In this paper, we propose a hybrid ensemble polynomial neural network (HEPNN) with the aid of polynomial neural network (PNN) and hybrid ensemble polynomials neurons (HEPNs). Two types of HEPNs including ensemble radial-based-function polynomial neuron (ERPN) and ensemble polynomial neuron (EPN) are proposed. ERPN and EPN are generalized polynomial neurons based on ensemble architecture. To address the problem of multiple covariance in traditional PNN neural networks, correlation coefficients and performance are utilized to select neurons replacing the original selection of nodes by performance only. The main strategies of HEPNN design are as follows: First, the first layer of the network consists of ERPN that are utilized to reflect the structure encountered between the data, while the second and higher layers consists of EPN, which reflect higher polynomial-order relationships between input and output data. Second, particle swarm optimization (PSO) is adopted to optimize the architecture of HEPNN. A comparative study shows the proposed HEPNN has better performance than other state-of-art models reported in literature.

Privacy issue is a main concern restricting data sharing and cross-organization collaborations. While Privacy-Preserving Machine Learning techniques such as Multi-Party Computations (MPC), Homomorphic Encryption, and Federated Learning are proposed to solve this problem, no solution exists with both strong security and high performance to run large-scale, complex machine learning models. This paper presents PPMLAC, a novel chipset architecture to accelerate MPC, which combines MPC's strong security and hardware's high performance, eliminates the communication bottleneck from MPC, and achieves several orders of magnitudes speed up over software-based MPC. It is carefully designed to only rely on a minimum set of simple hardware components in the trusted domain, thus is robust against side-channel attacks and malicious adversaries. Our FPGA prototype can run mainstream large-scale ML models like ResNet in near real-time under a practical network environment with non-negligible latency, which is impossible for existing MPC solutions.

This paper demonstrates how ideas from generative programming and software synthesis can help support the development of bulk-synchronous distributed memory kernels. These ideas are realized in a new language called MSL, a C-like language that combines synthesis features with high level notations for array manipulation and bulk-synchronous parallelism to simplify the semantic analysis required for synthesis. The paper shows that by leveraging these high level notations, it is possible to scale the synthesis and automated bug-finding technologies that underlie MSL to realistic computational kernels. Specifically, we demonstrate the methodology through case studies implementing non-trivial distributed kernels -- both regular and irregular -- from the NAS parallel benchmarks. We show that our approach can automatically infer many challenging details from these benchmarks and can enable high level implementation ideas to be reused between similar kernels. We also demonstrate that these high level notations map easily to low level C code and show that the performance of this generated code matches that of handwritten Fortran.

Software construction today often involves the use of large frameworks. The challenge in this type of programming is that object-oriented frameworks tend to grow exceedingly intricate; they spread functionality among numerous classes, and any use of the framework requires knowledge of many interacting components. We present a system named MATCHMAKER that from a simple query synthesizes code that interacts with the framework. The query consists of names of two framework classes, and our system produces code enabling interaction between them. MATCHMAKER relies on a database of dynamic program traces called DELIGHT that uses novel abstraction-based indexing techniques to answer queries about the evolution of heap connectivity in a matter of seconds.

A replay tool aiming to reproduce a program's execution interposes itself at an appropriate replay interface between the program and the environment. During recording, it logs all non-deterministic side effects passing through the interface from the environment and feeds them back during replay. The replay interface is critical for correctness and recording overhead of replay tools.

Soft hang is an action that was expected to respond instantly but instead drives an application into a coma. While the application usually responds eventually, users cannot issue other requests while waiting. Such hang problems are widespread in productivity tools such as desktop applications; similar issues arise in server programs as well. Hang problems arise because the software contains blocking or time-consuming operations in graphical user interface (GUI) and other time-critical call paths that should not.

Region-based memory management is a popular scheme in systems software for better organization and performance. In the scheme, a developer constructs a hierarchy of regions of different lifetimes and allocates objects in regions. When the developer deletes a region, the runtime will recursively delete all its subregions and simultaneously reclaim objects in the regions. The developer must construct a consistent placement of objects in regions; otherwise, if a region that contains pointers to other regions is not always deleted before pointees, an inconsistency will surface and cause dangling pointers, which may lead to either crashes or leaks.