Proof assistants offer tactics to apply proof by induction, but these tactics rely on inputs given by human engineers. We address this problem with SeLFiE, a domain-specific language to encode experienced users' expertise on how to apply the induct tactic in Isabelle/HOL: when we apply an induction heuristic written in SeLFiE to an inductive problem and arguments to the induct tactic, the SeLFiE interpreter examines both the syntactic structure of the problem and semantics of the relevant constants to judge whether the arguments to the induct tactic are plausible for that problem according to the heuristic.

Since modernity, ethic has been progressively fragmented into specific communities of practice. The digital revolution enabled by AI and Data is bringing ethical wicked problems in the crossroads of technology and behavior. However, the need of a comprehensive and constructive ethical framework is emerging as digital platforms connect us globally. The unequal structure of the global system makes that dynamic changes and systemic problems impact more on those that are most vulnerable. Ethical frameworks based only on the individual-level are not longer sufficient. A new ethical vision must comprise the understanding of the scales and complex interconnections of social systems. Many of these systems are internally fragile and very sensitive to external factors and threats, which turns into unethical situations that require systemic solutions. The high scale nature of digital technology that expands globally has also an impact at the individual level having the risk to make humans beings more homogeneous, predictable and ultimately controllable. To preserve the core of humanity ethic must take a stand to preserve and keep promoting individual rights and uniqueness and cultural heterogeneity tackling the negative trends and impact of digitalization. Only combining human-centered and collectiveness-oriented digital development it will be possible to construct new social models and human-machine interactions that are ethical. This vision requires science to enhance ethical frameworks and principles with the actionable insights of relationships and properties of the social systems that may not be evident and need to be quantified and understood to be solved. Artificial Intelligence is both a risk and and opportunity for an ethical development, thus we need a conceptual construct that drives towards a better digitalizated world.

Deliberate manipulation of digital images can be innocuous (e.g., to improve the quality and appearance of an image) or carried with malicious intent (e.g., to alter the semantic content of the image, or to establish an alibi). The diffusion of fake images has implications on judicial systems, global economy, financial health, and even homeland and national security. Not surprisingly, there have been interest from the digital forensics, and more specifically image forensics, community in recent years to detect deliberate manipulation of digital images. There have also been interest from the commercial market, as suggested in a recent study [1]. Image forensics, an emerging forensic discipline, seeks to determine the history of an image (e.g., its origin), the processing it underwent, etc, in order to determine the authenticity of the images [2].

By definition, diversity implies active and inclusive participation. When it comes to Artificial Intelligence Systems (AIS), it's even more critical to establish strategies and partnerships that can foster genuine and holistic inclusion regarding design for these important technologies. Since 2016, City AI has become the world's leading non-profit connecting the AI community globally and supporting local AI ecosystems to develop further. AI provides one of the greatest opportunities for humanity. Yet the access to its expertise and resources is limited.

Tensor completion refers to the task of estimating the missing data from an incomplete measurement or observation, which is a core problem frequently arising from the areas of big data analysis, computer vision, and network engineering. Due to the multidimensional nature of high-order tensors, the matrix approaches, e.g., matrix factorization and direct matricization of tensors, are often not ideal for tensor completion and recovery. Exploiting the potential periodicity and inherent correlation properties appeared in real-world tensor data, in this paper, we shall incorporate the low-rank and sparse regularization technique to enhance Tucker decomposition for tensor completion. A series of computational experiments on real-world datasets, including internet traffic data, color images, and face recognition, show that our model performs better than many existing state-of-the-art matricization and tensorization approaches in terms of achieving higher recovery accuracy. Naturally, these data would be stored as where rank(F) represents the rank of the underlying matrix higher-order tensor (a.k.a., multidimensional array), which F and M R Following the spirit of matrix completion model [1], [2], [3], [4], to name just a few.

Disentanglement is a highly desirable property of representation due to its similarity with human's understanding and reasoning. This improves interpretability, enables the performance of down-stream tasks, and enables controllable generative models. However, this domain is challenged by the abstract notion and incomplete theories to support unsupervised disentanglement learning. We demonstrate the data itself, such as the orientation of images, plays a crucial role in disentanglement and instead of the factors, and the disentangled representations align the latent variables with the action sequences. We further introduce the concept of disentangling action sequences which facilitates the description of the behaviours of the existing disentangling approaches. An analogy for this process is to discover the commonality between the things and categorizing them. Furthermore, we analyze the inductive biases on the data and find that the latent information thresholds are correlated with the significance of the actions. For the supervised and unsupervised settings, we respectively introduce two methods to measure the thresholds. We further propose a novel framework, fractional variational autoencoder (FVAE), to disentangle the action sequences with different significance step-by-step. Experimental results on dSprites and 3D Chairs show that FVAE improves the stability of disentanglement.

Multimodal neural machine translation (NMT) has become an increasingly important area of research over the years because additional modalities, such as image data, can provide more context to textual data. Furthermore, the viability of training multimodal NMT models without a large parallel corpus continues to be investigated due to low availability of parallel sentences with images, particularly for English-Japanese data. However, this void can be filled with comparable sentences that contain bilingual terms and parallel phrases, which are naturally created through media such as social network posts and e-commerce product descriptions. In this paper, we propose a new multimodal English-Japanese corpus with comparable sentences that are compiled from existing image captioning datasets. In addition, we supplement our comparable sentences with a smaller parallel corpus for validation and test purposes. To test the performance of this comparable sentence translation scenario, we train several baseline NMT models with our comparable corpus and evaluate their English-Japanese translation performance. Due to low translation scores in our baseline experiments, we believe that current multimodal NMT models are not designed to effectively utilize comparable sentence data. Despite this, we hope for our corpus to be used to further research into multimodal NMT with comparable sentences.

The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust. In this paper, we focus on the model-agnostic debiasing strategies and explore how to (or is it possible to) make the NLI models robust to multiple distinct adversarial attacks while keeping or even strengthening the models' generalization power. We firstly benchmark prevailing neural NLI models including pretrained ones on various adversarial datasets. We then try to combat distinct known biases by modifying a mixture of experts (MoE) ensemble method and show that it's nontrivial to mitigate multiple NLI biases at the same time, and that model-level ensemble method outperforms MoE ensemble method. We also perform data augmentation including text swap, word substitution and paraphrase and prove its efficiency in combating various (though not all) adversarial attacks at the same time. Finally, we investigate several methods to merge heterogeneous training data (1.35M) and perform model ensembling, which are straightforward but effective to strengthen NLI models.

The Levenshtein distance is a text similarity metric that measures the distance between 2 words. It has a number of applications, including text autocompletion and autocorrection. For either of these use cases, the word entered by a user is compared to words in a dictionary to find the closest match, at which point a suggestion(s) is made. The dictionary may contain thousands of words, and thus the response of the application for comparing 2 words will likely take a few milliseconds. The Levenshtein distance is usually calculated by preparing a matrix of size (M 1)x(N 1)--where M and N are the lengths of the 2 words--and looping through said matrix using 2 for loops, performing some calculations within each iteration.

In gradient boosting, predictions are made from an ensemble of weak learners. Unlike a random forest that creates a decision tree for each sample, in gradient boosting, trees are created one after the other. Previous trees in the model are not altered. Results from the previous tree are used to improve the next one. In this piece, we'll take a closer look at a gradient boosting library called CatBoost.