Project scheduling includes various problems of high pract ical relevance. Such problems arise in many areas and include different constraints and objectives. Usually pro ject scheduling problems require scheduling of a set of proj ect activities over a period of time and assignment of resources to these activities. Typical constraints include time windows for activities, precedence constraints between the ac tivities, assignment of appropriate resources etc. The aim is to find feasible schedules that optimize several criteria su ch as the minimization of total completion time. In this paper we investigate solving a real-world project sc heduling problem that arises in an industrial test laborato ry of a large company. This problem, Industrial Test Laborator y Scheduling (TLSP), which is an extension of the well known Resource-Constrained Project Scheduling Problem (R CPSP), was originally described in [1, 2]. It consists of a grouping stage, where smaller activities (tasks) are join ed into larger jobs, and a scheduling stage, where those jobs are scheduled and have resources assigned to them. In this wo rk, we deal with the second stage and assume that a grouping of tasks into jobs is already provided.

That's how much data humanity generates every single day. And the amount is increasing; we've created 90% of the world's data in the last two years alone. It should come as no surprise, then, that businesses today are drowning in data. That's because much of that data is unstructured; it takes the form of documents, social media content and other qualitative information that doesn't reside in conventional databases and is can't be parsed by traditional algorithms or machine analysis. Cognitive services not only cut through the deluge of data, but also bring meaning to it through human-like understanding of natural language queries.

His work is focused on the ethics of technology, including such topics as artificial intelligence (AI) and ethics, the ethics of space exploration and use, the ethics of technological manipulation of humans, the ethics of mitigation of and adaptation towards risky emerging technologies, and various aspects of the impact of technology and engineering on human life and society, including the relationship of technology and religion, particularly the Catholic Church. He spoke to Charles Camosy.] Camosy: Can you tell us how you became interested and indeed expert in AI ethics? Green: My undergraduate degree was in genetics from the University of California, Davis, and I worked in molecular biology and biotech there, but ultimately I discovered that lab work was not for me. I had dreamed of being a scientist since I was a child, so this was very confusing and I didn't know what to do with myself, so I made what turned out to be one of the best decisions of my life and joined the Jesuit Volunteers International.

The Artificial Intelligence (AI) in accounting market size is expected to grow from USD 666 million in 2019 to USD 4,791 million by 2024, at a Compound Annual Growth Rate (CAGR) of 48.4% during the forecast period. The AI in accounting is driven by various factors, such as the growing need to automate accounting processes and support enhanced data-based advisory and decision making. However, growing concerns over high criticality of data volume and quality, and investment related issues with integration of AI in accounting can hinder the growth of the market. Services segment to grow at a higher CAGR during the forecast period The AI in accounting market based on component is segmented into solutions and services.The services segment is expected to grow at a rapid pace during the forecast period. The growth of this segment can be attributed to the increasing deployment of AI in accounting software tools and solutions, which leads to increasing the demand for pre- and post-deployment services.

Are robots coming for your job? According to a Dell Technologies survey, 82% of leaders expect their employees and machines to work as "integrated teams". And many employees look forward to artificial intelligence (AI) that can help them do their job better. But not everybody has such a rosy outlook. In Australia, the report "Australia's Future Workforce" predicts about 40% of jobs could be lost to robotics, automation and artificial intelligence in the next 10-15 years.

Agricultural fields are no less than a battlefield. Irrespective of terrain, geography and type, crops have to compete against scores of different weeds, species of hungry insects, nematodes and a broad array of diseases. Weeds, or invasive plants, aggressively compete for soil nutrients, light and water, posing a serious threat to agricultural production and biodiversity. Weeds directly and indirectly result in tremendous losses to the farm sector, which convert to billions each year worldwide. To combat these challenges, the farm sector is looking at Artificial Intelligence (AI) based solutions.

Adobe and Evergage have topped the latest Forrester Wave rating digital intelligence platform providers based on product offering, strategy and market presence. The Q4 Digital Intelligence Platform report placed both Adobe and Evergage in the leaders' quadrant out a total field of nine vendors. In its commentary, the analyst firm highlighted Adobe's best-of-breed capabilities and scale as key reasons for its leadership position, noting the core components of Adobe Analytics, Experience Platform and Adobe Target as providing a wide array of services for marketing and customer engagement. Specifically, these address data, analytics and experience optimisation and are also well integrated into a wider platform play, Forrester stated. Second in the pack was smaller player, Evergage, which Forrester praised for its all-in-one, tightly integrated offering.

We consider Social Distance Games (SDGs), that is cluster formation games in which the utility of each agent only depends on the composition of the cluster she belongs to, proportionally to her harmonic centrality, i.e., to the average inverse distance from the other agents in the cluster. Under a non-cooperative perspective, we adopt Nash stable outcomes, in which no agent can improve her utility by unilaterally changing her coalition, as the target solution concept. Although a Nash equilibrium for a SDG can always be computed in polynomial time, we obtain a negative result concerning the game convergence and we prove that computing a Nash equilibrium that maximizes the social welfare is NP-hard by a polynomial time reduction from the NP-complete Restricted Exact Cover by 3-Sets problem. We then focus on the performance of Nash equilibria and provide matching upper bound and lower bounds on the price of anarchy of Θ(n), where n is the number of nodes of the underlying graph. Moreover, we show that there exists a class of SDGs having a lower bound on the price of stability of 6/5 − ε, for any ε > 0. Finally, we characterize the price of stability 5 of SDGs for graphs with girth 4 and girth at least 5, the girth being the length of the shortest cycle in the graph.

The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of differential privacy. \textit{Sensitivity} and \textit{privacy budget} are two key design aspects for the effectiveness of differential private models. Existing solutions for GBDT with differential privacy suffer from the significant accuracy loss due to too loose sensitivity bounds and ineffective privacy budget allocations (especially across different trees in the GBDT model). Loose sensitivity bounds lead to more noise to obtain a fixed privacy level. Ineffective privacy budget allocations worsen the accuracy loss especially when the number of trees is large. Therefore, we propose a new GBDT training algorithm that achieves tighter sensitivity bounds and more effective noise allocations. Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds. Furthermore, we design a novel boosting framework to allocate the privacy budget between trees so that the accuracy loss can be reduced. Our experiments show that our approach can achieve much better model accuracy than other baselines.

Gradient Boosting Decision Trees (GBDTs) have become very successful in recent years, with many awards in machine learning and data mining competitions. There have been several recent studies on how to train GBDTs in the federated learning setting. In this paper, we focus on horizontal federated learning, where data samples with the same features are distributed among multiple parties. However, existing studies are not efficient or effective enough for practical use. They suffer either from the inefficiency due to the usage of costly data transformations such as secure sharing and homomorphic encryption, or from the low model accuracy due to differential privacy designs. In this paper, we study a practical federated environment with relaxed privacy constraints. In this environment, a dishonest party might obtain some information about the other parties' data, but it is still impossible for the dishonest party to derive the actual raw data of other parties. Specifically, each party boosts a number of trees by exploiting similarity information based on locality-sensitive hashing. We prove that our framework is secure without exposing the original record to other parties, while the computation overhead in the training process is kept low. Our experimental studies show that, compared with normal training with the local data of each owner, our approach can significantly improve the predictive accuracy, and achieve comparable accuracy to the original GBDT with the data from all parties.