Effective Emoticon Suggestion Technique Based on Active Emotional Input Using Facial Expressions and Heart Rate Signals.

The evolution of mobile communication technology has brought about significant changes in the way people communicate. However, the lack of nonverbal cues in computer-mediated communication can make the accurate interpretation of emotions difficult. This study proposes a novel approach for using emotions as active input in mobile systems. This approach combines psychological and neuroscientific principles to accurately and comprehensively assess an individual's emotions for use as input in mobile systems. The proposed technique combines facial and heart rate information to recognize users' five prime emotions, which can be implemented on mobile devices using a front camera and a heart rate sensor. A user evaluation was conducted to verify the efficacy and feasibility of the proposed technique, and the results showed that users could express emotions faster and more accurately, with average recognition accuracies of 90% and 82% for induced and intended emotional expression, respectively. The proposed technique has the potential to enhance the user experience and provide more personalized and dynamic interaction with mobile systems.

BackProx: Secure Backscatter-Assisted Proximity Detection for Passive Keyless Entry and Start Systems.

A passive keyless entry and start (PKES) system is an electronic lock for an automobile that provides the great convenience of opening the door when the user is in proximity. However, the system suffers from relay attacks. Recent studies revealed that relayed signals result in valid packets that are sufficient to unlock doors. In particular, the adversary causes proximity errors by injecting a certain time delay before relaying to manipulate the phase rotation in the response signal. To this end, we present a novel relay-resilient proximity detection solution, BackProx, which uses pseudo-random frequency hopping with the assistance of a reference backscattering device. Since the relay adversary transmits the relayed signals from the key fob at long distances, the signals should propagate over longer distances, resulting in inevitable significant phase rotation with different frequencies. Inspired by this finding, BackProx uses an additional backscattering device to ensure the proximity of the key fob using the invariant characteristics of radio frequency signals in the physical layer (i.e., phase rotation). Our evaluation demonstrates the effectiveness of BackProx in resisting three types of relay attacks. The results show that it achieved a 98% true positive rate at close range and a 0.3% false positive rate at long range.

Efficient Integrity-Tree Structure for Convolutional Neural Networks through Frequent Counter Overflow Prevention in Secure Memories.

Advancements in convolutional neural network (CNN) have resulted in remarkable success in various computing fields. However, the need to protect data against external security attacks has become increasingly important because inference process in CNNs exploit sensitive data. Secure Memory is a hardware-based protection technique that can protect the sensitive data of CNNs. However, naively applying secure memory to a CNN application causes significant performance and energy overhead. Furthermore, ensuring secure memory becomes more difficult in environments that require area efficiency and low-power execution, such as the Internet of Things (IoT). In this paper, we investigated memory access patterns for CNN workloads and analyzed their effects on secure memory performance. According to our observations, most CNN workloads intensively write to narrow memory regions, which can cause a considerable number of counter overflows. On average, 87.6% of total writes occur in 6.8% of the allocated memory space; in the extreme case, 93.9% of total writes occur in 1.4% of the allocated memory space. Based on our observations, we propose an efficient integrity-tree structure called Countermark-tree that is suitable for CNN workloads. The proposed technique reduces overall energy consumption by 48%, shows a performance improvement of 11.2% compared to VAULT-128, and requires a similar integrity-tree size to VAULT-64, a state-of-the-art technique.