Our framework Phonesis is a machine-learned model of spoken language as an embodied mechanism over real human behaviour based on multimodal corpora (MOCHA-TIMIT, GRID, VoxCeleb2). Unlike approaches based on simulations, it combines a Speaker model (for interpreting the visual input and intention and converting it into realistic speech) and a Listener model (for the audio interpretation). Phonesis is trained in the end-to-end, which means that it is highly accurate in articulatory prediction, speech quality, and intent decoding. The results of ablation studies have indicated that joint optimization enhances performance. Zero-shot evaluation VoxCeleb2 has a high degree of generalization, indicating that it can be used in voice rehabilitation, brain-computer interfaces, and language understanding.
In the context of big data, balancing individual privacy with the need for data utility remains a critical challenge. This research presents the Bottom-Up k-Concealment (BU-KC) framework, a pioneering solution for Privacy-Preserving Big Data Publishing (PPBDP). At its core, the k-concealment model addresses the limitations of traditional k-anonymity by minimizing excessive generalizations, ensuring stronger privacy protection while preserving data utility. Combined with the Bottom-Up Generalization (BUG) strategy, BU-KC demonstrates superior performance compared to Top-Down Specialization (TDS) in terms of computational efficiency, scalability, and privacy preservation, while concurrently sustaining data utility. Furthermore, the integration of Apache Spark’s distributed computing paradigm enables us to effectively mitigate scalability constraints and processing bottlenecks commonly observed in the anonymization of large-scale datasets.
Cuckoo Hashing has been considered the state of the art since its origin. In Cuckoo Hashing, the search operation is executed sequentially in the tables, and thus, Cuckoo Hashing is Sequential Cuckoo Hashing. Based on the size of the table, Cuckoo Hashing is implemented in two ways: Symmetric Sequential Cuckoo Hashing and Asymmetric Sequential Cuckoo Hashing. Cuckoo Hashing suffers from higher insertion latency, inefficient memory usage, and high data migration. This work investigates the performance of both versions of Cuckoo Hashing under the light of two newly proposed performance indicators: Degree of Dexterity and Table Reference Count per key.
Creating non-invasive diagnosis procedures using speech is a very promising research area in biomedical engineering. Screening for an upper respiratory tract infection (URTI) or common cold using speech signals may be advantageous in terms of preventing its spread. In this study, we have proposed the various intrinsic mode multi-domain feature fusions for diagnosing the URTIs. First, each active speech frame is decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition to capture the non-linear and non-stationary characteristics of pathological speech. Then spectral and entropy domain features are extracted from each IMF and used as features for classification. The URTIC and the newly recorded PRECRU URTIS database are employed to evaluate the efficacy of the proposed features. A transformer-based framework is employed to evaluate the discriminative capacity of the features, focusing on their capability to simulate long-range relationships with features. The proposed features outperform state-of-the-art methods, achieving a UAR of 70.82% and 72.24%, respectively, on the URTIC database's development and test partitions and 76.06% on the PREC-RU URTIS database. The results emphasize the efficacy of integrating intrinsic mode spectral and entropy features for reliable URTI identification
Social media and social networking have spread widely in everyday life, so it is important to use them in collaborative learning. Forming appropriate learning groups is, therefore, an important objective. This paper presents a novel approach based on a Genetic Algorithm (GA) for dynamic learners grouping in a Social Network Learning system (SNL). It offers some improved attributes applied for grouping learners and new genetic operators applied in the GA. The efficiency of the proposed approach was evaluated by comparing the groups formed using the proposed GA with randomly formed groups, resulting in the conclusion that the proposed GA is more effective and that the groups formed are more efficient.
