Deep generative systems that learn probabilistic models from a corpus of existing music do not explicitly encode knowledge of a musical style, compared to traditional rule-based systems. Thus, it can be difficult to determine whether deep models generate stylistically correct output without expert evaluation, but this is expensive and time-consuming. Therefore, there is a need for automatic, interpretable, and musically-motivated evaluation measures of generated music. In this paper, we introduce a grading function that evaluates four-part chorales in the style of J.S. Bach along important musical features. We use the grading function to evaluate the output of a Transformer model, and show that the function is both interpretable and outperforms human experts at discriminating Bach chorales from model-generated ones.

We advocate the use of conformal prediction (CP) to enhance rule-based multi-label classification (MLC). In particular, we highlight the mutual benefit of CP and rule learning: Rules have the ability to provide natural (non-)conformity scores, which are required by CP, while CP suggests a way to calibrate the assessment of candidate rules, thereby supporting better predictions and more elaborate decision making. We illustrate the potential usefulness of calibrated conformity scores in a case study on lazy multi-label rule learning.

Ravelin, the London-based company using machine learning to help companies fight fraud when accepting online payments, has raised $20 million in new funding. The Series C round is led by Draper Esprit, with participation from existing investors Amadeus Capital Partners, BlackFin Tech, and Passion Capital. Ravelin disclosed $10 million in Series B funding in September 2018. Launched in 2016, Ravelin utilises machine learning and graph network technologies to help online businesses reduce losses to fraud and improve acceptance rates of orders. The idea is to do away with cruder, rule-based systems and use machine learning to negate false positives and give merchants more confidence accepting customers/transactions. With regards to product-market fit, Ravelin says it first found success with large-scale food and cab-ride marketplaces, but has since expanded into travel, ticketing, entertainment, gaming, gambling, and retail.

On a family trip a few months back, I was flipping through an airline magazine and landed on the puzzles page. There were three puzzles, "Easy", "Medium", and "Hard". At the top of the page a word that would become my obsession over the next couple of months: "Sudoku". I had heard about Sudoku puzzles, but I had never really considered trying one. I grabbed a pencil from one of the kids and started with the "Easy" puzzle. It took me quite some time (and I tore the paper in one spot after erasing too many times) but I eventually completed the puzzle.

Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings. However, existing embedding models are subject to at least one of the following limitations: (1) theoretical inexpressivity, (2) lack of support for prominent inference patterns (e.g., hierarchies), (3) lack of support for KBC over higher-arity relations, and (4) lack of support for incorporating logical rules. Here, we propose a spatio-translational embedding model, called BoxE, that simultaneously addresses all these limitations. BoxE embeds entities as points, and relations as a set of hyper-rectangles (or boxes), which spatially characterize basic logical properties. This seemingly simple abstraction yields a fully expressive model offering a natural encoding for many desired logical properties. BoxE can both capture and inject rules from rich classes of rule languages, going well beyond individual inference patterns. By design, BoxE naturally applies to higher-arity KBs. We conduct a detailed experimental analysis, and show that BoxE achieves state-of-the-art performance, both on benchmark knowledge graphs and on more general KBs, and we empirically show the power of integrating logical rules.

Amid a global pandemic, economic recession and simmering racial tensions around the world, Israel's threat to formally annex parts of occupied Palestinian territory presents yet another international crisis in the making. This is because, with this outrageous move, the Israeli government threatens to unravel the rules-based system of international relations. Today's international law regime was established in the first half of the 20th century not only to regulate relations between states but also to assist the movements for self-determination across the world and oversee the end of colonialism. The looming Israeli annexation of Palestinian land and the global inaction on it evidence the failure of this regime to help end colonialism and put its very raison d'etre in question. Much of the narrative in international diplomatic circles around the issue of annexation has revolved around deterrence, with the rationale being the threat of tangible consequences to annexation will lead to a reconsideration of the move. Yet this narrative fails to acknowledge that we have reached a point, where Israel will annex yet another chunk of Palestinian territory precisely because deterrence has not worked.

It wasn't too long ago that talking to a computer and having it not only understand, but speak back, was confined to the realm of science fiction, like that of the shipboard computers of Star Trek. The technology of the 24th century's Starship Enterprise is reality in the 21st century thanks to natural language processing (NLP), a machine learning-driven discipline that gives computers the ability to understand, process, and respond to spoken words and written text. Make no mistake: NLP is a complicated field that one can spend years studying. This guide contains the basics about NLP, details how it can benefit businesses, and explains where to get started with its implementation. Natural language processing (NLP) is a cross-discipline approach to making computers hear, process, understand, and duplicate human language.

Associations are the specific measurable constraints on interestingness used in association rule learning. Regardless of the rules being employed to classify new data, the associations need to be defined by constraints to determine what is both interesting and relevant. Support – How frequently the pattern/items occur in the dataset. Confidence – How often the rule being used has been true (conditional probability). Lift – Actual success rate of the target model (rule) over the expected success from random chance.

The rule extraction literature contains the notion of a fidelity-accuracy dilemma: when building an interpretable model of a black box function, optimising for fidelity is likely to reduce performance on the underlying task, and vice versa. I reassert the relevance of this dilemma for the modern field of explainable artificial intelligence, and highlight how it is compounded when the black box is an agent interacting with a dynamic environment. I then discuss two independent research directions - building white box agents and interpreting black box agents - which are both coherent and worthy of attention, but must not be conflated by researchers embarking on projects in the domain of agent interpretability.

This research is about the development a fuzzy decision support system for the diagnosis of coronary artery disease based on evidence. The coronary artery disease data sets taken from University California Irvine (UCI) are used. The knowledge base of fuzzy decision support system is taken by using rules extraction method based on Rough Set Theory. The rules then are selected and fuzzified based on information from discretization of numerical attributes. Fuzzy rules weight is proposed using the information from support of extracted rules. UCI heart disease data sets collected from U.S., Switzerland and Hungary, data from Ipoh Specialist Hospital Malaysia are used to verify the proposed system. The results show that the system is able to give the percentage of coronary artery blocking better than cardiologists and angiography. The results of the proposed system were verified and validated by three expert cardiologists and are considered to be more efficient and useful.