Imagine a building that tells you – before it happens – that the heating is about to fail. Some companies are using machine learning to do just that. Software firm CGnal, based in Milan, Italy, recently analysed a year's worth of data from the heating and ventilation units in an Italian hospital. Sensors are now commonly built into heating, ventilation and air conditioning units, and the team had records such as temperature, humidity and electricity use, relating to appliances in operating theatres and first aid rooms as well as corridors. They trained a machine learning algorithm on data from the first half of 2015, looking for differences in the readings of similar appliances.

Statistical characteristics of network traffic have attracted a significant amount of research for automated network intrusion detection, some of which looked at applications of natural statistical laws such as Zipf's law, Benford's law and the Pareto distribution. In this paper, we present the application of Benford's law to a new network flow metric "flow size difference", which have not been studied before by other researchers, to build an unsupervised flow-based intrusion detection system (IDS). The method was inspired by our observation on a large number of TCP flow datasets where normal flows tend to follow Benford's law closely but malicious flows tend to deviate significantly from it. The proposed IDS is unsupervised, so it can be easily deployed without any training. It has two simple operational parameters with a clear semantic meaning, allowing the IDS operator to set and adapt their values intuitively to adjust the overall performance of the IDS. We tested the proposed IDS on two (one closed and one public) datasets, and proved its efficiency in terms of AUC (area under the ROC curve). Our work showed the "flow size difference" has a great potential to improve the performance of any flow-based network IDSs.

Several of the preprocessing steps necessary for indexing as discussed in Chapter 2: detecting a document's encoding (ASCII, Unicode UTF-8 etc; page 2.1.1);

In rare disease physician targeting, a major challenge is how to identify physicians who are treating diagnosed or underdiagnosed rare diseases patients. Rare diseases have extremely low incidence rate. For a specified rare disease, only a small number of patients are affected and a fractional of physicians are involved. The existing targeting methodologies, such as segmentation and profiling, are developed under mass market assumption. They are not suitable for rare disease market where the target classes are extremely imbalanced. The authors propose a graphical model approach to predict targets by jointly modeling physician and patient features from different data spaces and utilizing the extra relational information. Through an empirical example with medical claim and prescription data, the proposed approach demonstrates better accuracy in finding target physicians. The graph representation also provides visual interpretability of relationship among physicians and patients. The model can be extended to incorporate more complex dependency structures. This article contributes to the literature of exploring the benefit of utilizing relational dependencies among entities in healthcare industry.

About a decade ago, spam brought email to near-ruin. The contest to save your inbox was on, with two of the world's biggest tech companies vying for the title of top spam-killer. By February 2012, Microsoft boasted that its spam filters were removing all but 3 percent of the junk messages from Hotmail, the company's online email service at the time. Google responded by claiming that its service, Gmail, removed all but about one percent of spam messages, adding that its false positives rate--legitimate mail misidentified as spam--was also about one percent. It was a point of pride for the two companies, particularly Microsoft, whose Hotmail service once carried such a poor reputation for spam. And the relative success of both showed that heuristic technologies--which identify spam based on a pre-defined rules--were working.

Police may soon have a new way to catch pedophiles who distribute child abuse photos anonymously online. The technology could also help law enforcement agencies in other ways, such as identifying smartphone thieves who take pictures with the stolen gadgets and then post their snapshots on the Internet. Riccardo Satta, scientific project officer of the European Commission Joint Research Center's Institute for the Protection and Security of the Citizen, described the work he did with fellow researcher Pasquale Stirparo at the Computers, Privacy and Data Protection Conference in Brussels held in January.* The key is the ability to spot a unique, unremovable pattern--or signature--that each digital camera imprints on photographs. By comparing the signature from a specific camera with those found in images posted to social media, a forensic investigator would be able to establish that all the images had been taken by the same camera.

The test is still experimental, unavailable to the public outside of clinical trials. Its developers say it needs further study in many more women to determine whether the early findings hold up. If it does come to market, it will not be for several years, and its use might initially be limited to women at high risk. Dr. Emanuel F. Petricoin, who helped create the test, said, ''I'm all too aware as an F.D.A. scientist of promising early results that start to fail as you go into the real world.'' Ovarian cancer is not common, but it is often deadly.

A high school junior has created a computer brain that can diagnose breast cancer with 99 percent sensitivity. Seventeen-year-old Brittany Wenger of Sarasota, Fla., wrote a breast cancer-diagnosing app based on an artificial neural network, basically a computer program whose structure is inspired by the way brain cells connect with one another. She won grand prize at the Google Science Fair for her invention in ceremony held in Palo Alto, Calif. Like other artificial intelligence programs, artificial neural networks "learn" what to do by analyzing examples they're given and they perform better if they get more examples. In addition, they're able to detect patterns in data that are too complex for human brains or other types of programs to analyze.

We revisit the notion of probably approximately correct implication bases from the literature and present a first formulation in the language of formal concept analysis, with the goal to investigate whether such bases represent a suitable substitute for exact implication bases in practical use-cases. To this end, we quantitatively examine the behavior of probably approximately correct implication bases on artificial and real-world data sets and compare their precision and recall with respect to their corresponding exact implication bases. Using a small example, we also provide qualitative insight that implications from probably approximately correct bases can still represent meaningful knowledge from a given data set.

In recent years, Artificial Intelligence (AI) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line. In short, big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models. Some appear almost unreasonable, like AI that can own itself -- AI DAOs. All of them are opportunities. This article will explore these applications. Before we discuss applications, let's first review what's different about blockchains compared to traditional big-data distributed databases like MongoDB. We can think of blockchains as "blue ocean" databases: they escape the "bloody red ocean" of sharks competing in an existing market, opting instead to be in a blue ocean of uncontested market space. Famous blue ocean examples are Wii for video game consoles (compromise raw performance, but have new mode of interaction), or Yellow Tail for wines (ignore the pretentious specs for wine lovers; make wine more accessible to beer lovers). By traditional database standards, traditional blockchains like Bitcoin are terrible: low throughput, low capacity, high latency, poor query support, and so on.