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Modular exponentiation is a basic operation in cryptosystems. Generally, the performance of this operation has a tremendous impact on the efficiency of the whole application. The efficiency of the modular exponentiation, in turn, depends mainly on that of modular multiplications as the former is somehow a repetition of the latter. One of the methods that computes the modular power is the sliding-window method, which pre-processes the exponent into zero and nonzero partitions. Zero partitions allow for a reduction of the number of modular multiplications required in the exponentiation process. In this paper, we devise a novel System-on-Chip (SoC) implementation for computing modular exponentiation using the sliding-window method. We also propose a hardware-only implementation for that operation. The partitioning strategy used in both approaches allows constant-length nonzero partitions, which increases the average number of zero partitions and so decreases that of nonzero partitions. The partitioning strategy allows variable-length zero partitions. The hardware/software co-design implements the modular multiplication on hardware and the rest of the system in software. We provide a useful comparison of the SoC-based implementation against hardware-only implementation. Both of the proposed implementations can be used in any industrial embedded system that needs to secure the handled information.

Involving users in new product design and development is in itself not a new phenomenon. Academic research on the subject, however, is relatively new. Since this new millennium research on user involvement in innovations has multiplied, exhibiting that firms who actively involve users in their innovation process can benefit from this initiative, even though literature also warns us for relying too much and too actively on the participation of customers in innovation. However, research is mainly descriptive and explanatory of nature, and is seldom a prescription for firm managers on what to do should they decide to involve their customers in an innovation initiative.

This paper addresses this omission in literature and tries to give some first building blocks for a protocol for firms that want to involve their customers in innovations. This protocol will be the result of the author's further research on literature, previously executed projects by the author, expert interviews and a design and development process, in continuation of this paper.

An increasing number of corporations engage with users in co-innovation of products and services. But there are a number of competing perspectives on how best to integrate these understandings into existing corporate innovation development processes. This paper maps out three of the dominant approaches, compares them in terms of goals, methods and basic philosophy, and shows how they may beneficially enrich one another. We will present an industrial innovation case that has been instrumental to the development of what we have termed "Participatory Innovation". Based on this, we will list the challenges such an approach sets to innovation management, and discuss research directions of what we see as fundamental to the development of the field of user-driven innovation.

Researchers in the learning sciences have explored a collaborative approach to developing innovations that fit into real classroom contexts. The co-design process relies on teachers' ongoing involvement with the design of educational innovations, which typically employ technology as a critical support for practice. To date, investigators have described the application and results of co-design, but they have not defined the process nor explored how it plays out over time. In this paper, we define co-design as a highly-facilitated, team-based process in which teachers, researchers, and developers work together in defined roles to design an educational innovation, realize the design in one or more prototypes, and evaluate each prototype's significance for addressing a concrete educational need. We suggest seven key process components and use data from a systematic set of interviews to illustrate the roles of teachers and researchers in co-design and describe how tensions in the process can unfold and be resolved over time.

The combination of informal learning and mobile outdoor games can be seen as a relevant arena for conducting novel learning activities that involve children in different tasks including physical motion, problem solving, inquiry and collaboration. These are activities that support different cognitive and social aspects of learning. Co-design and human centric design practices have been the focus of current research efforts in the field of educational technologies but not as prevalent in mobile games to support learning. In our current research we are exploring which design methods are appropriate for developing innovative ways of learning supported by mobile games. This paper presents all those aspects related to the design and implementation of a mobile game called Skattjakt (Treasure Hunt in Swedish). The outcome of our activities has provided us with valuable results that can help additionally to integrate outside informal learning with more formal classroom activities. Moreover, we believe that involving children in the design process of mobile games may give us new insights regarding the nature of their learning practices while learning with games.

3D point cloud deep learning has received significant attention due to its wide applications, such as augmented/mixed reality, autonomous vehicles/drones, and robots. Despite its remarkable accuracy, the computational cost and sparse nature of 3D point cloud deep learning models greatly hinder their use in real-world, latency-sensitive applications. In this paper, we develop efficient algorithms, systems, and hardware for 3D deep learning to overcome the computational challenges (especially sparsity) of 3D point cloud data, making it more applicable in a broader range of real-world scenarios. From the algorithm perspective, we introduce novel 3D building blocks (PVCNN and SPVNAS^∗, https://pvnas.mit.edu) that are tailor-made for point cloud data to reduce sparse and irregular overheads. On the system level, we develop a high-performance library (TorchSparse^∗, https://torchsparse.mit.edu) that can handle sparse and irregular workloads on general-purpose hardware, which is typically optimized for dense and regular workloads. Furthermore, we develop a specialized hardware accelerator (PointAcc^∗, https://pointacc.mit.edu) that can support various types of point cloud deep learning primitives and mitigate memory bottlenecks for sparse point cloud computing.

Microstructured optical fibres have increased degrees of flexibility that allow tailoring of optical properties for various applications. Three different algorithms for calculating mode properties and structural or confinement loss in these fibres are compared. A number of biologically-inspired approaches to optimising the fibre design (hole size, shape and arrangement) within the context and constraints of a particular fabrication technology are presented.