Unsupervised word segmentation in audio utterances is challenging as, in speech, there is typically no gap between words. In a preliminary experiment, we show that recent deep self-supervised features are very effective for word segmentation but require supervision for training the classification head. To extend their effectiveness to unsupervised word segmentation, we propose a pseudo-labeling strategy. Our approach relies on the observation that the temporal gradient magnitude of the embeddings (i.e. the distance between the embeddings of subsequent frames) is typically minimal far from the boundaries and higher nearer the boundaries. We use a thresholding function on the temporal gradient magnitude to define a psuedo-label for wordness. We train a linear classifier, mapping the embedding of a single frame to the pseudo-label. Finally, we use the classifier score to predict whether a frame is a word or a boundary. In an empirical investigation, our method, despite its simplicity and fast run time, is shown to significantly outperform all previous methods on two datasets.

In the deeply interconnected world we live in, pieces of information link domains all around us. As graph databases embrace effectively relationships among data and allow processing and querying these connections efficiently, they are rapidly becoming a popular platform for storage that supports a wide range of domains and applications. As in the relational case, it is expected that data preserves a set of integrity constraints that define the semantic structure of the world it represents. When a database does not satisfy its integrity constraints, a possible approach is to search for a ‘similar’ database that does satisfy the constraints, also known as a repair. In this work, we study the problem of computing subset and superset repairs for graph databases with data values using a notion of consistency based on having a set of Reg-GXPath expressions as integrity constraints. We show that for positive fragments of Reg-GXPath these problems admit a polynomial-time algorithm, while the full expressive power of the language renders them intractable.

We are currently in a great era for researchers and scientists studying and developing in the field of complex systems. Half of the physics Nobel prize of 2021 was awarded to the physicist Giorgio Parisi for his contributions to the theory of complex systems [9] and the other half to two meteorologists Syukuro Manabe and Klaus Hasselmann to the modeling of the Earth's climate [10]. Parisi has made significant contributions to the literature on complex systems, including areas such as spin glass [11, 12, 13], stochastic resonance [14], surface growth [15], multifractality [16], and bird flocking [17].

Along with the rapid evolution of mobile communication technologies, such as 5G, there has been a drastically increase in telecom fraud, which significantly dissipates individual fortune and social wealth. In recent years, graph mining techniques are gradually becoming a mainstream solution for detecting telecom fraud. However, the graph imbalance problem, caused by the Pareto principle, brings severe challenges to graph data mining. This is a new and challenging problem, but little previous work has been noticed. In this paper, we propose a Graph ATtention network with COst-sensitive BOosting (GAT-COBO) for the graph imbalance problem. First, we design a GAT-based base classifier to learn the embeddings of all nodes in the graph. Then, we feed the embeddings into a well-designed cost-sensitive learner for imbalanced learning. Next, we update the weights according to the misclassification cost to make the model focus more on the minority class. Finally, we sum the node embeddings obtained by multiple cost-sensitive learners to obtain a comprehensive node representation, which is used for the downstream anomaly detection task. Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors. In addition, our model is also helpful for solving the widespread over-smoothing problem in GNNs. The GAT-COBO code and datasets are available at https://github.com/xxhu94/GAT-COBO.

The present study aims to explore the capabilities of Language Models (LMs) in tackling high-stakes multiple-choice tests, represented here by the Exame Nacional do Ensino M\'edio (ENEM), a multidisciplinary entrance examination widely adopted by Brazilian universities. This exam poses challenging tasks for LMs, since its questions may span into multiple fields of knowledge, requiring understanding of information from diverse domains. For instance, a question may require comprehension of both statistics and biology to be solved. This work analyzed responses generated by GPT-3.5 and GPT-4 models for questions presented in the 2009-2017 exams, as well as for questions of the 2022 exam, which were made public after the training of the models was completed. Furthermore, different prompt strategies were tested, including the use of Chain-of-Thought (CoT) prompts to generate explanations for answers. On the 2022 edition, the best-performing model, GPT-4 with CoT, achieved an accuracy of 87%, largely surpassing GPT-3.5 by 11 points. The code and data used on experiments are available at https://github.com/piresramon/gpt-4-enem.

Non-Fungible Tokens (NFTs) are non-interchangeable assets, usually digital art, which are stored on the blockchain. Preliminary studies find that female and darker-skinned NFTs are valued less than their male and lighter-skinned counterparts. However, these studies analyze only the CryptoPunks collection. We test the statistical significance of race and gender biases in the prices of CryptoPunks and present the first study of gender bias in the broader NFT market. We find evidence of racial bias but not gender bias. Our work also introduces a dataset of gender-labeled NFT collections to advance the broader study of social equity in this emerging market.

San Diego, California, March 28, 2023 (GLOBE NEWSWIRE) -- HUMBL, Inc. (OTCQB: HMBL) HUMBL announced today the launch of its Artificial Intelligence (AI) and Automated Machine Learning initiatives across its consumer, commercial and Latin America business units. On the commercial side, HUMBL kicked off its AI / Automated Machine Learning initiatives with the announcement of its first commercial sales contract in its HUMBL Latin America subsidiary, with the sale of AI / Automated Machine Learning services for a leading IT / Telecommunications provider in the Latin America region in the form of a $60,000 (USD) contract for initial deliverables and a total contract value of $195,000 (USD) over three years, pending the achievement of milestones by HUMBL Latin America. "Artificial Intelligence is an accelerant to the principles of web3," said Brian Foote, CEO of HUMBL. "The use of public data sets to create more autonomous, intelligent outcomes for consumers, as well as the corporations and governments that serve them, is an excellent use of automated machine learning technologies," continued Foote. "The use of AI can help our clients model for more predictive outcomes around things like credit scoring, default rates, churn rates, healthcare patterns and more; driving more tailored experiences for consumers, while driving revenues and improved efficiencies for corporations and governments."

It started in the Caribbean Sea. Jaime Ascencio, then a business development engineer working across Latin America, was eager to find sustainable ways to combat the coastal erosion that was eating away at the region's treasured beaches--and threatening the tourism dollars brought in by its seaside resorts. "If there is no sand, there are no guests," he says. But Ascensio, who knew that artificial reefs could make for natural breakwaters, could only find solutions that were neither sustainable nor stable enough to resist the force of the waves. So he went on to get a master's in coastal engineering at the celebrated Delft University of Technology in the Netherlands--and developed one himself.

Clarifai is a leading, full-lifecycle deep learning AI platform for computer vision, natural language processing, and audio recognition. We help organizations transform unstructured images, video, text, and audio data into structured data at a significantly faster and more accurate rate than humans would be able to do on their own. Founded in 2013 by Matt Zeiler, Ph.D. Clarifai has been a market leader in AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai continues to grow with employees remotely based throughout the United States, Estonia, Argentina and India. We have raised $100M in funding to date, with $60M coming from our most recent Series C, and are backed by industry leaders like Menlo Ventures, Union Square Ventures, Lux Capital, New Enterprise Associates, LDV Capital, Corazon Capital, Google Ventures, NVIDIA, Qualcomm and Osage.

This paper explores a novel application of textual semantic similarity to user-preference representation for rating prediction. The approach represents a user's preferences as a graph of textual snippets from review text, where the edges are defined by semantic similarity. This textual, memory-based approach to rating prediction enables review-based explanations for recommendations. The method is evaluated quantitatively, highlighting that leveraging text in this way outperforms both strong memory-based and model-based collaborative filtering baselines.