Code Partitioning Research Articles

Research on Decomposition and Offloading Strategies for Complex Divisible Computing Tasks in Computing Power Networks

With the continuous emergence of intelligent network applications and complex tasks for mobile terminals, the traditional single computing model often fails to meet the greater requirements of computing and network technology, thus promoting the formation of a new computing power network architecture, of ‘cloud, edge and end’ three-level heterogeneous computing. For complex divisible computing tasks in the network, task decomposition and offloading help to realize a distributed execution of tasks, thus reducing the overall running time and improving the utilization of fragmented resources in the network. However, in the process of task decomposition and offloading, there are problems, such as there only being a single method of task decomposition; that too large or too small decomposition granularity will lead to an increase in transmission delay; and the pursuit of low-delay and low-energy offloading requirements. Based on this, a complex divisible computing task decomposition and offloading scheme is proposed. Firstly, the computational task is decomposed into multiple task elements based on code partitioning, and then a density-peak-clustering algorithm with an improved adaptive truncation distance and clustering center (ATDCC-DPC) is proposed to cluster the task elements into subtasks based on the task elements themselves and the dependencies between the task elements. Secondly, taking the subtasks as the offloading objects, the improved Double Deep Q-Network subtask offloading algorithm (ISO-DDQN) is proposed to find the optimal offloading scheme that minimizes the delay and energy consumption. Finally, the proposed algorithms are verified by simulation experiments, and the scheme in this paper can effectively reduce the task delay and energy consumption and improve the service experience.

Przestępstwo czynu nierządnego z nieletnim w kodeksach karnych państw zaborczych i kodeksie karnym z 1932 roku. Przyczynek do badań

The crime of pedophilia, as a separate type of prohibited act, appeared in Poland together with the criminal codes of the partitioning countries. These provisions were in many cases very casuistic, providing a different standard of protection for minors. The Criminal Code of 1932 introduced a uniform standard of protection through the provision of Article 203 penalizing the act of fornication with a minor. The author discusses not only the provisions of the partitioning codes and the Polish Criminal Code of 1932 relating to the crime of pedophilia, but also points to the conflict of these provisions with the provisions of the marriage law in force in Poland, which in some cases allowed children under 15 years of age to marry.

Multi-Modality Deep Restoration of Extremely Compressed Face Videos.

Arguably the most common and salient object in daily video communications is the talking head, as encountered in social media, virtual classrooms, teleconferences, news broadcasting, talk shows, etc. When communication bandwidth is limited by network congestions or cost effectiveness, compression artifacts in talking head videos are inevitable. The resulting video quality degradation is highly visible and objectionable due to high acuity of human visual system to faces. To solve this problem, we develop a multi-modality deep convolutional neural network method for restoring face videos that are aggressively compressed. The main innovation is a new DCNN architecture that incorporates known priors of multiple modalities: the video-synchronized speech signal and semantic elements of the compression code stream, including motion vectors, code partition map and quantization parameters. These priors strongly correlate with the latent video and hence they are able to enhance the capability of deep learning to remove compression artifacts. Ample empirical evidences are presented to validate the superior performance of the proposed DCNN method on face videos over the existing state-of-the-art methods.

The Extent of Heavy Metal Pollution by Chemical Partitioning and Risk Assessment Code of Sediments of Sewage-Fed Fishery Ponds at East Kolkata Wetland, a Ramsar Site, India

Sewage contaminated with toxic metals is being naturally treated in ponds at East Kolkata Wetlands (EKW) for fish cultivation traditionally. From the aquatic phase, these metals get settled on sediment and undergo partitioning. Some fractions are available and may accumulate in the cultivated products. The present study highlights the degree of partition of heavy metals Lead (Pb), Cadmium (Cd) and Chromium (Cr) in these pond sediments. The study reveals that Pb and Cr are significant (39% and 30%, respectively) in Fe-Mn bound fractions. Residual fraction is in the order of Pb > Cr > Cd. The easily available fraction of metals recorded higher in Cd (43.0%) and Cr (42.0%).The risk assessment code (RAC) analysis records that these metals could be considered as potential risk group of metals for higher mobility and availability in these pond ecosystems and is a significant concern for aquaculture products.

HTPC: heterogeneous traffic-aware partition coding for random packet spraying in data center networks

Modern data center networks typically adopt multi-rooted tree topologies such leaf-spine and fat-tree to provide high bisection bandwidth. Load balancing is critical to achieve low latency and high throughput. Although the per-packet schemes such as Random Packet Spraying (RPS) can achieve high network utilization and near-optimal tail latency in symmetric topologies, they are prone to cause significant packet reordering and degrade the network performance. Moreover, some coding-based schemes are proposed to alleviate the problem of packet reordering and loss. Unfortunately, these schemes ignore the traffic characteristics of data center network and cannot achieve good network performance. In this paper, we propose a Heterogeneous Traffic-aware Partition Coding named HTPC to eliminate the impact of packet reordering and improve the performance of short and long flows. HTPC smoothly adjusts the number of redundant packets based on the multi-path congestion information and the traffic characteristics so that the tailing probability of short flows and the timeout probability of long flows can be reduced. Through a series of large-scale NS2 simulations, we demonstrate that HTPC reduces average flow completion time by up to 60% compared with the state-of-the-art mechanisms.

Four-Dimensional Hurwitz Signal Constellations, Set Partitioning, Detection, and Multilevel Coding

The Hurwitz lattice provides the densest four-dimensional packing. This fact has motivated research on four-dimensional Hurwitz signal constellations for optical and wireless communications. This work presents a new algebraic construction of finite sets of Hurwitz integers that is inherently accompanied by a respective modulo operation. These signal constellations are investigated for transmission over the additive white Gaussian noise (AWGN) channel. It is shown that these signal constellations have a better constellation figure of merit and hence a better asymptotic performance over an AWGN channel when compared with conventional signal constellations with algebraic structure, e.g., two-dimensional Gaussian-integer constellations or four-dimensional Lipschitz-integer constellations. We introduce two concepts for set partitioning of the Hurwitz integers. The first method is useful to reduce the computational complexity of the symbol detection. This suboptimum detection approach achieves near-maximum-likelihood performance. In the second case, the partitioning exploits the algebraic structure of the Hurwitz signal constellations. We partition the Hurwitz integers into additive subgroups in a manner that the minimum Euclidean distance of each subgroup is larger than in the original set. This enables multilevel code constructions for the new signal constellations.

Modeling of thin sheet forming processes by combining solid-shell finite element with isotropic elastoviscoplastic model. Application to magnetic pulse forming processes

Sheet metal alloys are used in many industries to save material, reduce weight and improve the overall performance of products. For the last decades, many types of elements have been developed to resolve the locking problems encountered in the simulation of thin structures. Among these approaches, a family of assumed-strain solid-shell elements has proved to be very efficient and attractive in simulating thin 3D structures with various constitutive models. Furthermore, these elements are able to account for anisotropic behavior of thin structures since isotropic yield functions cannot capture the real physics of some forming processes. In this work, von Mises isotropic yield criterion with Johnson-cook hardening model are combined with a linear prismatic solid-shell element to simulate sheet metal forming processes. A new element assembly technique has been developed to permit the assembly of prismatic elements in a tetrahedral element-based software. This technique splits the prism into multiple tetrahedral elements in such a way that all the cross-terms are accounted for. Furthermore, a tetrahedral based partitioning code has been modified to account for the new prismatic element shape without changing the core structure of the code. More accurate results were obtained using low number of solid-shell elements compared to its counterpart tetrahedral element (MINI element). This reduction in the number of elements accelerated the simulation, especially in the coupled magnetic-structure simulation used for magnetic pulse forming process. The proposed element and criteria are implemented into FORGE (in-house code developed at CEMEF) for simulating magnetic pulse forming process.

Algorithm and VLSI Architecture Designs of a Lossless Embedded Compression Encoder for HD Video Coding Systems

The demand for visual quality has been advanced by high display resolutions and frame rates. Nevertheless, these two issues have caused tremendous memory bandwidth in a video coding system. In this study, an efficient lossless embedded compression (EC) algorithm is proposed to save memory bandwidth, while keeping visual quality. The proposed lossless EC algorithm incorporates three core techniques: tree partition, half-pixel prediction and group-based binary coding. Tree partition classifies a [Formula: see text] block into Trunk, Branch and Leaf. With tree partition, half-pixel prediction produces individual residues for Trunk, Branch and Leaf. Group-based binary coding converts theses residues to efficient codewords. The lossless compression ratio (CR) of the proposed EC is as high as 2.24 on average, saving memory bandwidth by 55.4%. This EC algorithm is implemented using CMOS 0.18[Formula: see text][Formula: see text]m technology. The maximum throughput can reach 6.4[Formula: see text]Gpixels/s, which can accommodate 3840 × 2160@60fps. The experiment results demonstrate that this study presents better hardware efficiency of 337[Formula: see text]Gpixels/J and 83.5[Formula: see text]Kpixels/s/gate.

Perceptual image hashing based on structural fractal features of image coding and ring partition

Perceptual image hashing finds increasing attention in several multimedia security applications. However, reaching the trade-off balance between the two most important properties of image hashing− robustness and discrimination, still remains the most restive challenge in hashing schemes. In this study, a robust image hashing technique is proposed by incorporating ring partition and fractal image coding. The scheme starts by normalizing the image to help in extracting its local features. Then the concept of ring partition is introduced in order to make our hash rotation invariant by dividing the image into 5 different rings to form a secondary image that possesses the invariant property. Further, image coding is introduced by extracting the structural fractal features to exploit dimensionality reduction and compression, hence, generating a robust hash. To ensure the system’s security, encryption is performed on the generated fractal elements before the final hash construction. We conduct series of experiments to evaluate the performance of our scheme. The achieved result shows that our scheme is robust against several content-preserving attacks such as image rotation, JPEG compression, gamma correction, gaussian low pass filtering, image scaling, cropping, brightness adjustment and contrast adjustment. In addition, the receiver operating characteristics is used to show the discriminative capability and robustness of our scheme as compared to other state-of-art schemes in the literature.

Towards Automatic High-Level Code Deployment on Reconfigurable Platforms: A Survey of High-Level Synthesis Tools and Toolchains

Heterogeneous computing systems with tightly coupled processors and reconfigurable logic blocks provide great scope to improve software performance by executing each section of code on the processor or custom hardware accelerator that best matches its requirements and the system optimisation goals. This article is motivated by the idea of a software tool that can automatically accomplish the task of deploying code, originally written for a conventional computer, to the processors and reconfigurable logic blocks in a heterogeneous system. We undertake an extensive survey of high-level synthesis tools to determine how close we are to this vision, and to identify any capability gaps. The survey is structured according to a new framework that clearly expresses the relationships between the many tools surveyed. We find that none of the existing tools can deploy general high-level code without manual intervention. Logic synthesis from arbitrary high-level code remains an open problem with dynamic data structures, function pointers and recursion all presenting challenges. Other challenges include automating the tasks of code partitioning, optimisation and design space exploration.

Detection of Repackaged Android Malware with Code-Heterogeneity Features

During repackaging, malware writers statically inject malcode and modify the control flow to ensure its execution. Repackaged malware is difficult to detect by existing classification techniques, partly because of their behavioral similarities to benign apps. By exploring the app's internal different behaviors, we propose a new Android repackaged malware detection technique based on code heterogeneity analysis . Our solution strategically partitions the code structure of an app into multiple dependence-based regions (subsets of the code). Each region is independently classified on its behavioral features. We point out the security challenges and design choices for partitioning code structures at the class and method level graphs, and present a solution based on multiple dependence relations. We have performed experimental evaluation with over 7,542 Android apps. For repackaged malware, our partition-based detection reduces false negatives (i.e., missed detection) by 30-fold, when compared to the non-partition-based approach. Overall, our approach achieves a false negative rate of 0.35 percent and a false positive rate of 2.97 percent.

High Quality Color Image Compression for Discrete Transform Domain Downward Conversion Block Based Image Coding

Text and image data are important elements for information processing almost in all the computer applications. Uncompressed image or text data require high transmission bandwidth and significant storage capacity. Designing and compression scheme is more critical with the recent growth of computer applications. Among the various spatial domain image compression techniques, multi-level Block partition Coding (ML-BTC) is one of the best methods which has the least computational complexity. The parameters such as Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are measured and it is found that the implemented methods of BTC are superior to the traditional BTC. This paves the way for a nearly error free and compressed transmission of the images through the communication channel.

Statistical mechanics of spike events underlying phase space partitioning and sequence codes in large-scale models of neural circuits.

Cortical circuits operate in an inhibition-dominated regime of spiking activity. Recently, it was found that spiking circuit models in this regime can, despite disordered connectivity and asynchronous irregular activity, exhibit a locally stable dynamics that may be used for neural computation. The lack of existing mathematical tools has precluded analytical insight into this phase. Here we present analytical methods tailored to the granularity of spike-based interactions for analyzing attractor geometry in high-dimensional spiking dynamics. We apply them to reveal the properties of the complex geometry of trajectories of population spiking activity in a canonical model of locally stable spiking dynamics. We find that attractor basin boundaries are the preimages of spike-time collision events involving connected neurons. These spike-based instabilities control the divergence rate of neighboring basins and have no equivalent in rate-based models. They are located according to the disordered connectivity at a random subset of edges in a hypercube representation of the phase space. Iterating backward these edges using the stable dynamics induces a partition refinement on this space that converges to the attractor basins. We formulate a statistical theory of the locations of such events relative to attracting trajectories via a tractable representation of local trajectory ensembles. Averaging over the disorder, we derive the basin diameter distribution, whose characteristic scale emerges from the relative strengths of the stabilizing inhibitory coupling and destabilizing spike interactions. Our study provides an approach to analytically dissect how connectivity, coupling strength, and single-neuron dynamics shape the phase space geometry in the locally stable regime of spiking neural circuit dynamics.

Flexible-Rate SIC-Free NOMA for Downlink VLC Based on Constellation Partitioning Coding

Visible light communication (VLC) systems utilizing conventional superposition coding/successive interference cancellation (SPC/SIC)-based non-orthogonal multiple access (NOMA) suffer from the error propagation effect due to imperfect SIC. In this letter, we propose a flexible-rate SIC-free NOMA technique for downlink VLC systems, based on constellation partitioning coding (CPC) and uneven constellation demapping (UCD). By using CPC/UCD, SIC is not required and hence error propagation can be eliminated in NOMA-based VLC systems. Moreover, by selecting a proper bit allocation scheme, flexible-rate multiple access can be supported in the VLC system applying CPC/UCD-based NOMA. Proof-of-concept two-user VLC experiments verify that, compared with conventional SPC/SIC-based NOMA, the bit error rate performance of the near user can be greatly improved by using CPC/UCD-based NOMA, and hence the effective power allocation ratio range can be substantially extended.

Code-Partitioning Offloading Schemes in Mobile Edge Computing for Augmented Reality

Augmented reality (AR) is one of the emerging use cases relying on ultra-reliable and low-latency communications (uRLLC). The AR service is composed of multiple dependent computational-intensive components. Due to the limited capability of user equipment (UE), it is difficult to meet the stringent latency and reliability requirements of AR service merely by local processing. To solve the problem, it is viable to offload parts of the AR task to the network edge, i.e., mobile edge computing (MEC), which is expected to extend the computing capability of the UE. However, MEC also incurs extra communication latency and errors on the wireless channel; therefore, it is challenging to make an optimum offload decision. So far, a little of state-of-the-art work has considered both the latency and reliability of the MEC-enabled AR service. In this paper, we study the scenario multiple edge nodes cooperate to complete the AR task. The dependency of task components is modeled by a directed acyclic graph through code partitioning. We aim to minimize the service failure probability (SFP) of the MEC-enabled AR service considering reliability and latency. We design an integer particle swarm optimization (IPSO)-based algorithm. Although the solution of IPSO-based algorithm approaches the optimum of the problem, it is infeasible to use IPSO for real-time AR services in practice due to the relatively high computational complexity. Hence, we propose a heuristic algorithm, which achieves a performance close to that of the IPSO-based algorithm with much lower complexity. Compared with state-of-the-art work, the heuristic algorithm can significantly improve the probability to fulfill the targeted SFP in various network conditions. Due to the generic characteristics, the proposed heuristic algorithm is applicable for AR services, as well as for many other use cases in uRLLC.

How to Fillet a Penguin: Runtime Data Driven Partitioning of Linux Code

In many modern operating systems (OSs), there exists no isolation between different kernel components, i.e., the failure of one component can affect the whole kernel. While microkernel OSs introduce address space separation for large parts of the OS, their improved fault isolation comes at the cost of performance. Despite significant improvements in modern microkernels, monolithic OSs like Linux are still prevalent in many systems. To achieve fault isolation in addition to high performance and code reuse in these systems, approaches to move only fractions of kernel code into user mode have been proposed. These approaches solely rely on static code analyses for deciding which code to isolate, neglecting dynamic properties like invocation frequencies. We propose to augment static code analyses with runtime data to achieve better estimates of dynamic properties for common case operation. We assess the impact of runtime data on the decision what code to isolate and the impact of that decision on the performance of such “microkernelized” systems. We extend an existing tool chain to implement automated code partitioning for existing monolithic kernel code and validate our approach in a case study of two widely used Linux device drivers and a file system.

Fuzzy SVM-Based Coding Unit Decision in HEVC

The latest video compression standard, High Efficiency Video Coding (HEVC), has greatly improved the coding efficiency compared to the predecessor H.264/AVC. However, equipped with the quadtree structure of coding tree unit partition and other sophisticated coding tools, HEVC brings a significant increase in the computational complexity. To address this issue, a coding unit (CU) decision method based on fuzzy support vector machine (SVM) is proposed for rate-distortion-complexity (RDC) optimization, where the process of CU decision is formulated as a cascaded multi-level classification task. The optimal feature set is selected according to a defined misclassification cost and a risk area is introduced for an uncertain classification output. To further improve the RDC performance, different regulation parameters in SVM are adopted and outliers in training samples are eliminated. Additionally, the proposed CU decision method is incorporated into a joint RDC optimization framework, where the width of risk area is adaptively adjusted to allocate flexible computational complexity to different CUs, aiming at minimizing computational complexity under a configurable constraint in terms of RD performance degradation. Experimental results show that the proposed approach can reduce 58.9% and 55.3% computational complexity on average with the values of Bjønteggard delta peak-signal-to-noise ratio as -0.075 dB and -0.085 dB and the values of Bjøntegaard delta bit rate as 2.859% and 2.671% under low delay P and random access configurations, respectively, which has outperformed the state-of-the-art fast algorithms based on statistical information and machine learning.

BOAT: A Block-Streaming App Execution Scheme for Lightweight IoT Devices

The contradiction between the limited capability of lightweight Internet-of-Things (IoT) devices and ever-increasing user demands is a fundamental and challenging problem in the era of IoT. One of the most important challenges is that lightweight IoT devices are generally embedded with fixed applications on resource-constrained hardware, and cannot enable on-demand service provisioning. In this paper, we propose BOAT, a block-streaming application execution scheme based on transparent computing (TC), which can remotely retrieve the necessary parts of traditional applications from edge servers on demand and run them on IoT devices locally. Specifically, we first exploit TC to build a scalable IoT system, where the applications of lightweight IoT devices are stored in edge servers or cloud but can be dynamically loaded on IoT devices in a block-streaming way. Moreover, we present a code partition approach to split the codes of a whole service into numerous functional blocks at the edge side. Such that, IoT devices only need to load necessary blocks of an application to obtain the requested services without loading the whole application codes. A lightweight I/O virtualization mechanism and a fine-grained relocation technology are then developed to support the block-streaming service loading. Our experimental results on a lightweight wearable device demonstrate that the proposed scheme can efficiently achieve flexible service provisioning with improved scalability and reduced service loading delay and energy consumption.

Trellis-Coded Multilevel Coset Codes

ABSTRACTTwo novel designs of multilevel coset codes are proposed. The systems are designed by combining convolutional codes and set partitioning of a binary linear block code. Several example codes are given. The simulation results show that the proposed coding schemes outperform the existing convolutional codes of similar trellis complexity.

Optimal Code Partitioning Over Time and Hierarchical Cloudlets

This letter proposes a task scheduling scheme designed for code partitioning over time and the hierarchical cloudlets in a mobile edge network. To this end, we define the so called energy-time cost parameters to optimally schedule tasks over time and hierarchical cloudlet locations. Accordingly, we investigate two different optimization scenarios. In particular, the first scenario aims at finding the optimal task scheduling for given radio parameters. In the second scenario, we carry out the optimization of both the task scheduling and the mobile device's transmission power. More importantly, we show that by adopting the proposed code partitioning scheme in this letter, the transmission power optimization problem becomes a disjoint problem from the task scheduling problem.