Fine-grained Techniques Research Articles

Tactics And Techniques Classification In Cyber Threat Intelligence

Abstract Completing the classification of tactics and techniques in cyber threat intelligence (CTI) is an important way to obtain tactics, techniques and procedures (TTPs) and portray the behavior of cyber attacks. However, the high level of abstraction of tactics and techniques information and their presence in CTI, usually in the form of natural language text, make it difficult for traditional manual analysis methods and feature engineering-based machine learning methods to complete the classification of tactics and techniques effectively. Meanwhile, flat deep learning methods do not perform well in classifying more fine-grained techniques due to their inability to exploit the hierarchical relationship between tactics and techniques. Therefore, this paper regards the tactics and techniques of TTPs defined in Adversarial Tactics, Techniques and Common Knowledge knowledge base as labels and proposes a Convolutional Neural Network (CNN) model based on hierarchical knowledge migration and attention mechanism for classifying tactics and techniques in CTI, named HM-ACNN (CNN based on hierarchical knowledge migration and attention mechanism). HM-ACNN classifies tactics and techniques into two phases, and the underlying network model for both phases is the Attention-based CNN network. The first step in HM-ACNN is converting the CTI text into a two-dimensional image based on the word embedding model, and then start training the classification of tactics through the CNN structure based on the attention mechanism before the classification of techniques. Secondly, after the tactics classification training is completed, the tactic-to-technique knowledge migration is then completed by transforming the parameters of the CNN layer and the attention layer in the tactics classification process based on the special hierarchical relationship between tactics and techniques. Then, the classification of techniques is finished by fine-tuning. The experimental results show that HM-ACNN performs well in the tactics and techniques classification tasks, and the metric F1 values reach 93.66% and 86.29%, which are better than other models such as CNN, Recurrent Neural Network and CRNN (Recurrent Convolutional Neural Networks).

Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach

Over the last decade, remote sensing technology has advanced dramatically, resulting in significant improvements on image quality, data volume, and application usage. These images have essential applications since they can help with quick and easy interpretation. Many standard detection algorithms fail to accurately categorize a scene from a remote sensing image recorded from the earth. A method that uses bilinear convolution neural networks to produce a lessweighted set of models those results in better visual recognition in remote sensing images using fine-grained techniques. This proposed hybrid method is utilized to extract scene feature information in two times from remote sensing images for improved recognition. In layman's terms, these features are defined as raw, and only have a single defined frame, so they will allow basic recognition from remote sensing images. This research work has proposed a double feature extraction hybrid deep learning approach to classify remotely sensed image scenes based on feature abstraction techniques. Also, the proposed algorithm is applied to feature values in order to convert them to feature vectors that have pure black and white values after many product operations. The next stage is pooling and normalization, which occurs after the CNN feature extraction process has changed. This research work has developed a novel hybrid framework method that has a better level of accuracy and recognition rate than any prior model.

Incremental and Modular Context-sensitive Analysis

AbstractContext-sensitive global analysis of large code bases can be expensive, which can make its use impractical during software development. However, there are many situations in which modifications are small and isolated within a few components, and it is desirable to reuse as much as possible previous analysis results. This has been achieved to date through incremental global analysis fixpoint algorithms that achieve cost reductions at fine levels of granularity, such as changes in program lines. However, these fine-grained techniques are neither directly applicable to modular programs nor are they designed to take advantage of modular structures. This paper describes, implements, and evaluates an algorithm that performs efficient context-sensitive analysis incrementally on modular partitions of programs. The experimental results show that the proposed modular algorithm shows significant improvements, in both time and memory consumption, when compared to existing non-modular, fine-grain incremental analysis techniques. Furthermore, thanks to the proposed intermodular propagation of analysis information, our algorithm also outperforms traditional modular analysis even when analyzing from scratch.

Multi-Perspective Attention Network for Fast Temporal Moment Localization

Temporal moment localization (TML) aims to retrieve the temporal interval for a moment semantically relevant to a sentence query. This is challenging because it requires understanding a video, a sentence, and the relationship between them. Existing TML methods have shown impressive performances by modeling interactions between videos and sentences using fine-grained techniques. However, these fine-grained techniques require a high computational overhead, making them impractical. This work proposes an effective and efficient multi-perspective attention network for temporal moment localization. Inspired by the way humans understand an image from multiple perspectives and different contexts, we devise a novel multi-perspective attention mechanism consisting of perspective attention and multi-perspective modal interactions. Specifically, a perspective attention layer based on multi-head attention takes two memory sequences, one as the base and the other as the reference memory, as inputs. Perspective attention assesses the two different memories, models the relationship, and encourages the base memory to focus on features related to the reference memory, providing an understanding of the base memory from the perspective of the reference memory. Furthermore, multi-perspective modal interactions model the complex relationship between a video and sentence query, and obtain the modal-interacted memory, consisting of a visual feature that selectively learned query-related information. Similar to the heavyweight fine-grained TML methods, the proposed network obtains the accurate complex relationship while being lightweight like coarse-grained TML methods. We also adopt a fast action recognition network to efficiently extract visual features, which reduce the computational overhead. Through experiments on three TML benchmark datasets, we demonstrate the effectiveness and efficiency of the proposed network.

Exploiting coarse-grained reused-based opportunities in Big Data multi-query optimization

Multi-query optimization in Big Data becomes a promising research direction due to the popularity of massive data analytical systems (e.g., MapReduce and Flink). The multi-query is translated into jobs. These jobs are routinely submitted with similar tasks to the underling Big Data analytical systems. These similar tasks are considered complicated and computation overhead. Therefore, there are some existing techniques that have been proposed for exploiting sharing tasks in Big Data multi-query optimization (e.g., MRShare and Relaxed MRShare). These techniques are heavily tailored relaxed optimizing factors of fine-grained reused-based opportunities. In accordance with Big Data multi-query optimization, the existing fine-grained techniques are only concerned with equal tuples size and uniform data distribution. These issues are not applicable to the real-world distributed applications which depend on coarse-grained reused-based opportunities, such as non-equal tuples size and non-uniform data distribution. These two issues receive more-attention in Big Data multi-query optimization, to minimize the data read from or written back to Big Data infrastructures (e.g., Hadoop). In this paper, Multi-Query Optimization using Tuple Size and Histogram (MOTH) system has been proposed to consider the granularity of the reused-based opportunities. The proposed MOTH system exploits the coarse-grained of the fully and partially reused-based opportunities among queries with considering non-equal tuples size and non-uniform data distribution to avoid repeated computations. According to the proposed MOTH system, a combined technique has been introduced for estimating the coarse-grained reused-based opportunities horizontally and vertically. The horizontal estimation of non-equal tuples size has been done by extracting metadata in column-level, while the vertical estimation of non-uniform data distribution is concerned with using pre-computed histogram in row-level. In addition, the MOTH system estimates the coarse-grained reused-based opportunities with considering slow storage (i.e., limited physical resources or fewer allocated virtualized resources) to produce the accurate estimation of the reused results costs. Then, a cost-based heuristic algorithm has been introduced to select the best reused-based opportunity and generate an efficient multi-query execution plan. Because the partial reused-based opportunities have been considered, extra computations are needed to retrieve the non-derived results. Also, a partial reused-based optimizer has been tailored and added to the proposed MOTH system to reformulate the generated multi-query plan to improve the shared partial queries. According to the experimental results of the proposed MOTH system using TPC-H benchmark, it is found that multi-query execution time has been reduced by considering the granularity of the reused results.

CHILL: a system for fine-grained mapping of chained high impact long-latency load phases on tightly coupled heterogeneous multi-cores

With increasing power and application demands, heterogeneous multi-core processors are becoming more prevalent. However, the key to proper utilisation of heterogeneous multi-cores is assigning, or mapping, the right application to the right core type. Recent work has shown that fine-grained mapping takes advantage of short program phases with highly variant performance requirements, and can elicit greater benefits from tightly coupled heterogeneous multi-cores. In this paper, we show that bottlenecks in performance can occur in fine-grained program phases during chains of high impact long-latency loads. We design a system that detects these bottleneck phases, and propose accelerating these phases on the out-of-order core for better performance and energy efficiency. Our system operates within 10% of performance, and 2.6% of energy to an oracle resource mapper. This translates to a 44.4% performance gain, and 9.2% energy savings over existing fine-grained mapping techniques.

Semi-supervised Textual Analysis and Historical Research Helping Each Other: Some Thoughts and Observations

Future historians will describe the rise of the World Wide Web as the turning point of their academic profession. As a matter of fact, thanks to an unprecedented amount of digitization projects and to the preservation of born-digital sources, for the first time they have at their disposal a gigantic collection of traces of our past. However, to understand trends and obtain useful insights from these very large amounts of data, historians will need more and more fine-grained techniques. This will be especially true if their objective will turn to hypothesis-testing studies, in order to build arguments by employing their deep in-domain expertise. For this reason, we focus our paper on a set of computational techniques, namely semi-supervised computational methods, which could potentially provide us with a methodological turning point for this change. As a matter of fact these approaches, due to their potential of affirming themselves as both knowledge and data driven at the same time, could become a solid alternative to some of the today most employed unsupervised techniques. However, historians who intend to employ them as evidences for supporting a claim, have to use computational methods not anymore as black boxes but as a series of well known methodological approaches. For this reason, we believe that if developing computational skills will be important for them, a solid background knowledge on the most important data analysis and results evaluation procedures will become far more capital.

Comparison of Design Models: A Systematic Mapping Study

Context: Model comparison plays a central role in many software engineering activities. However, a comprehensive understanding about the state-of-the-art is still required. Goal: This paper aims at classifying and performing a thematic analysis of the current literature. Method: For this, we have followed well-established empirical guidelines to define and perform a systematic mapping study. Results: Some studies (14 out of 40) provide generic model comparison techniques, rather than specific ones for UML diagrams. Conclusion: Fine-grained techniques are still required to support ever-present and complex model comparison tasks during the evolution of design models.

Real-time stereo to multi-view conversion system based on adaptive meshing

The stereo to multi-view conversion technology plays an important role in the development and promotion of three-dimensional television, which can provide adequate supply of high-quality 3D content for autostereoscopic displays. This paper focuses on a real-time implementation of the stereo to multi-view conversion system, the major parts of which are adaptive meshing, sparse stereo correspondence, energy equation construction and virtual-view rendering. To achieve the real-time performance, we make three main contributions. First, we introduce adaptive meshing to reduce the computational complexity at the expense of slight decrease in quality. Second, we use a simple and effective method based on block matching algorithm to generate the sparse disparity map. Third, for the module of block-saliency calculation, sparse stereo correspondence and view synthesis, novel parallelization strategies and fine-grained optimization techniques based on graphic processing units are used to accelerate the executing speed. Experimental results show that the system can achieve real-time and semi-real-time performance when rendering 8 views with the image resolution of 1280 × 720 and 1920 × 1080 on Tesla K20. The images and videos presented finally are both visually realistic and comfortable.

A Review on Reagents and Processes of Fine-Grained Cassiterite Flotation in Mining Engineering

Tin resources are abundant and complicated in China. Most of tin resources come from cassiterite. Normally the main beneficiation method of coarse cassiterite ore is gravity separation. However, as for the fine-grained cassiterite, flotation and joint processing techniques are generally selected to improve the low recovery from gravity separation. This paper outlines different kinds of cassiterite, collectors and depressants for cassiterite flotation and their using conditions and some reinforced methods in beneficiation of the fine-grained cassiterite for reference in mining industry.

Motion Detection in Low Resolution Grayscale Videos Using Fast Normalized Cross Correrelation on GP-GPU

Motion estimation (ME) has been widely used in many computer vision applications, such as object tracking, object detection, pattern recognition and video compression. The most popular block based similarity measures are the sum of absolute differences (SAD), the sum of squared differences (SSD) and the normalized cross correlation (NCC). Similarity measure obtained using NCC is more robust under varying illumination changes as compared to SAD and SSD. However NCC is computationally expensive and application of NCC using full or exhaustive search method further increases required computational time. Relatively efficient way of calculating the NCC is to pre-compute sum-tables to perform the normalization referred to as fast NCC (FCC). In this paper we propose real time implementation of full search FCC algorithm applied to gray scale videos using NVIDIA’s Compute Unified Device Architecture (CUDA). We present fine-grained optimization techniques for fully exploiting computational capacity of CUDA. Novel parallelization strategies adopted for extracting data parallelism substantially reduce computational time of exhaustive FCC. We show that by efficient utilization of global, shared and texture memories available on CUDA, we can obtain the speedup of the order of 10x as compared to the sequential implementation of FCC.

Judgment processes in older adults' drug benefit evaluations

Background The Medicare Prescription Drug, Improvement and Modernization Act of 2003 will provide drug coverage to older adults through a variety of mechanisms, including stand-alone prescription drug benefits. Variation in cost-sharing and utilization controls is permitted, leading potentially to a wide variety of prescription benefit plans. However, little is known regarding the manner in which beneficiaries will integrate information to form plan evaluations. Objectives The objectives of this study were to assess and compare the use of compensatory and configural judgment models in older adults' evaluations of drug benefit plans. Methods Three focus groups were conducted with a total of 19 seniors to elicit relevant drug plan attributes. A separate group of 32 seniors then judged the suitability of 48 drug benefit profiles based on these attributes. Within-subjects regression analysis was used to reveal each participant's judgment policy. Results Focus groups suggested that copayment, premium, deductible, formulary use, and mail-service use were relevant plan attributes. Regression analyses showed that while most participants used compensatory judgment models in evaluation formation, 12 (37.5%) subjects used conjunctive judgment models. Conclusions Configural judgment processes are used frequently by older adults when evaluating drug benefit plans. Future research using more fine-grained techniques (eg, process tracing) might help further elucidate judgment processes in this context.

Genetic variability among endangered Chinese giant salamanders, Andrias davidianus.

The endangered Chinese giant salamander (Andrias davidianus) is endemic to mainland China. Genetic divergence among six populations of the species was investigated by means of isozyme electrophoresis and mitochondrial DNA (mtDNA) sequences. Forty allozyme loci were resolved for all populations; the amount of genetic divergence among populations was comparable to that in other amphibians. mtDNA sequences showed a similar level of divergence. The population from Huangshan is distinct from other populations, indicating the existence of localized divergence. Both allozyme and mtDNA data failed to associate the populations into a pattern corresponding to the three Chinese river systems, which may be the consequence of human relocation. Conservation policies should emphasize the protection of localized populations and cessation of human-facilitated introductions. Future studies should focus on investigating the divergence among localized populations from isolated mountain regions, particularly using more fine-grained techniques such as microsatellite DNA.