Reviewerly/exHarmony huggingface dataset:
Bitcoin and Its Mining on the Equilibrium Path

Bitcoin as a major cryptocurrency has come up as a shooting star of the 2017 and 2018 headlines. After exploding its price twenty times just in the twelve months of 2017, the tone has changed dramatically in 2018 after major price corrections and increasing concerns about its mining power consumption and overall sustainability. The dynamics and interaction between Bitcoin price and its mining costs have become of major interest. Here we show that these two quantities are tightly interconnected and they tend to a common long-term equilibrium. Mining costs adjust to the cryptocurrency price with the adjustment time of several months up to a year. Current developments suggest that we have arrived at a new era of Bitcoin mining where marginal (electricity) costs and mining efficiency play the prime role. Presented results open new avenues towards interpreting past and predicting future developments of the Bitcoin mining framework.
Recent Trends in Block Chain Technology

Blockchain is a peer-to-peer (P2P) distributed ledger technology that provides openness and confidence for a new age of transactional applications. The fundamental fabric for bitcoin is blockchain, which is a design pattern made up of three core elements: a distributed network, a public ledger, and digital transactions. Digital transactions are recorded in a public ledger by members of the distributed network. Members of the network run algorithms to test and validate the planned transaction before adding it to the network. The latest transaction is applied to the public ledger if a number of the network participants believe that the transaction is legitimate. In minutes or seconds, changes to the public ledger are mirrored in all copies of the blockchain. A transaction is immutable after it has been added and cannot be reversed or deleted. No one user of the network has the ability to tamper with or change data, and everybody in the network has a full copy of the blockchain. Blockchain is a peer-to-peer (P2P) network of nodes made up of network members.
Head Count Tracking System to Maintain Social Distance during Pandemic

Being in an exceptionally jam- packed spot like arena, metro stations or sacred spots on Hajj influences the human degree of solace as well as principally the human degree of wellbeing. High Crowd may result on pushing, mass frenzy, charge, swarm pound and causing a general control misfortune. Group the board and observing is essential for keeping up open wellbeing and is a significant exploration theme. Building up a strong group checking framework (CMS) is a difficult as it includes tending to many central questions, for example, thickness variety, sporadic conveyance of articles, impediments, present assessment, and so forth Group gathering at different spots like emergency clinics, parks, arenas, air terminals, social and strict focuses are generally checked by Close Circuit Television(CCTV) cameras.
Object-Aware Attention in Few-Shot Learning

Embedding networks trained with a limited number of samples have a poor capability in localizing objects. Therefore, models of few-shot learning (FSL) are easily affected by object-irrelevant information in the background, which will lead to low accuracy. An Object-Aware Attention (OAA) mechanism is proposed to improve the generalization ability of models. In OAA module, object-relevant area is obtained by a fully convolutional network to guide the network in extracting object-relevant features. Besides, a general few-shot learning framework with OAA as a plug-and-play module is proposed, in which original images and object-aware images are fused to get the rectified prototypes. Under the general framework, the performance of most existing few-shot learning methods can be improved effectively. Comprehensive experiments show that the OAA can improve the accuracy of four mainstream baselines significantly. On benchmark mini-ImageNet, the method achieves a state-of-the-art performance on the 5-way-1-shot task and 5-way-5-shot task.
AUTOMATED SYSTEM FOR DETECTION OF NON-STANDARD ACTIONS USING SCRIPTURAL ANALYSIS OF THE TEXT

The scenario (narrative schemas) is some established (in society) sequence of steps to achieve the set goal and contains the most complete information about all possible ways of development of the described situation (with selection points and branches). The creation of the XML platform gave rise to a new high-tech and technologically more advanced stage in the development of the Web. As a result, the XML platform becomes a significant component in the technology of information systems development, and the tendency of their integration at the level of corporations, agencies, ministries only strengthens the position of XML in the field of information technology in general. A system for automatic detection of non-standard scripts in text messages has been developed. System programming consists of stages of ontology formation, sentence parsing and scenario comparison. the classic natural language processing (NLP) method, which supports the most common tasks such as tokenization, sentence segmentation, tagging of a part of speech, extraction of named entities, partitioning, parsing and co-referential resolution, is used for parsing sentences in the system. Maximum entropy and machine learning based on perceptrons are also possible. Ontologies are stored using OWL technology. The object-target sentence parsers with the described OWL are compared in the analysis process. From a SPARQL query on a source object, query models are returned to the table object. The table class is the base class for all table objects and provides an interface for accessing values in the rows and columns of the results table. If the table object has exactly three columns, it can be used to build a new data source object. This provides a convenient mechanism for retrieving a subset of data from one data source and adding them to another. In the context of the RDF API, a node is defined as all statements about the subject of a URI. The content of the table is compared with the semantics of the sentence. If the sentence scenario does not match the OWL ontology model, there is a possibility of atypical object actions. In this case, a conclusion is formed about the suspicion of the message. For more correct use of possibilities of the analysis of the text it is necessary to form the case of ontologies or to use existing (Akutan, Amazon, etc.) taking into account their features. To increase the ontologies of objects, it is possible to use additional neural network teaching methods.
One-Shot Example Videos Localization Network for Weakly-Supervised Temporal Action Localization

This paper tackles the problem of example-driven weakly-supervised temporal action localization. We propose the One-shot Example Videos Localization Network (OSEVLNet) for precisely localizing the action instances in untrimmed videos with only one trimmed example video. Since the frame-level ground truth is unavailable under weakly-supervised settings, our approach automatically trains a self-attention module with reconstruction and feature discrepancy restriction. Specifically, the reconstruction restriction minimizes the discrepancy between the original input features and the reconstructed features of a Variational AutoEncoder (VAE) module. The feature discrepancy restriction maximizes the distance of weighted features between highly-responsive regions and slightly-responsive regions. Our approach achieves comparable or better results on THUMOS’14 dataset than other weakly-supervised methods while it is trained with much less videos. Moreover, our approach is especially suitable for the expansion of newly emerging action categories to meet the requirements of different occasions.
Semantic Similarity Retrieval System Based on the Chinese Minority Dictionary Wa Volume

With the rapid development of information retrieval technology, related research has also made great progress. However, in the specific field of minority information resources, the problem of insufficient scalability has caused users to retrieve the accurate information required by the specific field still very difficult. We use the Word2vec method to calculate the semantic similarity of specific vocabulary based on the “The Chinese Minority Dictionary Wa Volume (abbreviated as Wa Volume)” and Chinese Wikipedia. Finally, we experimentally verify that the semantic similarity based on the Wa Volume in the field of ethnic minorities is higher than that based on Chinese Wikipedia.
On embedding Lambek calculus into commutative categorial grammars

Abstract We consider tensor grammars, which are an example of ‘commutative’ grammars, based on the classical (rather than intuitionistic) linear logic. They can be seen as a surface representation of abstract categorial grammars (ACG) in the sense that derivations of ACG translate to derivations of tensor grammars and this translation is isomorphic on the level of string languages. The basic ingredients are tensor terms, which can be seen as encoding and generalizing proof nets. Using tensor terms makes the syntax extremely simple and a direct geometric meaning becomes transparent. Then we address the problem of encoding noncommutative operations in our setting. This turns out possible after enriching the system with new unary operators. The resulting system allows representing both ACG and Lambek grammars as conservative fragments, while the formalism remains, as it seems to us, rather simple and intuitive.
BSMRL: Bribery Selfish Mining with Reinforcement Learning

As the second valuable cryptocurrency, Ether arouses a lot of concern and discussion, leading to various potential attacks. Although the threshold of the mining power is rather high for the attackers, machine learning is accessary to a tyrant’s crimes. Thus the attackers can maximize their rewards by optimizing their attacking strategies. In this paper, we propose a novel intelligent attacking strategy (aka. BSMRL). More specifically, attackers optimize, combining with bribery attacks, their strategies to maximize the rewards by utilizing reinforcement learning. Simulation results indicate that BSMRL can greatly reduce the threshold for the attackers with higher rewards. Compared with the normal selfish miners, intelligent attackers are more dangerous to the system of Ethereum.
A Novel Method of Cropped Images Forensics in Social Networks

Operation chain forensics has achieved many advanced results, however, the spread of images in Social Networks has brought greater challenges to forensic research. The processing of images by Social Networks reduces the effectiveness of current forensics methods. An example is that if images are exchanged through Social Networks, the features they rely on will no longer be valid for detecting cropped images. To solve this problem, this paper proposes a novel method to complete the task of cropped forensics on Social Networks. By analyzing the image processing characteristics of Social Networks, we designed a special feature named "block artifacts grayscale" (BAGS) for crop detection based on block artifacts generated in JPEG compression. The proposed method is proved more accurate than some previous algorithms in cropped forensics in Social Networks, and it is also robust to serious re-compression. The proposed method modeled on Social Networks can promote other forensics issues. Moreover, the method provides a good direction for determining the process of enhanced filtering in Social Networks.
Quantized Field Theory of Fuzzy Space

The proposed work handles the concept of fuzzy space as a set of real numbers with a set finite membership function.Extending standard arithmetic operations through a fuzzy space, the fuzzy Green's function is created here with an analysis of its behavior inside and outside the light cone.The fuzzy causality principle is generalized to field models.Also, this work demonstrates the ability to use fuzzy space to regularize divergences in quantum field theory.The passage to the limit to a system of interacting particles enables the obtaining of the dissipative projection operator, represented earlier.The Liouville equation is solved here by successive approximations in the range of times much larger than the typical scale of fuzziness, by assuming the interaction as a small parameter.As well, here was applied the standard diagram technique.
Deconstruction of cultural space: death of the text

Starting from the understanding of the “death” of the text as a process of its deconstruction, fragmentation, simplification, when the text becomes a signal instead of a message, we will also consider more modern transformations of its role and system organization, leading to symbolic “death” through complication and transition to hypertext. The reasons for converting the text of a message into a narrative and into a participant in intertextual relations. The final departure to the pragmatic aspect of information exchange in various fields (interpersonal communication, advertising, journalism, didactics). Without death, a ritualized act, with the dominance of network forms of communication, its inclusion in the space of network content is impossible. Now, after the publication of the text, it has by and large only two ways: either it is interpreted and retransmitted in such a way that inevitably leads to the inflation of the content, or it is archived, which is equivalent to a “funeral”. In any of the variants, this is the “death” of the original author’s text, the fate of which we are considering. Speaking of the “death of the text”, we mean the transformation of the very general concept (communicative intention) that defines the functionality of modern textual activity. The leading characteristics of relevant information as a unit of communication are its trigger (motivating) potential and the ability to act as network content (liquidity). The features that characterize the text in the traditional sense: coherence, integrity, i.e. the ability to demonstrate the qualities of the “system”, gradually lose their significance in social practice.
A More Focus on Multi-degradation Method for Single Image Super-Resolution

Single Image Super-resolution (SISR) aims at reconstructing a High-Resolution (HR) image from a Low-Resolution (LR) one. Recent works, especially in the deep learning-based approach, mainly define and resolve the problems of LR images degraded by a fixed degradation kernel, typically bicubic interpolation. However, this assumption can hardly be practical since an input image may suffer from many other deteriorations (e.g. blur or noise). Previous works tackle such multi-degradations by proposing new models, targeting at lessening the restrictions of learning-based method and taking advantages of CNN architecture. Unfortunately, they ignore the existing state-of-the-art CNN-based SISR models that are trained on a fixed degradation kernel. In this work, we introduce a context-extending module that generates on-the-fly more realistic types of degradation. We also come up with a comprehensive cross-degradation loss function enabling the model to better adapt real-world conditions. With this proposal, we can generalize arbitrary end-to-end learning-based networks. Evaluating by Peak Signal-to-Noise Ratio (PSNR) metric, our proposed method outperforms the EDSR baseline a significant amount of 34.5% (from 20.14dB to 27.09dB) on noisy images meanwhile sustaining the comparable results on the bicubic downsampling factor.
An Empirical Study of Uncertainty Gap for Disentangling Factors

Disentangling factors has proven to be crucial for building interpretable AI systems. Disentangled generative models would have explanatory input variables to increase the trustworthiness and robustness. Previous works apply a progressive disentanglement learning regime where the ground-truth factors are disentangled in an order. However, they didn't answer why such an order for disentanglement is important. In this work, we propose a novel metric, namely Uncertainty Gap, to evaluate how the uncertainty of generative models changes given input variables. We generalize the Uncertainty Gap to image reconstruction tasks using BCE and MSE. Extensive experiments on three commonly-used benchmarks also demonstrate the effectiveness of our Uncertainty Gap in evaluating both informativeness and redundancy of given variables. We empirically find that the significant factor with the largest Uncertainty Gap should be disentangled before insignificant factors, which indicates that a suitable order of disentangling factors facilities the performance.
Data Collection, Sharing and Analysis to Tackle Undeclared Work: A Toolkit

Collecting, sharing and analysing data can improve enforcement authority performance not only in detecting undeclared work but also preventing non-compliance. The aim of this toolkit is to support enforcement authorities in the Western Balkans to improve their knowledge and awareness of how to develop efficient databases to detect, prevent and predict undeclared work. The objectives of this toolkit are (i) to discuss the challenges faced around data collection, sharing and analysis and (ii) to offer tips on how data collection, sharing and analysis could be improved, not least by reporting good practices.
Online-GCN: an online interactive segmentation method based on graph convolutional network

Interactive image segmentation can improve segmentation performance using manual intervention. Traditional interactive segmentation methods have unsatisfactory segmentation accuracy for images with complex background. Deep learning-based methods depend on large and accurate annotated datasets. In this paper, we propose an online interactive segmentation method based on graph convolutional network (GCN), which includes the superiorities of these two types of methods. We present a pre-segmentation stage to get an initial segmentation of the image, then propose an interactive GCN (iGCN) module to further improve the accuracy of the initial segmentation. Moreover, iGCN module is trained online without any pre-training burden. Experimental results show that our method outperforms several state-of-the-art methods on GrabCut and Berkeley datasets.
Security Analysis of Zhang et al. Authenticated Encryption Scheme

Authenticated encryption schemes play an essential role in distributed environments where users may want to access remote servers while preserving their security and privacy. Zhang et al. (Applied Mathematics and Computation, Vol. 162, Issue 3, 2005) identified security pitfalls in Tseng et al.’s schemes that allow the signer to repudiate her signature. Moreover, Zhang et al. presented a new modification in order to mitigate these pitfalls. We elaborate on the lacking of achieving the desired non-repudiation property in the scheme that was presented by Zhang et al.
Sentence Punctuation for Collaborative Commentary Generation in Esports Live-Streaming

To solve the existing sentence punctuation problem for collaborative commentary generation in Esports live-streaming, this paper presents two strategies for sentence punctuation for text sequences of game commentary, that is, punctuating sentences by two or three text sequence(s) originally punctuated by Youtube to obtain a complete sentence of commentary. We conducted comparative experiments utilizing and fine-tuning a state-of-the-art pre-trained generative language model among two strategies and the baseline to generate collaborative commentary. Both objective evaluations by automatic metrics and subjective analyses showed that our strategy of punctuating sentences by two text sequences outperformed the baseline.
Industry-Academia Research Collaborations During and After COVID-19

There exists a long-standing challenge of building successful research collaborations between industry and academia in software engineering. There are multiple reasons contributing to this issue, including different timelines, impact metrics, expectations, and perceptions of researchers and practitioners, altogether leading to the issue known as the industryacademia collaboration gap. After the onset of the COVID-19 pandemic, most researchers and practitioners were sent to work from home, relying on virtual collaboration with their peers, which might have given rise to an even wider industry-academia collaboration gap. At the 8th International Workshop on Software Engineering Research and Industrial Practice, held virtually at the International Conference on Software Engineering 2021, we investigate the impact that working from home has had on research collaborations between industry and academia. We also take a look at how remote work may change research collaborations in the future. In this report, we present takeaways from keynote talks on this matter, share insights from the panel of six experts on the topic of industry-academia collaborations, and finally summarize findings from related paper presentations.
Thesaurus and subject heading lists as Linked Data

Abstract Most libraries put a lot of effort into developing subject headings or thesauri, which are used to index and retrieve information. Nevertheless, in the library field, controlled vocabularies are associated to authority records as authority files. In order to become findable by search engines, these authority files should be modelled on semantic vocabularies. This research proposes an authority-record conversion process for publishing thesauri and subject headings as linked data, by using the Simple Knowledge Organization Systems data model. To this purpose, we undertook a bibliographic and documentary research on the World Wide Web Consortium recommendation guidelines, which were used to produce a set of procedures and technologies to support the conversion proposal. This research provides evidences that controlled vocabularies are an important resource for improving information retrieval on the web. The proposed conversion process works as a quick guide for controlled vocabulary integration and reuse among users and systems on the linked data environment. Although the proposal was originally intended for a library setting, it can be applied and tested in another type of institution, such as documentation centres, museums, or cultural heritage archives. It can also be used in other linked open data projects.