However, the existing approaches often require users to walk on a specific road at a normal walking speed to improve recognition accuracy. User authentication and verification by gait data based on smartphones’ inertial sensors has gradually attracted increasing attention due to their compact size, portability and affordability. Finally, we conclude with four future research opportunities. Besides, we summarized the artificial intelligence solutions for the physical security and safety of IoT equipment. Afterward, we discuss, among other aspects, anti-theft and anti-vandalism schemes along with circuit and system design, additional sensing devices, biometry and behavior analysis, and tracking methods. Therefore, this paper provides an overview of IoT equipment's physical security and safety to draw attention to new research opportunities in this area. However, an important aspect that is often overlooked in security literature is IoT equipment's physical security and safety, namely, preventing IoT equipment from vandalism and theft. Cyber-security and privacy countermeasures are widely used in IoT equipment, and many studies have been conducted. The connectivity and intelligence of IoT equipment offer improved services, but several technical challenges have emerged in recent years that hinder the widespread application of IoT, e.g., security and safety. We evaluate the proposed method using the open dataset of Nokia Mobile Data Challenge, and experimental results show the effectiveness of the proposed method in personalized location recommendation. To efficiently reduce model parameters, factorization machines are employed to construct the recommendation model, which models feature interactions as the inner products of latent vectors with matrix factorization. Demographic features and location features are also extracted. In this paper, we propose a personalized location recommendation method using mobile phone usage information, which transforms the location recommendation problem into a regression task, and extracts six types of mobile phone usage features to profile users. In addition, abundant mobile phone usage information can be recorded when users are using their phones, e.g., the use frequency of Apps, which can fully reveal the diverse characteristics of different users. However, such little information is not sufficient to profile users accurately. To address this problem, the existing studies usually exploit other information, e.g., demographic features, to characterize users. The cold-start problem is still a great challenge in personalized location recommendation, which makes it difficult to infer a new user’s preferences, because a new user generally has never visited any location at the start. To customize these headphones, developers can unlock smart experiences with the Everest Elite 750NC by downloading the CES 2017 Innovation award winning JBL EVEREST ELITE SDK currently available at Dare to listen.Location recommendation has become a hot research area in recent years. The My JBL Headphones App features include over-the-air updates that future-proof these headphones, and TruNote™ Auto Sound Calibration that personalizes the audio performance based on ear cup fit, delivering the most authentic version of music possible. Meticulously crafted from premium materials and conceived for a snug fit and long-lasting comfort, these sleek and elegant headphones come in metallic finishes and colors. A 3-hour quick recharge, an echo cancelling microphone for hands-free calls, Legendary JBL Pro Audio Sound, compact hard carrying case and flat-fold design elevate these headphones to travel companion par excellence. In Adaptive Noise Cancelling (ANC) mode, control what you want to hear for up to 15 hours. Smartly engineered, enjoy wireless freedom with up to 20 hours of listening pleasure on a single charge. Your life, your music, your comfort – the all-new JBL Everest Elite 750NC is truly shaped around you.
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