Reviewerly/exHarmony huggingface dataset:
A model of organizational culture for enhancing organizational commitment in telecom industry: Evidence from Vietnam

The aim of this study is to investigate the influence of organizational culture on organizational commitment in the context of the Vietnam telecom industry. The model was tested with a sample of 324 employees working for telecom companies. The result from the partial least squares method (PLS) using SmartPLS 3.0 pr ogram revealed a good fit between collected data and measurement scales which were introduced and developed in the Western contexts. Besides, the findings showed that the six dimensions of organizational culture had positive impacts on organizational commitment decreasingly: innovativeness, teamwork, training and development, reward and recognition, organizational communication, empowerment. This discovery highlighted the importance of organizational culture in enhancing employee commitment towards the organization as well as calls the management attention to these dimensions and long-term development policies
A GEOSPATIAL KNOWLEDGE GRAPH PROTOTYPE FOR NATIONAL TOPOGRAPHIC MAPPING

Abstract. Knowledge graphs are a form of database representation and handling that show the potential to better meet the challenges of data interoperability, semi-automated information reasoning, and information retrieval. Geospatial knowledge graphs (GKG) have at their core specialized forms of applied ontology that provide coherent spatial context to a domain of information including non-spatial attributes. This paper discusses research toward the development of a prototype GKG based on national topographic databases of geospatial feature instances, attributes, properties, metadata, and annotations. The challenges are to capture and represent geographic semantics inherent in the source data, to align such graph models with standards where possible, to test logical computations, and to visualize the data using a cartographic user interface. Data integration from outside sources was tested through SPARQL and GeoSPARQL queries. Called the MapKB, the approaches applied in this prototype use a number of software components to build a system architecture aligned with those objectives and are composed entirely of free and open-source software. The system and ontology design were validated through reasoning and competency questions. Technical aspects of the prototype software succeeded, but customization was found to be needed for user-based design.
A Robust graph attention network with dynamic adjusted Graph

Graph Attention Networks(GATs) are useful deep learning models to deal with the graph data. However, recent works show that the classical GAT is vulnerable to adversarial attacks. It degrades dramatically with slight perturbations. Therefore, how to enhance the robustness of GAT is a critical problem. Robust GAT(RoGAT) is proposed in this paper to improve the robustness of GAT based on the revision of the attention mechanism. Different from the original GAT, which uses the attention mechanism for different edges but is still sensitive to the perturbation, RoGAT adds an extra dynamic attention score progressively and improves the robustness. Firstly, RoGAT revises the edges weight based on the smoothness assumption which is quite common for ordinary graphs. Secondly, RoGAT further revises the features to suppress features' noise. Then, an extra attention score is generated by the dynamic edge's weight and can be used to reduce the impact of adversarial attacks. Different experiments against targeted and untargeted attacks on citation data on citation data demonstrate that RoGAT outperforms most of the recent defensive methods.
Extremes of Markov random fields on block graphs: max-stable limits and structured Hüsler-Reiss distributions

We study the joint occurrence of large values of a Markov random field or undirected graphical model associated to a block graph. On such graphs, containing trees as special cases, we aim to generalize recent results for extremes of Markov trees. Every pair of nodes in a block graph is connected by a unique shortest path. These paths are shown to determine the limiting distribution of the properly rescaled random field given that a fixed variable exceeds a high threshold. The latter limit relation implies that the random field is multivariate regularly varying and it determines the max-stable distribution to which component-wise maxima of independent random samples from the field are attracted. When the sub-vectors induced by the blocks have certain limits parametrized by H\"usler-Reiss distributions, the global Markov property of the original field induces a particular structure on the parameter matrix of the limiting max-stable H\"usler-Reiss distribution. The multivariate Pareto version of the latter turns out to be an extremal graphical model according to the original block graph. Thanks to these algebraic relations, the parameters are still identifiable even if some variables are latent.
SeSPHR A Methodology for Secure Sharing of Personal Health Records in the Cloud

The widespread acceptance of cloud-based services in the healthcare sector has resulted in cost effective and convenient exchange of Personal Health Records (PHRs) among several participating entities of the e-Health systems. Nevertheless, storing the confidential health information to cloud servers is susceptible to revelation or theft and calls for the development of methodologies that ensure the privacy of the PHRs. Therefore, we propose a methodology called SeSPHR for secure sharing of the PHRs in the cloud. The SeSPHR scheme ensures patient-centric control on the PHRs and preserves the confidentiality of the PHRs. The patients store the encrypted PHRs on the un-trusted cloud servers and selectively grant access to different types of users on different portions of the PHRs. A semi- trusted proxy called Setup and Re-encryption Server (SRS) is introduced to set up the public/private key pairs and to produce the re- encryption keys. Moreover, the methodology is secure against insider threats and also enforces a forward and backward access control. Furthermore, we formally analyze and verify the working of SeSPHR methodology through the High Level Petri Nets (HLPN). Performance evaluation regarding time consumption indicates that the SeSPHR methodology has potential to be employed for securely sharing the PHRs in the cloud. Key Words:: Access Control, Cloud Computing, Personal Health Records, Privacy.
An Exploration of Students’ Willingness to Communicate in Thai EFL Online Classroom

This study aims at exploring the level of Thai EFL undergraduate students’ willingness to communicate in English language online classrooms. The crucial evidence from students’ voices was investigated through the mixed-methods paradigm, called exploratory sequential research design which employed a qualitative, followed by a quantitative approach to answer the main research question that investigated the levels of Thai EFL undergraduate students’ willingness to communicate in English language online classrooms. In the first phase, qualitative data was collected and analyzed from focus group interviews to elicit the rich data in deep detail and explain some outstanding points of view from the participants at the university level. Then, the results from the qualitative phase were used as a guideline to develop the questionnaires which were relevant to the willingness to communicate in online classrooms. One thousand one hundred and nineteen participants (N=1,119) responded to the questionnaires. Results showed that the salient factors that influenced the level of students’ willingness to communicate in online classrooms were communication channels, students’ beliefs, peer influence, teachers’ characteristics, teaching methods, and online classroom atmosphere. The findings of this study can create teachers’ awareness when teaching online and can be used to seek out how to improve themselves and adjust their own teaching approach to enhance their students’ online language learning outcomes in the future. Recommendations for further research are addressed in the end.
Few-Shot Object Detection by Attending to Per-Sample-Prototype

Few-shot object detection aims to detect instances of specific categories in a query image with only a handful of support samples. Although this takes less effort than obtaining enough annotated images for supervised object detection, it results in a far inferior performance compared to the conventional object detection methods. In this paper, we propose a meta-learning-based approach that considers the unique characteristics of each support sample. Rather than simply averaging the information of the support samples to generate a single prototype per category, our method can better utilize the information of each support sample by treating each support sample as an individual prototype. Specifically, we introduce two types of attention mechanisms for aggregating the query and support feature maps. The first is to refine the information of few-shot samples by extracting shared information between the support samples through attention. Second, each support sample is used as a class code to leverage the information by comparing similarities between each support feature and query features. Our proposed method is complementary to the previous methods, making it easy to plug and play for further improvement. We have evaluated our method on PASCAL VOC and COCO benchmarks, and the results verify the effectiveness of our method. In particular, the advantages of our method are maximized when there is more diversity among support data.
Training Robust Zero-Shot Voice Conversion Models with Self-supervised Features

Unsupervised Zero-Shot Voice Conversion (VC) aims to modify the speaker characteristic of an utterance to match an unseen target speaker without relying on parallel training data. Recently, self-supervised learning of speech representation has been shown to produce useful linguistic units without using transcripts, which can be directly passed to a VC model. In this paper, we showed that high-quality audio samples can be achieved by using a length resampling decoder, which enables the VC model to work in conjunction with different linguistic feature extractors and vocoders without requiring them to operate on the same sequence length. We showed that our method can outperform many baselines on the VCTK dataset. Without modifying the architecture, we further demonstrated that a) using pairs of different audio segments from the same speaker, b) adding a cycle consistency loss, and c) adding a speaker classification loss can help to learn a better speaker embedding. Our model trained on LibriTTS using these techniques achieves the best performance, producing audio samples transferred well to the target speaker's voice, while preserving the linguistic content that is comparable with actual human utterances in terms of Character Error Rate.
A Data Analytics Approach to the Cyber Crime Underground Economy

Abstract: Despite the rapid escalation of cyber threats, there has still been little research into the foundations of the subject or methodologies that could serve to guide Information Systems researchers and practitioners who deal with cyber security. In addition, little is known about Crime-as-a-Service (CaaS), a criminal business model that underpins the cybercrime underground. This research gap and the practical cybercrime problems we face have motivated us to investigate the cybercrime underground economy by taking a data analytics approach from a design science perspective. To achieve this goal, we proposed data analysis framework for analyzing the cybercrime underground, CaaS and crime ware definitions, and an associated classification model. In addition, we develop an example application to demonstrate how the proposed framework and classification model could be implemented in practice. We then use this application to investigate the cybercrime underground economy by analyzing a large dataset obtained from the online hacking community. By taking a design science research approach, this study contributes to the design artifacts, foundations, and methodologies in this area. Moreover, it provides useful practical insights to practitioners by suggesting guidelines as to how governments and organizations in all industries can prepare for attacks by the cybercrime underground.
Reframing the Economic Rationale of Music Publishers’ Relationships with Rights Management Entities – Shifting to a Systematic Approach

Abstract As international competition and cooperation in collective rights management evolves, the ecosystem of rights management entities grows. Consequently, the scope of inter-RME organisation across copyright management channels is becoming more complex and thus difficult to manage. Based on the insights of interviews with nine German music publishers and the best practices specified in the Information Technology Infrastructure Library standard for supplier management , this paper re-conceptualises a unified model for RME Relationship Management for music publishers.
Data-driven Numerical Invariant Synthesis with Automatic Generation of Attributes

Abstract We propose a data-driven algorithm for numerical invariant synthesis and verification. The algorithm is based on the ICE-DT schema for learning decision trees from samples of positive and negative states and implications corresponding to program transitions. The main issue we address is the discovery of relevant attributes to be used in the learning process of numerical invariants. We define a method for solving this problem guided by the data sample. It is based on the construction of a separator that covers positive states and excludes negative ones, consistent with the implications. The separator is constructed using an abstract domain representation of convex sets. The generalization mechanism of the decision tree learning from the constraints of the separator allows the inference of general invariants, accurate enough for proving the targeted property. We implemented our algorithm and showed its efficiency.
Classification of beneficiaries for the rehabilitation of uninhabitable houses using the K-Nearest Neighbor algorithm

The registrars for rehabilitation programs for uninhabitable settlements are increasing every year. The large data processing of registrants may result in inaccuracies and need a long time to determine livable houses (RTLH) and unfit for habitation (non RTLH). This study aims to apply the K-Nearest Neighbor algorithm in classifying the eligibility of recipients of uninhabitable house rehabilitation assistance. The data used in this study were 1289 data with 13 attributes from the Jepara Regency Public Housing and Settlement Service. Data processing begins with attribute selection, categorization, outlier data cleaning, and data normalization and method application. The proposed system has the best classification at k of 5 with an accuracy of 97.93%, 96.88% precision, 99.53% recall, and an AUC value of 0.964.
Straight Forward Constructive Deep Learning Neural Network (SFC-DLNN) algorithm

Abstract Straight Forward Constructive Deep Learning Neural Network (SFC-DLNN) algorithm is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology, SFC-DLNN begins with a minimal network (perceptron), then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, the input-side weights of the new architecture are generated. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating others, more complex feature space is then created where the data is likely to be linearly separable. The SFC-DLNN architecture has several advantages over existing algorithms: it learns quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes.We obtain from our built model (SFC-DLNN) an accuracy and specificity of 83:5% from a simulated data set using the uniform distribution. This is not the best but is enough to approve the model prediction capacity.
MAPLIBRE-RS: TOWARD PORTABLE MAP RENDERERS

Abstract. Map renderers play a crucial role in Web, desktop, and mobile applications. In this context, code portability is a common problem, often addressed by maintaining multiple code bases: one for theWeb, usually written in JavaScript, and one for desktop and mobile, often written in C/C++. The maintenance of several code bases slows down innovation and makes evolution time-consuming. In this paper, we review existing open-source map renderers, examine how they address this problem, and identify the downsides of the current strategies. With a proof of concept, we demonstrate that Rust, WebAssembly, and WebGPU are now sufficiently mature to address this problem. Our new open-source map renderer written in Rust runs on all platforms and showcases good performance. Finally, we explain the challenges and limitations encountered while implementing a modern map renderer with these technologies.
High-Precision Target Detection of Remote Sensing Image Based on Feature Enhancement with 6G Technology

We provide remote sensing image enhancement technology based on 6G nursing urine technology to solve the main problems and current challenges of target detection in remote sensing imaging. High-resolution images: to get a good image, first look at the remote control image, and objective findings on the remote control image play an important role in this process. Military and civilian use: at present, many advanced object detection algorithms have achieved success in natural imaging, but their development is limited by two factors: large variation in object size and low detection accuracy, the scale, rotation direction, and distribution density of remote sensing images. On the other hand, the first remote sensing image is usually a high-resolution, large-scale image, which requires more time than blank image detection. The model is fine-tuned on the ICDAR2015 training package for 60,000 repetitions. The same model experimentally reduced the image to 1280 × 768 and displayed the results in one dimension. Therefore, in order to solve the problem of low detection due to changes in azimuth, rotation direction, and velocity distribution, it is necessary to further study methods for detecting large-capacity remote sensing objects using abundant high-precision remote sensing technologies, sensing imaging service on remote sensing maps.
A Survey on Federated Learning PoisoningAttacks and Defenses

Abstract As one kind of distributed machine learning technique, federated learning enables multiple clients to build a model across decentralized datacollaboratively without explicitly aggregating the data. Due to its abilityto break data silos, federated learning has received increasing attentionin many fields, including finance, healthcare, and education. However,the invisibility of clients’ training data and the local training process result in some security issues. Recently, many works have beenproposed to research the security attacks and defenses in federatedlearning, but there has been no special survey on poisoning attacks onfederated learning and the corresponding defenses. In this paper, weinvestigate the most advanced schemes on federated learning poisoningattacks and defenses and point out the future directions in these areas.
FAKE NEWS &amp; HATE SPEECH: A Colloquium Report

Nigeria, the proverbial giant of Africa, has in recent times, been beset with the politics of paternalism which has translated into heightened consciousness in ethnic nationalism and extremist immoderation, all too manifest in the now very dominant paradigm of fake news and hate speech. Amidst this threatening state of confusion, the nation-state witnesses its highest level of polarization among the citizenry and records a harvest of kidnappings, abductions, raping and ritual killings, all of which the government blames on fake news and hate speech. &#x0D; As part of its contribution in the attempt to mitigate and possibly proffer a solution to the twin monsters of fake news and hate speech, the Olusegun Obasanjo Centre for African Studies (OOCAS) at the National Open University of Nigeria (NOUN) Abuja organized a one-day Colloquium on Fake News and Hate Speech. This report, therefore, is a descriptive analysis that highlights the culture of impunity with which political elites incite and polarize the citizenry, especially on the contentious issues of religion and ethnicity.&#x0D; The paper applies the critical discourse analysis approach to evaluate the colloquium proceedings - the views, positions and propositions of discussants/participants and the ensuing communiqué that fake news and hate speech are largely products of mainstream and social media; with governments and political elites as the main culprits who exploit the volatile issues of religion, politics and ethnicity to polarize and incite.&#x0D; The paper concludes with nine communiqué recommendations including, that the mass media, especially the mainstream media, should invest more in the practice of investigative journalism and Nigeria’s governments, in general, should provide good governance, equity and justice to the citizenry; that, participants and discussants believe will curb the menace of fake news and hate speech.
ANALISIS KAIDAH KEBAHASAAN DALAM NOVEL SESUAP RASA KARYA CATZ LINK TRISTAN SEBAGAI ALTERNATIF PEMILIHAN BAHAN AJAR BAHASA INDONESIA KELAS XII

Learning about novels is an important lesson that must be mastered by students. However, in reality, students find it difficult to analyze novels because of a lack of understanding of the material. One of the reasons is the selection of Indonesian language teaching materials that are less attractive. Therefore, the author researched the linguistic rules of the novel so that it can be used as alternative teaching material. This study aims to analyze the linguistic rules in the novel Sesuap Rasa by Catz Link Tristan as a choice of Indonesian language teaching materials for class XII. The research method used is qualitative with data collection techniques, namely documentation, and literature study. The author uses data sources in the form of 61.53% of the number of pages in the novel. The results of the analysis show that 61.53% of the number of pages in the novel Sesuap Rasa by Catz Link Tristan contains 6 novel linguistic rules consisting of 471 adverbs of time, 1122 personal pronouns, 591 material verbs, 470 mental verbs, 517 adjectives. , and 979 dialogues. Based on the results of the study, the linguistic rules of the novel that have been analyzed can be used as teaching materials by the demands of the 2013 Curriculum. The results of the validation of Indonesian class XII teaching materials in the form of modules obtained an overall average score of 4.61 with a percentage of 92.2% included in the "Very Good" category.