The rapid urbanization of cities and increasing vehicular congestion have posed significant challenges to traffic management and safety. This study explores the transformative potential of artificial intelligence (AI) and machine vision technologies in revolutionizing traffic systems. By leveraging advanced surveillance cameras and deep learning algorithms, this research proposes a system for real-time detection of vehicles, traffic anomalies, and driver behaviors. The system integrates geospatial and weather data to adapt dynamically to environmental conditions, ensuring robust performance in diverse scenarios. Using YOLOv8 and YOLOv11 models, the study achieves high accuracy in vehicle detection and anomaly recognition, optimizing traffic flow and enhancing road safety. These findings contribute to the development of intelligent traffic management solutions and align with the vision of creating smart cities with sustainable and efficient urban infrastructure.

Kurdish, an Indo-European language spoken by over 30 million speakers, is considered a dialect continuum and known for its diversity in language varieties. Previous studies addressing language and speech technology for Kurdish handle it in a monolithic way as a macro-language, resulting in disparities for dialects and varieties for which there are few resources and tools available. In this paper, we take a step towards developing resources for language and speech technology for varieties of Central Kurdish, creating a corpus by transcribing movies and TV series as an alternative to fieldwork. Additionally, we report the performance of machine translation, automatic speech recognition, and language identification as downstream tasks evaluated on Central Kurdish varieties. Data and models are publicly available under an open license at https://github.com/sinaahmadi/CORDI.

Road safety is a crucial issue that affects not only the individuals involved in road accidents, but also society as a whole. The cost of road accidents in terms of human lives lost, physical and emotional suffering, and financial losses is enormous. Thus, it is important to understand the factors that contribute to road accidents and to develop strategies to reduce the number and severity of these incidents. One of the most important steps in this process is the identification of "black spots," areas where the number of accidents is significantly higher compared to other parts of the road network. The identification of black spots is crucial for prioritizing road safety interventions and evaluating their effectiveness in reducing the number of accidents. These events can range from minor incidents, such as fender benders, to serious crashes, resulting in fatalities or severe injuries. Thus, identifying these areas provides insights into the underlying causes of these accidents. For example, black spot analysis can reveal the presence of road design or infrastructure issues that may contribute to accidents, such as poor lighting, confusing road signs, and a lack of pedestrian crossings.

What even is intelligence exactly? It's a very hard thing to define. Typically, attempts to define it start with, "the ability to…" and then devolve into overly complicated language that really aims to obscure the fact that we don't know how to end the sentence. The best attempts keep it simple with something like, "the ability to solve mental tasks, in the abstract." What constitutes "intelligence" is dictated by goals arising from one's environment.

In this paper, we introduce the first large vocabulary speech recognition system (LVSR) for the Central Kurdish language, named Jira. The Kurdish language is an Indo-European language spoken by more than 30 million people in several countries, but due to the lack of speech and text resources, there is no speech recognition system for this language. To fill this gap, we introduce the first speech corpus and pronunciation lexicon for the Kurdish language. Regarding speech corpus, we designed a sentence collection in which the ratio of di-phones in the collection resembles the real data of the Central Kurdish language. The designed sentences are uttered by 576 speakers in a controlled environment with noise-free microphones (called AsoSoft Speech-Office) and in Telegram social network environment using mobile phones (denoted as AsoSoft Speech-Crowdsourcing), resulted in 43.68 hours of speech. Besides, a test set including 11 different document topics is designed and recorded in two corresponding speech conditions (i.e., Office and Crowdsourcing). Furthermore, a 60K pronunciation lexicon is prepared in this research in which we faced several challenges and proposed solutions for them. The Kurdish language has several dialects and sub-dialects that results in many lexical variations. Our methods for script standardization of lexical variations and automatic pronunciation of the lexicon tokens are presented in detail. To setup the recognition engine, we used the Kaldi toolkit. A statistical tri-gram language model that is extracted from the AsoSoft text corpus is used in the system. Several standard recipes including HMM-based models (i.e., mono, tri1, tr2, tri2, tri3), SGMM, and DNN methods are used to generate the acoustic model. These methods are trained with AsoSoft Speech-Office and AsoSoft Speech-Crowdsourcing and a combination of them. The best performance achieved by the SGMM acoustic model which results in 13.9% of the average word error rate (on different document topics) and 4.9% for the general topic.

In a quiet corner of rural Hampshire, a robot called Rachel is pootling around an overgrown field. With bright orange casing and a smartphone clipped to her back end, she looks like a cross between an expensive toy and the kind of rover used on space missions. Up close, she has four USB ports, a disc-like GPS receiver, and the nuts and bolts of a system called Lidar, which enables her to orient herself using laser beams. She cost around £2,000 to make. Every three seconds, Rachel takes a closeup photograph of the plants and soil around her, which will build into a forensic map of the field and the wider farm beyond. After 20 minutes or so of this, she is momentarily disturbed by two of the farm's dogs, unsure what to make of her.

Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points. Finite mixture models, Dirichlet process mixture models, and Pitman--Yor process mixture models make this assumption, as do all other infinitely exchangeable clustering models. However, for some applications, this assumption is inappropriate. For example, when performing entity resolution, the size of each cluster should be unrelated to the size of the data set, and each cluster should contain a negligible fraction of the total number of data points. These applications require models that yield clusters whose sizes grow sublinearly with the size of the data set. We address this requirement by defining the microclustering property and introducing a new class of models that can exhibit this property. We compare models within this class to two commonly used clustering models using four entity-resolution data sets.