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As multiprocessor (or multicore) real-time systems become popular, there has been much research on multiprocessor real-time scheduling algorithms. This work evaluates EDZL (Earliest Deadline until Zero Laxity), a scheduling algorithm for real-time multiprocessor systems. First, we compare the performance of EDZL schedulability tests. We measure and compare the ratio of task sets admitted by each test. We also investigate the dominance between EDZL schedulability tests and discover that the union of the demand-based test and the utilization-based test is an effective combination. Second, we compare the schedulability of EDZL and EDF(k). We prove that the union of the EDZL schedulability tests dominates the EDF(k) schedulability test, i.e., the union of the EDZL schedulability tests can admit all task sets admitted by the EDF(k) schedulability test. We also compare the schedulability of EDZL and EDF(k) through scheduling simulation by measuring the ratio of successfully scheduled task sets. EDZL can successfully schedule 7.0% more task sets than EDF(k).

Vehicle digital forensics includes the process of collecting and analysing digital data stored in vehicles to find evidence related to traffic accidents or crime scenes. Through this process, we can reconstruct digital events by recognizing various information such as driver behaviour, driving patterns, vehicle destinations, and smartphones connected to a vehicle. Recently, many vehicle digital forensic studies have been conducted, but few of them have dealt with Android-based infotainment (or audio video navigation, AVN) systems. While many AVN systems adopt Android as the operating system, digital forensics of Android-based AVN systems is not easy. This is because Android-based AVN systems support various storage devices and data formats and have various data sources, making consistent data collection or analysis difficult. In this article, we perform digital forensics on four AVN systems: two Android 4.2.2 Jellybean-based and two Android 4.4.2 KitKat-based, aiming to develop a data acquisition and analysis method appropriate for each Android version. As a data collection method for the AVN system forensics, logical data acquisition is performed on the Jellybean-based systems and physical data acquisition on the KitKat-based systems. For the collected data, we identify and analyse Bluetooth data, navigation data, and system logs individually. Next, we investigate the differences in the storage structure and file location of major digital artefacts depending on the Android versions and show the limitations of the individual data analysis. Finally, we construct a timeline for the driver’s activities by integrating and analysing the diverse artefacts which consist of Bluetooth data, navigation data, and system logs.

Teleoperation, in which humans and robots work together to improve work performance, is growing explosively. However, the work performance of teleoperation is not yet excellent. Master–slave systems with different kinematics and workspaces need space-transformation control techniques. These techniques cause psychological fatigue to an operator with poor manipulation skills. In this study, we propose an intuitive master design that focuses on fatigue. Large workspaces reduce mental fatigue; however, they lead to physical fatigue problems. To solve this problem, we reflect the role of actuators in the design, through functional separation using movement and gravity compensation. This study proposes the design and prototype fabrication of an intuitive master K-handler to improve remote-work performance. The K-handler features six degrees of freedom (DoF), an anthropomorphic structure, and a lightweight nature. It has a reach long enough to cover the workspace of the human arm to reduce mental fatigue. In addition, gravity compensation, which can reduce the operator’s physical fatigue during operation, is possible in all workspace areas.

Conventional assembly methods using robots need to change end-effectors or operate two robot arms for assembly. In this study, we propose a screwdriving gripper that can perform the tasks required for the assembly using a single robot arm. The proposed screwdriving gripper mimics a human-two-handed operation and has three features: (1) it performs pick-and-place, peg-in-hole, and screwdriving tasks required for assembly with a single gripper; (2) it uses a flexible link that complies with the contact force in the environment; and (3) it employs the same joints as the pronation and supination of the wrist, which help the manipulator to create a path. We propose a new gripper with 3 fingers and 12 degrees of freedom to implement these features; this gripper is composed of grasping and screwdriving parts. The grasping part has two fingers with a roll-yaw-pitch-pitch joint configuration. Its pitch joint implements wrist pronation and supination. The screwdriving part includes one finger with a roll-pitch-pitch joint configuration and a flexible link that can comply with the environment; this facilitates compliance based on the direction of the external force. The end of the screwdriving finger has a motor with a hex key attached, and an insert tip is attached to the back of the motor. A prototype of the proposed screwdriving gripper is manufactured, and a strategy for assembly using a prototype is proposed. The features of the proposed gripper are verified through screwdriving task experiments using a cooperative robotic arm. The experiments showed that the screwdriving gripper can perform tasks required for the assembly such as pick and place, peg-in-hole, and screwdriving.

Malware family classification is grouping malware samples that have the same or similar characteristics into the same family. It plays a crucial role in understanding notable malicious patterns and recovering from malware infections. Although many machine learning approaches have been devised for this problem, there are still several open questions including, “Which features, classifiers, and evaluation metrics are better for malware familial classification”? In this paper, we propose a machine learning approach to Android malware family classification using built-in and custom permissions. Each Android app must declare proper permissions to access restricted resources or to perform restricted actions. Permission declaration is an efficient and obfuscation-resilient feature for malware analysis. We developed a malware family classification technique using permissions and conducted extensive experiments with several classifiers on a well-known dataset, DREBIN. We then evaluated the classifiers in terms of four metrics: macrolevel F1-score, accuracy, balanced accuracy (BAC), and the Matthews correlation coefficient (MCC). BAC and the MCC are known to be appropriate for evaluating imbalanced data classification. Our experimental results showed that: (i) custom permissions had a positive impact on classification performance; (ii) even when the same classifier and the same feature information were used, there was a difference up to 3.67% between accuracy and BAC; (iii) LightGBM and AdaBoost performed better than other classifiers we considered.

We present the first provably-secure three-party password-only authenticated key exchange (PAKE) protocol that can run in only two communication rounds. Our protocol is generic in the sense that it can be constructed from any two-party PAKE protocol. The protocol is proven secure in a variant of the widely-accepted model of Bellare, Pointcheval and Rogaway (2000) without any idealized assumptions on the cryptographic primitives used. We also investigate the security of the two-round, three-party PAKE protocol of Wang, Hu and Li (2010) and demonstrate that this protocol cannot achieve implicit key authentication in the presence of an active adversary.