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Firmware vulnerabilities raise serious security concerns with the rapid growth in connected embedded devices. Fuzzing is an effective dynamic testing technique to find those vulnerabilities; however, firmware fuzzing is very limited by hardware dependence, such as on-chip and off-chip peripherals. The latest elegant approaches are making substantial progress in hardware-independent firmware fuzzing, but there is room for further improvement. We observe that hardware-independent peripheral modeling is scalable but slow at the register level; in contrast, at the abstract function level, it is fast but has limited scalability. Firmware fuzzing is still challenging in terms of achieving both scalability and efficiency. To address this problem, we present a dual-level approach that leverages register level modeling and selective function level modeling in a hybrid manner. Our method starts firmware fuzzing at the register level and connects peripheral handlers while executing hardware abstraction layer functions. We evaluate our method in terms of efficiency, scalability, and effectiveness with four real-world firmware and demonstrate the possibility of relatively fast and scalable firmware fuzzing that combines the benefits of the two levels.

Background: Bedridden patients and disabled persons need help from a guardian or caregiver even in the activities of daily living. In particular, for transfer of them, the use of a transfer lift has been recommended. However, the existing devices have limitations in terms of support according to the individual’s characteristics. This study aimed to develop a transfer lift device utilizing the user’s body fitting sling equipped with an air tube inside the sling. Results: Customized sling lifts are generally floor-based lifts that consist of a sling that holds the user’s body and a lift that moves the sling to the desired position. One characteristic feature of the device is an air tube, which is used on the sling to allow the sling to be adjusted to the user’s body. Conclusion: The device will be useful in the advancement and commercialization of customized sling lifts, to ensure the safe and efficient transfer of persons with disabilities.

BACKGROUND We investigated the effects of the upper-body flexibility exercises on the golf performance of a female amateur golfer. CASE REPORT The participant was a 43-year-old woman who performed a general golf swing exercise (30 min) and an upper-body flexibility exercise (20 min) 3 times a week, for a total of 6 times in 2 weeks. The maximum rotation angle of the upper body was measured using a goniometer. To measure the X-factor, the numerical value was measured after subtracting the rotation angle of the lower-body from the rotation angle of the upper body when the participant stopped making a back-swing top motion. A camera measuring instrument was used to measure the clubhead speed and carry distance of the golf ball when she hit the ball with a no. 7 iron club. After the exercises, the maximum rotation angle of the participant's upper body increased from 40° to 69°, and the X-factor increased from 10° to 24°. The clubhead speed increased from 29.4 m/s to 34.4 m/s, and the carry distance increased from 84 m to 106 m. CONCLUSIONS The participant responded positively to the upper-body flexibility exercises, and there was improved upper-body mobility, X-factor, clubhead speed, and carry distance. Our results showed that upper-body flexibility exercises with a general golf swing exercise for female amateur golfers may improve golf performance.

Objective To investigate the relationship between sitting balance, trunk control, and mobility, as well as whether the sitting balance and trunk control can predict mobility level in sub-acute stroke survivors. Methods This is a observational and cross-sectional study. Fifty-five hemiplegic stroke survivors were participated in this study. The Timed Up and Go Test (TUG) was used to estimate mobility, and the Sitting Balance Scale (SBS) was used to examining sitting balance. The Trunk Impairment Scale (TIS), Trunk Control Test (TCT), and Postural Assessment Scale for Stroke-trunk control (PASS-TC) were used for examining the trunk control. Spearman’s correlation was used to analyze the relationship between TUG, SBS, TIS, TCT, and PASS-TC. Results The TUG is significantly correlated with SBS (r = -0.78), TIS (r = -0.76), TCT (r = -0.65), and PASS-TC (r = -0.67). In addition, the receiver operation characteristic (ROC) curve showed as cut-off value of SBS as >28.5, TIS > 16.5, TCT >82, and PASS-TC >10.5. The area under the ROC curve in each of the four tests is moderately accurate for predicting the mobility of sub-acute stroke survivors (0.84 ~0.90) (0.7 < AUC ≤ 9 (moderate informative)). Implications The SBS showed the highest correlation for mobility using TUG in the hemiplegic stroke survivors. Also, SBS was revealed as the most dominant examination tool predicting the mobility by TUG, it can be explained the sitting postural balance is the variable predicting the mobility in survivors of sub-acute stroke.

FPGAs are field-programmable and reconfigurable integrated circuits; consequently, they entail numerous security concerns. For example, malicious functions such as hardware Trojans (HTs), can be inserted into the circuits in both development and deployment stages, as malicious fabrication and modification are possible even after deployment. Therefore, to detect HTs in FPGAs effectively, it is necessary to exploit both netlists available at the development stage and bitstreams available at deployment stage; this is in contrast with existing approaches, which require source code or gate-level netlists. In achieving this, we encounter two major challenges: effectively exploiting FPGA netlists closer to bitstreams for HTs detection and reverse-engineering bitstreams to netlists at an acceptable level. To address these problems, we develop a bidirectional mechanism for detecting HTs in FPGAs at any stage. To the best of our knowledge, this is the first study on bidirectional HT detection in FPGAs. To address the first challenge, we focus on LUT-level netlists; regarding the second challenge, we directly reverse-engineer bitstreams to LUT-level netlists. For HTs detection, we employ features extracted from LUT-level netlists, which can also be derived from reversed bitstreams and used to identify HTs. We design and implement our system for experimental studies. The experiments achieve a TPR of more than 99.3% and an FPR of less than 0.15% for 15 TrustHub benchmarks in forward and backward (reverse) directions for FPGA Virtex-5 devices.