Reviewerly/exHarmony huggingface dataset:
Handling Structural Mismatches in Real-time Opera Tracking

Algorithms for reliable real-time score following in live opera promise a lot of useful applications such as automatic subtitles display, or real-time video cutting in live streaming. Until now, such systems were based on the strong assumption that an opera performance follows the structure of the score linearly. However, this is rarely the case in practice, because of different opera versions and directors' cutting choices. In this paper, we propose a two-level solution to this problem. We introduce a real-time-capable, high-resolution (HR) tracker that can handle jumps or repetitions at specific locations provided to it. We then combine this with an additional low-resolution (LR) tracker that can handle all sorts of mismatches that can occur at any time, with some imprecision, and can re-direct the HR tracker if the latter is ‘lost’ in the score. We show that the combination of the two improves tracking robustness in the presence of strong structural mismatches.
Radiación fotosintéticamente activa evaluada en la ciudad de Zacatecas

The present investigation is related to one of the most important processes for the development of life on Earth; photosynthesis, an essential process in the cycle and development of living beings, centered on solar radiation that is useful for plants to carry out this process, Photosynthetically Active Radiation (PAR). The objective of this work is to generate information on the PAR through a database to collaborate in the decision-making of farmers in the area. For this purpose, a quantum sensor installed in building 6 of the UAZ Siglo XXI Campus was used. According to Abal (2013), in agricultural and production planning, it is especially important to have a detailed knowledge of incident solar radiation on the earth's surface (Abal and Durañona, 2013). When collecting, treating and analyzing the data, it was found that the daily average PAR is 819.52 μmol of photons m-2 s-1 (179.47 W m-2), if only the sunny hours are taken into account. It can be concluded that according to the PAR received in the evaluation region and the type of nutrients in the soil, other crop alternatives to those traditionally used can be sought.
On the Cost of ASIC Hardware Crackers: A SHA-1 Case Study

In February 2017, the SHA-1 hashing algorithm was practically broken using an identical-prefix collision attack implemented on a GPU cluster, and in January 2020 a chosen-prefix collision was first computed with practical implications on various security protocols. These advances opened the door for several research questions, such as the minimal cost to perform these attacks in practice. In particular, one may wonder what is the best technology for software/hardware cryptanalysis of such primitives. In this paper, we address some of these questions by studying the challenges and costs of building an ASIC cluster for performing attacks against a hash function. Our study takes into account different scenarios and includes two cryptanalytic strategies that can be used to find such collisions: a classical generic birthday search, and a state-of-the-art differential attack using neutral bits for SHA-1.
Modified <scp>SeDaSc</scp> system for efficient data sharing in the cloud

Summary Organizations tend to store their sensitive data on trusted cloud data storage server to protect it from outside security attacks. However, storing data on the cloud has serious security issues like data misuse from the cloud vendors themselves and attacks from outsources. In this manner, the organizations tend to encrypt their data before storing it in the cloud to protect against the misuse from the cloud vendors and other outer attacks. This article offers an enhancement to Secure Data Sharing in the Cloud (SeDaSc) system in terms of enhancing security metrics related to data privacy and disk storage handling. In the proposed system, the cryptographic server (CS) has been treated a semi‐trusted entity by not giving direct access to the data directly. The data encryption is carried at the client side. While, the keys generation and other related cryptographic operations as the access control list (ACL) management are carried at the CS. The client generates the hash of the file needed to be shared in the cloud and then sends such hash to the CS. Then, the CS uses the hash to generate the cryptographic key and sends it to the client for encryption process. Further, the proposed system achieves better disk management by only updating the ACL in case of uploading the same file more than once. The CS stores generated key hash and whenever receives a new file, the CS compares the hash of the generated key with other stored hashes. The hash computation in key generation in the proposed system makes the key generation process varies with data size in compare with the key generation process in SeDaSc that is independent on data size. Furthermore, in the case of identical hashes, the ACL will only be updated. Therefore, the identical files neither will be encrypted nor uploaded to cloud twice. The proposed system has been represented mathematically and graphically using high level petri net model and simulated by python language environment.
<i>MAP2XANES</i>: a Jupyter interactive notebook for elemental mapping and XANES speciation

MAP2XANES is an intuitive Jupyter notebook that automatizes the analysis of synchrotron X-ray fluorescence imaging and X-ray absorption spectroscopy for the characterization of complex and heterogeneous samples. The notebook uses basic modules and functions from Numpy, Scipy, Pandas, iPywidgets and Matplotlib libraries for a powerful data reduction process that, in just a few clicks, guides the user through the visualization of elemental maps, space-resolved absorption spectra and their automatized analysis. In particular, by means of linear combination fit of the XANES spectra, the notebook determines the chemical species distribution in the sample under investigation. The direct output of the analysis process is the correlation between the different elemental distributions and the spatial localization of the chemical species detected. An application to mineralogy is thus presented, analyzing the Mn 2+ , Mn 3+ and Mn 4+ distribution in a mineral sample of hausmannite (Mn 2+ Mn 2 3+ O 4 ), courtesy of the Museum of Natural Science of Barcelona.
Role of Knowledge Infrastructure Capabilities in Product/Service Innovation

This research explores the direct and indirect effects of knowledge infrastructure capabilities in creating product/service innovation in small entrepreneurial companies in Jordan. The mediating effects of knowledge-based dynamic capabilities and moderating role of innovation culture and emotional intelligence are also investigated. To this end, questionnaire surveys were developed and administered to employees and managers of 29 small entrepreneurial companies, resulting in 202 respondents. Validation and reliability analyses were performed followed by structural equation modelling for hypotheses testing. The results demonstrated the positive direct effect of knowledge infrastructure capabilities on creating product/service innovation. The indirect effect was confirmed as knowledge-based dynamic capabilities partially mediated the link between knowledge infrastructure capabilities and product/service innovation, and this relationship was moderated by innovation culture and emotional intelligence. This research highlights that the innovation culture and emotional intelligence encourage and sustain employee actions. This moderating influence can help better understand the complex nature of product/service innovation. Moreover, the variables used in this study have been thoroughly examined in developed countries, application of this model is deemed appropriate in developing countries such as Jordan to provide empirical evidence from a non-Western perspective.
Contribution to Arabic Text Classification Using Machine Learning Techniques

With the increase of text stored in electronic format, it is no longer possible for humans to understand all the incoming data or even categorize it. We need an automatic text classification system in order to classify them into predefined classes and quickly retrieve information. Text classification can be achieved by machine learning, it requires a set of approaches for vectorization and classification. In vectorization phase, this work proposes two approaches (BOW and TF-IDF), but in the classification phase, the algorithms of machine learning used are: RL, SVM and ANN. At the end, a comparison study is given.
Topic Sequence Embedding for User Identity Linkage from Heterogeneous Behavior Data

In social media, user identity linkage is a vital information security issue of identifying users’ private information across multiple online social networks. With the popularity of behavior-rich social services, existing methods attempt to align users through encoding behaviors. However, most of the efforts suffer from the high variety and heterogeneity of behavior data across social networks, resulting in a limitation of modeling user intrinsic characteristics. To address the above issues, we focus on keyword-based topics to formulate user’s variety behaviors for user identity linkage. In this paper, a novel Topic Sequence Embedding (TSeqE) method is proposed to embed contextual information of topics to represent users’ intrinsic characteristics for identity linkage. Furthermore, we introduce a domain-adversarial training strategy to tackle the behavior heterogeneity problem. Our experiments on two real-world datasets demonstrate that TSeqE produces a significant improvement compared with several strong baselines.
Virtual Reconstruction of a the Ambisonic Concert Hall of the Royal Danish Academy of Music

In this paper we present a simulation and validation of the Ambisonic concert hall at the Royal Danish Academy of Music (RDAM), which was inaugurated on September 19th, 2019 in Copenhagen.Validation with experts showed that the simulated ambisonic room was perceived to match the acoustic- and spatial attributes of its real counterpart.
Pointer Networks for Arbitrary-Shaped Text Spotting

Current text spotting methods perform text detection and text recognition separately. However, in complex scenes where bounding boxes of texts with various shapes are often overlapped, text detection becomes error-prone. By contrast, character detection is more non-ambiguous and easier to learn. In this paper, we present a highly efficient one-stage method named PointerNet for arbitrary-shaped text spotting. Unlike previous methods, PointerNet does not rely on text detection and opens a novel spotting-by-character-detection paradigm. In particular, to connect characters to texts, we propose a simple yet highly effective strategy named pointer that learns the 2D offset from the center of the current character to the center of the subsequent character. Evaluations demonstrate that our PointerNet achieves state-of-the-art performance and is more efficient than current methods (75ms vs. 133ms compared with FOTS). Our code will be publicly available.
The study and concept of technology (a perspective from methodology and culturology)

&amp;nbsp; This article raises the question about the nature of technology, namely whether there is universal technology that evolves and develops in culture, or there are different technologies that are interrelated in genesis. The author examines the pros and cons, as well as other problems, the solution of which sheds light on the nature of technology. The conclusion is made that the invariant characteristics of technology (technology as an artifact; synergy of natural effects and human activity; social body of a person; conceptualization; factors of sociality) do not represent the ontology of technology, but rather the methodology. In order to solve the dilemma, the author analyzed the two current states of technology &amp;#8210; engineering and technology. Technology is defines as an activity on creating technical products and as a technical environment that is essential for human life. It is claimed that the technology is a result of the activity turn and turn, which was later associated with management, including research and rational restructuring of the activity by specialists trained for solving such tasks. Ultimately, the author inclines towards the thesis of multiple technologies. The article discussses the Internet as a new type of technology. &amp;nbsp;
Improved Depth Estimation Algorithm via Superpixel Segmentation and Graph-cut

Depth information is a critical factor for increasing the quality of immersive media. However, depth estimation approaches still have issues in maintaining the depth continuity, and there is inconsistency between the depth edges and the corresponding color edges. The proposed algorithm aims to solve this problem. The proposed depth-estimation method uses a graph-cut on a superpixel basis. We present a novel energy function used in graph-edge weights, and add preprocessing and local depth refinement to remove superpixel noise. Simulation results demonstrate that the proposed algorithm provides a more accurate depth image, which maintains global continuity, compared to the other conventional methods.
Kansei Transition Analysis by Time-series Change of Media Content

In this paper, we present a new concept, a waveform model of Kansei transition for time-series media content. It is important to apply the time-series change of media content to Kansei information processing. For example, the impression of music media content changes over time. In our model, we represent Kansei transition by time-series change of media content as waveforms. We realize new Kansei similarity by comparison with Kansei transitions represented by waveforms applying a signal processing technique. Through new Kansei similarity, it is possible to realize media content retrieval and recommendation systems corresponding to the time-series Kansei transition of media content. Our model consists of two modules: a high-order media-Kansei transformation module and a waveform similarity computation module. The high-order media-Kansei transformation module extracts each Kansei magnitude by each time from the features of media content. The waveform similarity computation module computes similarities between each waveform represented as Kansei transition.
Employing Data Augmentation for Recognition of Hand Gestures Using Deep Learning

Hand gestures are a form of non-verbal communication. Apart from traditional input devices, hand gestures are used for interaction with computers too. Communication via hand gestures finds many applications in the real world. Different people have hands with different shapes and orientations, which is termed as nonlinearity. The nonlinearity affects the performance of hand gesture models. A convolutional neural network (CNN) is an approach of neural networks, specifically known as deep learning. CNN is used to recognize and classify images. Sometimes, CNN could not correctly understand the hand gesture due to nonlinearity. Data augmentation helps CNN to understand the nonlinearity and complexity of images better. Data augmentation generates enormous data from lesser data, thus increasing the data adversity. Data augmentation uses various operations like zooming, rotating, shifting, shearing, and scaling to generate more data from the existing data. This article executes a CNN model using augmented data for recognition of static hand gestures. The dataset consists of 10 different hand gestures. The experimented CNN model has been trained using 10000 images and tested using 1000 images. The changes in the output of CNN with and without data augmentation have been highlighted. The CNN model employing data augmentation achieved an accuracy of 98.10%, whereas the CNN model excluding the data augmentation process attained an accuracy of 94.90% only.
Ranking Synthetic Features for Generative Zero-Shot Learning

Zero-Shot Learning (ZSL) is an emerging learning paradigm that addresses the problem of recognizing unseen classes during training. Several studies have shown ZSL can be improved using synthetic samples of unseen classes, usually generated with a GAN and conditioned on some high- level descriptions of the desired class. This paper proposes a new generative adversarial network architecture to improve synthetic feature generation by applying a ranking step at training time. We combined two classifiers' results at the zeroshot classification step to ensure improved classification accuracy. Then we evaluated the proposed architecture using the widely used dataset AWA. Our results show an improvement of classification accuracy of 2.3% in ZSL setting and 0.15% in GZSL setting compared to the state-of-the-art.
Embodying a Narrative: Spatializing Abstract Narrative Themes for Forensic Exploration

Immersive virtual experiences are becoming more prominent in journalism, yet they largely focus on realistic simulations of the real world. We introduce a novel VR experience that incorporates surrealism and foregrounds the abstract concepts in the journalistic story of Occupy City Hall, the June 2020 sit-in protest in New York, NY. The experience’s virtual environment contains multimedia primary sources augmented with abstracted representations of their interrelated underlying themes. The user experiences an ambiguous interface, which is designed to provoke curiosity, exploration, and critical thinking. The experience has been live-streamed in 2D to audience observers, who responded positively to the diverse concrete and conceptual content, but felt the ambiguous interface was not supplemented with sufficient guidance. Future user studies will involve the full VR experience, to determine the impact of ambiguous interfaces on user engagement, enjoyment, and conceptual learning, as well as comparing this form of journalistic media to others.
Service Providers’ Competence Identification in Knowledge-Intensive Crowdsourcing Context

Competence analysis of service providers is of great importance for a knowledge-intensive crowdsourcing platform to examine service provider'(SPs) effectiveness and contribution and therefore to improve its management and operation efficiency and accuracy. As the information highway highly-developed, there are a huge amount of online crowdsourcing communities where talent workers can exchange experience and ideas, which enables competence analysis using text mining. In this paper, we developed a competence identification framework to analyze and recognize SPs' competence in online knowledge-intensive crowdsourcing (KIC) context. We firstly crawled the experience sharing articles, which contained excellent SPs' experiences and ideas about the efforts that should be made to be successful, from several Chinese online crowdsourcing communities and then text mining techniques were applied to analyze these unstructured texts. Each sentence in the corpora was tokenized into several words, after which the words were clustered as different topics using Latent Dirichlet Allocation (LDA) model based on their underlying semantics. Furthermore, based on the LDA outputs, we identified six clusters of crowdsourcing SPs' competence and thus constructed the competence system on the basis of Spencer's competence dictionary and human intervention. Finally, the descriptions of the competence system were presented.
Recent Developments in Tibetan NLP

research-article Share on Recent Developments in Tibetan NLP Authors: Long Congjun Chinese Academy of Social Sciences Chinese Academy of Social SciencesView Profile , Nathan W. Hill Trinity College Dublin Trinity College DublinView Profile Authors Info & Claims ACM Transactions on Asian and Low-Resource Language Information ProcessingVolume 20Issue 2March 2021 Article No.: 19pp 1–3https://doi.org/10.1145/3453692Online:23 April 2021Publication History 0citation84DownloadsMetricsTotal Citations0Total Downloads84Last 12 Months69Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Design and Implementation of an Oral English Learning System Based on Speech Recognition Technology

With the development of network technology, the use of intelligent equipment, People's Daily life style has also changed accordingly. In recent years, the application scope of big data has been expanding, and the research and development of artificial intelligence has been promoted gradually. Among them, the research of speech recognition technology is an important research direction. If breakthroughs are made in speech recognition technology, it is possible for humans to communicate with machines that can understand human language and respond to it accordingly. The integration of intelligent devices into people's lives has provided great convenience, and the research on speech recognition technology is undoubtedly an important part, which determines whether the communication between people and devices can be realized smoothly.
RAPOT: An Adaptive Multifactor Risk Assessment Framework on Public Opinion for Trial Management

Since public opinion from social media has a growing impact and supervision on trial, risk assessment on public opinion is increasingly important in refined trial management. However, the tremendous amount of public opinion and the insufficient historical logs of trial procedures bring challenges to risk assessment on public opinion. To address this, we propose an adaptive multifactor risk assessment framework on public opinion with fuzzy numbers. Initially, we establish a multilayer indicator model for assessing the risk of public opinion (POR) with multilayer analysis and decision methods. Then, we explore the association rules hidden in the process logs to update the indicator model periodically. Moreover, we design a public opinion analysis module for indicator evaluation, including analysis in public opinion sentiment, hot search, and social media coverage to deal with big data on social media. Especially, the public opinion sentiment is classified by topic-based BiLSTM (T-BiLSTM), which is more accurate. Finally, the fuzzy number similarity is employed to determine POR’s level in the nine-level risk system. Experimental results validate the efficiency of our framework when assessing the POR.
Sustainable computing

Energy consumption by computers is expanding exponentially along with big data and AI processing. The trend can be broken by adopting alternate approaches to CPU and GPU design. Specifically, Adiabatic Reversible Logic (ARL) has been proposed as the solution. This essay surveys the technology of ARL and gives early examples of actual reversible machines.