Corpus for development of routing algorithms in opportunistic networks

We have designed a collection of scenarios, a corpus, for its use in the study and development of routing algorithms for opportunistic networks. To obtain these scenarios, we have followed a methodology based on characterizing the space and choosing the best exemplary items in such a way that the corpus as a whole was representative of all possible scenarios. Until now, research in this area was using some sets of non-standard network traces that made it difficult to evaluate algorithms and perform fair comparisons between them. These developments were hard to assess in an objective way, and were prone to introduce unintentional biases that directly affected the quality of the research. Our contribution is more than a collection of scenarios; our corpus provides a fine collection of network behaviors that suit the development of routing algorithms, specifically in evaluating and comparing them. If the scientific community embraces this corpus, the community will have a global-agreed methodology where the validity of results would not be limited to specific scenarios or network conditions, thus avoiding self-produced evaluation setups, availability problems and selection bias, and saving time. New research in the area will be able to validate the routing algorithms already published. It will also be possible to identify the scenarios better suit specific purposes, and results will be easily verified. The corpus is available free to download and use

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Multimedia Development of English Vocabulary Learning in Primary School

In this paper, we describe a prototype of web-based intelligent handwriting education system for autonomous learning of Bengali characters. Bengali language is used by more than 211 million people of India and Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. This research project was aimed to develop an intelligent Bengali handwriting education system. As an intelligent tutor, the system can automatically check the handwriting errors, such as stroke production errors, stroke sequence errors, stroke relationship errors and immediately provide a feedback to the students to correct themselves. Our proposed system can be accessed from smartphone or iPhone that allows students to do practice their Bengali handwriting at anytime and anywhere. Bengali is a multi-stroke input characters with extremely long cursive shaped where it has stroke order variability and stroke direction variability. Due to this structural limitation, recognition speed is a crucial issue to apply traditional online handwriting recognition algorithm for Bengali language learning. In this work, we have adopted hierarchical recognition approach to improve the recognition speed that makes our system adaptable for web-based language learning. We applied writing speed free recognition methodology together with hierarchical recognition algorithm. It ensured the learning of all aged population, especially for children and older national. The experimental results showed that our proposed hierarchical recognition algorithm can provide higher accuracy than traditional multi-stroke recognition algorithm with more writing variability

Data Scarcity in Event Analysis and Abusive Language Detection

Lack of data is almost always the cause of the suboptimal performance of neural networks. Even though data scarce scenarios can be simulated for any task by assuming limited access to training data, we study two problem areas where data scarcity is a practical challenge: event analysis and abusive content detection} Journalists, social scientists and political scientists need to retrieve and analyze event mentions in unstructured text to compute useful statistical information to understand society. We claim that it is hard to specify information need about events using keyword-based representation and propose a Query by Example (QBE) setting for event retrieval. In the QBE setting, we assume that there are a few example sentences mentioning the event class a user is interested in and we aim to retrieve relevant events using only the examples as a query. Traditional event detection approaches are not applicable in this setting as event detection datasets are constructed based on pre-defined schemas which limits them to a small set of event and event-argument types. Moreover, the amount of annotated data in event detection datasets is limited that only allows us to build a retrieval corpus for evaluation. Thus we assume that there are no relevance judgments to train an event retrieval model -- except for the few examples of a specific event type. We create three QBE evaluation settings from three event detection datasets: PoliceKilling, ACE, and IndiaPoliceEvents. For the PoliceKilling dataset, where a relevant sentence describes a police killing event, we show that a query model constructed from the NLP features extracted from the few given examples is effective compared to event detection baselines. For the ACE dataset, where there are thirty-three types of events, we construct a QBE setting for each type and show that a sentence embedding approach effectively transfers for event matching. Finally, we conducted a unified evaluation of all three datasets using the sentence-embedding-based model and showed that it outperforms strong baselines. We further examine the effect of data scarcity in abusive language detection. We first study a specific type of abusive language -- hate speech. Neural hate speech detection models trained from one dataset poorly generalize to another dataset from a different domain. This is because characteristics of hate speech vary based on racial and cultural aspects. Our data scarcity scenario assumes that we have a hate speech dataset from a domain and it needs to generalize to a test set from another domain using the unlabeled data from the test domain only. Thus we assume zero target domain data in this scenario. To tackle the data scarcity, we propose an unsupervised domain adaptation approach to augment labeled data for hate speech detection. We evaluate the approach with three different models (character CNNs, BiLSTMs, and BERT) on three different collections. We show our approach improves Area under the Precision/Recall curve by as much as 42% and recall by as much as 278%, with no loss (and in some cases a significant gain) in precision. Finally, we examine the cross-lingual abusive language detection problem. Abusive language is a superclass of hate speech that includes profanity, aggression, offensiveness, cyberbullying, toxicity, and hate speech itself. There is a large collection of abusive language detection datasets in English such as Jigsaw. For other languages there exist datasets for abusive language detection but with very limited data. We propose a cross-lingual transfer learning approach to learn an effective neural abusive language classifier for such low-resource languages with help from a dataset from a resource-rich language. The framework is based on a nearest-neighbor architecture and is thus interpretable by design. It is a modern instantiation of the classic k-nearest neighbor model, as we use transformer representations in all its components. Unlike prior work on neighborhood-based approaches, we encode the neighborhood information based on query-neighbor interactions. We propose two encoding schemes and show their effectiveness using both qualitative and quantitative analyses. Our evaluation results on eight languages from two different datasets for abusive language detection show sizable improvements in F1 over strong baselines

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others