Neural program embedding can be helpful in analyzing large software, a task that is challenging for traditional logic-based program analyses due to their limited scalability. A key focus of recent machine-learning advances in this area is on modeling program semantics instead of just syntax. Unfortunately evaluating such advances is not obvious, as program semantics does not lend itself to straightforward metrics. In this paper, we introduce a benchmarking framework called COSET for standardizing the evaluation of neural program embeddings. COSET consists of a diverse dataset of programs in source-code format, labeled by human experts according to a number of program properties of interest. A point of novelty is a suite of program transformations included in COSET. These transformations when applied to the base dataset can simulate natural changes to program code due to optimization and refactoring and can serve as a "debugging" tool for classification mistakes. We conducted a pilot study on four prominent models--TreeLSTM [1], gated graph neural network (GGNN) [2], AST-Path neural network (APNN) [3], and DYPRO [4]. We found that COSET is useful in identifying the strengths and limitations of each model and in pinpointing specific syntactic and semantic characteristics of programs that pose challenges.

When we look at all of the different types of HR technologies that are out there, and there's now hundreds of them that have some form of machine learning or AI at the core of their platform, and have an HR use case. One of the biggest areas that we're seeing an impact is in recruitment. Over a third of all of the vendors that are out there that are using some form of AI in their platform are doing so to try and disrupt recruitment and there are a few different ways that they're doing that, these include: To learn more about how AI is impacting Recruitment, you can read an earlier blog post that I wrote on this topic by going here. So I'm not talking about employee engagement or engagement surveys when I talk about Employee Experience. What I'm talking about is the experience that people have in your organisation using HR technology. I don't think there are many employees out there that enjoy the experience of going into a typical HR system that's been around for 10-15 years.

When compared to all other programming language, python is extremely simple, easy to learn, interpret and implement. Due to this reason it became very popular and trending programming right now. The job demand for python programmers are high. Python engineers have some of the highest salaries in the industry. There are plenty of Python scientific packages for data visualization, machine learning, natural language processing, complex data analysis and more.

Like most other teachers, I joined the profession to improve the world. But the job I loved so much eventually exhausted me: and like so many others, I left. I was tired of the laborious paperwork (too often solely for the benefit of inspectors). I was tired of having to give evidence for every judgement I made in ridiculous detail. I was tired of flagging up children who needed further support for it to never materialise.

A 12-month training program that will help people prepare for the future of work. The Future of Work Monthly will teach students the 12 crucial skills they need to succeed in the new world of work. Each month students will receive a video from me that will introduce the particular skill for the month, why it's important, and how to practice that skill. For the remainder of that month they will then receive a weekly email providing specific tactics and strategies for how they can practice that skill in their day to day lives.

Of course we discussed how PR is evolving and Martin had some interesting insight into how PR people need to bring visuals into their pitches and try to get a little bit more savvy with photos, video and design. We then spent a lot of time talking about the fascinating intersection between PR and AI (artificial intelligence). Not to toot my own horn, but this was really one of the first times an interviewee brought a topic to the table that I honestly hadn't thought of before. Martin is currently completing a Master in Communications Management from McMaster/Syracuse and researching AI, relationships and communications. His thesis was on the relationships humans have (or will have) with machines, which he calls the human/AI agent relationship.

Understanding learning materials (e.g. test questions) is a crucial issue in online learning systems, which can promote many applications in education domain. Unfortunately, many supervised approaches suffer from the problem of scarce human labeled data, whereas abundant unlabeled resources are highly underutilized. To alleviate this problem, an effective solution is to use pre-trained representations for question understanding. However, existing pre-training methods in NLP area are infeasible to learn test question representations due to several domain-specific characteristics in education. First, questions usually comprise of heterogeneous data including content text, images and side information. Second, there exists both basic linguistic information as well as domain logic and knowledge. To this end, in this paper, we propose a novel pre-training method, namely QuesNet, for comprehensively learning question representations. Specifically, we first design a unified framework to aggregate question information with its heterogeneous inputs into a comprehensive vector. Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way. Here, a novel holed language model objective is developed to extract low-level linguistic features, and a domain-oriented objective is proposed to learn high-level logic and knowledge. Moreover, we show that QuesNet has good capability of being fine-tuned in many question-based tasks. We conduct extensive experiments on large-scale real-world question data, where the experimental results clearly demonstrate the effectiveness of QuesNet for question understanding as well as its superior applicability.

Transcribing content from structural images, e.g., writing notes from music scores, is a challenging task as not only the content objects should be recognized, but the internal structure should also be preserved. Existing image recognition methods mainly work on images with simple content (e.g., text lines with characters), but are not capable to identify ones with more complex content (e.g., structured symbols), which often follow a fine-grained grammar. To this end, in this paper, we propose a hierarchical Spotlight Transcribing Network (STN) framework followed by a two-stage "where-to-what" solution. Specifically, we first decide "where-to-look" through a novel spotlight mechanism to focus on different areas of the original image following its structure. Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly. Moreover, we propose two implementations on the basis of STN, i.e., STNM and STNR, where the spotlight movement follows the Markov property and Recurrent modeling, respectively. We also design a reinforcement method to refine the framework by self-improving the spotlight mechanism. We conduct extensive experiments on many structural image datasets, where the results clearly demonstrate the effectiveness of STN framework.

This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty. This can be interpreted as a form of domain randomization and/or generative pretraining during training. To this end, the usage of the Pointer-Generator softens the requirement of having the answer within the context, enabling us to construct diverse training samples for learning. Additionally, we propose a new Introspective Alignment Layer (IAL), which reasons over decomposed alignments using block-based self-attention. We evaluate our proposed method on the NarrativeQA reading comprehension benchmark, achieving state-of-the-art performance, improving existing baselines by $51\%$ relative improvement on BLEU-4 and $17\%$ relative improvement on Rouge-L. Extensive ablations confirm the effectiveness of our proposed IAL and CL components.

In recent years, deep neural networks have been applied to many off-line machine learning tasks. Despite their state-of-of-the-art performance, the theory behind their generalization abilities is still not complete. When turning to the online domain even much less is known and understood both from the practical use and the theoretical side. Thus, the main focus of this paper is exploring the theoretical guarantees of deep neural networks in online learning under general stochastic processes. In the traditional online learning setting, and in particular in sequential prediction under uncertainty, the learner is evaluated by a loss function that is not entirely known at each iteration [8]. In this work, we study online prediction focusing on the challenging case where the unknown underlying process is stationary and ergodic, thus allowing observations to depend on each other arbitrarily. Many papers before have considered online learning under stationary and ergodic sources and in various application domains. For example, in online portfolio selection, [19, 16, 17, 42, 26] proposed nonparametric online strategies that guarantee, under mild conditions, convergence to the best possible outcome. 1