Reviewerly/exHarmony huggingface dataset:
Improving innovation performance in the hotel sector

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Companies aiming to enhance their innovation performance should increase the emphasis on key knowledge management practices. The impact on innovation can be further strengthened through efforts to boost the organization's capacity to learn and engage in creative activities. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
History and Development of Advertising through Media Technology

Advertising has experienced many important changes over the years, as it has had to adapt to new media and audiences. This article is about the history and purpose of advertising. Advertising has been used as a form of commercial communication for a long time, in order to promote the sale of a product or service. It can be used to encourage or persuade people. This article provides an overview of advertising history, with a focus on different periods. We can predict which future advertising technologies, types, media, effects, and fields will be developed based on the changing environments of the 4th industrial revolution era. The report provides useful information for the advertising industry and experts who need to be ready for future changes.
Fraud Detection via Contrastive Positive Unlabeled Learning

Many online system fraud detection techniques employ deep learning models for identifying malicious user activity sessions. In many real-world scenarios, few labeled malicious and many unlabeled sessions exist. In such scenarios, the fraud detection problem can be effectively addressed through the Positive Unlabeled (PU) learning technique. Despite this fact, possible malicious sessions can be extremely diverse, which makes learning a good decision boundary challenging. In this paper, we present a novel contrastive positive unlabeled learning (ConPU) model for fraud detection and in particular, propose a contrastive loss function for PU learning. ConPU indirectly approximates the cluster center of normal sessions in the representation space by using distributions of unlabeled sessions and malicious sessions, predicts labels of sessions in the unlabeled set by analyzing their proximity between normal and malicious session cluster centers in the representation space, and then incorporates both positive pairs and negative pairs into the contrastive loss function. As a result, ConPU can derive separable representations as well as accurate cluster centers of normal and malicious sessions in the representation space. We theoretically demonstrate the efficacy o f o ur d eveloped l oss f unction i n C onPU. Additionally, we empirically evaluate ConPU on benchmark datasets, in which, ConPU demonstrates substantial performance improvement over state-of-the-art baselines.
Distributed Incremental Machine Learning for Big Time Series Data

Today’s highly instrumented systems generate large amounts of time series data from many different domains. In order to create meaningful insights from these data, techniques are needed to handle the collection, processing, and analysis at scale. The high frequency and volume of data that is generated introduces several challenges including data transformation, managing concept drift, the operational cost of model re-training and tracking, and scaling hyperparameter optimization.Incremental machine learning can provide a viable solution to handle these kinds of data. Further, distributed machine learning can be an efficient technique to improve performance, increase accuracy, and scale to larger input sizes.In this paper, we introduce a framework that combines the computational capabilities of Apache Spark and the workflow parallelization of Ray for distributed incremental learning. We conduct an empirical analysis of our framework for time series forecasting using the Walmart M5 dataset. The system can perform a parameter search on streaming data with concept drift producing a robust pipeline that fits high-volume data effectively. The results are encouraging and substantiate system proficiency over traditional big data analysis approaches that exclusively use either offline or online training.
Stream2Graph: Dynamic Knowledge Graph for Online Learning Applied in Large-scale Network

Knowledge Graphs (KG) are valuable information sources that store knowledge in a domain (healthcare, finance, e-commerce, cyber-security.). Most industrial KGs are dynamic by nature as they are updated regularly with streaming data (customer activity, network traffic, application logs, IT process). However, extracting insights from continuously updated data comes with major challenges, particularly in big data settings. In this paper, we address the following challenges: 1) ingesting heterogeneous data, 2) training and deployment of predictive models on continuously evolving data, and 3) implementation of data pipelines for updating and maintaining the KG in production. We cover multiple aspects of this process, from knowledge collection to its operationalization. We propose Stream2Graph, a stream-based system for building and updating the knowledge base dynamically in real time. Then we show how graph features can be used in downstream online machine learning models. The solution speeds up big data stream learning and knowledge extraction to enhance Graph-based AI applications. Experimental results show the effectiveness of our solution for knowledge base construction and improvement of big data learning capabilities. Using data from Stream2Graph resulted in speedups for training and inference time in the range from 547x to 2000x in downstream ML models. Finally, we provide the lessons learned from applying graph-based online learning on large-scale network processing high-velocity streaming data.
Confli-T5: An AutoPrompt Pipeline for Conflict Related Text Augmentation

Recent advances in natural language processing (NLP) and Big Data technologies have been crucial for scientists to analyze political unrest and violence, prevent harm, and promote global conflict management. Government agencies and public security organizations have invested heavily in deep learning-based applications to study global conflicts and political violence. However, such applications involving text classification, information extraction, and other NLP-related tasks require extensive human efforts in annotating/labeling texts. While limited labeled data may drastically hurt the models’ performance (over-fitting), large demands on annotation tasks may turn real-world applications impracticable. To address this problem, we propose Confli-T5, a prompt-based method that leverages the domain knowledge from existing political science ontology to generate synthetic but realistic labeled text samples in the conflict and mediation domain. Our model allows generating textual data from the ground up and employs our novel Double Random Sampling mechanism to improve the quality (coherency and consistency) of the generated samples. We conduct experiments over six standard datasets relevant to political science studies to show the superiority of Confli-T5. Our codes are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Mask recognition system based on anchor

During the epidemic prevention and control phase, masks must be worn in public places according to the epidemic prevention regulations. However, manual detection may result in missed detections when there is a high flow of people. Therefore, using computers to complement manual detection can reduce costs and improve efficiency. For the recognition system of mask-wearing situation, this system utilizes the target detection lightweight model; the difference between the two is that the former model is optimized for mobile, which is more suitable for deployment on devices with limited computing power such as mobile or edge detection. And the model is based on the backbone network of FaceBoxes face detector to complete the detection of wearing masks. After the experimental demonstration results, only 5 sample results of the 50 face images we tested (including 23 photos with masks and 27 without masks) showed errors. The system had a 90% correct rate in natural scenes throughout the day.
Gradual Further Pre-training Architecture for Economics/Finance Domain Adaptation of Language Model

We propose a new pre-trained architecture for economics/finance domain adaptation of language models in this paper. Pre-trained language models have become commonplace in a wide range of language processing applications. Because they learn from generic documents such as Wikipedia, many pre-trained language models are not fully adapted to the domain. As a result, there are two approaches: one is to develop a domain-specific pre-trained language model, and the other is to adapt the model learned on general documents to the domain through further pre-training with domain texts. However, no definitive better method has been discovered, and each project is working on it in different ways. As a result, this study focuses on the Japanese language’s economics/financial domains and investigates how pre-trained language models can be better adapted to domain-specific tasks.
Graggle: A Graph-based Approach to Document Clustering

Document recommendation systems have traditionally relied upon high-dimensional vector representations that scale poorly in corpora with diverse vocabularies. Existing graph-based approaches focus on the metadata of documents and, unfortunately, ignore the content of the papers. In this work, we have designed and implemented a new system we call Graggle, which builds a graph to model a corpus. Nodes are papers, and edges represent significant words shared between them. We then leverage modern graph learning techniques to turn this graph into a highly efficient tool for dimensionality reduction. Documents are represented as low-dimensional vector embeddings generated with a graph autoencoder. Our experiments show that this approach outperforms traditional document vector-based and text autoencoding approaches on labeled data. Additionally, we have applied this technique to a repository of unlabeled research documents about the novel coronavirus to demonstrate its effectiveness as a real-world tool.
Detector entanglement: Quasidistributions for Bell-state measurements

Measurements in the quantum domain can exceed classical notions. This concerns fundamental questions about the nature of the measurement process itself, as well as applications, such as their function as building blocks of quantum information processing protocols. In this paper we explore the notion of entanglement for detection devices in theory and experiment. A method is devised that allows one to determine nonlocal quantum coherence of positive-operator-valued measures via negative contributions in a joint distribution that fully describes the measurement apparatus under study. This approach is then applied to experimental data for detectors that ideally project onto Bell states. In particular, we describe the reconstruction of the aforementioned entanglement quasidistributions from raw data and compare the resulting negativities with those expected from theory. Therefore, our method provides a versatile toolbox for analyzing measurements regarding their quantum-correlation features for quantum science and quantum technology.
Developing student literacy management to support national economic development in Indonesia

The development and management of student literacy data have become a central issue discussed recently; its purpose is to support the Government's agenda to improve the national economy, including Indonesia. We searched for data online on several sources related to financial literacy in student countries and the goals of increasing the country's economy. We examine this location under the lychee phenomenon approach. We involve a comprehensive data coding system, evaluation, and interpretation of data to get relevant conclusions and valid answers to problems. Based on the existing data and discussion of the results, we can conclude that efforts to develop student literacy are significantly carried out by every Government, not only by Indonesia but by several other developed countries; this is because, as much as possible, the Government develops literacy governance for generations of students, so they will get a generation that can improve national economic development. Hopefully, these results can be helpful for further studies by academics, decision-makers, and the wider community.
Metaphors in Minangkabau Language: Meaning Shift and Change

Metaphor is a tool to explain the nature of shifts and changes in meaning because it is able to serve the thoughts and feelings of language users. Metaphor serves as a source of strong motivation to express such thoughts and feelings and becomes expressive means of language. Not all meanings can be conveyed literally, and if speakers feel that there are no words with literal meaning available, they will convey the meaning figuratively which can be more captivating to the listener's attention or can bring out complexities that cannot be done in other ways. Minang native speakers consider settlement in the outside of deliberation to be bad. To avoid disputes, they must be good at finding and uttering expressions whose meanings are different from the sentences that are said, so, they do not immediately feel offended or humiliated. The utterance in (8) means ‘to clean village from all threats and disturbance.’ The analogy of red in (10) refers to saga fruit and the sentential meaning of (11) is associated to a person who has capability to posit him-/her-self. Meanwhile, the sentence in (13) might mean ‘what is hoped appears.’ There are similarities in the nature of animals equated with human character, for example, in Minang, the phrases pipik andak menjadi elang ‘the sparrow wants to be an eagle’ and gajah dilulue ular lidih ‘elephant is wrapped around by a small snake’ refer to a weak against a strong person or can be described as a person who is not aware of his abilities. Keywords: Metaphor, ontological, structural, inference, Minangkabau, meaning shift and change
IL SIGNIFICATO ESTENSIVO DI ABBIGLIAMENTO E IL LIMITE D’USO „ENOLOGIA“ NEL VOCABOLARIO ZINGARELLI

In this paper we illustrate how dictionary authors recorded the extensive meaning of the Italian noun abbigliamento (clothing), referring to the sectorial language “enology” in the Zingarelli 2021 dictionary. Our research has presented the nineteenth-century attestations of this meaning, both within the Italian and French sources. Our analysis also showed the increase in the oenological voices attested in the Zingarelli in the period between 1970 and 2021 and, in particular, those that developed new meanings inherent to this sectorial language in the period from 1983 to 2021. Semantic neology, often underestimated, as Cortelazzo states, also shows its vitality in the items related to enology, showing that even an old word, as Marri suggests, can provide new meanings, as we have seen for abbigliamento.
Gambaran E-learning Readiness pada Mahasiswa Unika Atma Jaya selama Masa Pandemi COVID-19

The Covid-19 pandemic has undoubtedly had quite a significant impact on many aspects of human society, including a students’ learning experiences. As of March 2020, in lieu of recent policies to mitigate the spread of Covid-19, all institutions in Indonesia have been required to do distance learning, including universities. This sudden change could possibly have an impact on the learning process on a student’s learning. Seeing as most universities use E-Learning as the preferred method of distance learning, this study aims to study the readiness of students to participate in E-Learning. This study uses a quantitative descriptive method to analyse the E-Learning Readiness (ELR) of Faculty of Psychology students from Unika Atma Jaya from batches 2018-2021. This study involves 180 participants aged 17-24. Analysis shows that generally students are ready to use E-Learning (ELR score = 3,5 where above 3,4 is considered ready, but not optimal), specifically in the dimensions Learner Control and Motivation For Learning. When further broken into two groups 2018-2019 (experienced face to face before pandemic) and 2020-2021 (E-Learning only), 2018-2019 students show a trend of having lesser readiness levels than their 2020-2021 peers. Even if according to t-score analysis shows no statistical difference between the overall ELR levels of students in batches 2018-2019 and 2020-2021, there are still differences in ELR scores when analysing individual dimensions, particularly the dimensions Computer Internet Self-Efficacy, Motivation For Learning, and Learner Control.
STOP &amp; ACT – Impro-Theater as Psycho-Drama

The article presents the technique of “Stop &amp; Act” developed by the author, a special kind of improvisational theater. It is a derivative of psycho-drama based on Jacob Levi Moreno and intended for larger groups. In improvised play, “challenging” issues, e.g. unresolved or still “pending” conflicts, interests or other explosive topics, are openly discussed and acted out in short theater scenes, without too much staging. The goals, rules, target audience, benefits of impro-theater are given in the article. The most important aspects of the “Stop &amp; Act” were described such as: the preparation stage, the script, the game, the actors, go-go-girls, the stage, the audience, the presenter, the feedback and the investment.
Recognition Method of Sports Training Based on Intelligent Information Platform

With the wide development of sports nowadays, many kinds of sport applications have appeared and changed the living styles of people all around the world. There are many factors that are important for sports, such as the environment and exercise. At present, there are still some shortcomings in sports training management all around the world. For example, the unscientific is unclear, the training purpose is not strong, and the assistant decision-making means are backward. In this way, these issues jointly make the sport training process difficult. In this way, this paper applies the multi-agent technology to the system, designs an intelligent information sports training management and recognition method based on intelligent agents, and carries on the concrete implementation in certain environments. The corresponding experimental results indicate that the accuracy and intelligence of the training recognition system can be improved greatly and effectively.
Measurement point cloud registration method for complex mechanical parts based on improved ICP

Point cloud registration is the key step of 3D non-contact precision measurement for complex mechanical parts based on machine vision. Due to extreme dependence on initial position, slow iterative convergence and many wrong corresponding point pairs, Iterative Closest Point (ICP) algorithm could not satisfy the requirements of point cloud registration efficiency and precision for measurement of large quantities of complex mechanical parts, so an improved ICP method of measurement point cloud registration for complex mechanical parts by combining ICP with Intrinsic Shape Signature-Fast Point Feature Histogram (ISS-FPFH) feature is proposed. In order to reduce the registration quantity of the point cloud and keep the original subtle features on surface of point cloud, the voxel filter based on the point close to center of gravity is proposed to preprocess for point cloud down sampling. It is difficult for traditional ICP algorithm to determine the appropriate initial position and will lead to the registration failure of multi-view measurement point cloud, the Sample Consensus Initial Registration (SAC-IA) algorithm based on ISS-FPFH is applied for coarse registration. In order to solve the problems of slow iterative convergence and many wrong corresponding point pairs of traditional ICP algorithm, the point-to-plane ICP algorithm by combining with normal vector angle constraint is proposed for fine registration. Through experimental comparison and analysis, the results show that the accuracy and efficiency of the method proposed in this paper can meet requirements of 3D non-contact precision measurement for large quantities of complex mechanical parts.
Enablers and inhibitors in agile teams

Mobile application development requires fast development due to shorter go-to market initiatives. In this context, understanding agile teams is crucial for the mobile software development industry. Thus, identifying and understanding enablers and inhibitors of agile teams in active learning environments has become a research opportunity among the software engineering research community and it is the main goal of this study. To achieve this study goal, we have performed a one-month case study with 50 individuals as part of a two-year mobile development course that teaches iOS development to undergraduate students in a Challenge Based Learning environment. In the course, students experience an agile environment developing mobile applications, dedicating 20 hours a week for the development of real world projects. The case study was conducted in two stages, both of which were held during the COVID-19 pandemic. On the first stage, we have found 33 positive aspects as well as 33 negative aspects reported by participants. However, on the second stage participants have reported 30 positive aspects and 35 negative aspects. The set of both positive aspects and negative aspects partially repeats from the first stage to the second stage. We concluded that team relationship, communication and engagement positively influence mobile application development in agile environments. At the same time, this study also presented negative influences for mobile application development teams, such as teams working remotely (because of COVID-19), lack of knowledge and bad planning.
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions

Despite the remarkable performance and generalization levels of deep learning models in a wide range of artificial intelligence tasks, it has been demonstrated that these models can be easily fooled by the addition of imperceptible yet malicious perturbations to natural inputs. These altered inputs are known in the literature as adversarial examples. In this paper, we propose a novel probabilistic framework to generalize and extend adversarial attacks in order to produce a desired probability distribution for the classes when we apply the attack method to a large number of inputs. This novel attack paradigm provides the adversary with greater control over the target model, thereby exposing, in a wide range of scenarios, threats against deep learning models that cannot be conducted by the conventional paradigms. We introduce four different strategies to efficiently generate such attacks, and illustrate our approach by extending multiple adversarial attack algorithms. We also experimentally validate our approach for the spoken command classification task and the Tweet emotion classification task, two exemplary machine learning problems in the audio and text domain, respectively. Our results demonstrate that we can closely approximate any probability distribution for the classes while maintaining a high fooling rate and even prevent the attacks from being detected by label-shift detection methods.