scholarly journals The memory forensic research oriented to segment heap in Windows 10 system

2021 ◽  
Vol 39 (5) ◽  
pp. 1139-1149
Author(s):  
Jiqiang ZHAI ◽  
Pan CHEN ◽  
Xiao XU ◽  
Hailu YANG
Keyword(s):  
Structural Information ◽  
Experimental Results ◽  
Starting Position ◽  
Forensic Research ◽  
Internal Information ◽  
Feature Values ◽  
Memory Object ◽  
Cyber Criminals

The current forensic research on heaps mainly extracts information from the heap of Linux and the NT heap of Windows. However, the study of how to extract the information on the segment heap in the Windows 10 from dump files is not sufficient. To reproduce the internal information on the segment heap, this paper proposes a method for locating and extracting the internal information on the segment heap in the Windows 10 according to the field offset in the vtype description information of memory object. The method uses the pool scanning technology to locate the process object, obtains the starting position of the process heap and scans the process heap according to the structural information on the process object and the process environment block object. Then it locates the position of the segment heap with its feature values, thereby extracting its internal information. Based on the analysis results, five forensic plugins for extracting the information on the segment heap were developed on the Volatility framework. The experimental results show that this method can effectively extract the information on the address of each segment heap and its internal components in the memory and on the size of committed memory, etc. The information can help investigators to analyze the digital traces left in the memory by cyber criminals or cyber attackers.

Towards a General Theory of Information

2018 ◽  
pp. 4459-4469
Author(s):  
Laura L. Pană
Keyword(s):  
Information Structure ◽  
Structural Information ◽  
Technical Information ◽  
Information Efficiency ◽  
Internal Information ◽  
New Research ◽  
Interdisciplinary Study ◽  
Information Aesthetics ◽  
Information Functional ◽  
Information Values

Contemporary society is an information society, based on Information Sciences and Information Technology. Technical information is therefore the most accessed and further promoted. This article aims to push toward completeness the study of information by analyzing the variety of information types and by presenting the hierarchy of information levels of existence. Specific features of natural, social and human information are highlighted. The internal information structure of distinct domains and levels of natural and social existence is explored. Information types such as structural information, systemic information, functional information or free information are characterized and defined, from various perspectives. An interdisciplinary study of information is thus accomplished, by using findings from several scientific and philosophical disciplines, from Information Epistemology or Information Aesthetics to Neuroinformatics and Neurorobotics. New research topics such as information values, information efficiency and information responsibility are proposed at the end of article.

scholarly journals End-to-End Game-Focused Learning of Adversary Behavior in Security Games

2020 ◽  
Vol 34 (02) ◽  
pp. 1378-1386
Author(s):  
Andrew Perrault ◽  
Bryan Wilder ◽  
Eric Ewing ◽  
Aditya Mate ◽  
Bistra Dilkina ◽  
...  
Keyword(s):  
Expected Utility ◽  
Optimization Problem ◽  
Predictive Accuracy ◽  
Experimental Results ◽  
Limited Data ◽  
Two Stage ◽  
Security Games ◽  
End To End ◽  
Feature Values ◽  
Adversary Model

Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary. Motivated by green security, where the defender may only observe an adversary's response to defense on a limited set of targets, we study the problem of learning a defense that generalizes well to a new set of targets with novel feature values and combinations. Traditionally, this problem has been addressed via a two-stage approach where an adversary model is trained to maximize predictive accuracy without considering the defender's optimization problem. We develop an end-to-end game-focused approach, where the adversary model is trained to maximize a surrogate for the defender's expected utility. We show both in theory and experimental results that our game-focused approach achieves higher defender expected utility than the two-stage alternative when there is limited data.

Cross-Media Retrieval Based on Query Modality and Semi-Supervised Regularization

2017 ◽  
Vol 21 (7) ◽  
pp. 1211-1220
Author(s):  
Yihe Liu ◽  
◽  
Huaxiang Zhang ◽  
Li Liu ◽  
Lili Meng ◽  
...  
Keyword(s):  
Semantic Information ◽  
Structural Information ◽  
Unlabeled Data ◽  
Experimental Results ◽  
Retrieval Method ◽  
Cross Media ◽  
Feature Spaces ◽  
Media Objects ◽  
The Cross ◽  
Original Feature

Existing cross-media retrieval methods usually learn one same latent subspace for different retrieval tasks, which can only achieve a suboptimal retrieval. In this paper, we propose a novel cross-media retrieval method based on Query Modality and Semi-supervised Regularization (QMSR). Taking the cross-media retrieval between images and texts for example, QMSR learns two couples of mappings for different retrieval tasks (i.e. using images to search texts (Im2Te) or using texts to search images (Te2Im)) instead of learning one couple of mappings. QMSR learns two couples of projections by optimizing the correlation between images and texts and the semantic information of query modality (image or text), and integrates together the semi-supervised regularization, the structural information among both labeled and unlabeled data of query modality to transform different media objects from original feature spaces into two different isomorphic subspaces (Im2Te common subspace and Te2Im common subspace). Experimental results show the effectiveness of the proposed method.

scholarly journals A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Daiji Tanaka ◽  
Katsuhiro Honda ◽  
Seiki Ubukata ◽  
Akira Notsu
Keyword(s):  
Supervised Learning ◽  
Structural Information ◽  
Experimental Results ◽  
Virtual Sample ◽  
Supervised Clustering ◽  
Virtual Samples ◽  
Additional Costs ◽  
Class Labels ◽  
Classification Quality

Although the goal of clustering is to reveal structural information from unlabeled datasets, in cases with partial structural supervisions, semi-supervised clustering is expected to improve partition quality. However, in many real applications, it may cause additional costs to provide an enough amount of supervised objects with class labels. A virtual sample approach is a practical technique for improving classification quality in semi-supervised learning, in which additional virtual samples are generated from supervised objects. In this research, the virtual sample approach is adopted in semi-supervised fuzzy co-clustering, where the goal is to reveal object-item pairwise cluster structures from cooccurrence information among them. Several experimental results demonstrate the characteristics of the proposed approach.

Selectivity-Based Keyword Extraction Method

2016 ◽  
Vol 12 (3) ◽  
pp. 1-26 ◽  
Author(s):  
Slobodan Beliga ◽  
Ana Meštrović ◽  
Sanda Martinčić-Ipšić
Keyword(s):  
Extraction Method ◽  
Structural Information ◽  
Experimental Results ◽  
Linguistic Knowledge ◽  
Centrality Measures ◽  
Average Weight ◽  
Keyword Extraction ◽  
Source Text ◽  
Single Node ◽  
Node Centrality

In this work the authors propose a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The authors show that selectivity-based keyword extraction slightly outperforms an extraction based on the standard centrality measures: in/out-degree, betweenness and closeness. Therefore, they include selectivity and its modification – generalized selectivity as node centrality measures in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network. The experimental results point out that selectivity-based keyword extraction has a great potential for the collection-oriented keyword extraction task.

scholarly journals A network embedding model for pathogenic genes prediction by multi-path random walking on heterogeneous network

2019 ◽  
Vol 12 (S10) ◽  
Author(s):  
Bo Xu ◽  
Yu Liu ◽  
Shuo Yu ◽  
Lei Wang ◽  
Jie Dong ◽  
...  
Keyword(s):  
Heterogeneous Network ◽  
Structural Information ◽  
Single Gene ◽  
Prediction Method ◽  
Experimental Results ◽  
Network Embedding ◽  
Pathogenic Genes ◽  
Phenotype Data ◽  
Multiple Paths ◽  
Complex Structural

Abstract Background Prediction of pathogenic genes is crucial for disease prevention, diagnosis, and treatment. But traditional genetic localization methods are often technique-difficulty and time-consuming. With the development of computer science, computational biology has gradually become one of the main methods for finding candidate pathogenic genes. Methods We propose a pathogenic genes prediction method based on network embedding which is called Multipath2vec. Firstly, we construct an heterogeneous network which is called GP−network. It is constructed based on three kinds of relationships between genes and phenotypes, including correlations between phenotypes, interactions between genes and known gene-phenotype pairs. Then in order to embedding the network better, we design the multi-path to guide random walk in GP−network. The multi-path includes multiple paths between genes and phenotypes which can capture complex structural information of heterogeneous network. Finally, we use the learned vector representation of each phenotype and protein to calculate the similarities and rank according to the similarities between candidate genes and the target phenotype. Results We implemented Multipath2vec and four baseline approaches (i.e., CATAPULT, PRINCE, Deepwalk and Metapath2vec) on many-genes gene-phenotype data, single-gene gene-phenotype data and whole gene-phenotype data. Experimental results show that Multipath2vec outperformed the state-of-the-art baselines in pathogenic genes prediction task. Conclusions We propose Multipath2vec that can be utilized to predict pathogenic genes and experimental results show the higher accuracy of pathogenic genes prediction.

NET-REGULAR PLACEMENT FOR HIGH PERFORMANCE CIRCUITS

1993 ◽  
Vol 04 (03) ◽  
pp. 245-260 ◽  
Author(s):  
KOK-HOO YEAP ◽  
MAJID SARRAFZADEH
Keyword(s):  
Standard Deviation ◽  
High Performance ◽  
Structural Information ◽  
Experimental Results ◽  
Signal Delay ◽  
Circuit Structure ◽  
Regular Placement

We examine a placement strategy incorporating the knowledge of underlying circuit structure targeted for high performance circuits. We measure the "net regularity" using the standard deviation of the locations of its terminals. Experimental results showed that exploiting circuit structural information led to substantially more regular nets, compared to placement without structural information. Benchmark circuits from MCNC showed 1.9 to 26 times increase in the number of nets with zero standard deviation. Furthermore, the length of the longest net in a chip was reduced from 5 to 22%. The placement strategy is crucial for high performance circuits since regular nets have relatively fewer vias and reducing the longest net length improves signal delay.

Towards a General Theory of Information

2019 ◽  
pp. 212-224
Author(s):  
Laura L. Pană
Keyword(s):  
Information Structure ◽  
Structural Information ◽  
Technical Information ◽  
Information Efficiency ◽  
Internal Information ◽  
New Research ◽  
Interdisciplinary Study ◽  
Information Aesthetics ◽  
Information Functional ◽  
Information Values

Contemporary society is an information society based on information sciences and information technology. Technical information is therefore the most accessed and further promoted. This chapter aims to push toward completeness the study of information by analyzing the variety of information types and by presenting the hierarchy of information levels of existence. Specific features of natural, social, and human information are highlighted. The internal information structure of distinct domains and levels of natural and social existence is explored. Information types such as structural information, systemic information, functional information, or free information are characterized and defined from various perspectives. An interdisciplinary study of information is thus accomplished by using findings from several scientific and philosophical disciplines from information epistemology or information aesthetics to neuroinformatics and neurorobotics. New research topics such as information values, information efficiency, and information responsibility are proposed at the end of the chapter.

A Comparison of Open Source Data Mining Tools for Breast Cancer Classification

2017 ◽  
pp. 636-651 ◽  
Author(s):  
Ahmed AbdElhafeez Ibrahim ◽  
Atallah Ibrahin Hashad ◽  
Negm Eldin Mohamed Shawky
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Open Source ◽  
Experimental Results ◽  
Classification Task ◽  
Open Source Data ◽  
Source Data ◽  
Precise Tool ◽  
Feature Values ◽  
Mining Tools

Data Mining is a field that interconnects areas from computer science, trying to discover knowledge from databases in order to simplify the decision making. Classification is a Data Mining chore that learns from a set of instances in order to precisely classify the target class for new instances. Open source Data Mining tools can be used to make classification. This paper compares four tools: KNIME, Orange, Tanagra and Weka. Our goal is to discover the most precise tool and technique for breast cancer classifications. The experimental results show that some tools achieve better results more than others. Also, using fusion classification task verified to be better than the single classification task over the four datasets have been used. Also, we present a comparison between using complete datasets by substituting missing feature values and incomplete ones. The experimental results show that some datasets have better accuracy when using complete datasets.

Stiffness Teaching and Motion Assist System Using Functional Electrical Stimulation and Electromyogram Signals

2004 ◽  
Vol 16 (5) ◽  
pp. 446-455 ◽  
Author(s):  
Masaaki Uechi ◽  
◽  
Yutaka Naito ◽  
Duk Shin ◽  
Makoto Sato ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Hand Movement ◽  
Joint Stiffness ◽  
Experimental Results ◽  
Joint Torque ◽  
Internal Information

In skilful tasks and sports, not only movement but also joint stiffness and the force are important. It is easy to watch and replicate the movement, but not to replicate the stiffness and the force. Muscle tensions cause our movement, which can be measured by EMG. Using the signals from EMG, we develop the technique to estimate the joint torque, joint stiffness, and equilibrium posture. So, while watching the hand movement, we can also feel the force and the joint torque are used. In this paper, we propose a motion assist system (MAS) that uses the pieces of internal information that are joint stiffness and joint torque based on muscle tensions. The experimental results show that the joint torque and hand stiffness were transmitted precisely using functional electrical stimulation (FES). This system will be useful not only for learning a skill, but also for supporting elder persons.

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