Detecting Persistent User Behavior Using Probabilistic Counting in Network-Wide View

Persistent user behavior monitoring, which deals with finding users that occur persistently over a measurement period, is one hot topic in traffic measurement. It is significant for many applications, such as anomaly detection. Former works concentrate on monitoring frequent user behavior, such as users occurring frequently either over one measurement period or on one monitor. They have paid little attention to detect persistent user behavior over a long measurement period on multiple monitors. However, persistent users do not necessarily appear frequently in a short measurement period, but appear persistently in a long measurement period. Due to limited resource on monitors, it is not practical to collect a tremendous amount of network traffic in a long measurement period on one single monitor. Moreover, since network attackers deliberately send packets flowing through the entire managed network, it is difficult to detect abnormal behavior on one single monitor. To solve the above challenges, a novel method for detecting persistent user behavior called DPU is proposed, and it contains both online distributed traffic collection in a long measurement period on multiple monitors and offline centralized user behavior detection on the central server. The key idea of DPU is that we design the compact distributed synopsis data structure to collect the relevant information with users occurring in a long measurement period on each monitor, and we can reconstruct user IDs using simple calculations and bit settings to find users with persistent behavior on the basis of estimated occurrence frequency of users on the central server when user IDs are unknown in advance. The experiments are conducted on real traffic to evaluate the performance of detecting persistent user behavior, and the experimental results illustrate that our method can improve about 30% estimation accuracy, 40% detection precision, and accelerate about 3 times in comparison with the related method.

Wearable, Multimodal, Biosignal Acquisition System for Potential Critical and Emergency Applications

For emergency or intensive-care units (ICUs), patients with unclear consciousness or unstable hemodynamics often require aggressive monitoring by multiple monitors. Complicated pipelines or lines increase the burden on patients and inconvenience for medical personnel. Currently, many commercial devices provide related functionalities. However, most devices measure only one biological signal, which can increase the budget for users and cause difficulty in remote integration. In this study, we develop a wearable device that integrates electrocardiography (ECG), electroencephalography (EEG), and blood oxygen machines for medical applications with the hope that it can be applied in the future. We develop an integrated multiple-biosignal recording system based on a modular design. The developed system monitors and records EEG, ECG, and peripheral oxygen saturation (SpO2) signals for health purposes simultaneously in a single setting. We use a logic level converter to connect the developed EEG module (BR8), ECG module, and SpO2 module to a microcontroller (Arduino). The modular data are then smoothly encoded and decoded through consistent overhead byte stuffing (COBS). This developed system has passed simulation tests and exhibited proper functioning of all modules and subsystems. In the future, the functionalities of the proposed system can be expanded with additional modules to support various emergency or ICU applications.

Does Using Multiple Computer Monitors for Office Tasks Affect User Experience?

Objective To compare the impact of multiple computer monitor configurations on health and performance outcomes compared to the use of a single monitor. Background Multiple monitor configurations are used in office settings to promote increased productivity by providing more screen space; however, it is unknown if there are health-related trade-offs to increased productivity. Method A systematic review was conducted according to the PRISMA statement guidelines and adapted the best evidence synthesis. Results Eighteen studies were included in our review. There was strong evidence that implementing dual monitors is in line with users’ preference. There was also moderate evidence for controlled laboratory studies demonstrating that multiple monitors may increase task efficiency with decreased desktop interaction; however, implementing multiple monitors may also result in nonneutral neck postures for users. Conclusion More research needs to be conducted on biomechanical exposures when using larger displays. Longitudinal field studies should be conducted to determine the influence of monitor interventions on health, productivity, and well-being. All studies must consider task complexity and user positioning and should measure health and productivity outcomes together. Researchers must also consider up-to-date purchasing trends when choosing the monitor configurations and sizes for their studies. Application Regulatory bodies and practitioners can use the results to develop evidence-based monitor guidelines and inform decision-making in practice, respectively. Researchers can use this information to design future studies on monitor configurations that incorporate current purchasing trends.

Implementation of a Data Acquisition and Integration Device in the Neurologic Intensive Care Unit

The neurologic intensive care unit has evolved into a data-rich, complex arena. Various neurologic monitors, collectively referred to as multimodality monitoring, provide clinicians with a plethora of real-time information about a comatose patient’s condition. The time and cognitive burden required to synthesize the available data and reach meaningful clinical conclusions can be overwhelming. The Moberg Component Neuromonitoring System (Moberg Research, Inc) is a data acquisition and integration device that collects data from multiple monitors, displaying them on a single screen in a way that highlights physiological trends throughout a patient’s clinical course. Implementation of the Moberg Component Neuromonitoring System in the neurologic intensive care unit can improve understanding of a patient’s neurophysiology, enhance clinical decision-making, and improve quality of care. Use of a staged process of implementation including exploration, installation, initial implementation, and full implementation can bring technology to the bedside in a sustainable fashion.

Look, Don’t Touch

With each new technology interface introduced in the environment, users spend more time switching between and managing these interfaces. When the interfaces involve screen-based displays and controls, eye movements may provide an intuitive and efficient means of switching between screens. This research focused on evaluating manual keyboard and gaze-based control methods for switching control between displays of a simulated surveillance system. Results showed that gaze-based tracking was faster and produced lower subjective workload than using a manual keyboard. Operators’ performance was also consistent with Keystroke-Level Model–Goals Operators Methods Selection predictions for each control method. These findings identify gaze-based control as a viable method for switching control between multiple monitors.

Design of Software Platform for Online BCI System

In the paper a software platform is developed for online BCI system based on motor imagery. The programming for multiple monitors is applied to display the notice information of performing imaginary movement. The hybrid programming of using Matlab engine under VC++ is implemented to make full use of the function of signal processing and pattern recognition of Matlab. Both offline EEG acquisition and analysis and online test can be realized with the software platform. And it has high universality and friendly interface.