Look-ahead with mini-bucket heuristics for MPE

The paper investigates the potential of look-ahead in the con-text of AND/OR search in graphical models using the Mini-Bucket heuristic for combinatorial optimization tasks (e.g., MAP/MPE or weighted CSPs). We present and analyze the complexity of computing the residual (a.k.a Bellman update) of the Mini-Bucket heuristic and show how this can be used to identify which parts of the search space are more likely to benefit from look-ahead and how to bound its overhead. We also rephrase the look-ahead computation as a graphical model, to facilitate structure exploiting inference schemes. We demonstrate empirically that augmenting Mini-Bucket heuristics by look-ahead is a cost-effective way of increasing the power of Branch-And-Bound search.Postprint (published version

Digital supply chain surveillance using artificial intelligence: definitions, opportunities and risks

Digital Supply Chain Surveillance (DSCS) is the proactive monitoring and analysis of digital data that allows firms to extract information related to a supply network, without the explicit consent of firms involved in the supply chain. AI has made DSCS to become easier and larger-scale, posing significant opportunities for automated detection of actors and dependencies involved in a supply chain, which in turn, can help firms to detect risky, unethical and environmentally unsustainable practices. Here, we define DSCS, review priority areas using a survey conducted in the UK. Visibility, sustainability, resilience are significant areas that DSCS can support, through a number of machine-learning approaches and predictive algorithms. Despite anecdotal narrative on the importance of explainability of algorithmic results, practitioners often prefer accuracy over explainability; however, there are significant differences between industrial sectors and application areas. Using a case study, we highlight a number of concerns on the unchecked use of AI in DSCS, such as bias or misinterpretation resulting in erroneous conclusions, which may lead to suboptimal decisions or relationship damage. Building on this, we develop and discuss a number of illustrative cases to highlight risks that practitioners should be aware of, proposing key areas of further research

The Acquisition of Physical Knowledge in Generative Neural Networks

As children grow older, they develop an intuitive understanding of the physical processes around them. Their physical understanding develops in stages, moving along developmental trajectories which have been mapped out extensively in previous empirical research. Here, we investigate how the learning trajectories of deep generative neural networks compare to children's developmental trajectories using physical understanding as a testbed. We outline an approach that allows us to examine two distinct hypotheses of human development - stochastic optimization and complexity increase. We find that while our models are able to accurately predict a number of physical processes, their learning trajectories under both hypotheses do not follow the developmental trajectories of children.Comment: Published as a conference paper at ICML 202

Soccer Coach Decision Support System

The savage essence and nature of sports means those who work on it hunt for the win. The sport enterprise is undergoing a gigantic digital transformation focused on imaging, real time and data analysis employed in the competitions. Conventional process methods in sports management such as fitness and health establishments, training, growth and match or game realisation are all being revolutionized by the sport digitization. In team sports it is well known that is needful an enough and simple digital methodology to organize and construct a feasible strategy. Digitization in sports is perpetually evolving and requires pervasive challenges. The sports and athletics digitization success is based on what is being done with collection of more data. Competitive advantages go to those who produce powerful operations using the data and acting on it in real time. The potential impact of these sport features in sport team operations is powerful. Data does not ride all decisions, but it empowers knowledgeable decisions. In these world circumstances, our vision with this system was born from a dream helping soccer sport management systems embrace and improve its contest success. Our perspective problem is how a decision support system for soccer coaches helps them to take enhancement decisions better. To face this problem we have created a soccer coach decision support system. This system is organised in two joined components; the first simulates the prediction of the soccer match winner through a data driven neural network. This component output activates the second to operate the logic rules learning and provides the stats, analysis, decision making and additionally plans improvements like drills and training procedures. This helps on the preparation towards upcoming matches as well as being aligned with their style and playing concepts. Future scalability and development, will analyse the mental and moral features of the teams by virtue of their athlete’s behavior changes

Deep Learning for Automatic Detection and Facial Recognition in Japanese Macaques: Illuminating Social Networks

Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for animals and time-intensive for researchers. In recent years, the integration of deep learning in research offered new methodological perspectives through automatization of complex tasks. Harnessing object detection and recognition technologies is increasingly used by researchers to achieve identification on video footage. This study represents a preliminary exploration into the development of a non-invasive tool for face detection and individual identification of Japanese macaques (Macaca fuscata) through deep learning. The ultimate goal of this research is, using identifications done on the dataset, to automatically generate a social network representation of the studied population. The current main results are promising: (i) the creation of a Japanese macaques' face detector (Faster-RCNN model), reaching a 82.2% accuracy and (ii) the creation of an individual recognizer for K{\=o}jima island macaques population (YOLOv8n model), reaching a 83% accuracy. We also created a K{\=o}jima population social network by traditional methods, based on co-occurrences on videos. Thus, we provide a benchmark against which the automatically generated network will be assessed for reliability. These preliminary results are a testament to the potential of this innovative approach to provide the scientific community with a tool for tracking individuals and social network studies in Japanese macaques

Deep reinforcement learning for industrial applications

In recent years there has been a growing attention from the world of research and companies in the field of Machine Learning. This interest, thanks mainly to the increasing availability of large amounts of data, and the respective strengthening of the hardware sector useful for their analysis, has led to the birth of Deep Learning. The growing computing capacity and the use of mathematical optimization techniques, already studied in depth but with few applications due to a low computational power, have then allowed the development of a new approach called Reinforcement Learning. This thesis work is part of an industrial process of selection of fruit for sale, thanks to the identification and classification of any defects present on it. The final objective is to measure the similarity between them, being able to identify and link them together, even if coming from optical acquisitions obtained at different time steps. We therefore studied a class of algorithms characteristic of Reinforcement Learning, the policy gradient methods, in order to train a feedforward neural network to compare possible malformations of the same fruit. Finally, an applicability study was made, based on real data, in which the model was compared on different fruit rolling dynamics and with different versions of the network

Automated vulnerability detection in source code

Technological advances have facilitated instant global connectivity, transforming the way we interact with the world. Software, propelled by this evolution, plays a pivotal role in our daily lives, being present in virtually every facet of our existence. Programmers, who form the bedrock of the business structure, create source code comprising hundreds or even thousands of lines, encompassing essential functionalities for software to operate seamlessly. However, owing to the inherent complexity of these functionalities and their interdependencies, it is common for errors to escape notice in the code, inadvertently reaching the software production phase and resulting in code vulnerabilities Each year, the number ofidentified software vulnerabilities, either publicly disclosed or discovered internally, increases. These vulnerabilities pose a significant risk of exploitation, potentially leading to data breaches or service interruptions. Therefore, the goal of this project is to develop a tool capable of analyzing code written in C and C++ to detect vulnerabilities before the code is deployed to end users. To achieve this goal, we leveraged existing work in this area by using a dataset of open-source functions written in C and C++. This dataset contains approximately 1.27 million functions categorized into five different Common Weakness Enumerations (CWEs). Preprocessing was performed to optimize the performance of the models used. The models were trained on function snippets only, without considering any external context of the code, thus simplifying the problem and increasing processing efficiency. The results obtained are promising, with the trained models showing high performance in identifying and classifying vulnerabilities. In addition, these results can serve as a benchmark for direct comparisons between different approaches.O avanço tecnológico permitiu uma conexão global instantânea, transformando a maneira como interagimos com o mundo. Os softwares, impulsionados por essa evolução, desempenham um papel crucial em nosso cotidiano, estando presentes em praticamente todos os aspectos de nossas vidas. Os programadores, fundamentais na estrutura empresarial, desenvolvem o código-fonte composto por centenas ou até milhares de linhas, incorporando as funcionalidades essenciais para o pleno funcionamento dos softwares. No entanto, devido à complexidade intrínseca dessas funcionalidades e suas interdependências, é comum que erros passem despercebidos no código, chegando inadvertidamente à fase de produção do software e resultando em vulnerabilidades de código. Anualmente, observa-se um aumento no número de vulnerabilidades de software que são identificadas e divulgadas publicamente ou descobertas internamente. Essas vulnerabilidades representam um sério risco e podem resultar em fuga de informações ou interrupção de serviços. Assim, este projeto visa desenvolver uma ferramenta capaz de analisar o código escrito em C e C++ para identificar vulnerabilidades antes que esse código chegue ao consumidor final. Para alcançar esse objetivo, utilizamos como ponto de partida diversos trabalhos já realizados nessa área, fazendo uso de um conjunto de dados contendo funções de código aberto escritas em C e C++. Esse conjunto de dados engloba cerca de 1.27 milhões de funções categorizadas por cinco diferentes Common Weakness Enumerations (CWEs). Realizamos um pré-processamento para otimizar o desempenho dos modelos utilizados. Os modelos foram treinados apenas em trechos de funções, sem considerar qualquer contexto externo sobre o código, simplificando assim o problema e melhorando a eficiência do processamento. Os resultados obtidos são promissores, pois os modelos treinados foram capazes de identificar e classificar as vulnerabilidades com alto desempenho, estes resultados podem também servir como base para comparação direta entre diferentes abordagens

Exploring out of distribution: Deep neural networks and the human brain

Deep neural networks have achieved state-of-the-art performance across a wide range of tasks. Convolutional neural networks, with their ability to learn complex spatial features, have surpassed human-level accuracy on many image classification problems. However, these architectures are still often unable to make accurate predictions when the test data distribution differs from that of the training data. In contrast, humans naturally excel at such out-of-distribution generalizations. Novel solutions have been developed to improve a deep neural net\u27s ability to handle out-of-distribution data. The advent of methods such as Push-Pull and AugMix have improved model robustness and generalization. We are interested in assessing whether or not such models achieve the most human-like generalization across a wide variety of image classification tasks. We identify AugMix as the most human-like deep neural network under our set of benchmarks. Identifying such models sheds light on human cognition and the analogy between neural nets and the human brain. We also show that, contrary to our intuition, transfer learning worsens the performance of Push-Pull

Application of Reinforcement Learning for optimizing the inventory management of clinical trials

On the one hand, clinical trials are of great importance in discovering new treatments for diseases. They teach research things that cannot be learned in the laboratory, that is, what does and does not work in humans. What’s more, they are a really helpful tool for deciding whether the side effects of a new drug or treatment are acceptable compared to the potential benefits. Since results are not known at the beginning, this process is quite uncertain and effective management of doses is required. On the other hand, Reinforcement Learning is a Machine Learning paradigm different from Supervised Learning and Unsupervised Learning. Unlike these two methods, Reinforcement Learning is used when we want a system to accomplish a task by understanding the task by itself and the system must learn based on the “trial and error” rule. In recent years, several Reinforcement Learning applications have brought great advances in different fields. However, this new paradigm has not been used for optimizing the inventory management of clinical trials, so that’s why this master thesis addresses this line of investigation. The contributions of this master thesis are: (1) The definition of the Markov Decision Pro- cess formulation for the current problem, (2) The application of Tabular Methods to solve the problem, and (3) The improvement of the used Tabular Methods by modifying their respective Q-Tables. Results show a considerable improvement after the Q-Table’s modification. However, the system does not achieve a great performance mainly due to the high dimensionality of the problem