A careful analysis of the Large Language Model (LLM) results, generated through anonymized representations of the original dataset, is crucial to precisely evaluate the data-sharing procedure's limitations and facilitate valuable collaborations among Internet-based cognitive behavioral therapy (ICBT) companies and third parties. This paper presents an experimental study of fine-tuning 27 LMs for a multiclass classification task to identify depression severity using 40,191 tweets labeled by human annotators. We fine-tune 14 Bidirectional Encoder Representations from Transformers (BERT), 6 Robustly Optimized BERT Pretraining Approaches (RoBerta), 3 Generative Pretraining (GPT), and 4 Text-to-Text Transfer Transformer (T5) based LMs to classify confidential and anonymized tweets. We report that T5, through conditional generation, outperforms widely adopted BERT, RoBerta, and GPT types for classifying confidential and anonymized tweets. Anonymizing personal information safeguards user privacy and often increases LM performance. Case sensitivity can potentially improve or harm the performance of domain-specific LMs for original and anonymized text.

Crime forecasting techniques can play a leading role in hindering crime occurrences, especially in areas under possible threat. In this paper, we propose Locality Aware Temporal Factorization Machines (LTFMs) for crime prediction. Its locality representation module deploys a spatial encoder to estimate the regional dependencies using Graph Convolutional Networks (GCNs). Then, the Point of Interest (POI) encoder computes the weighted attentive aggregation of location, crime, and POI latent representations. The dynamic crime representation module utilizes the transformer-based positional encodings to capture the dependencies among space, time, and crime categories. The encodings learnt from locality representation and crime category encoders, are projected into a factorization machine-based architecture via a shared feed-forward network. An extensive comparison with state-of-art techniques, using Chicago and New York's criminal records, shows the significance of LTFMs.

Gene Network Graphs (GNGs) are comprised of biomedical data. Deriving structural information from these graphs remains a prime area of research in the domain of biomedical and health informatics. In this paper, we propose Gene Vectors Embodied Deep Attentional Factorization Machines (GDFMs) for the gene to gene interaction prediction. We first initialize GDFM with vector embeddings learned from gene locality configuration and an expression equivalence criterion that preserves their innate similar traits. GDFM uses an attention-based mechanism that manipulates different positions, to learn the representation of sequence, before calculating the pairwise factorized interactions. We further use hidden layers, batch normalization, and dropout to stabilize the performance of our deep structured architecture. An extensive comparison with several state-of-the-art approaches, using Ecoli and Yeast datasets for gene-gene interaction prediction shows the significance of our proposed framework.

Low-literate people are unable to use many mainstream social networks due to their text-based interfaces even though they constitute a major portion of the world population. Specialized speech-based networks (SBNs) are more accessible to low-literate users through their simple speech-based interfaces. While SBNs have the potential for providing value-adding services to a large segment of society they have been hampered by the need to operate in low-income segments on low budgets. The knowledge of influential users and communities in such networks can help in optimizing their operations. In this paper, we present a self-organizing map (SOM) for discovering and visualizing influential communities of users in SBNs. We demonstrate how a friendship graph is formed from call data records and present a method for estimating influences between users. Subsequently, we develop a SOM to cluster users based on their influence, thus identifying community-level influences and their roles in information propagation. We test our approach on Polly, a SBN developed for job ads dissemination among low-literate users. For comparison, we identify influential users with the benchmark greedy algorithm and relate them to the discovered communities. The results show that influential users are concentrated in influential communities and community-level information propagation provides a ready summary of influential users.

Multi Query Optimization in federated database systems is a well-studied area. Studies have shown that similar problem arises in wide range of applications, e.g., distributed stream processing systems and wireless sensor networks. In this paper, a general distributed multiquery processing problem motivated by the need to speedup data acquisition in federated databases using evolutionary algorithm is studied. We setup a simple framework in which each individual in population is evolved in terms of cost, uniform labeling of hyper edges and validity of resource constraints through a number of generations. Variations of our general problem can be shown to be NP-Hard. Our extensive empirical evaluation over five different synthetic datasets shows a significant improvement of 8 percent in results as compared to the state-of-the-art methods.