Effective storytelling relies on a delicate balance between meeting the reader's prior expectations and introducing unexpected developments. In the domain of detective fiction, this tension is known as fair play, which includes the implicit agreement between the writer and the reader as to the range of possible resolutions the mystery story may have. In this work, we present a probabilistic framework for detective fiction that allows us to define desired qualities. Using this framework, we formally define fair play and design appropriate metrics for it. Stemming from these definitions is an inherent tension between the coherence of the story, which measures how much it ``makes sense'', and the surprise it induces. We validate the framework by applying it to LLM-generated detective stories. This domain is appealing since we have an abundance of data, we can sample from the distribution generating the story, and the story-writing capabilities of LLMs are interesting in their own right. Results show that while LLM-generated stories may be unpredictable, they generally fail to balance the trade-off between surprise and fair play, which greatly contributes to their poor quality.

Disjointness checks are among the most important constraint checks in a knowledge base and can be used to help detect and correct incorrect statements and internal contradictions. Wikidata is a very large, community-managed knowledge base. Because of both its size and construction, Wikidata contains many incorrect statements and internal contradictions. We analyze the current modeling of disjointness on Wikidata, identify patterns that cause these disjointness violations and categorize them. We use SPARQL queries to identify each ``culprit'' causing a disjointness violation and lay out formulas to identify and fix conflicting information. We finally discuss how disjointness information could be better modeled and expanded in Wikidata in the future.

We consider pure-exploration problems in the context of stochastic sequential adaptive experiments with a finite set of alternative options. The goal of the decision-maker is to accurately answer a query question regarding the alternatives with high confidence with minimal measurement efforts. A typical query question is to identify the alternative with the best performance, leading to ranking and selection problems, or best-arm identification in the machine learning literature. We focus on the fixed-precision setting and derive a sufficient condition for optimality in terms of a notion of strong convergence to the optimal allocation of samples. Using dual variables, we characterize the necessary and sufficient conditions for an allocation to be optimal. The use of dual variables allow us to bypass the combinatorial structure of the optimality conditions that relies solely on primal variables. Remarkably, these optimality conditions enable an extension of top-two algorithm design principle, initially proposed for best-arm identification. Furthermore, our optimality conditions give rise to a straightforward yet efficient selection rule, termed information-directed selection, which adaptively picks from a candidate set based on information gain of the candidates. We outline the broad contexts where our algorithmic approach can be implemented. We establish that, paired with information-directed selection, top-two Thompson sampling is (asymptotically) optimal for Gaussian best-arm identification, solving a glaring open problem in the pure exploration literature. Our algorithm is optimal for $\epsilon$-best-arm identification and thresholding bandit problems. Our analysis also leads to a general principle to guide adaptations of Thompson sampling for pure-exploration problems. Numerical experiments highlight the exceptional efficiency of our proposed algorithms relative to existing ones.

By using Facial Recognition with the combination of Azure Cloud Services or Amazon Web Services etc, if a police officer is wearing a helmet with a webcam and scanning a crime scene, he can identify suspects who have a past criminal history. It would need immense computing power but that would compensate through the use of cloud servers and connectivity on the go wherever the officers are. The biggest advantage of this would be to identify any suspects based on past criminal activity and detain them for questioning. Another advantage of Facial Recognition would be to identify the suspect through police offer's inquiry. If the suspect is not carrying his ID and tells the police officer a fake name, the wearable headset or glasses worn by the police officer would inform him about the person's actual identity.

Major advancements are being made in the field of Facial Recognition but the technology is still in the early stages of development. Right now, it is possible to accurately identify persons from a series of images provided if we have a small database from which the algorithm has to search from but if the database goes extensive with images of many persons, the time for the person to be identified can exceed by a huge margin. And in practical scenarios, we need results in a matter of seconds or so. Therefore it is not convenient to deploy this tech right now but it still presents a promising future in the area of crime fighting and tracking of fugitives. By using Facial Recognition with the combination of Azure Cloud Services or Amazon Web Services etc, if a police officer is wearing a helmet with a webcam and scanning a crime scene, it is possible for him to identify suspects who have a past criminal history.

An artificial intelligence (AI)-trained facial recognition system (FRS) has been installed at the Puratchi Thalaivar Dr. MGR Central railway station for detecting known culprits passing through the gates and alerting authorities. "For the first time, we have introduced the CCTV camera device backed by artificial intelligence. In the existing system, we capture the picture and video of any suspect. But we have to manually analyse the footage to detect their movement. The new system will automatically alert us about known culprits," said a senior police officer of the Government Railway Police (GRP).

The drone attack that brought Gatwick airport to a standstill last December could have been an "inside job", according to police, who said the perpetrator may have been operating the drone from within the airport. Sussex police told BBC Panorama that the fact an insider may have been behind the attack was "treated as a credible line of enquiry from the earliest stages of the police response". Gatwick's chief operating officer, Chris Woodroofe, believes the perpetrator was familiar with the airport's operational procedures and had a clear view of the runway or possibly infiltrated its communication network. "It was clear that the drone operators had a link into what was going on at the airport," he told Panorama, in his first interview since the incident. He said the culprit had carefully picked a drone that would remain undetected by the airport's DJI Aeroscope detection system being tested at the time.

This paper introduces DATA Agent, a system which creates murder mystery adventures from open data. In the game, the player takes on the role of a detective tasked with finding the culprit of a murder. All characters, places, and items in DATA Agent games are generated using open data as source content. The paper discusses the general game design and user interface of DATA Agent, and provides details on the generative algorithms which transform linked data into different game objects. Findings from a user study with 30 participants playing through two games of DATA Agent show that the game is easy and fun to play, and that the mysteries it generates are straightforward to solve.

I used to live in a building that had a communal kitchen for over 100 students. As you might imagine, there were almost always dishes that weren't washed in the sink. A group at my school pitched the idea to put up a Nest Cam to catch culprits and call them out on it using the Nest Cam feed. To illustrate my point, let's say you found dirty dishes at 12 pm, and you hadn't been in the kitchen for a day. Think about the way that you would search for the person who left the dishes.

This paper presents a framework for generating adventure games from open data. Focusing on the murder mystery type of adventure games, the generator is able to transform open data from Wikipedia articles, OpenStreetMap and images from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves around the murder of a person with a Wikipedia article and populates the game with suspects who must be arrested by the player if guilty of the murder or absolved if innocent. Starting from only one person as the victim, an extensive generative pipeline finds suspects, their alibis, and paths connecting them from open data, transforms open data into cities, buildings, non-player characters, locks and keys and dialog options. The paper describes in detail each generative step, provides a specific playthrough of one WikiMystery where Albert Einstein is murdered, and evaluates the outcomes of games generated for the 100 most influential people of the 20th century.