Reviewerly/exHarmony huggingface dataset:
Relation-changing models meet paraconsistency

Switch graphs are graph-like structures characterized by embedding higher-level edges (edges that link to other edges) to describe reactive phenomena. When an edge of such structure is traversed, the accessibility relation of this graph can be changed by adding/removing edges. Relation-changing models have been used to represent phenomena in diverse fields (from Biology to Computer Science) and some modal languages were introduced recently. In this paper we introduce four-valued local information in switch graphs, and propose a paraconsistent logic to study these systems.
A time sequence coding based node-structure feature model oriented to node classification

Graph data mining is an important method for managing complex systems in artificial intelligence (AI). As an important branch of graph data mining, node classification is widely applied in paper classification in citation networks, user classification in social networks, etc. At present, many graph neural networks (GNNs) have been proposed to realize node classification by obtaining node embeddings encoded by aggregating neighborhood features. Most GNNs use one-hot encoding as the initial node feature when there is no node attribute in the graph. However, one-hot encoding is a global encoding technique that cannot reflect the environmental context of specific nodes. To this end, we proposed a graph structure sequential coding (GSSC) model, including a learnable node-structure feature X, sampler, encoder and classifier, to obtain the structural embeddings which can better reflect the topological structure of non-attribute graphs. For non-attribute graphs, we utilized the learnable feature X as the initial node-structure feature, which could be simultaneously trained with the GSSC model. In addition, we used a decoupling scheme to separate the sampling and encoding processes of GSSC. Therefore, we could use different samplers for sparse and dense graphs. In addition, different sampling orders (i.e., temporality) in node sequences could reflect different semantic associations between the nodes. Therefore, we innovatively employed time sequence models (TSMs) as encoders to encode node sequences with temporality. Based on these TSMs, semantic information of the head node in the node sequence can be captured. The experimental results on five datasets confirmed that our GSSC model performs better than other representative methods in node classification for graphs without node attributes.
The impact of HRM practices and employee behavior on career success

Objective and subjective criteria are essential measures of organizational success. However, prior studies ignored these criteria when assessing employees’ career success. This study aims to examine the impact of HRM practices and employee behavior on career success for employees in the Greater Amman Municipality. This study adopted a survey for data collection, as 375 questionnaires were distributed to employees working at Greater Amman Municipality in Jordan. Four weeks were given to respondents to complete the survey. After the allotted time had passed, 246 questionnaires had been gathered, with a response rate of 65.6%. Of these, 246 questionnaires were deemed suitable for further analysis. Convenience sampling has been used as a key technique for collecting data. For data analysis, SPSS (version 25) and SmartPLS (version 3.3.9) statistical software were used. The findings indicated that objective and subjective criteria significantly affect career success. The results also showed that HRM practices and employee behavior significantly and positively influenced career success. Furthermore, employee behavior mediated the relationship between HRM practices and career success. Since each facet reflects a significant and distinctive component of career success, academics and researchers should focus on both the objective and subjective dimensions of career success.
studying The Boundaries of The Tectonic Zones South of Latitude 32◦ of Iraq Using Trend Surface Analysis method

A geophysical study investigation was conducted in the southern part of Iraq bounded by coordinates; longitudes (39◦ - 48◦) East and latitude (29◦ - 32◦) North. This study includes analysis and interpretation of the potential data (gravity and magnetic) derived from the Bouguer anomaly map and Aeromagnetic total field intensity map of Iraq both of them is within the scale of (1: 1,000,000) and do comparison with the information available from geological maps, which include Tectonic, Hydrological and Geological information. Gravity and Magnetic maps of the study area were digitized at a grid interval of (1×1) cm which is equivalent to (10×10) km on the land. The trend surface technique was applied on the Bouguer anomaly map of the area. Through these analyses there are three tectonic boundaries have been proposed (A, B, and C). Results were compared with potential analyses of available geological information which match some faults proposed through data analysis with other geological information.
Software science view on quantum circuit algorithms

We show that, on the abstraction level of quantum circuit diagrams, quantum circuit algorithms belong to the species of interactive sequential algorithms that we studied in earlier work. This observation leads to a natural specification language for quantum circuit algorithms.
A dimensional model of interaction style variation in spoken dialog

In spoken dialog people vary their interaction styles, and dialog systems should be able to do the same. Previous work has elucidated various aspects of style variation and adaptation, but a general model has been lacking. Here we present a dimensional model of the space of interaction styles, derived from a large data set and prosody-based features. The 8 dimensions of this model cover many previously-noted aspects of style and include some novel ones. This model may be useful for selecting data for dialog model pretraining and fine-tuning, for investigating demographic differences, and for dialog system style adaptation. However, regarding individual differences in interaction style, we find individual style tendencies to be surprisingly weak, with a predictive model based on individual tendencies outperforming a speaker-independent model by only 3.6%.
Historical Data Gathering Method for Container Based Microservice

As the provision of cloud-based services becomes smoother, cloud-native development methodology are on the rise. The microservice architecture can be used the cloud-native methodology. Microservice architecture is a scaled-down size of a single function or small service unit and a function execution flow is formed through loosely coupling between microservices. When adopting a microservice architecture, it is necessary to trace the execution flow and collect historical data in order to configure a smooth function flow between microservices. In the current microservice architecture, however, there is a lack of research on historical data elements or execution flow tracking for microservice execution. Therefore, a historical data collection method based on the execution architecture and flow of microservice based on container are proposed in this paper. For the construction of the method, the architecture and technique for collecting historical data were presented in units of microservice, container, and container orchestration framework. In addition, in order to show that historical data collection is possible based on the proposed method, a case study was conducted and simulated based on the actual environment. The poposed method is expected to be used as a basis for research on service management and monitoring based on microservice architecture.
Implementing ICARE training model to enhance Indonesian students’ emceeing skills: Its process and impact

Having a good command of public speaking skills has been considered a pivotal modal for students to gain academic achievement and professional success. Practicing to be a master of ceremony can be a means of elevating public speaking skills. However, mastering the emceeing skills is not an easy task since it requires the students’ competencies to make use of linguistic and non-linguistic resources. The purpose of this study is to elucidate the results of the implementation of the ICARE model (Introduction, Connection, Application, Reflection, and Extension) to enhance students’ linguistic- and non-linguistic-related emceeing skills. This research employed a quantitative and qualitative approaches, using an observation sheet, interview protocol, and pre and posttest. The results showed that the training activities focused on establishing a group collaboration and promoted confidence in linguistic and non-linguistic mastery. The observation results revealed that the application of the ICARE model was considered helpful since it could facilitate teaching and learning activities in a more contextual way, with active, creative, and fun characteristics (joyful learning) and obtained a positive response from the students. They could relate it to everyday life. The statistical results of the pre and posttest scores demonstrated the effectiveness of the ICARE model in enhancing the students’ language and non-language-related emceeing skills. The results affirm the importance of mastering linguistic and non-linguistic elements that contribute to increased public speaking skills.
Payroll Information System for SMP PGRI 16 Sidoarjo

A salary is a form of result in the form of money or goods given to individuals or groups. The payroll calculation system process that is currently running is still using the Microsoft Excel application. But now everything has developed rapidly, and with the existence of information technology, everything can be done more efficiently. Information technology has now grown very far and has begun to be widely applied by anyone and in any case, including the payroll system. The application of information technology to payroll can increase the effectiveness of the treasurer's performance in a school. SMP PGRI 16 Sidoarjo is one of the agencies that has not implemented information technology in the payroll sector, it became the basis for the formation of this information system.
Towards a moderate realist foundation for ontological knowledge organization systems: The question of the naturalness of classifications

Several authors emphasise the need for a change in classification theory due to the influence of a dogmatic and monistic ontology supported by an outdated essentialism. These claims tend to focus on the fallibility of knowledge, the need for a pluralistic view, and the theoretical burden of observations. Regardless of the legitimacy of these concerns, there is the risk, when not moderate, to fall into the opposite relativistic extreme. Based on a narrative review of the literature, we aim to reflectively discuss the theoretical foundations that can serve as a basis for a realist position supporting pluralistic ontological classifications. The goal is to show that, against rather conventional solutions, objective scientific-based approaches to natural classifications are presented to be viable, allowing a proper distinction between ontological and taxonomic questions. Supported by critical scientific realism, we consider that such an approach is suitable for the development of ontological Knowledge Organisation Systems (KOSs). We believe that ontological perspectivism can provide the necessary adaptation to the different granularities of reality.
THE PROSPECTS OF METHODOLOGICAL SUPPORT FOR ADVANCED TRAINING COURSES TARGETED AT RUSSIAN LANGUAGE AND LITERATURE TEACHERS IN THE REPUBLIC OF TAJIKISTAN IN THE CONTEXT OF THIRD-GENERATION FSES

The paper presents options for methodological support of advanced training courses for teachers of Russian language and literature working in Russian-Tajik schools of the Republic of Tajikistan. The tasks assigned by the leaders of the two countries to the large team participating in the humanitarian program provided meaningful content of advanced training courses, interaction with verbal teachers in the classroom and during their direct pedagogical activity. The requirements of the Federal State Educational Standard (FSES) of the third generation for the organization of the educational process allowed us to identify problematic issues that require development and long-term solutions. In this regard, prospects for further cooperation were identified, which will allow to provide the necessary methodological support to teachers of the Russian language and literature of the Republic of Tajikistan, both within the framework of philological school education and in the field of work with gifted bilingual students.
Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification

QR codes (short for Quick Response codes) were originally developed for use in the automotive industry to track factory inventories and logistics, but their popularity has expanded significantly in the past few years due to the widespread applications of smartphones and mobile phone cameras. QR codes can be used for a variety of purposes, including tracking inventory, advertising, electronic ticketing, and mobile payments. Although they are convenient and widely used to store and share information, their accessibility also means they might be forged easily. Digital forensics can be used to recognize direct links of printed documents, including QR codes, which is important for the investigation of forged documents and the prosecution of forgers. The process involves using optical mechanisms to identify the relationship between source printers and the duplicates. Techniques regarding computer vision and machine learning, such as convolutional neural networks (CNNs), can be implemented to study and summarize statistical features in order to improve identification accuracy. This study implemented AlexNet, DenseNet201, GoogleNet, MobileNetv2, ResNet, VGG16, and other Pretrained CNN models for evaluating their abilities to predict the source printer of QR codes with a high level of accuracy. Among them, the customized CNN model demonstrated better results in identifying printed sources of grayscale and color QR codes with less computational power and training time.
Improved angelization technique against background knowledge attack for 1:M microdata

With the advent of modern information systems, sharing Electronic Health Records (EHRs) with different organizations for better medical treatment, and analysis is beneficial for both academic as well as for business development. However, an individual's personal privacy is a big concern because of the trust issue across organizations. At the same time, the utility of the shared data that is required for its favorable use is also important. Studies show that plenty of conventional work is available where an individual has only one record in a dataset (1:1 dataset), which is not the case in many applications. In a more realistic form, an individual may have more than one record in a dataset (1:M). In this article, we highlight the high utility loss and inapplicability for the 1:M dataset of the θ-Sensitive k-Anonymity privacy model. The high utility loss and low data privacy of (p, l)-angelization, and (k, l)-diversity for the 1:M dataset. As a mitigation solution, we propose an improved (θ∗, k)-utility algorithm to preserve enhanced privacy and utility of the anonymized 1:M dataset. Experiments on the real-world dataset reveal that the proposed approach outperforms its counterpart, in terms of utility and privacy for the 1:M dataset.
The deep neural network approach to the reference class problem

Abstract Methods of machine learning (ML) are gradually complementing and sometimes even replacing methods of classical statistics in science. This raises the question whether ML faces the same methodological problems as classical statistics. This paper sheds light on this question by investigating a long-standing challenge to classical statistics: the reference class problem (RCP). It arises whenever statistical evidence is applied to an individual object, since the individual belongs to several reference classes and evidence might vary across them. Thus, the problem consists in choosing a suitable reference class for the individual. I argue that deep neural networks (DNNs) are able to overcome specific instantiations of the RCP. Whereas the criteria of narrowness, reliability, and homogeneity, that have been proposed to determine a suitable reference class, pose an inextricable tradeoff to classical statistics, DNNs are able to satisfy them in some situations. On the one hand, they can exploit the high dimensionality in big-data settings. I argue that this corresponds to the criteria of narrowness and reliability. On the other hand, ML research indicates that DNNs are generally not susceptible to overfitting. I argue that this property is related to a particular form of homogeneity. Taking both aspects together reveals that there are specific settings in which DNNs can overcome the RCP.
Noise Reduction Using Singular Value Decomposition with Jensen–Shannon Divergence for Coronary Computed Tomography Angiography

Coronary computed tomography angiography (CCTA) is widely used due to its improvements in computed tomography (CT) diagnostic performance. Unlike other CT examinations, CCTA requires shorter rotation times of the X-ray tube, improving the temporal resolution and facilitating the imaging of the beating heart in a stationary state. However, reconstructed CT images, including those of the coronary arteries, contain insufficient X-ray photons and considerable noise. In this study, we introduce an image-processing technique for noise reduction using singular value decomposition (SVD) for CCTA images. The threshold of SVD was determined on the basis of minimization of Jensen-Shannon (JS) divergence. Experiments were performed with various numerical phantoms and varying levels of noise to reduce noise in clinical CCTA images using the determined threshold value. The numerical phantoms produced 10% higher-quality images than the conventional noise reduction method when compared on a quantitative SSIM basis. The threshold value determined by minimizing the JS-divergence was found to be useful for efficient noise reduction in actual clinical images, depending on the level of noise.
CH-Bench: a user-oriented benchmark for systems for efficient distant reading (design, performance, and insights)

Abstract Data science deals with the discovery of information from large volumes of data. The data studied by scientists in the humanities include large textual corpora. An important objective is to study the ideas and expectations of a society regarding specific concepts, like “freedom” or “democracy,” both for today’s society and even more for societies of the past. Studying the meaning of words using large corpora requires efficient systems for text analysis, so-called distant reading systems. Making such systems efficient calls for a specification of the necessary functionality and clear expectations regarding typical work loads. But this currently is unclear, and there is no benchmark to evaluate distant reading systems. In this article, we propose such a benchmark, with the following innovations: As a first step, we collect and structure various information needs of the target users. We then formalize the notion of word context to facilitate the analysis of specific concepts. Using this notion, we formulate queries in line with the information needs of users. Finally, based on this, we propose concrete benchmark queries. To demonstrate the benefit of our benchmark, we conduct an evaluation, with two objectives. First, we aim at insights regarding the content of different corpora, i.e., whether and how their size and nature (e.g., popular and broad literature or specific expert literature) affect results. Second, we benchmark different data management technologies. This has allowed us to identify performance bottlenecks.
Simulatable verifiable random function from the LWE assumption

A verifiable random function (VRF) is a pseudorandom function F that can be publicly verified. A simulatable VRF (sVRF) is an important variant of a VRF, which additionally provides simulatability. Informally, the simulatability of a VRF depicts the ability to simulate a valid proof π that y=F(sk,x) for any input x and any output value y. A (simulatable) VRF can be used in the E-Cash, E-Lottery, blockchain and constructing the multi-theorem non-interactive zero-knowledge (NIZK) proof. However, up to now, the existing constructions of an sVRF either rely on non-standard assumptions (e.g., the Q-type ones), or are built in the random oracle model, or resort to time-consuming techniques like the Cook-Levin reduction. In this paper, we design the first sVRF from the LWE assumption in the standard model (free of a random oracle) without using a Cook-Levin reduction. In our construction of an sVRF, we take as building blocks a pseudorandom function, a trapdoor fully homomorphic commitment (FHC) scheme, and a NIZK proof system for a language specified by FHC. Our trapdoor FHC is the key technical tool, which helps the simplification of the underlying NIZK language, thus making possible an instantiation of a NIZK proof from LWE without a Cook-Levin reduction. Together with an LWE-based PRF, we obtain an sVRF scheme from LWE.
A Canonical Context-Preserving Representation for Open IE: Extracting Semantically Typed Relational Tuples from Complex Sentences

Modern systems that deal with inference in texts need automatized methods to extract meaning representations (MRs) from texts at scale. Open Information Extraction (IE) is a prominent way of extracting all potential relations from a given text in a comprehensive manner. Previous work in this area has mainly focused on the extraction of isolated relational tuples. Ignoring the cohesive nature of texts where important contextual information is spread across clauses or sentences, state-of-the-art Open IE approaches are thus prone to generating a loose arrangement of tuples that lack the expressiveness needed to infer the true meaning of complex assertions. To overcome this limitation, we present a method that allows existing Open IE systems to enrich their output with additional meta information. By leveraging the semantic hierarchy of minimal propositions generated by the discourse-aware Text Simplification (TS) approach presented in Niklaus et al. (2019), we propose a mechanism to extract semantically typed relational tuples from complex source sentences. Based on this novel type of output, we introduce a lightweight semantic representation for Open IE in the form of normalized and context-preserving relational tuples. It extends the shallow semantic representation of state-of-the-art approaches in the form of predicate-argument structures by capturing intra-sentential rhetorical structures and hierarchical relationships between the relational tuples. In that way, the semantic context of the extracted tuples is preserved, resulting in more informative and coherent predicate-argument structures which are easier to interpret. In addition, in a comparative analysis, we show that the semantic hierarchy of minimal propositions benefits Open IE approaches in a second dimension: the canonical structure of the simplified sentences is easier to process and analyze, and thus facilitates the extraction of relational tuples, resulting in an improved precision (up to 32%) and recall (up to 30%) of the extracted relations on a large benchmark corpus.