The technocrat epoch is overflowing with new technologies and such cutting-edge facilities accompany the risks and pitfalls. Robotic process automation is another innovation that empowers the computerization of high-volume, manual, repeatable, everyday practice, rule-based, and unmotivating human errands. The principal objective of Robotic Process Automation is to supplant monotonous human errands with a virtual labor force or a computerized specialist playing out a similar work as the human laborer used to perform. This permits human laborers to zero in on troublesome undertakings and critical thinking. Robotic Process Automation instruments are viewed as straightforward and strong for explicit business process computerization. Robotic Process Automation comprises intelligence to decide if a process should occur. It has the capability to analyze the data presented and provide a decision based on the logic parameters set in place by the developer. Moreover, it does not demand for system integration, like other forms of automation. Be that as it may since the innovation is yet arising, the Robotic Process Automation faces a few difficulties during the execution.

Although machine learning based algorithms have been extensively used for detecting phishing websites, there has been relatively little work on how adversaries may attack such "phishing detectors" (PDs for short). In this paper, we propose a set of Gray-Box attacks on PDs that an adversary may use which vary depending on the knowledge that he has about the PD. We show that these attacks severely degrade the effectiveness of several existing PDs. We then propose the concept of operation chains that iteratively map an original set of features to a new set of features and develop the "Protective Operation Chain" (POC for short) algorithm. POC leverages the combination of random feature selection and feature mappings in order to increase the attacker's uncertainty about the target PD. Using 3 existing publicly available datasets plus a fourth that we have created and will release upon the publication of this paper, we show that POC is more robust to these attacks than past competing work, while preserving predictive performance when no adversarial attacks are present. Moreover, POC is robust to attacks on 13 different classifiers, not just one. These results are shown to be statistically significant at the p < 0.001 level.

Strategic test allocation plays a major role in the control of both emerging and existing pandemics (e.g., COVID-19, HIV). Widespread testing supports effective epidemic control by (1) reducing transmission via identifying cases, and (2) tracking outbreak dynamics to inform targeted interventions. However, infectious disease surveillance presents unique statistical challenges. For instance, the true outcome of interest - one's positive infectious status, is often a latent variable. In addition, presence of both network and temporal dependence reduces the data to a single observation. As testing entire populations regularly is neither efficient nor feasible, standard approaches to testing recommend simple rule-based testing strategies (e.g., symptom based, contact tracing), without taking into account individual risk. In this work, we study an adaptive sequential design involving n individuals over a period of {\tau} time-steps, which allows for unspecified dependence among individuals and across time. Our causal target parameter is the mean latent outcome we would have obtained after one time-step, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint. We propose an Online Super Learner for adaptive sequential surveillance that learns the optimal choice of tests strategies over time while adapting to the current state of the outbreak. Relying on a series of working models, the proposed method learns across samples, through time, or both: based on the underlying (unknown) structure in the data. We present an identification result for the latent outcome in terms of the observed data, and demonstrate the superior performance of the proposed strategy in a simulation modeling a residential university environment during the COVID-19 pandemic.

Spreadsheets are widely used for table manipulation and presentation. Stylistic formatting of these tables is an important property for both presentation and analysis. As a result, popular spreadsheet software, such as Excel, supports automatically formatting tables based on rules. Unfortunately, writing such formatting rules can be challenging for users as it requires knowledge of the underlying rule language and data logic. We present CORNET, a system that tackles the novel problem of automatically learning such formatting rules from user examples in the form of formatted cells. CORNET takes inspiration from advances in inductive programming and combines symbolic rule enumeration with a neural ranker to learn conditional formatting rules. To motivate and evaluate our approach, we extracted tables with over 450K unique formatting rules from a corpus of over 1.8M real worksheets. Since we are the first to introduce conditional formatting, we compare CORNET to a wide range of symbolic and neural baselines adapted from related domains. Our results show that CORNET accurately learns rules across varying evaluation setups. Additionally, we show that CORNET finds shorter rules than those that a user has written and discovers rules in spreadsheets that users have manually formatted.

The rise of AI methods to make predictions and decisions has led to a pressing need for more explainable artificial intelligence (XAI) methods. One common approach for XAI is to produce a post-hoc explanation, explaining why a black box ML model made a certain prediction. Formal approaches to post-hoc explanations provide succinct reasons for why a prediction was made, as well as why not another prediction was made. But these approaches assume that features are independent and uniformly distributed. While this means that "why" explanations are correct, they may be longer than required. It also means the "why not" explanations may be suspect as the counterexamples they rely on may not be meaningful. In this paper, we show how one can apply background knowledge to give more succinct "why" formal explanations, that are presumably easier to interpret by humans, and give more accurate "why not" explanations. In addition, we show how to use existing rule induction techniques to efficiently extract background information from a dataset, and also how to report which background information was used to make an explanation, allowing a human to examine it if they doubt the correctness of the explanation.

Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events. Whereas previous weakly supervised approaches required a knowledge-base of such events, or corresponding financial figures, our approach requires no such additional data, and can be employed to extract economic events related to companies which are not even mentioned in the training data.

Lithuania's Foreign Minister, Gabrielius Landsbergis, talked with Fox News Digital about Russia, China and the'global rules-based order' on the 20th anniversary of his country joining NATO. Lithuania commemorated its entry into NATO this last week and its long-standing partnership with the U.S. as leaders look ahead to the increasingly complex security landscape developing around the world. President George W. Bush visited the Lithuanian capital of Vilnius 20 years ago to welcome the country into the still-growing NATO alliance, applauding the character of member states to "stand in the face of evil, to have the courage to always face danger." "President [George W.] Bush made the most famous speech any American has ever made in Lithuania exactly 20 years ago," Lithuanian Foreign Minister Gabrielius Landsbergis told Fox News Digital in an exclusive interview. "That was even before we were a member of NATO, and it was probably the most important security guarantee that we got before Article Five started covering us with its umbrella."

Lithuania's Foreign Minister, Gabrielius Landsbergis, talked with Fox News Digital about Russia, China and the'global rules-based order' on the 20th anniversary of his country joining NATO. Lithuania commemorated its entry into NATO this last week and its long-standing partnership with the U.S. as leaders look ahead to the increasingly complex security landscape developing around the world. President George W. Bush visited the Lithuanian capital of Vilnius 20 years ago to welcome the country into the still-growing NATO alliance, applauding the character of member states to "stand in the face of evil, to have the courage to always face danger." "President [George W.] Bush made the most famous speech any American has ever made in Lithuania exactly 20 years ago," Lithuanian Foreign Minister Gabrielius Landsbergis told Fox News Digital in an exclusive interview. "That was even before we were a member of NATO, and it was probably the most important security guarantee that we got before Article Five started covering us with its umbrella."

Rule-based machine learning techniques consist of learning classifier systems, association rule learning, artificial immune systems, and any other technique that relies on a collection of rules containing contextual knowledge. Although rule-based machine learning is essentially a rule-based system, it is unique from traditional rule-based systems, which are often hand-crafted, and other rule-based decision-makers. It is because rule-based machine learning uses a learning algorithm to identify good rules automatically, rather than requiring a human to manually design and curate a rule set using prior domain expertise.

We consider concept generalization at a large scale in the diverse and natural visual spectrum. Established computational modes (i.e., rule-based or similarity-based) are primarily studied isolated and focus on confined and abstract problem spaces. In this work, we study these two modes when the problem space scales up, and the $complexity$ of concepts becomes diverse. Specifically, at the $representational \ level$, we seek to answer how the complexity varies when a visual concept is mapped to the representation space. Prior psychology literature has shown that two types of complexities (i.e., subjective complexity and visual complexity) (Griffiths and Tenenbaum, 2003) build an inverted-U relation (Donderi, 2006; Sun and Firestone, 2021). Leveraging Representativeness of Attribute (RoA), we computationally confirm the following observation: Models use attributes with high RoA to describe visual concepts, and the description length falls in an inverted-U relation with the increment in visual complexity. At the $computational \ level$, we aim to answer how the complexity of representation affects the shift between the rule- and similarity-based generalization. We hypothesize that category-conditioned visual modeling estimates the co-occurrence frequency between visual and categorical attributes, thus potentially serving as the prior for the natural visual world. Experimental results show that representations with relatively high subjective complexity outperform those with relatively low subjective complexity in the rule-based generalization, while the trend is the opposite in the similarity-based generalization.