Make sure, of course, to always gamble smart [Hack #35], regardless of what you play, though when it comes to the level of risk you take, some games [Hacks #39 and #40] are better than others [Hack #41].
HACK Smart Draw-----FROM---BRAVO-6---------------
Although this chapter is full of hacks aimed at particular games, many of them games of chance, there are a variety of tips and tools that are useful across the board for all gamblers. Much mystery, superstition, and mathematical confusion pervade the world of gambling, and knowing a little more about the geography of this world should help you get around. This hack shows how to gamble smarter by teaching you about the following things:
The basic strategies described earlier in this hack assume that you have no idea what cards still remain in the deck. They assume that the original distribution of cards still remains for a single deck, or six decks, or whatever number of decks is used in a particular game. The moment any cards have been dealt, however, the actual odds change, and, if you know the new odds, you might choose different options for how you play your hand.
Armed with all the wisdom you likely now have as a statistician (unless this is the first hack you turned to in this book), you might have already made a few interesting observations about this payoff schedule.
This hack presents some tips and tools for moving from novice to semi-pro. These are some simple hunks of knowledge and quick rules of thumb for making decisions. Like the other poker hacks in this book, they provide strategy tips based purely on statistical probabilities, which assume a random distribution of cards in a standard 52-card deck.
As you can see in Table 4-18, using wild cards lessens the chance of getting two pair. But why would this be? Surely adding a wild card means that sometimes I can turn a one-pair hand into a two-pair hand. This is true, but why would I? Imagine a player has one pair in her hand, and she gets a wild card as her fifth card. Yes, she could match that wild card up with a singleton and call it a pair, declaring a hand with two pairs. On the other hand, it would be smarter for her to match it up with the pair she already has and declare three of a kind. Given the option between two pair and three of a kind, everyone would choose the stronger hand.
With revised rankings, three of a kind would be worth less than two pair, so now smart players would use their wild card to make two pair instead of three of kind, so two pair would quickly become more common than three of a kind.
Smart devices play a main role in IoE [7]. They are equipped with multicommunication interfaces, such as Wi-Fi, Bluetooth, near-field communication (NFC), and cellular communication. In addition, they are equipped with a massive number of sensors. Moreover, they have embedded operating systems (OSs) that are referred to as IoT OSs [3]. When smartphones are mentioned in this survey, we are referring to smartphones, tablets, and smartwatches since they have the same characteristics with few industrial differences. According to the statistics reported by Statista ( -of-smartphone-usersworldwide), the number of smartphones worldwide exceeded 2.8 billion with an estimation of 5 billion in 2019. Smartphones have been employed heavily in controlling and monitoring the process of hundreds of smart home products. For example, WeMo (Belkin Wemo: home automation, -automation/c/wemo-home-automation/) product allows the users to control multiple features in their houses, such as power usage of different appliances. This product is controlled by smartphones. Another example is Apple HomeKit ( ) for security and surveillance systems. A third example is Reemo ( ) that converts houses into smart homes. Smartphones play a monitoring and controlling role in these applications. However, smartphone capabilities and sensors allow them to play a greater role in health, identification, localization, and tracking.
The rest of this paper is organized as follows: Section 2 overviews smart device architecture and their internal components. Section 3 shows how data mining and IoE collide in the area of smart devices. Section 4 shows the useful applications of hidden data extraction. Section 5 shows the disadvantages of extracting sensor hidden data and the methods to start digging the smart device hidden data. Finally, we conclude this work in Section 6.
This processor is a hardware isolated component that has connections with subscriber identity module (SIM) cards, microphone, and speakers. It is responsible for the cellular communication, SMS, and data over the cellular network. It is equipped with real-time operating systems (RTOS). This processor is isolated to allow voice calls to continue in a normal way even if the other components and applications of the smartphone are overloaded. Finally, this processor is responsible for the handoff process between cellular network cells. It is worth mentioning that all of these processors may be designed in the SoC method to allow shared memory access.
Hidden data research has shown that the data sensed from these sensors can be utilized and interpreted to show other information as in the following sections. Moreover, Section 3 shows how the communication and networking parts equipped in smart devices can be leveraged as hidden data-harvesting sensors. This leads to the categorization of smart device sensors according to their functionalities into active and passive sensors. Any sensor may act as an active or a passive sensor according to its usage. In other words, if the data harvested from a sensor is leveraged in the same way as the smart device designers or developers designed it, it is called an active functionality. However, if the collected data has been interpreted in new ways, these sensors are functioning in a passive way. If the sensors are leveraged in this way, hidden information problem occurs. In the following sections, different smart device sensors are introduced.
(1) Heat Maps. One of the new data visualization methods of multitouching or gesture on a smartphone screen is known as heat maps [13]. Developers have developed multiple methods to generate these maps [14]. Figure 2 shows an example of these maps.
These maps as mentioned earlier are used for data visualization purposes. Many smartphone applications have been written to utilize these maps to debug written applications and study user behaviors when debugging application issues, such as Appsee [15]. Moreover, many works have been conducted to study touch gesture utilizing touch maps for health diagnoses, such as Down syndrome [16], perceived difficulty [17], and issues in fine motor skills and eyes [18].
(2) Touchscreen as A Passive Sensor. All the examples that we will show utilize the touchscreen in an active way: touching speed, delay, typing time, and gestures. However, researchers found another method to obtain useful data from the touchscreen that can be utilized with other smartphone sensors to study sleeping behaviors of the users by counting how many times the touchscreen opens and closes [19]. Moreover, it can be utilized with the alarm application to study how fast users respond to wake-up alarms [20].
Three main sensors are embedded in modern smart devices for motion detection: accelerometer, gyroscope, and magnetometer. The accelerometer detects changes in the device displacement, orientation, and tilt around three axes by measuring acceleration forces. Its operational theory depends on the value changes of capacitance while a movable mass freely moves between the fixed plates in the MEMS. The total voltage changes from all plates can be recorded and utilized. Figure 3 shows the simple 2D structure of the accelerometer.
Two main multimedia sensors are embedded in smart devices: camera and fingerprint and microphone. In the following sections, the camera image acquisition process and fingerprint sensors are introduced.
(1) Camera. Shooting a photo with a smart device camera passes five different complex stages. The process starts by collecting the light through the camera lens and focusing the light on the internal filter. Subsequently, the output RGB colors are passed to the main camera sensor, the CCD/CMOS sensor. In this stage, each color is manipulated as separated components. To view the last image, color interpolation and image postprocessing step are required. Each one of these stages leaves a fingerprint on the obtained image. This glitch may be utilized to track any photo back to the camera that took it as shown in the following sections. Figure 5 shows the image harvesting pipelining procedure of a smartphone camera.
There are other sensors embedded in smart devices, such as proximity and battery temperature sensor. However, few applications associated with these sensors are found in the literature. The battery temperature sensor has been used in health applications to tackle death situations when the body temperature drops rapidly [43]. For the proximity sensors, to the best of our knowledge, no applications or research has been conducted to infer different information from its harvested data.
As mentioned, the problem is not in harvesting the data itself. The real problem is how to connect the data from different sensors to focus on another hidden meaning. The mining process is not also an issue; machine learning algorithms are useful in finding models for the required focused information [44]. This process is like hacking a system. Information is harvested from active and passive probing, such as the sensor data. Subsequently, mining is leveraged to find errors, breaches, and bugs in the system. Finally, algorithms are written to exploit the system. The hard step in the data mining for the big data system is to connect the inputs. In other words, extract useful features from the data and to find information of the harvested data.
Machine learning algorithms (MLAs), supervised and unsupervised, are used heavily in different well-known applications, such as spam filtering, expert systems, and friend suggestions in a social network. Many programming libraries in all programming languages have been written to allow the implementation of MLA in few lines. This allows researchers to focus on the developed application and the interpretation of the data. Figure 8 shows the most popular MLA utilized in the conducted smart device sensor hidden data extraction works. As shown in the figure, the number of these algorithms is massive and they cannot be introduced in one paper. However, three main algorithms will be introduced in the next sections: random forest, support vector machine (SVM), and artificial neural network (ANN). These algorithms have been selected since they have been leveraged in more than 70% of the conducted research surveyed in this paper. 2ff7e9595c
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