Smart wristbands and watches have become an important accessory to fitness, but their application to healthcare is still in a fledgling state. Their long-term wear facilitates extensive data collection and evolving sensitivity of smart wristbands allows them to read various body vitals. In this paper, we hypothesized the use of heart rate variability (HRV) measurements to drive an algorithm that can pre-empt the onset or worsening of an affliction. Due to its significance during the time of the study, SARS-Cov-2 was taken as the case study, and a hidden Markov model (HMM) was trained over its observed symptoms. The data used for the analysis was the outcome of a study hosted by Welltory. It involved the collection of SAR-Cov-2 symptoms and reading of body vitals using Apple Watch, Fitbit, and Garmin smart bands. The internal states of the HMM were made up of the absence and presence of a consistent decline in standard deviation of NN intervals (SSDN), the root mean square of the successive differences (rMSSD) in R-R intervals, and low frequency (LF), high frequency (HF), and very low frequency (VLF) components of the HRV measurements. The emission probabilities of the trained HMM instance confirmed that the onset or worsening of the symptoms had a higher probability if the HRV components displayed a consistent decline state. The results were further confirmed through the generation of probable hidden states sequences using the Viterbi algorithm. The ability to pre-empt the exigent state of an affliction would not only lower the chances of complications and mortality but may also help in curbing its spread through intelligence-backed decisions.
We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has become universal, including at sites of cultural heritage. ARTYCUL used the video streams of closed-circuit televisions (CCTV) cameras installed in such premises to detect human figures, and their coordinates with respect to the camera frames were used to visualize the density of visitors around the specific display items. Such a framework that can display the popularity of artifacts would aid the curators towards a more optimal organization. Moreover, it could also help to gauge if a certain display item were neglected due to incorrect placement. While items of similar interest can be placed in vicinity of each other, an online recommendation system may also use the reputation of an artifact to catch the eye of the visitors. Artificial intelligence-based solutions are well suited for analysis of internet of things (IoT) traffic due to the inherent veracity and volatile nature of the transmissions. The work done for the development of ARTYCUL provided a deeper insight into the avenues for applications of IoT technology to the cultural heritage domain, and suitability of ML to process real-time data at a fast pace. While we also observed common issues that hinder the utilization of IoT in the cultural domain, the proposed framework was designed keeping in mind the same obstacles and a preference for backward compatibility.
The internet of things has brought in innovations in the daily lives of users. The enthusiasm and openness of consumers have fuelled the manufacturers to dish out new devices with more features and better aesthetics. In an attempt to keep up with the competition, the manufacturers are not paying enough attention to cyber security of these smart devices. The gravity of security vulnerabilities is further aggravated due to their connected nature. As a result, a compromised device would not only stop providing the intended service but could also act as a host for malware introduced by an attacker. This study has focused on 10 manufacturers, namely Fitbit, D-Link, Edimax, Ednet, Homematic, Smarter, Osram, Belkin Wemo, Philips Hue, and Withings. The authors studied the security issues which have been raised in the past and the communication protocols used by devices made by these brands. It was found that while security vulnerabilities could be introduced due to lack of attention to details while designing an IoT device, they could also get introduced by the protocol stack and inadequate system configuration. Researchers have iterated that protocols like TCP, UDP, and mDNS have inherent security shortcomings and manufacturers need to be mindful of the fact. Furthermore, if protocols like EAPOL or Zigbee have been used, then the device developers need to be aware of safeguarding the keys and other authentication mechanisms. The authors also analysed the packets captured during setup of 23 devices by the above-mentioned manufacturers. The analysis gave insight into the underlying protocol stack preferred by the manufacturers. In addition, they also used count vectorizer to tokenize the protocols used during device setup and use them to model a multinomial classifier to identify the manufacturers. The intent of this experiment was to determine if a manufacturer could be identified based on the tokenized protocols. The modelled classifier could then be used to drive an algorithm to checklist against possible security vulnerabilities, which are characteristic of the protocols and the manufacturer history. Such an automated system will be instrumental in regular diagnostics of a smart system. The authors then wrapped up this report by suggesting some measures a user can take to protect their local networks and connected devices.
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