Long-term unemployment (LTU) is a challenge for both jobseekers and public employment services. Statistical profiling tools are increasingly used to predict LTU risk. Some profiling tools are opaque, black-box machine learning models, which raise issues of transparency and fairness. This paper investigates whether interpretable models could serve as an alternative, using administrative data from Switzerland. Traditional statistical, interpretable, and black-box models are compared in terms of predictive performance, interpretability, and fairness. It is shown that explainable boosting machines, a recent interpretable model, perform nearly as well as the best black-box models. It is also shown how model sparsity, feature smoothing, and fairness mitigation can enhance transparency and fairness with only minor losses in performance. These findings suggest that interpretable profiling provides an accountable and trustworthy alternative to black-box models without compromising performance.

The injection molding process is a traditional technique for making products in various industries such as electronics and automobiles via solidifying liquid resin into certain molds. Although the process is not related to creating the main part of engines or semiconductors, this manufacturing methodology sets the final form of the products. Re-cently, research has continued to reduce the defect rate of the injection molding process. This study proposes an optimal injection molding process control system to reduce the defect rate of injection molding products with XAI (eXplainable Artificial Intelligence) ap-proaches. Boosting algorithms (XGBoost and LightGBM) are used as tree-based classifiers for predicting whether each product is normal or defective. The main features to control the process for improving the product are extracted by SHapley Additive exPlanations, while the individual conditional expectation analyzes the optimal control range of these extracted features. To validate the methodology presented in this work, the actual injection molding AI manufacturing dataset provided by KAMP (Korea AI Manufacturing Platform) is employed for the case study. The results reveal that the defect rate decreases from 1.00% (Original defect rate) to 0.21% with XGBoost and 0.13% with LightGBM, respectively.

Underwater acoustic target recognition has emerged as a prominent research area within the field of underwater acoustics. However, the current availability of authentic underwater acoustic signal recordings remains limited, which hinders data-driven acoustic recognition models from learning robust patterns of targets from a limited set of intricate underwater signals, thereby compromising their stability in practical applications. To overcome these limitations, this study proposes a recognition framework called M3 (Multi-task, Multi-gate, Multi-expert) to enhance the model's ability to capture robust patterns by making it aware of the inherent properties of targets. In this framework, an auxiliary task that focuses on target properties, such as estimating target size, is designed. The auxiliary task then shares parameters with the recognition task to realize multi-task learning. This paradigm allows the model to concentrate on shared information across tasks and identify robust patterns of targets in a regularized manner, thereby enhancing the model's generalization ability. Moreover, M3 incorporates multi-expert and multi-gate mechanisms, allowing for the allocation of distinct parameter spaces to various underwater signals. This enables the model to process intricate signal patterns in a fine-grained and differentiated manner. To evaluate the effectiveness of M3, extensive experiments were implemented on the ShipsEar underwater ship-radiated noise dataset. The results substantiate that M3 has the ability to outperform the most advanced single-task recognition models, thereby achieving the state-of-the-art performance.

Tabular data are omnipresent in various sectors of industries. Neural networks for tabular data such as TabNet have been proposed to make predictions while leveraging the attention mechanism for interpretability. However, the inferred attention masks are often dense, making it challenging to come up with rationales about the predictive signal. To remedy this, we propose InterpreTabNet, a variant of the TabNet model that models the attention mechanism as a latent variable sampled from a Gumbel-Softmax distribution. This enables us to regularize the model to learn distinct concepts in the attention masks via a KL Divergence regularizer. It prevents overlapping feature selection by promoting sparsity which maximizes the model's efficacy and improves interpretability to determine the important features when predicting the outcome. To assist in the interpretation of feature interdependencies from our model, we employ a large language model (GPT-4) and use prompt engineering to map from the learned feature mask onto natural language text describing the learned signal. Through comprehensive experiments on real-world datasets, we demonstrate that InterpreTabNet outperforms previous methods for interpreting tabular data while attaining competitive accuracy.

Federated Transfer Learning (FTL) is the most general variation of Federated Learning. According to this distributed paradigm, a feature learning pre-step is commonly carried out by only one party, typically the server, on publicly shared data. After that, the Federated Learning phase takes place to train a classifier collaboratively using the learned feature extractor. Each involved client contributes by locally training only the classification layers on a private training set. The peculiarity of an FTL scenario makes it hard to understand whether poisoning attacks can be developed to craft an effective backdoor. State-of-the-art attack strategies assume the possibility of shifting the model attention toward relevant features introduced by a forged trigger injected in the input data by some untrusted clients. Of course, this is not feasible in FTL, as the learned features are fixed once the server performs the pre-training step. Consequently, in this paper, we investigate this intriguing Federated Learning scenario to identify and exploit a vulnerability obtained by combining eXplainable AI (XAI) and dataset distillation. In particular, the proposed attack can be carried out by one of the clients during the Federated Learning phase of FTL by identifying the optimal local for the trigger through XAI and encapsulating compressed information of the backdoor class. Due to its behavior, we refer to our approach as a focused backdoor approach (FB-FTL for short) and test its performance by explicitly referencing an image classification scenario. With an average 80% attack success rate, obtained results show the effectiveness of our attack also against existing defenses for Federated Learning.

Video content is a must have for businesses and content creators wanting to compete in this highly visual environment. Reports have shown that more than 80% of online traffic is video traffic, and an increasing amount of people prefer it over other forms of online content like text and images. Most online publishers rely on social networks to reach audiences, and video content provides more organic reach than other types. At the same time, it has traditionally been both time-consuming and costly to produce and disseminate video content. Artificial intelligence (AI) is changing this outlook, making it easier than ever to generate video.

SEO (Search Engine Optimization) requires a multifaceted strategy that includes researching competition, analyzing what keywords are capable of driving traffic, creating an external and internal link building strategy, and optimizing page loading speed. Below we feature the best SEO tools to increase your odds of ranking high in Google. This powerful SEO platform offers a range of tools that replaces the functionality of other products that includes Google Trends, MOZ, Hootsuite and SimilarWeb. Traffic Analysis – Benchmark your website traffic against competitors to see where you stand. See their estimated total traffic, top traffic sources, bounce rate, time on page, and more to inform your next strategy.

The AI presenter speaking in the video, which can be a deep fake, a 3D avatar, a real-life avatar, or even a customized avatar, can get its script through speech or text. This synthetic app has advanced customization features that make video creation very straightforward.

Artificial intelligence (AI) is not only affecting industries like business and healthcare. It is also playing an increasing role in the creative industries by ushering in a new era of AI-generated art. AI technologies and tools are often widely accessible to anyone, which is helping to create an entirely new generation of artists. We often hear that AI is going to automate away or take over all human tasks, including those in art, film, and other creative industries. But this is far from the case. AI is a supplemental tool that artists can use to explore new creative territory.

Chatbots are specialized computer programs that can interact with customers through audio or text. Through the use of artificial intelligence (AI), chatbots can simulate humans, and the best technologies are often indistinguishable from their human counterparts. An increasing number of companies are implementing AI chatbots into their business processes, and they are helping better market products to customers. They are incredibly valuable to nearly every business, but especially those looking to guide customers and keep them engaged. On top of all of this, chatbots can create personality for a brand and lead to more personalized experiences for customers. Chatbots are expected to become a $1 billion market within the next few years, and the majority of businesses will use them in some way.