficulties of CS speech recognition lie in alternating languages and the lack of transcribed data. Therefore, this paper uses the recently successful self-supervised learning (SSL) methods to leverage many unlabeled speech data without CS. We show that hidden representations of SSL models offer frame-level language identity even if the models are trained with English speech only. Jointly training CTC and language identification modules with self-supervised speech representations improves CS speech recognition performance. Furthermore, using multilingual speech data for pre-training obtains the best CS speech recognition.
A Style and Semantic Memory Mechanism for Domain Generalization

Mainstream state-of-the-art domain generalization algorithms tend to prioritize the assumption on semantic invariance across domains. Meanwhile, the inherent intra-domain style invariance is usually underappreciated and put on the shelf. In this paper, we reveal that leveraging intra-domain style invariance is also of pivotal importance in improving the efficiency of domain generalization. We verify that it is critical for the network to be informative on what domain features are invariant and shared among instances, so that the network sharpens its understanding and improves its semantic discriminative ability. Correspondingly, we also propose a novel "jury" mechanism, which is particularly effective in learning useful semantic feature commonalities among domains. Our complete model called STEAM can be interpreted as a novel probabilistic graphical model, for which the implementation requires convenient constructions of two kinds of memory banks: semantic feature bank and style feature bank. Empirical results show that our proposed framework surpasses the state-of-the-art methods by clear margins.
How Developers Modify Pull Requests in Code Review

In pull-based development process, contributors submit their code to open-source projects by pull requests, which are accepted or rejected by reviewers. Contributors may modify their code, which causes several iterations of code review process, and makes code reviews time-consuming for both contributors and reviewers. In this article, we set out to study pull request modifications in a code review process. We collect nine projects on GitHub with 104 307 pull requests, and investigate pull request modifications through analyzing added commits after pull requests’ submission. By studying four research questions, we conclude our major findings as follow. First, 34.56 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of collected pull requests have modifications. Pull requests with modifications have longer lifetime but higher pass rates. Second, we conclude eight modification types indicating why pull requests are modified. Third, we propose a novel method called MClassify to automatically classify pull request modifications, which achieves the accuracy of 0.807. Fourth, various modification types affect code review differently from the perspective of lifetime and pass rate. Pull requests with source control system management modifications have the longest lifetime. These findings enable developers and researchers to understand a pull-based code review process better and make improvements.
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) is a challenging problem when given image category labels but requires to learn object localization models. Optimizing a convolutional neural network (CNN) for classification tends to activate local discriminative regions while ignoring complete object extent, causing the partial activation issue. In this paper, we argue that partial activation is caused by the intrinsic characteristics of CNN, where the convolution operations produce local receptive fields and experience difficulty to capture long-range feature dependency among pixels. We introduce the token semantic coupled attention map (TS-CAM) to take full advantage of the self-attention mechanism in visual transformer for long-range dependency extraction. TS-CAM first splits an image into a sequence of patch tokens for spatial embedding, which produce attention maps of long-range visual dependency to avoid partial activation. TS-CAM then re-allocates category-related semantics for patch tokens, enabling each of them to be aware of object categories. TS-CAM finally couples the patch tokens with the semantic-agnostic attention map to achieve semantic-aware localization. Experiments on the ILSVRC/CUB-200-2011 datasets show that TS-CAM outperforms its CNN-CAM counterparts by 7.1%/27.1% for WSOL, achieving state-of-the-art performance.
Functions-as-constructors higher-order unification: extended pattern unification

Abstract Unification is a central operation in constructing a range of computational logic systems based on first-order and higher-order logics. First-order unification has several properties that guide its incorporation in such systems. In particular, first-order unification is decidable, unary, and can be performed on untyped term structures. None of these three properties hold for full higher-order unification: unification is undecidable, unifiers can be incomparable, and term-level typing can dominate the search for unifiers. The so-called pattern subset of higher-order unification was designed to be a small extension to first-order unification that respects the laws governing λ -binding (i.e., the equalities for α , β , and η -conversion) but which also satisfied those three properties. While the pattern fragment of higher-order unification has been used in numerous implemented systems and in various theoretical settings, it is too weak for many applications. This paper defines an extension of pattern unification that should make it more generally applicable, especially in proof assistants that allow for higher-order functions. This extension’s main idea is that the arguments to a higher-order, free variable can be more than just distinct bound variables. In particular, such arguments can be terms constructed from (sufficient numbers of) such bound variables using term constructors and where no argument is a subterm of any other argument. We show that this extension to pattern unification satisfies the three properties mentioned above.
Functional Central Limit Theorems for Stick-Breaking Priors

We obtain the strong law of large numbers, Glivenko-Cantelli theorem, central limit theorem, functional central limit theorem for various Bayesian nonparametric priors which include the stick-breaking process with general stick-breaking weights, the two-parameter Poisson-Dirichlet process, the normalized inverse Gaussian process, the normalized generalized gamma process, and the generalized Dirichlet process. For the stick-breaking process with general stick-breaking weights, we introduce two general conditions such that the central limit theorem and functional central limit theorem hold. Except in the case of the generalized Dirichlet process, since the finite dimensional distributions of these processes are either hard to obtain or are complicated to use even they are available, we use the method of moments to obtain the convergence results. For the generalized Dirichlet process we use its marginal distributions to obtain the asymptotics although the computations are highly technical.
WESTERN SCIENCE HUMILIATION AS A SYMPTOM OF THE WHOLE CIVILIZATION DECLINE

Some aspects of the radical change of value orientations in modern western science are considered. Influenced by leftist ideology and under the slogans of abstract justice, the leadership of European and North American institutions is trying to expand the presence of new members in the scientific and educational spheres on the quota basis, taking into account racial and gender rather than meritocratic characteristics (high-quality basic and university education level, persistence to obtain new knowledge, developed mental abilities in this area). It has been shown that the rapid legislative or illegal introduction of quotas for minorities instead of increasing their scientific level and well-being can only limit the recruiting of talented people of any gender or race into science and will result in the decline of the world science as a whole.
DATABOOK : a standardised framework for dynamic documentation of algorithm design during Data Science projects

This paper proposes a standard documentation framework for Data Science projects, called Databook. It is a result of five years of action-research on multiple projects in several sectors of activity in France, and of a confrontation of standard theoretical Data Science processes, such as CRISP_DM, with the reality of the field. As a vector for knowledge sharing and capitalisation, the Databook has been identified as one of the main facilitators of Human Data Mediation. Transformed into an operational prototype of simple and minimalist documentation, it has since been tested then on about a hundred Data Science projects, has proven its benefits for the internal and external efficiency of Data Science projects, and can be turned into a more ambitious standard framework for data patrimony valorisation and data quality governance.
Feature Extraction Model with Group-Based Classifier for Content Extraction from Video Data

In today's PC illustration, numerous object locations of videos are quite critical duties to accomplish. Swiftly and reliably recognising and distinguishing the multiple aspects of a video is a crucial attribute for collaborating with one's condition (object). The core issue is that in theory, to ensure that no significant aspect is missing; all aspects of a content in a video must be scanned for elements on various different scales. It requires some investment and effort anyway, to really arrange the substance of a given content region and both time and computational limits that an operator can spend on classification are constrained. Two presumption procedures for accelerating the standard identifier are performed by the proposed method and demonstrate their capability by performing both identification efficiency and velocity. The main enhancement of our group-based classifier focuses on accelerating the grouping of sub features by planning the problem as a selection procedure for consecutive features. The subsequent improvement gives better multiscale features to distinguish objects of all sizes without rescaling the information image from a video. Extracting contents from video is an assortment of successive images with a steady time interim. So video can give more data about contents in it when situations are changing regarding time. Along these lines, physically taking care of contents with features are very unimaginable. In the proposed work, it is suggested that a Group-based Video Content Extraction Classifier (GbCCE) extracts content from a video by extracting relevant features using a group-based classifier. The proposed method is distinct from conventional approaches and the findings indicate that better output is demonstrated by the proposed method.