scholarly journals Instruction Sequences Expressing Multiplication Algorithms

2018 ◽  
Vol 2018 (1) ◽  
pp. 39-66
Author(s):  
J.A. Bergstra ◽  
◽  
C.A. Middelburg ◽  
Keyword(s):  
Instruction Sequences

scholarly journals Working Memory and the Enactment Effect in Early Alzheimer’s Disease

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Lara A. Charlesworth ◽  
Richard J. Allen ◽  
Suzannah Morson ◽  
Wendy K. Burn ◽  
Celine Souchay
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Working Memory ◽  
Free Recall ◽  
Older Adult ◽  
Memory Task ◽  
Enactment Effect ◽  
The Subject ◽  
Instruction Sequences ◽  
Early Alzheimer’S Disease

This study examines the enactment effect in early Alzheimer’s disease using a novel working memory task. Free recall of action-object instruction sequences was measured in individuals with Alzheimer’s disease (n=14) and older adult controls (n=15). Instruction sequences were read out loud by the experimenter (verbal-only task) or read by the experimenter and performed by the participants (subject-performed task). In both groups and for all sequence lengths, recall was superior in the subject-performed condition than the verbal-only condition. Individuals with Alzheimer’s disease showed a deficit in free recall of recently learned instruction sequences relative to older adult controls, yet both groups show a significant benefit from performing actions themselves at encoding. The subject-performed task shows promise as a tool to improve working memory in early Alzheimer’s disease.

Improving Similarity Measure for Java Programs Based on Optimal Matching of Control Flow Graphs

2015 ◽  
Vol 25 (07) ◽  
pp. 1171-1197 ◽  
Author(s):  
Dehong Qiu ◽  
Jialin Sun ◽  
Hao Li
Keyword(s):  
Graph Matching ◽  
Control Flow ◽  
Basic Block ◽  
New Approach ◽  
Java Program ◽  
Java Programs ◽  
Instruction Sequences ◽  
High Level ◽  
Flow Graphs ◽  
Basic Blocks

Measuring program similarity plays an important role in solving many problems in software engineering. However, because programs are instruction sequences with complex structures and semantic functions and furthermore, programs may be obfuscated deliberately through semantics-preserving transformations, measuring program similarity is a difficult task that has not been adequately addressed. In this paper, we propose a new approach to measuring Java program similarity. The approach first measures the low-level similarity between basic blocks according to the bytecode instruction sequences and the structural property of the basic blocks. Then, an error-tolerant graph matching algorithm that can combat structure transformations is used to match the Control Flow Graphs (CFG) based on the basic block similarity. The high-level similarity between Java programs is subsequently calculated on the matched pairs of the independent paths extracted from the optimal CFG matching. The proposed CFG-Match approach is compared with a string-based approach, a tree-based approach and a graph-based approach. Experimental results show that the CFG-Match approach is more accurate and robust against semantics-preserving transformations. The CFG-Match approach is used to detect Java program plagiarism. Experiments on the collection of benchmark program pairs collected from the students’ submission of project assignments demonstrate that the CFG-Match approach outperforms the comparative approaches in the detection of Java program plagiarism.

Instruction Clustering Analysis for Unknown Network Protocol's Abnormal Behavior

2015 ◽  
Vol 15 (03n04) ◽  
pp. 1540002
Author(s):  
YANJING HU ◽  
QINGQI PEI ◽  
LIAOJUN PANG
Keyword(s):  
Clustering Analysis ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Abnormal Behavior ◽  
Automatic Clustering ◽  
Reverse Analysis ◽  
Message Format ◽  
Instruction Sequences ◽  
Analysis Platform ◽  
Abnormal Behaviors

Protocol's abnormal behavior analysis is an important task in protocol reverse analysis. Traditional protocol reverse analysis focus on the protocol message format, but protocol behavior especially the abnormal behavior is rare studied. In this paper, protocol behavior is represented by the labeled behavior instruction sequences. Similar behavior instruction sequences mean the similar protocol behavior. Using our developed virtual analysis platform HiddenDisc, we can capture a variety of known or unknown protocols' behavior instruction sequences. All kinds of executed or unexecuted instruction sequences can automatic clustering by our designed instruction clustering algorithm. Thereby we can distinguish and mine the unknown protocols' potential abnormal behavior. The mined potential abnormal behavior instruction sequences are executed, monitored and analyzed on HiddenDisc to determine whether it is an abnormal behavior and what is the behavior's nature. Using the instruction clustering algorithm, we have analyzed 1297 protocol samples, mined 193 potential abnormal instruction sequences, and determined 187 malicious abnormal behaviors by regression testing. Experimental results show that our proposed instruction clustering algorithm has high efficiency and accuracy, can mine unknown protocols' abnormal behaviors effectively, and enhance the initiative defense capability of network security.

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