Embedding Metadata-Enriched Graphs

This paper presents an on-going research where we studythe problem of embedding meta-data enriched graphs, with a focus onknowledge graphs in a vector space with transformer based deep neuralnetworks. Experimentally, we compare ceteris paribus the performance ofa transformer-based model with other non-transformer approaches. Dueto their recent success in natural language processing we hypothesizethat the former is superior in performance. We test this hypothesizesby comparing the performance of transformer embeddings with non-transformer embeddings on different downstream tasks. Our researchmight contribute to a better understanding of how random walks in-fluence the learning of features, which might be useful in the design ofdeep learning architectures for graphs when the input is generated withrandom walks.

Distilling vector space model scores for the assessment of constructed responses with bifactor Inbuilt Rubric method and latent variables

In this paper, we highlight the importance of distilling the computational assessments of constructed responses to validate the indicators/proxies of constructs/trins using an empirical illustration in automated summary evaluation. We present the validation of the Inbuilt Rubric (IR) method that maps rubrics into vector spaces for concepts’ assessment. Specifically, we improved and validated its scores’ performance using latent variables, a common approach in psychometrics. We also validated a new hierarchical vector space, namely a bifactor IR. 205 Spanish undergraduate students produced 615 summaries of three different texts that were evaluated by human raters and different versions of the IR method using latent semantic analysis (LSA). The computational scores were validated using multiple linear regressions and different latent variable models like CFAs or SEMs. Convergent and discriminant validity was found for the IR scores using human rater scores as validity criteria. While this study was conducted in the Spanish language, the proposed scheme is language-independent and applicable to any language. We highlight four main conclusions: (1) Accurate performance can be observed in topic-detection tasks without hundreds/thousands of pre-scored samples required in supervised models. (2) Convergent/discriminant validity can be improved using measurement models for computational scores as they adjust for measurement errors. (3) Nouns embedded in fragments of instructional text can be an affordable alternative to use the IR method. (4) Hierarchical models, like the bifactor IR, can increase the validity of computational assessments evaluating general and specific knowledge in vector space models. R code is provided to apply the classic and bifactor IR method.

On Generalized Galois Cyclic Orbit Flag Codes

Flag codes that are orbits of a cyclic subgroup of the general linear group acting on flags of a vector space over a finite field, are called cyclic orbit flag codes. In this paper, we present a new contribution to the study of such codes, by focusing this time on the generating flag. More precisely, we examine those ones whose generating flag has at least one subfield among its subspaces. In this situation, two important families arise: the already known Galois flag codes, in case we have just fields, or the generalized Galois flag codes in other case. We investigate the parameters and properties of the latter ones and explore the relationship with their underlying Galois flag code.

Klasifikasi Berita Hoaks Menggunakan Algoritma Vector Space Model

Sebuah berita terkait suatu informasi yang beredar di media cetak atau mainstream akan menjadikan opini publik tentang suatu masalah baik yang bersifat informasi positif atau negatif, perkembangan teknologi informasi sekarang ini menyebabkan penyebaran informasi bisa uptodate setiap harinya. Dengan semakin mudahnya sebuah informasi menyebar maka akan semakin mudah pula mempengaruhi kehidupan dalam sosial masyarakat sekarang ini. Namun pada kenyataannya informasi yang beredar di media itu tidak semuanya benar atau bisa dikatakan adanya suatu berita hoax atau tidak benar. Dalam penelitian ini bertujuan untuk mengklasifikasi sistem temu kembali informasi berita hoaks menggunakan metode vektor space model untuk memastikan kebenaran suatu berita apakah berita hoax atau tidak. Dalam penelitian tersebut menghasilkan klasifikasi kebenaran berita dengan akurasi terbaik pada K-6 sebesar 83%, artinya dengan akurasi tersebut bisa memvalidasi klasifikasi terkait informasi berita benar ataupun hoax sebesar 83%.

Learning to sense from events via semantic variational autoencoder

In this paper, we introduce the concept of learning to sense, which aims to emulate a complex characteristic of human reasoning: the ability to monitor and understand a set of interdependent events for decision-making processes. Event datasets are composed of textual data and spatio-temporal features that determine where and when a given phenomenon occurred. In learning to sense, related events are mapped closely to each other in a semantic vector space, thereby identifying that they contain similar contextual meaning. However, learning a semantic vector space that satisfies both textual similarities and spatio-temporal constraints is a crucial challenge for event analysis and sensing. This paper investigates a Semantic Variational Autoencoder (SVAE) to fine-tune pre-trained embeddings according to both textual and spatio-temporal events of the class of interest. Experiments involving more than one hundred sensors show that our SVAE outperforms a competitive one-class classification baseline. Moreover, our proposal provides desirable learning requirements to sense scenarios, such as visualization of the sensor decision function and heat maps with the sensor’s geographic impact.

Analyzing Vaccination Priority Judgments for 132 Occupations Using Word Vector Models

Most human societies conduct a high degree of division of labor based on occupation. However, determining the occupational field that should be allocated a scarce resource such as vaccine is a topic of debate, especially considering the COVID-19 situation. Though it is crucial that we understand and anticipate people’s judgments on resource allocation prioritization, quantifying the concept of occupation is a difficult task. In this study, we investigated how well people’s judgments on vaccination prioritization for different occupations could be modeled by quantifying their knowledge representation of occupations as word vectors in a vector space. The results showed that the model that quantified occupations as word vectors indicated high out-of-sample prediction accuracy, enabling us to explore the psychological dimension underlying the participants’ judgments. These results indicated that using word vectors for modeling human judgments about everyday concepts allowed prediction of performance and understanding of judgment mechanisms.