--Machine learning models have been found to be susceptible to adversarial examples that are often indistinguishable from the original inputs. These adversarial examples are created by applying adversarial perturbations to input samples, which would cause them to be misclassified by the target models. Attacks that search and apply the perturbations to create adversarial examples are performed in both white-box and black-box settings, depending on the information available to the attacker about the target. For black-box attacks, the only capability available to the attacker is the ability to query the target with specially crafted inputs and observing the labels returned by the model. Current black-box attacks either have low success rates, requires a high number of queries, or produce adversarial examples that are easily distinguishable from their sources. In this paper, we present AdversarialPSO, a black-box attack that uses fewer queries to create adversarial examples with high success rates. AdversarialPSO is based on the evolutionary search algorithm Particle Swarm Optimization, a population-based gradient-free optimization algorithm. It is flexible in balancing the number of queries submitted to the target vs the quality of imperceptible adversarial examples. The attack has been evaluated using the image classification benchmark datasets CIF AR-10, MNIST, and Imagenet, achieving success rates of 99.6%, 96.3%, and 82.0%, respectively, while submitting substantially fewer queries than the state-of-the-art. We also present a black-box method for isolating salient features used by models when making classifications. This method, called Swarms with Individual Search Spaces or SWISS, creates adversarial examples by finding and modifying the most important features in the input. The purpose of these two attacks is to help evaluate the robustness of machine learning models and to encourage the exploration of much-needed defenses. Deep learning (DL) is being used to solve a wide variety of problems in many different domains, such as image classification [1], malware detection [2], speech recognition [3], and medicine [4]. Despite state-of-the-art performances, DL models have been shown to suffer from a general flaw that makes them vulnerable to external attack. Adversaries can cause models to misclassify inputs by applying small perturbations to samples at test time [5].

Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN, to improve DNN's interpretability and predictive power, and to identify effective regularization methods for specific tasks. This study designs a particular DNN architecture with alternative-specific utility functions (ASU-DNN) by using prior behavioral knowledge. Unlike a fully connected DNN (F-DNN), which computes the utility value of an alternative k by using the attributes of all the alternatives, ASU-DNN computes it by using only k's own attributes. Theoretically, ASU-DNN can dramatically reduce the estimation error of F-DNN because of its lighter architecture and sparser connectivity. Empirically, ASU-DNN has 2-3% higher prediction accuracy than F-DNN over the whole hyperparameter space in a private dataset that we collected in Singapore and a public dataset in R mlogit package. The alternative-specific connectivity constraint, as a domain-knowledge-based regularization method, is more effective than the most popular generic-purpose explicit and implicit regularization methods and architectural hyperparameters. ASU-DNN is also more interpretable because it provides a more regular substitution pattern of travel mode choices than F-DNN does. The comparison between ASU-DNN and F-DNN can also aid in testing the behavioral knowledge. Our results reveal that individuals are more likely to compute utility by using an alternative's own attributes, supporting the long-standing practice in choice modeling. Overall, this study demonstrates that prior behavioral knowledge could be used to guide the architecture design of DNN, to function as an effective domain-knowledge-based regularization method, and to improve both the interpretability and predictive power of DNN in choice analysis.

Arrows: actions top, left, down, and right; encircled characters: state labels. The actions in states that are not reachable or lead to another LDBA state are not displayed. In all subfigures, the most likely paths are highlighted in red. the baby b, the only allowed action is left and when taken the following situations can happen: (i) the robot hits the wall with probability 0.1 and wakes the baby up; (ii) the robot moves left with probability 0. 8 or moves down with probability 0.1 . If the baby has been woken up, which means the robot could not leave in a single time step (represented by L TL as b null b), the robot should notify the adult (at state a); otherwise, the robot should directly go back to the charger (at state c). The full objective is specified in L TL as ϕ 2 nullnull d nullnullnullnull (1) (b null b) null ( b U (a c)) null nullnull null (2) a null ( a U b) null nullnull null (3) ( b null b nullnull b) ( a U c) null nullnull null (4) c ( a U b) null nullnull null (5) (b null b) a null nullnull null (6) null .

In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and person-alisation of Interactive Narratives (INs). INs are partially observable for the players and their goals are dynamic as a result. We used Receding Horizon IRL (RHIRL) to learn players' goals in the form of reward functions, and derive policies to imitate their behaviour. Our preliminary results suggest that RHIRL is able to learn action sequences to complete a game, and provided insights towards generating behaviour more similar to specific players.

Proceedings of the BMVC 2019 Workshop on Interpretable and Explainable Machine Vision, Cardiff, UK, September 12, 2019.

MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech Emna Rejaibi a,b,c, Ali Komaty d, Fabrice Meriaudeau e, Said Agrebi c, Alice Othmani a a Universit e Paris-Est, LISSI, UPEC, 94400 Vitry sur Seine, France b INSAT Institut National des Sciences Appliqu ees et de T echnologie, Centre Urbain Nord BP 676-1080, Tunis, Tunisie c Y obitrust, T echnopark El Gazala B11 Route de Raoued Km 3.5, 2088 Ariana, Tunisie d University of Sciences and Arts in Lebanon, Ghobeiry, Liban e Universit e de Bourgogne Franche Comt e, ImvIA EA7535/ IFTIM Abstract Major depression, also known as clinical depression, is a constant sense of despair and hopelessness. It is a major mental disorder that can a ff ect people of any age including children and that a ff ect negatively person's personal life, work life, social life and health conditions. Globally, over 300 million people of all ages are estimated to su ff er from clinical depression. A deep recurrent neural network-based framework is presented in this paper to detect depression and to predict its severity level from speech. Low-level and high-level audio features are extracted from audio recordings to predict the 24 scores of the Patient Health Questionnaire (a depression assessment test) and the binary class of depression diagnosis. To overcome the problem of the small size of Speech Depression Recognition (SDR) datasets, data augmentation techniques are used to expand the labeled training set and also transfer learning is performed where the proposed model is trained on a related task and reused as starting point for the proposed model on SDR task. The proposed framework is evaluated on the DAIC-WOZ corpus of the A VEC2017 challenge and promising results are obtained. An overall accuracy of 76.27% with a root mean square error of 0.4 is achieved in assessing depression, while a root mean square error of 0.168 is achieved in predicting the depression severity levels. Introduction Depression is a mental disorder caused by several factors: psychological, social or even physical factors. Psychological factors are related to permanent stress and the inability to successfully cope with di fficult situations. Social factors concern relationship struggles with family or friends and physical factors cover head injuries. Depression describes a loss of interest in every exciting and joyful aspect of everyday life. Mood disorders and mood swings are temporary mental states taking an essential part of daily events, whereas, depression is more permanent and can lead to suicide at its extreme severity levels.

--Code completion, one of the most useful features in the integrated development environments, can accelerate software development by suggesting the libraries, APIs, method names in real-time. Recent studies have shown that statistical language models can improve the performance of code completion tools through learning from large-scale software repositories. However, these models suffer from three major drawbacks: a) The hierarchical structural information of the programs is not fully utilized in the program's representation; b) In programs, the semantic relationships can be very long, existing LSTM based language models are not sufficient to model the long-term dependency. In this paper, we present a novel method that introduces the hierarchical structural information into the representation of programs by considering the path from the predicting node to the root node. T o capture the long-term dependency in the input programs, we apply Transformer-XL network as the base language model. Besides, we creatively propose a Multi-T ask Learning (MTL) framework to learn two related tasks in code completion jointly, where knowledge acquired from one task could be beneficial to another task. Experiments on three real-world datasets demonstrate the effectiveness of our model when compared with state-of-the-art methods. As the complexity and scale of the software developing continue to grow, code completion has become an essential feature of Integrated Development Environments (IDEs). It can speed up the process of software development by suggesting the next probable token based on existing code. However, traditional code completion tools rely on compile-time type information or heuristics rules to make recommendations [1], [2], which are costly and could not well capture human's programming patterns. To alleviate this problem, code completion research started to focus on learning from large-scale codebases in recent years. Based on the observation of source code's repeatability and predictability [3], statistical language models are generally used for modeling source code. N-gram is one of the most widely used language models [3]-[5]. Most recently, as the success of deep learning, source code modeling techniques have turned to Recurrent Neural Network (RNN) based models [2], [6]. In these models, a piece of source code is represented as source code token sequence or Abstract Syntactic Tree (AST) node sequence. Given a partial code sequence, the model computes the probability of the next token or AST node and recommends the one with the highest probability.

Beyond sharing datasets or simulations, we believe the Recommender Systems (RS) community should share Task Environments. In this work, we propose a high-level logical architecture that will help to reason about the core components of a RS Task Environment, identify the differences between Environments, datasets and simulations; and most importantly, understand what needs to be shared about Environments to achieve reproducible experiments. The work presents itself as valuable initial groundwork, open to discussion and extensions.

Over the last few decades, many distinct lines of research aimed at automating mathematics have been developed, including computer algebra systems (CASs) for mathematical modelling, automated theorem provers for first-order logic, SAT/SMT solvers aimed at program verification, and higher-order proof assistants for checking mathematical proofs. More recently, some of these lines of research have started to converge in complementary ways. One success story is the combination of SAT solvers and CASs (SAT+CAS) aimed at resolving mathematical conjectures. Many conjectures in pure and applied mathematics are not amenable to traditional proof methods. Instead, they are best addressed via computational methods that involve very large combinatorial search spaces. SAT solvers are powerful methods to search through such large combinatorial spaces---consequently, many problems from a variety of mathematical domains have been reduced to SAT in an attempt to resolve them. However, solvers traditionally lack deep repositories of mathematical domain knowledge that can be crucial to pruning such large search spaces. By contrast, CASs are deep repositories of mathematical knowledge but lack efficient general search capabilities. By combining the search power of SAT with the deep mathematical knowledge in CASs we can solve many problems in mathematics that no other known methods seem capable of solving. We demonstrate the success of the SAT+CAS paradigm by highlighting many conjectures that have been disproven, verified, or partially verified using our tool MathCheck. These successes indicate that the paradigm is positioned to become a standard method for solving problems requiring both a significant amount of search and deep mathematical reasoning. For example, the SAT+CAS paradigm has recently been used by Heule, Kauers, and Seidl to find many new algorithms for $3\times3$ matrix multiplication.

WIRE)--Guidewire Software, Inc. (NYSE: GWRE), provider of the industry platform Property and Casualty (P&C) insurers rely upon, today announced that specialist insurer Beazley has selected Guidewire Cyence Risk Analytics (Cyence) to leverage its internet-scale data collection capabilities to improve underwriting, capital and pricing efficiencies, and offer policy terms and premiums as quickly as possible to its customers. Beazley will implement Cyence data models to augment its existing data modeling applications for its cyber insurance line of business, with plans to expand to other lines of business in the future. "We are pleased that Beazley has expanded its Guidewire relationship with the selection of Cyence, and applaud the company's global mission of providing its clients with the highest standards of underwriting and claims service," said Mike Rosenbaum, chief executive officer, Guidewire Software. "Guidewire is committed to bringing smart insurance solutions to the market. We look forward to working with Beazley to infuse data analytics to help achieve its strategic goals around enterprise risk management and overall business growth."