FS3change: A Scalable Method for Change Pattern Mining

Deeply embedded systems powered by microcontrollers are becoming popular with the emergence of Internet-of-Things (IoT) technology. However, these devices primarily run C/C \({+}{+}\) code and are susceptible to memory bugs, which can potentially lead to both control data attacks and non-control data attacks. Existing defense mechanisms (such as control-flow integrity (CFI), dataflow integrity (DFI) and write integrity testing (WIT), etc.) consume a massive amount of resources, making them less practical in real products. To make it lightweight, we design a bitmap-based allowlist mechanism to unify the storage of the runtime data for protecting both control data and non-control data. The memory requirements are constant and small, regardless of the number of deployed defense mechanisms. We store the allowlist in the TrustZone to ensure its integrity and confidentiality. Meanwhile, we perform an offline analysis to detect potential collisions and make corresponding adjustments when it happens. We have implemented our idea on an ARM Cortex-M-based development board. Our evaluation results show a substantial reduction in memory consumption when deploying the proposed CFI and DFI mechanisms, without compromising runtime performance. Specifically, our prototype enforces CFI and DFI at a cost of just 2.09% performance overhead and 32.56% memory overhead on average.

The World Wide Web contributes greatly to the creation of an ever-increasing global information database. One of the important experience factors while accessing it lies with the time taken to load the webpages. The latency of the network and server's response affect the page-load performance. Caching and prefetching are well-known strategies for reducing the access latency and have become essential elements of research so as to provide the users with a faster browsing experience. In this paper we propose a method of predicting the client's forthcoming requests and assisting the client in faster page-load by selectively ‘hinting’ and ‘pushing’ the predicted resources to the client. We make use of the SPDY based forward proxy for communicating with the Browser and introduce ‘SPDY session’ prefetch, We also analyze the reduction in memory consumption because of our prediction procedure.

A sparse mesh for Compact Finite Difference—Fourier solvers with radius-dependent spectral resolution in circular domains

Recently, an integral equation discontinuous Galerkin method (IEDG) has been proposed to solve electromagnetic problems. One of the most attractive features of IEDG is the possibility to employ non-conformal mesh for the implementation of the combined field integral equation (CFIE) since the square integrable L2 basis is adopted and no continuity requirement is necessary across boundaries of adjacent elements. Moreover, IEDG provides great flexibility in terms of basis function such that different orders of basis functions with different element types can be mixed with the same discretization to best represent the currents locally. This feature also facilitates the incorporation of adaption techniques to IEDG, either through the use of higher order basis functions (p refinement) or subdivide the elements into multiple smaller pieces (h refinement). All these merits makes IEDG a very attractive candidate for the solution of multi-scale and wide-band problems such that geometry components that are smooth can be discretized using coarser elements while finer discretization can be adopted for more intricate structures. For wide-band simulations, IEDG allows one to adopt the discretization that is generated at a lower frequency for the calculation at higher frequencies through adaptively refining the elements. This helps to reduce the computational burden. However, adoption of higher frequency mesh at lower frequencies could result in the well known "sub-wavelength breakdown" problem for conventional MLFMM. The computational complexity deteriorates significantly for both multi-scale or low frequency problems. In this paper, IEDG is accelerated by hierarchical multi-level fast multipole method, especially for the problems that involve the dense mesh in a local or global scale. H-MLFMM separates the evanescent physics from radiation physics and employ different transformation of basis accordingly. For the evanescent regime, where densely discretized elements reside, it identifies a reduced set of numerically independent basis functions from the original ones via a skeletonalization algorithm, which in essence is a kernel independent algebraic data spacification algorithm. The reduced set of basis functions are named "skeletons" or "skeleton DoFs" and they are adopted to represent the interaction related with the original set of basis functions. Subsequently, significant reduction in memory consumption and CPU time per matrix-vector multiplication (MVP) are achieved through the compressed coupling matrices. With the H-MLFMM incorporated, IEDG offers a very attractive approach to solve for the wide-band EM response from a target via the mesh at midfrequency.

An appropriate mathematical framework for perfusion imaging based on slow projection acquisition like originating from C-arm systems has already been presented in literature. Due to the huge system matrix this approach in general has an extremely high computational effort and memory consumption, thus preventing the storage in the random access memory of a computer for realistic objects and spatial resolution. To overcome this difficulty we present an efficient storage scheme for the system matrix. The main idea is to effectively utilize the underlying geometric symmetries. In this paper we investigated the performance of this the beam-form independent framework focusing on the reduction of memory consumption. The obtained results demonstrate a reduction of the overall memory requirements up to three orders of magnitude compared to savings due to the simply utilization of the matrix sparsity of the matrixes. Future work will focus on the implementation of the method for large three dimensional objects as well as using higher order symmetries.

BackgroundMining logic rules from structured knowledge bases is the basis of knowledge engineering. Due to the NP‐hardness of the rule mining problem, logic rules cannot be efficiently induced from knowledge bases, especially large‐scale ones.IdeaIn this article, we propose a compact and efficient index structure for the maintenance of the intermediate data during top‐down rule mining, such that the memory consumption can be reduced and mining efficiency can be improved.Developing PointsThe index is based on a mapping from constant symbols to integers and the sorting of the mapped integers. Index update has been dissembled into four basic operations. Moreover, the index itself acts as the cache during top‐down mining.ValueMost contributions in existing works employ algorithmic and architectural optimizations to improve efficiency. Data‐oriented optimizations have also been explored to some extent, but the data efficiency is relatively low, and the memory consumption is thus becoming a new challenge for state‐of‐the‐art systems. We tackle this challenge in this article, and our technique has been proven more efficient than state‐of‐the‐art systems. We evaluate our method on six datasets which contain up to 160 K records and are frequently used as benchmarks in tasks related to knowledge engineering. The experimental results show that the proposed technique speeds up the rule mining procedure by on average and reduces memory consumption by up to 70%. The space overhead of the data structure is about twice that of the indexed records, which is more than 80% lower than that of the state‐of‐the‐art technique.

This paper proposes an efficient patch-based image matting approach to reduce memory consumption in processing high-resolution images. Most existing image matting techniques employ a global optimization over the whole set of image pixels, incurring a prohibitively high memory consumption. Inspired by 'divide and conquer', the image is divided into small patches self-adaptively according to the distribution of pixels, then the matting algorithm is applied in patch level. The memory consumption is related to the size of patch. Relationships between patches are also considered by locally linear embedding to maintain consistency through the whole image. Experimental results show that our method significantly reduces memory consumption while maintaining high-fidelity matting results on the benchmark dataset.

Many relational databases exhibit complex dependencies between data attributes, caused either by the nature of the underlying data or by explicitly denormalized schemas. In data warehouse scenarios, calculated key figures may be materialized or hierarchy levels may be held within a single dimension table. Such column correlations and the resulting data redundancy may result in additional storage requirements. They may also result in bad query performance if inappropriate independence assumptions are made during query compilation. In this paper, we tackle the specific problem of detecting functional dependencies between columns to improve the compression rate for column-based database systems, which both reduces main memory consumption and improves query performance. Although a huge variety of algorithms have been proposed for detecting column dependencies in databases, we maintain that increased data volumes and recent developments in hardware architectures demand novel algorithms with much lower runtime overhead and smaller memory footprint. Our novel approach is based on entropy estimations and exploits a combination of sampling and multiple heuristics to render it applicable for a wide range of use cases. We demonstrate the quality of our approach by means of an implementation within the SAP NetWeaver Business Warehouse Accelerator. Our experiments indicate that our approach scales well with the number of columns and produces reliable dependence structure information. This both reduces memory consumption and improves performance for nontrivial queries.

Radient: Scalable, memory efficient name lookup algorithm for named data networking

The graph stream is defined as rapid edge streams on a huge domain of nodes. Nowadays, graph streams play important roles in network traffic, social networks, and cloud troubleshooting. Therefore, various summary structures for graph streams are proposed to obtain approximate evaluation results. However, these structures either sacrifice accuracy for guaranteed throughput or compromise memory consumption for high precision. In view of the limitations, we propose Cuckoo Matrix. It only uses one adjacency matrix to complete high accuracy queries while assuring large throughput. Meanwhile, Cuckoo Matrix is capable of preserving the connectivity of edges for the purpose of supporting both structural queries and weight-based estimations. The experimental results show that Cuckoo Matrix improves insertion throughput by 25% and reduces memory consumption by 25% compared to the state-of-the-art, which meets the current requirements of graph stream summarization.

In order to solve the transponder collision problem in a RFID system, this paper proposes a hybrid algorithm which combines strengths of existing high-performance algorithms while avoiding their major drawbacks. Experiments show this hybrid algorithm uses fewer time slots and less total communication time compared to adaptive slot-count algorithm based on ALOHA as well as enhanced anti-collision algorithm based on binary tree, both of which are superior and well accepted algorithms in literature. Meanwhile, high computational intensity of adaptive slot-count algorithm is significantly relieved, and memory consumption of enhanced anti-collision algorithm is reduced to a negligible level. Results from simulations under large number of transponders and long transponder IDs, which is the case in real RFID applications, suggest the advantage of this hybrid algorithm is prominent.

Recently, dynamic explicit FEM developed for the crash analysis of structures is applicable to the collision problem with large deformation and elasto-plastic collapse. According to the simplicity of computational algorithms and reduction of memory consumption in computer, explicit scheme is becoming popular tools. In the present report, theory of dynamic explicit FEM is applied and the post buckling behavior with very large deformation has been investigated considering influence of the displacement velocity. Particularly, the second buckling behavior observed in the post buckling behavior has been focused on.

Energy consumption is one of the most important issues in resource-constrained embedded systems. Many such systems run Java-based applications due to Java's architecture-independent format (bytecode). Standard techniques for executing bytecode programs, e.g. interpretation or just-in-time compilation, have performance or memory issues that make them unsuitable for resource-constrained embedded systems.A superoperator-extended, lightweight Java Virtual Machine (JVM) can be used in resource-constrained embedded systems to improve performance and reduce memory consumption. This paper shows that such a JVM also significantly reduces energy consumption. This is due primarily to a considerable reduction in the number of memory accesses and thus in energy consumption in the instruction and data TLBs and caches and, in most cases, in DRAM energy consumption. Since the fraction of processor energy dissipated in these units is approximately 60%, the energy savings achieved are significant.The paper evaluates the number of load, store, and computational instructions eliminated by the use of proposed superoperators as compared to a simple interpreter on a set of embedded benchmarks. Using cache and DRAM per access energy we estimate the total processor/DRAM energy saved by using our JVM. Our results show that with 32KB caches the reduction in energy consumption ranges from 40% to 60% of the overall processor, plus DRAM energy. Even higher savings may be achieved with smaller caches and increased access to DRAM as DRAM access energy is fairly high.

Episode Rule Mining is a popular framework for discovering sequential rules from event sequential data. However, traditional episode rule mining methods only tell that the consequent event is likely to happen within a given time interval after the occurrence of the antecedent events. As a result, they cannot satisfy the requirement of many time sensitive applications, such as program security trading and intelligent transportation management due to the lack of fine-grained response time. In this study, we come up with the concept of fixed-gap episode to address this problem. A fixed-gap episode consists of an ordered set of events where the elapsed time between any two consecutive events is a constant. Based on this concept, we formulate the problem of mining precise-positioning episode rules in which the occurrence time of each event in the consequent is clearly specified. In addition, we develop a trie-based data structure to mine such precise-positioning episode rules with several pruning strategies incorporated for improving the performance as well as reducing memory consumption. Experimental results on real datasets show the superiority of our proposed algorithms.

The rapid adoption of video conference systems has created a demand to automate the meeting summary process, aiming to boost productivity and reduce memory consumption. This paper presents a meeting summarization framework based on an Artificial Neural Network (ANN) that can develop concise and rich context summaries for real-time or recorded meetings. The collection of data is carried out with the help of audio, video, and text reports from a public dataset and live meetings of based cloud database. Preprocessing involves converting speech into text, filtering out noise, performing speaker diarization, and removing irrelevant content store in the cloud of the network. The Natural Language Processing (NLP) technique of feature extraction employs TF-IDF, word embeddings, and other topic modelling to capture semantics in the cloud of the network in performance. An ANN is used to classify and prioritize important discussion points, decisions, and action items in the cloud. The measures of performance, being ROUGE score 83%, precision 77%, recall 77%, and F1-score 85%, are used. The experimental evidence indicates that the ANN model offers high summarization quality and relevance, allowing for efficient review of meetings and avoiding the need to spend time writing notes.