PenSLR: Persian end-to-end Sign Language Recognition Using Ensembling

Sign Language Recognition (SLR) is a fast-growing field that aims to fill the communication gaps between the hearing-impaired and people without hearing loss. Existing solutions for Persian Sign Language (PSL) are limited to word-level interpretations, underscoring the need for more advanced and comprehensive solutions. Moreover, previous work on other languages mainly focuses on manipulating the neural network architectures or hardware configurations instead of benefiting from the aggregated results of multiple models. In this paper, we introduce PenSLR, a glove-based sign language system consisting of an Inertial Measurement Unit (IMU) and five flexible sensors powered by a deep learning framework capable of predicting variable-length sequences. We achieve this in an end-to-end manner by leveraging the Connectionist Temporal Classification (CTC) loss function, eliminating the need for segmentation of input signals. To further enhance its capabilities, we propose a novel ensembling technique by leveraging a multiple sequence alignment algorithm known as Star Alignment. Furthermore, we introduce a new PSL dataset, including 16 PSL signs with more than 3000 time-series samples in total. We utilize this dataset to evaluate the performance of our system based on four word-level and sentence-level metrics. Our evaluations show that PenSLR achieves a remarkable word accuracy of 94.58% and 96.70% in subject-independent and subject-dependent setups, respectively. These achievements are attributable to our ensembling algorithm, which not only boosts the word-level performance by 0.51% and 1.32% in the respective scenarios but also yields significant enhancements of 1.46% and 4.00%, respectively, in sentence-level accuracy.

Deep Reinforcement Learning for Online Error Detection in Cyber-Physical Systems

Reliability is one of the major design criteria in Cyber-Physical Systems (CPSs). This is because of the existence of some critical applications in CPSs and their failure is catastrophic. Therefore, employing strong error detection and correction mechanisms in CPSs is inevitable. CPSs are composed of a variety of units, including sensors, networks, and microcontrollers. Each of these units is probable to be in a faulty state at any time and the occurred fault can result in erroneous output. The fault may cause the units of CPS to malfunction and eventually crash. Traditional fault-tolerant approaches include redundancy time, hardware, information, and/or software. However, these approaches impose significant overheads besides their low error coverage, which limits their applicability. In addition, the interval between error occurrence and detection is too long in these approaches. In this paper, based on Deep Reinforcement Learning (DRL), a new error detection approach is proposed that not only detects errors with high accuracy but also can perform error detection at the moment due to very low inference time. The proposed approach can categorize different types of errors from normal data and predict whether the system will fail. The evaluation results illustrate that the proposed approach has improved more than 2x in terms of accuracy and more than 5x in terms of inference time compared to other approaches.

A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management

The process of continuously reallocating funds into financial assets, aiming to increase the expected return of investment and minimizing the risk, is known as portfolio management. Processing speed and energy consumption of portfolio management have become crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces and environment uncertainty, which their limited onboard resources cannot offset. Emerging neuromorphic chips inspired by the human brain increase processing speed by up to 1000 times and reduce power consumption by several orders of magnitude. This paper proposes a spiking deep reinforcement learning (SDRL) algorithm that can predict financial markets based on unpredictable environments and achieve the defined portfolio management goal of profitability and risk reduction. This algorithm is optimized forIntel's Loihi neuromorphic processor and provides 186x and 516x energy consumption reduction is observed compared to the competitors, respectively. In addition, a 1.3x and 2.0x speed-up over the high-end processors and GPUs, respectively. The evaluations are performed on cryptocurrency market between 2016 and 2021 the benchmark.