Reviewerly/exHarmony huggingface dataset:
CPTAM: Constituency Parse Tree Aggregation Method

Diverse Natural Language Processing tasks employ constituency parsing to understand the syntactic structure of a sentence according to a phrase structure grammar. Many state-of-the-art constituency parsers are proposed, but they may provide different results for the same sentences, especially for corpora outside their training domains. This paper adopts the truth discovery idea to aggregate constituency parse trees from different parsers by estimating their reliability in the absence of ground truth. Our goal is to consistently obtain high-quality aggregated constituency parse trees. We formulate the constituency parse tree aggregation problem in two steps, structure aggregation and constituent label aggregation. Specifically, we propose the first truth discovery solution for tree structures by minimizing the weighted sum of Robinson-Foulds (RF) distances, a classic symmetric distance metric between two trees. Extensive experiments are conducted on benchmark datasets in different languages and domains. The experimental results show that our method, CPTAM, outperforms the state-of-the-art aggregation baselines. We also demonstrate that the weights estimated by CPTAM can adequately evaluate constituency parsers in the absence of ground truth.MSC codesConstituency parse treeTruth discoveryOptimization
Unambiguous State Discrimination with Intrinsic Coherence

We investigate the discrimination of pure-mixed (quantum filtering) and mixed-mixed states and compare their optimal success probability with the one for discriminating other pairs of pure states superposed by the vectors included in the mixed states. We prove that under the equal-fidelity condition, the pure-pure state discrimination scheme is superior to the pure-mixed (mixed-mixed) one. With respect to quantum filtering, the coherence exists only in one pure state and is detrimental to the state discrimination for lower dimensional systems; while it is the opposite for the mixed-mixed case with symmetrically distributed coherence. Making an extension to infinite-dimensional systems, we find that the coherence which is detrimental to state discrimination may become helpful and vice versa.
Information Augmentation for Few-shot Node Classification

Although meta-learning and metric learning have been widely applied for few-shot node classification (FSNC), some limitations still need to be addressed, such as expensive time costs for the meta-train and difficult of exploring the complex structure inherent the graph data. To address in issues, this paper proposes a new data augmentation method to conduct FSNC on the graph data including parameter initialization and parameter fine-tuning. Specifically, parameter initialization only conducts a multi-classification task on the base classes, resulting in good generalization ability and less time cost. Parameter fine-tuning designs two data augmentation methods (i.e., support augmentation and shot augmentation) on the novel classes to generate sufficient node features so that any traditional supervised classifiers can be used to classify the query set. As a result, the proposed method is the first work of data augmentation for FSNC. Experiment results show the effectiveness and the efficiency of our proposed method, compared to state-of-the-art methods, in terms of different classification tasks.
A random energy approach to deep learning

Abstract We study a generic ensemble of deep belief networks (DBN) which is parametrized by the distribution of energy levels of the hidden states of each layer. We show that, within a random energy approach, statistical dependence can propagate from the visible to deep layers only if each layer is tuned close to the critical point during learning. As a consequence, efficiently trained learning machines are characterised by a broad distribution of energy levels. The analysis of DBNs and restricted Boltzmann machines on different datasets confirms these conclusions.
Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency

Structured pruning has been extensively studied on monolingual pre-trained language models and is yet to be fully evaluated on their multilingual counterparts.This work investigates three aspects of structured pruning on multilingual pre-trained language models: settings, algorithms, and efficiency.Experiments on nine downstream tasks show several counterintuitive phenomena: for settings, individually pruning for each language does not induce a better result; for algorithms, the simplest method performs the best; for efficiency, a fast model does not imply that it is also small.To facilitate the comparison on all sparsity levels, we present Dynamic Sparsification, a simple approach that allows training the model once and adapting to different model sizes at inference.We hope this work fills the gap in the study of structured pruning on multilingual pre-trained models and sheds light on future research.
Forensic Discoverability of iOS Vault Applications

Vault Applications store potentially sensitive information on a smartphone; and are available on Android and iOS.Using these applications could be used to hide potential evidence or illicit photos.After comparing five iOS photo vaults, each vault left evidence and photos behind.However, of the three forensic toolkits used, each produced different results in their scans of the phone.The media left behind was due to the photo vaults not protecting their information as claimed and using basic obfuscation techniques in place of security controls.Future research will look at how newer security controls are implemented and if they are easily discoverable.
A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification

Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).
Corrections to “A YouTube Spam Comments Detection Scheme Using Cascaded Ensemble Machine Learning Model”

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , the author would like to correct the Acknowledgment as follows:
A Staffing Recommender System based on Domain-Specific Knowledge Graph

In the economics environment, Job Matching is always a challenge involving the evolution of knowledge and skills. A good matching of skills and jobs can stimulate the growth of economics. Recommender System (RecSys), as one kind of Job Matching, can help the candidates predict the future job relevant to their preferences. However, RecSys still has the problem of cold start and data sparsity. The content-based filtering in RecSys needs the adaptive data for the specific staffing tasks of Bidirectional Encoder Representations from Transformers (BERT). In this paper, we propose a job RecSys based on skills and locations using a domain-specific Knowledge Graph (KG). This system has three parts: a pipeline of Named Entity Recognition (NER) and Relation Extraction (RE) using BERT; a standardization system for pre-processing, semantic enrichment and semantic similarity measurement; a domain-specific Knowledge Graph (KG). Two different relations in the KG are computed by cosine similarity and Term Frequency-Inverse Document Frequency (TF-IDF) respectively. The raw data used in the staffing RecSys include 3000 descriptions of job offers from Indeed, 126 Curriculum Vitae (CV) in English from Kaggle and 106 CV in French from Linx of Capgemini Engineering. The staffing RecSys is integrated under an architecture of Microservices. The autonomy and effectiveness of the staffing RecSys are verified through the experiment using Discounted Cumulative Gain (DCG). Finally, we propose several potential research directions for this research.
A comparison of presence and emotion between immersive virtual reality and desktop displays for musical multimedia

Visual and auditory immersion influence sense of presence, and in some cases emotional response.However, these have previously been tested separately and it is unknown whether and how immersion influences experiences combining both, as found in musical multimedia.A within-participants experiment compared participants' sense of presence (feeling of 'being there'), and induced and perceived emotions, when experiencing a classical concert performance presented over headphones on a desktop PC (2D video with headlocked sound reproduction) or in virtual reality (VR) (head-mounted display presenting 360° video and reactive spatial sound reproduction).Emotion was measured by self-report questionnaires based on a three-dimensional model (pleasantness, tense arousal and energetic arousal) and presence was measured using four items (Sense of physical space, Engagement, Ecological Validity and Negative Effects).Participants experienced a significant increase in pleasantness and all dimensions of presence in VR compared to the desktop condition.Ratings of felt arousal and perceived emotions did not significantly differ between conditions.The "engagement" component of presence was positively correlated with emotion felt and perceived, whereas "ecological validity" and "sense of physical space" were related to emotions experienced in the desktop condition only.The role of presence should therefore be considered when investigating emotional responses to musical multimedia.
Improving Subject Headings for Iowa Indigenous Peoples

By authorizing outdated terms for North American Indigenous peoples, the Library of Congress Subject Headings (LCSH) vocabulary deprioritizes or ignores the preferred names of the peoples being described. As a result, cataloging and metadata professionals constrained by LCSH often must apply names imposed during colonization. For example, in many library catalogs, works about people of the Meskwaki Nation in Iowa are labeled with “Fox Indians--Iowa” and “Sauk Indians--Iowa,” and Ioway peoples are described as “Iowa Indians.” As part of a diversity, equity, and inclusion (DEI) initiative at Iowa State University Library, a working group in the Metadata Services department undertook a project to build, publish, and use a controlled vocabulary of preferred terms for Indigenous communities with ties to land that is now part of the state of Iowa. This paper describes the working group’s research, outreach efforts, published vocabulary, and process for adding the preferred subject headings to library metadata.
STRATEGIC FLEXIBILITY MEDIATING THE IMPACT OF ENTREPRENEURIAL ORIENTATION ON ORGANIZATIONAL EXCELLENCE

The study aimed to analyze the impact of entrepreneurial orientation in achieving organizational excellence through strategic flexibility. This research is a quantitative study. The population of this study consists of all managers in the upper and middle management at the Jordanian Commercial Banks. The study conducted a comprehensive survey, by using its population as a sample, which totals 1023 managers. The data collection tool is a questionnaire that consists of 29 items was developed. The results show that the level of availability of entrepreneurial orientation, organizational excellence, and strategic flexibility has reached a high degree at Jordanian Commercial Banks. Other findings show that entrepreneurial orientation in its combined dimensions (innovative entrepreneurial orientation, proactive entrepreneurial orientation, and risk-taking entrepreneurial orientation) has a statistically significant impact on achieving organizational excellence through strategic flexibility in Jordanian commercial banks. From the findings, the authors conclude that strategic flexibility plays a partial mediating role between entrepreneurial orientation (with all its dimensions) and organizational excellence. At the same time, strategic flexibility fails to play a mediator role between entrepreneurial orientation dimensions and organizational excellence.Keywords: Entrepreneurial Orientation, Organizational Excellence, Strategic Flexibility, Commercial Banks, Jordan.JEL Classifications: L1, L2, M1DOI: https://doi.org/10.32479/irmm.12520
The Impact of COVID-19 Instigated Changes on Loneliness of Teachers and Motivation–Engagement of Students: A Psychological Analysis of Education Sector

Upon the spread of the global pandemic of COVID-19, education was transformed online in an abrupt manner.Amid this change, the education sector did not have room for proper decision-making and understanding of psychological effects.This theoretical analysis aims to contribute to the proposed Frontiers Research Topic, through (a) in-depth analysis of the pandemic status and behavioral psychology and (b) examining educational psychology from the perspective of teachers regarding sudden changes.As a result, implications are suggested based on interviews, linking to extant literature.The current research recognizes the difference between online learning and emergency remote education.While the former comprises prepared means of teaching and assessment, the latter is unaccompanied by such preparedness.Thus, there are variations in the outcomes of learning, motivation, and engagement.Scholars, teachers, deans, and educational managers can benefit from current results.
A computationally grounded logic of awareness

Abstract We introduce a multi-agent logic of explicit, implicit belief and awareness with a semantics using belief bases. The novelty of our approach is that an agent’s awareness is not a primitive but is directly computed from the agent’s belief base. We prove soundness and completeness of the logic relative to the belief base semantics. Furthermore, we provide a polynomial embedding of the logic of propositional awareness into it and a polynomial embedding of our logic into the logic of general awareness. Thanks to the latter embedding, we show that the satisfiability checking problem for our logic is PSPACE complete. Finally, we extend it by the notions of public announcement and private belief expansion.
An Efficient Feature Selection Method for Video-Based Activity Recognition Systems

Human activity recognition (HAR) is the examination of gestures and actions of humans from various resources such as depth or RGB cameras. In this work, we have designed a dynamic and robust feature selection algorithm for a HAR system, through which the system accurately recognizes various kinds of activities. In the proposed approach, we employed mutual information algorithm, which selects the prominent features from the extracted features. The proposed algorithm is the expansion of two methods like max-relevance and min-redundancy, respectively. This method has the capability to gather the assets of various extraction algorithms. But the procedure of selection may be unfair due to the dissimilarity between the classification power and redundancy of the features. To resolve this type of unfair selection, we stabilize both parts through the proposed algorithm that has autonomous upper limit of the mutual information function. Likewise, for the feature extraction and recognition, we used the symlet wavelet transform and hidden Markov model, respectively, for action classification. The proposed algorithm has been justified on depth-based database which has thirteen kinds of activities under comprehensive set of experiments. We showed that the proposed feature selection method achieved best classification accuracy against existing works.
Pattern-set Representations using Linear, Shallow and Tensor Subspaces

Pattern-set matching refers to a class of problems where learning takes place through sets rather than elements. Much used in computer vision, this approach presents robustness to variations such as illumination, intrinsic parameters of the signal capture devices, and pose of the analyzed object. Inspired by applications of subspace analysis, three new collections of methods are presented in this paper: (1) New representations for two-dimensional sets; (2) Shallow networks for image classification; and (3) Subspaces for tensor representation and classification. New representations are proposed with the aim of preserving the spatial structure and maintaining a fast processing time. We also introduce a technique to keep temporal structure, even using the principal component analysis, which classically does not model sequences. In shallow networks, we present two convolutional neural networks that do not require backpropagation, employing only subspaces for its convolution filters. These networks present advantages when the training time and hardware resources are scarce. Finally, to handle tensor data, such as video data, we propose methods that employ subspaces for representation in a compact and discriminative way. Our proposed work has been applied to several problems, such as 2D data representation, shallow networks for image classification, and tensor representation and learning.
DIGITAL TRANSFORMATION OF THE GLOBAL EDUCATIONAL ENVIRONMENT

The phenomenon of digitalization of education is interconnected with the features of the modern generation, which is rapidly integrating into the global educational space. Active involvement of personalized services in the lives of young people exacerbates the problem of finding new educational technologies, updating the content of education to meet individual educational needs, personal and professional self-determination in the context of their life and career growth. Digitalization of the educational environment expands the possibilities of communication, changes the social situation, transforms the motivational and value sphere, affects the personal and professional self-determination of students as a digital generation immersed in the digital environment and digital communications, the bearer of computer-generated values , mobile devices, Internet, social networks. The digitalization of education is a powerful trend in reforming and modernizing the global educational environment and involves the conversion of all types of information from various sources into digital language. Forecasts suggest the transition of the learning process to a digital environment as a turning point in the history of education. The article investigates the preconditions and current trends in the global digitalization of education. The necessity of using new digital pedagogical technologies is reasoned and the possibilities of access to various tools for expanding the field of blended learning are outlined. It is confirmed that the digitalization of the educational environment expands the boundaries of communication, changes the social situation, transforms the motivational and value sphere, affects the personal and professional self-determination of students. It was found that the introduction of the digital technologies in education is the dominant trend of reforming and modernizing the global educational environment and involves the conversion of all types of information into digital language.
Selecting Outstanding Patterns Based on Their Neighbourhood

The purpose of pattern mining is to help experts understand their data. Following the assumption that an analyst expects neighbouring patterns to show similar behavior, we investigate the interestingness of a pattern given its neighborhood. We define a new way of selecting outstanding patterns, based on an order relation between patterns and a quality score. An outstanding pattern shows only small syntactic variations compared to its neighbors but deviates strongly in quality. Using several supervised quality measures, we show experimentally that only very few patterns turn out to be outstanding. We also illustrate our approach with patterns mined from molecular data.
Analysis of Learning System in Higher Collection through a Technology 4.0 Approach in the Era of Covid-19 Pandemic

Analysis of learning management in higher education through a technology 4.0 approach. This study aims to obtain an overview of the implementation of learning management with a communication information technology approach (technology 4.0) to improve the learning management system in universities, especially the Bengkulu province UNIVED campus during the covid 19 pandemic. This research will be investigated further and more carefully on management factors. The results showed that: The availability of ICT network infrastructure services on the Unived campus was obtained by 80% of students and 93% of the academic community. In x2 (Availability of Student Academic Information Systems) on the Unived campus, the results of interviews were 82% of students and 94% of the academic community. In x3 (Availability of e learning applications) on the Unived campus, the results of interviews were 78% of students and 92% of the academic community. Based on the comparison of test data and interview data on the PT Unived campus, it was concluded that the Learning System (LS) = x in stages (ISM) = (A, C, and D) experienced an increasing trend (positive). Meanwhile, in the Individual stages of mastering ICT (B) there is a downward trend.
EFEKTIVITAS SICANTIK CLOUD PADA PELAYANAN PUBLIK PERIZINAN BERBASIS E- GOVERMENT DIKABUPATEN PURWAKARTA

The focus of the study is the application of the Si Cantik Cloud application. The purpose of this study is to obtain a general description of the effectiveness of Si Cantik in e-government-based licensing public services in Purwakarta Regency. The research method is descriptive qualitative. The research findings indicate that there are supporting factors, namely the commitment of organizational leaders in this case as illustrated in the publication of SOPs supporting the implementation of the Si Cantik Cloud application which can have implications for clarity of information for the business community. In conclusion, on the 5 dimensions used by the author in measuring effectiveness, information is obtained that the Si Cantik Cloud application implemented by the Purwakarta Regency Investment and One Stop Integrated Service (DPMPTSP) shows that the application is quite effective in being used and utilized in the provision of licensing and non-licensing services in Indonesia. Purwakarta Regency. The author recommends that the Purwakarta Regency Government can improve socialization and assistance to the community, and develop infrastructure designs in order to strengthen the effectiveness of implementing the Si Cantik Cloud application.&#x0D; Keywords: Effectiveness, Si Cantik Cloud, Public Service, E-government.