scholarly journals Reasoning with PCP-Nets

2021 ◽  
Vol 72 ◽  
pp. 1103-1161
Author(s):  
Cristina Cornelio ◽  
Judy Goldsmith ◽  
Umberto Grandi ◽  
Nicholas Mattei ◽  
Francesca Rossi ◽  
...  
Keyword(s):  
Experimental Results ◽  
Experimental Setting ◽  
Voting Theory ◽  
Aggregation Method ◽  
Probabilistic Uncertainty ◽  
Multi Agent ◽  
Conditional Preferences ◽  
The Given

We introduce PCP-nets, a formalism to model qualitative conditional preferences with probabilistic uncertainty. PCP-nets generalise CP-nets by allowing for uncertainty over the preference orderings. We define and study both optimality and dominance queries in PCP-nets, and we propose a tractable approximation of dominance which we show to be very accurate in our experimental setting. Since PCP-nets can be seen as a way to model a collection of weighted CP-nets, we also explore the use of PCP-nets in a multi-agent context, where individual agents submit CP-nets which are then aggregated into a single PCP-net. We consider various ways to perform such aggregation and we compare them via two notions of scores, based on well known voting theory concepts. Experimental results allow us to identify the aggregation method that better represents the given set of CP-nets and the most efficient dominance procedure to be used in the multi-agent context.

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

IMPROVED TIME-MULTIPLEXED FPGA ARCHITECTURE AND ALGORITHM FOR MINIMIZING COMMUNICATION COST DESIGNS

2013 ◽  
Vol 22 (05) ◽  
pp. 1350033
Author(s):  
CHI-CHOU KAO ◽  
YEN-TAI LAI
Keyword(s):  
Greedy Algorithm ◽  
Sequential Circuits ◽  
Precedence Constraints ◽  
Communication Cost ◽  
Experimental Results ◽  
Time Sharing ◽  
Fpga Architecture ◽  
The Given

The Time-Multiplexed FPGA (TMFPGA) architecture can improve dramatically logic utilization by time-sharing logic but it needs a large amount of registers among sub-circuits for partitioning the given sequential circuits. In this paper, we propose an improved TMFPGA architecture to simplify the precedence constraints so that the number of the registers among sub-circuits can be reduced for sequential circuits partitioning. To demonstrate the practicability of the architecture, we also present a greedy algorithm to minimize the maximum number of the registers. Experimental results demonstrate the effectives of the algorithm.

scholarly journals Model Checking Temporal Epistemic Logic under Bounded Recall

2020 ◽  
Vol 34 (05) ◽  
pp. 7071-7078
Author(s):  
Francesco Belardinelli ◽  
Alessio Lomuscio ◽  
Emily Yu
Keyword(s):  
Model Checking ◽  
Incomplete Information ◽  
Epistemic Logic ◽  
Upper Bounds ◽  
Experimental Results ◽  
Model Checker ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Bounded Recall

We study the problem of verifying multi-agent systems under the assumption of bounded recall. We introduce the logic CTLKBR, a bounded-recall variant of the temporal-epistemic logic CTLK. We define and study the model checking problem against CTLK specifications under incomplete information and bounded recall and present complexity upper bounds. We present an extension of the BDD-based model checker MCMAS implementing model checking under bounded recall semantics and discuss the experimental results obtained.

Experimental Investigations of the Machinability of Titanium Alloy TC4 with Different Hydrogen Contents

2011 ◽  
Vol 418-420 ◽  
pp. 1307-1311
Author(s):  
Jun Hu ◽  
Yong Jie Bao ◽  
Hang Gao ◽  
Ke Xin Wang
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Cutting Force ◽  
Hydrogen Content ◽  
Experimental Results ◽  
Cutting Conditions ◽  
Experimental Investigations ◽  
Cutting Condition ◽  
The Given

The experiments were carried out in the paper to investigate the effect of adding hydrogen in titanium alloy TC4 on its machinability. The hydrogen contents selected were 0, 0.25%, 0.49%, 0.63%, 0.89% and 1.32%, respectively. Experiments with varing hydrogen contents and cutting conditions concurrently. Experimental results showed that the cutting force of the titanium alloy can be obviously reduced and the surface roughness can be improved by adding appropriate hydrogen in the material. In the given cutting condition, the titanium alloy TC4 with 0.49% hydrogen content showed better machinability.

scholarly journals Hierarchical Classification Based on Label Distribution Learning

2019 ◽  
Vol 33 ◽  
pp. 5533-5540 ◽  
Author(s):  
Changdong Xu ◽  
Xin Geng
Keyword(s):  
Hierarchical Classification ◽  
Experimental Results ◽  
Challenging Problem ◽  
Training Set ◽  
True Label ◽  
Major Bottleneck ◽  
Novel Method ◽  
Class Labels ◽  
Label Distribution ◽  
The Given

Hierarchical classification is a challenging problem where the class labels are organized in a predefined hierarchy. One primary challenge in hierarchical classification is the small training set issue of the local module. The local classifiers in the previous hierarchical classification approaches are prone to over-fitting, which becomes a major bottleneck of hierarchical classification. Fortunately, the labels in the local module are correlated, and the siblings of the true label can provide additional supervision information for the instance. This paper proposes a novel method to deal with the small training set issue. The key idea of the method is to represent the correlation among the labels by the label distribution. It generates a label distribution that contains the supervision information of each label for the given instance, and then learns a mapping from the instance to the label distribution. Experimental results on several hierarchical classification datasets show that our method significantly outperforms other state-of-theart hierarchical classification approaches.

Mining Allocating Patterns in Investment Portfolios

2009 ◽  
pp. 110-135
Author(s):  
Yanbo J. Wang ◽  
Xinwei Zheng ◽  
Frans Coenen
Keyword(s):  
Data Mining ◽  
Portfolio Management ◽  
Association Rule ◽  
Common Type ◽  
Experimental Results ◽  
Transaction Database ◽  
Investment Portfolios ◽  
The Given

An association rule (AR) is a common type of mined knowledge in data mining that describes an implicative co-occurring relationship between two sets of binary-valued transaction-database attributes, expressed in the form of an ? rule. A variation of ARs is the (WARs), which addresses the weighting issue in ARs. In this chapter, the authors introduce the concept of “one-sum” WAR and name such WARs as allocating patterns (ALPs). An algorithm is proposed to extract hidden and interesting ALPs from data. The authors further indicate that ALPs can be applied in portfolio management. Firstly by modelling a collection of investment portfolios as a one-sum weighted transaction- database that contains hidden ALPs. Secondly the authors show that ALPs, mined from the given portfolio-data, can be applied to guide future investment activities. The experimental results show good performance that demonstrates the effectiveness of using ALPs in the proposed application.

Norm Emergence in Multi-Agent Societies

2011 ◽  
pp. 195-206
Author(s):  
Bastin Tony Roy Savarimuthu ◽  
Maryam Purvis ◽  
Stephen Cranefield
Keyword(s):  
Software Agents ◽  
Experimental Results ◽  
Multi Agent System ◽  
Artificial Agent ◽  
Agent System ◽  
Agent Societies ◽  
Multi Agent ◽  
Norm Emergence

Norms are shared expectations of behaviours that exist in human societies. Norms help societies by increasing the predictability of individual behaviours and by improving cooperation and collaboration among members. Norms have been of interest to multi-agent system researchers, as software agents intend to follow certain norms. But, owing to their autonomy, agents sometimes violate norms, which needs monitoring. In order to build robust MAS that are norm compliant and systems that evolve and adapt norms dynamically, the study of norms is crucial. Our objective in this chapter is to propose a mechanism for norm emergence in artificial agent societies and provide experimental results. We also study the role of autonomy and visibility threshold of an agent in the context of norm emergence.

Multi-agent soft constraint aggregation via sequential voting: theoretical and experimental results

2019 ◽  
Vol 33 (1-2) ◽  
pp. 159-191 ◽  
Author(s):  
Cristina Cornelio ◽  
Maria Silvia Pini ◽  
Francesca Rossi ◽  
Kristen Brent Venable
Keyword(s):  
Experimental Results ◽  
Soft Constraint ◽  
Constraint Aggregation ◽  
Multi Agent ◽  
Sequential Voting

A Fuzzy Adaptive K-SVD Dictionary Algorithm for Face Recogntion

2013 ◽  
Vol 347-350 ◽  
pp. 3797-3803 ◽  
Author(s):  
Xiao Ning Song ◽  
Zi Liu
Keyword(s):  
Iterative Method ◽  
Fuzzy Set ◽  
Sparse Coding ◽  
Sparse Representations ◽  
Experimental Results ◽  
Sparse Signal ◽  
Signal Representations ◽  
The Given ◽  
Class Assignment ◽  
Fuzzy Adaptive

Sparse representations using overcomplete dictionaries has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals. The K-SVD algorithm is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. However, the existing K-SVD algorithm is employed to dwell on the concept of a binary class assignment meaning that the multi-classes samples are assigned to the given classes definitely. The work proposed in this paper provides a novel fuzzy adaptive way to adapting dictionaries in order to achieve the fuzzy sparse signal representations, the update of the dictionary columns is combined with an update of the sparse representations by incorporated a new mechanism of fuzzy set, which is called fuzzy K-SVD. Experimental results conducted on the ORL and Yale face databases demonstrate the effectiveness of the proposed method.

Investors' Evaluations of Strategic Prior-Period Benchmark Disclosures in Earnings Announcements

2005 ◽  
Vol 80 (1) ◽  
pp. 243-268 ◽  
Author(s):  
Susan D. Krische
Keyword(s):  
Financial Performance ◽  
Performance Measures ◽  
Earnings Announcements ◽  
Quantitative Description ◽  
Experimental Results ◽  
Company Performance ◽  
Experimental Setting ◽  
Present Evidence ◽  
Financial Performance Measures ◽  
A Current

Schrand and Walther's (2000) archival evidence suggests that managers strategically disclose prior-period benchmarks in current earnings announcements, which, in turn, influences investors' judgments. Using a controlled experimental setting, I present evidence confirming that a transparent description of a transitory prior-period gain or loss affects how investors apply prior-period earnings when evaluating currentperiod earnings. I also provide evidence that this effect is likely to be unintentional on the part of investors, resulting from limitations in their memory for the prior-period event. Overall, the experimental results suggest that a quantitative description of the transitory prior-period gain or loss in a current earnings announcement helps investors to evaluate company performance. The results also highlight the need for consistency in reporting non-GAAP financial performance measures.

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