Suspicious Degree Research Articles

Shilling Attack Detection in User Based Recommendation System

The majority of the existing unsupervised methods for detecting shilling attacks are based on user rating patterns, ignoring the differences in rating behavior between legitimate users and attack users. These methods have low accuracy in detecting different shilling attacks without having any prior knowledge of the attack types. We provide a novel unsupervised shilling assault detection technique based on an examination of user rating behavior in order to overcome these constraints. By first examining the deviation of rating tendencies on each item, we are able to determine the target item(s) and the accompanying goals of the attack users. Based on the results of this study, a group of suspicious users is then created. Second, we examine the users' rating behaviors in terms of their rating and interest preferences. Finally, using measurements of user rating behavior, we determine the suspicious degree and identify attack users within the collection of suspicious users. The Movie Lens 1M dataset, the sampled Amazon review dataset, and the Netflix dataset all show how good the suggested detection model.

Ward_p_HMM: A Shilling Attack Detection Technique Using Ward_p Method and Hidden Markov Model

Collaborative Filtering Recommender Systems ( CFRSs ) are widely employed in several applications because of its satisfying performance in the customized recommendation. Recent studies show that CFRSs are at risk of shilling attacks where attackers inject shilling profiles into the system. Malicious user injected ratings not only severely impact genuineness of recommendations but also user's trustworthiness within recommendation systems. Existing unsupervised clustering technique uses Ward method which is an iterative method of low scalability . For addressing this issue, in this work an unsupervised SA detection technique named WardpHMM has been proposed. It uses Ward p and Hidden Markov Model (HMM). In this proposed method HMM is used to measure difference of user’s rating behavior. It generates User Suspicious Degree (USD) of each user by analyzing user’s Suspicious Degree Range of Items (SDRI) and User’s Matching Degree (UMD). Then Ward p method is applied to merge users based on USD and to acquire group of Attack Users (AUs). For performance analysis of the proposed technique Amazon-ratings sample dataset has been used. The performance comparison shows that proposed Ward p HMM technique outperforms baseline technique with respect to precision, recall and F1-score .

Fault Localization With Data Flow Information and an Artificial Neural Network

Fault localization is a technique for identifying the exact source code line with faults. It typically requires a lot of time and cost because, to locate the fault, a developer must track the execution of the failed program line by line. To reduce the fault localization efforts, many methods have been proposed. However, their localized suspicious code range is wide, and their fault localization effect is not high. To cope with this limitation, this paper computes the degree of fault suspiciousness of statements by using an artificial neural network and information of the executed test case, such as statement coverage, execution result, and definition-use pair. Compared to the approach that uses only statement coverage as input data for training an artificial neural network, the experiment results show higher accuracy in 15 types of faults out of 29 real fault types in the approach that the definition-use pair included.

Research on the construction of O2O teaching system of cross-cultural knowledge in College English based on MOOC

In order to improve the teaching effect of college English cross-cultural knowledge, with the support of recommendation algorithms, this paper builds a O2O teaching system of cross-cultural knowledge in college English based on MOOC, and provides service methods such as material resource learning, “smart grab-order” question answering, resource recommendation, LBS appointment teaching and offline teaching. Moreover, this paper constructs a learner and teacher interest model, introduces a suspicious degree update model into the interest model, uses a deep learning network algorithm to recommend composition resources, and uses a deep network adaptive learning algorithm to enhance the accuracy of composition resource recommendation. In addition, this paper constructs a system framework structure based on actual needs, and studies the realization process of each of its functional modules. Finally, this paper designs an experiment to evaluate the practical effect of O2O teaching system of cross-cultural knowledge in college English based on MOOC. The research results show that the system constructed in this paper meets the expected requirements.

Graph embedding-based approach for detecting group shilling attacks in collaborative recommender systems

Over the past decade, many approaches have been presented to detect shilling attacks in collaborative recommender systems. However, these approaches focus mainly on detecting individual attackers and rarely consider the collusive shilling behaviors among attackers, i.e., a group of attackers working together to bias the output of collaborative recommender systems by injecting fake profiles. Such shilling behaviors are generally termed group shilling attacks, which are more harmful to collaborative recommender systems than traditional shilling attacks. In this paper, we propose a graph embedding-based method to detect group shilling attacks in collaborative recommender systems. First, we construct a user relationship graph by analyzing the user rating behaviors and use a graph embedding method to obtain the low-dimensional vector representation of each node in the user relationship graph. Second, we employ the k-means++ clustering algorithm to obtain candidate groups based on the generated user feature vectors. Finally, we calculate the suspicious degree of each candidate group according to the attack group detection indicators and use the Ward’s hierarchical clustering method to cluster the candidate groups according to their suspicious degrees and obtain the attack groups. The experimental results on the Amazon and Netflix datasets show that the proposed method outperforms the baseline methods in detection performance.

Detecting suspicious entities in Offshore Leaks networks

The ICIJ Offshore Leaks Database represents a large set of relationships between people, companies, and organizations involved in the creation of offshore companies in tax-heaven territories, mainly for hiding their assets. This data are organized into four networks of entities and their interactions: Panama Papers, Paradise Papers, Offshore Leaks, and Bahamas Leaks. For instance, the entities involved in the Panama Papers networks are people or companies that had affairs with the Panamanian offshore law firm Mossack Fonseca, often with the purpose of laundering money. In this paper, we address the problem of searching the ICIJ Offshore Leaks Database for people and companies that may be involved in illegal acts. We use a collection of international blacklists of sanctioned people and organizations as ground truth for bad entities. We propose a new ranking algorithm, named Suspiciousness Rank Back and Forth (SRBF), that, given one of the networks in the ICIJ Offshore Leaks Database, leverages the network structure and the blacklist ground truth to assign a degree of suspiciousness to each entity in the network. We experimentally show that our algorithm outperforms existing techniques for node classification achieving area under the ROC curve ranging from 0.69 to 0.85 and an area under the recall curve ranging from 0.70 to 0.84 on three of the four considered networks. Moreover, our algorithm retrieves bad entities earlier in the rank than competitors. Further, we show the effectiveness of SRBF on a case study on the Panama Papers network.

Detecting shilling attacks in recommender systems based on analysis of user rating behavior

The existing unsupervised methods for detecting shilling attacks are mostly based on the rating patterns of users, ignoring the rating behavior difference between genuine users and attack users, and these methods suffer from low accuracy in detecting various shilling attacks without a priori knowledge of the attacks. To address these limitations, we propose a novel unsupervised shilling attack detection model based on an analysis of user rating behavior. First, we identify the target item(s) and the corresponding intentions of the attack users by analyzing the deviation of rating tendencies on each item, and based on this analysis, a set of suspicious users is constructed. Second, we analyze the users’ rating behaviors from an interest preference and rating preference perspective. In particular, we measure the diversity and memory of users’ interest preferences by entropy and block entropy, respectively, and we analyze the memory of user rating preferences by a self-correlation analysis. Finally, we calculate the suspicious degree and spot attack users in the set of suspicious users based on measurements of user rating behavior. Experimental results on the Netflix dataset, the MovieLens 1M dataset and the sampled Amazon review dataset demonstrate the effectiveness of the proposed detection model.

Management of intrahepatic splenosis:a case report and review of the literature

BackgroundSplenosis is the heterotopic autotransplantation and implantation of splenic tissue after splenic trauma or splenectomy. Considering that splenosis often occurs in the mesentery, omentum, and peritoneum, intrahepatic splenosis has seldom been reported. We report a rare case of isolated intrahepatic splenosis in a 54-year-old man who presented with a liver mass thought to be hepatocellular carcinoma.Case presentationA 54-year-old man was referred to our hospital for further evaluation of a liver lesion. The patient was asymptomatic and had a history of emergent splenectomy after a high-altitude falling accident. Abdominal contrast-enhanced computed tomography revealed a 4.5 × 3.3 cm lesion that was located in segment IV of the left liver lobe. The lesion had an inhomogeneous enhancement during the arterial phase and diminished enhancement during the portal and equilibrium phases. Similar radiological features were also observed on a contrast magnetic resonance imaging scan. Partial hepatectomy was performed with the suspicion of hepatocellular carcinoma. Pathological examination of the liver specimen revealed intrahepatic splenosis.ConclusionSplenosis should be considered in differential diagnosis of a liver mass discovered years after splenic trauma or surgery. A proposed scoring system may be helpful in evaluating the suspicious degree of intrahepatic mass to be splenosis. Invasive treatments are not recommended for asymptomatic patients, since the splenosis can provide beneficial immunologic function.

UD-HMM: An unsupervised method for shilling attack detection based on hidden Markov model and hierarchical clustering

The existing unsupervised methods usually require a prior knowledge to ensure the performance when detecting shilling attacks in collaborative filtering recommender systems. To address this limitation, in this paper we propose an unsupervised method to detect shilling attacks based on hidden Markov model and hierarchical clustering. We first use hidden Markov model to model user's history rating behaviors and calculate each user's suspicious degree by analyzing the user's preference sequence and the difference between genuine and attack users in rating behaviors. Then we use the hierarchical clustering method to group users according to user's suspicious degree and obtain the set of attack users. The experimental results on the MovieLens 1 M and Netflix datasets show that the proposed method outperforms the baseline methods in detection performance.

Graph Mining for Software Fault Localization: An Edge Ranking based Approach

Fault localization is considered one of the most challenging activities in the software debugging process. It is vital to guarantee software reliability. Hence, there has been a great demand for automated methods that can pinpoint faults for software developers. Various fault localization techniques that are based on graph mining have been proposed in the literature. These techniques rely on detecting discriminative sub-graphs between failing and passing traces. However, these approaches may not be applicable when the fault does not appear in a discriminative pattern. On the other hand, many approaches focus on selecting potentially faulty program components (statements or predicates) and then ranking these components according to their degree of suspiciousness. One of the difficulties encountered by such approaches is to understand the context of fault occurrence. To address these issues, this paper introduces an approach that helps in analyzing the context of execution traces based on control flow graphs. The proposed approach uses the edge-ranking of basic blocks in software programs using Dstar that proved to be more effective than many fault localization techniques. The proposed method helps in detecting some types of faults that could not be previously detected by many other approaches. Using Siemens benchmark, experiments show the effectiveness of the proposed technique compared to some well-known approaches such as Dstar, Tarantula, SOBER, Cause Transition and Liblit05. The percentage of localized faulty versions versus the percentage of code examined is taken as a measure. For instance, when the percentage of examined code is 30%, the proposed technique can localize nearly 81% of the faulty versions, which outperforms the other four techniques.

Model for the detection of signals in images with multiple suspicious locations

A signal detection model is presented that combines a signal model and a noise model providing mathematical descriptions of the frequency of appearance of the signals, and of the signal-like features naturally occurring in the background. We derive expressions for the likelihood functions for the whole ensemble of observed suspicious locations, in various possible combinations of signals and false signal candidates. As a result, this formalism is able to describe several new types of detection tests using likelihood ratio statistics. We have a global image abnormality test and an individual signal detection test. The model also provides an alternative mechanism in which is selected the combination of signal and noise features candidates that has the maximum likelihood. These tests can be analyzed with a variety of operating characteristic curves (ROC, LROC, FROC, etc.). In the mathematical formalism of the model, all the details characterizing the suspicious features are reduced to a single scalar function, which we name the signal specificity function, representing the frequency that a signal takes a certain value relative to the frequency of having a false signal with the same value in an image of given size. The signal specificity function ranks the degree of suspiciousness of the features found, and can be used to unify into a single score all the suspicious feature characteristics, and then apply the usual decision conventions as in the Swensson's detection model [Med. Phys. 23, 1709-1725 (1996)]. We present several examples in which these tests are compared. We also show how the signal specificity function can be used to model various degrees of accuracy of the observer's knowledge about image noise and signal statistical properties. Aspects concerning modeling of the human observer are also discussed.

Endodontic management of an impacted premolar

This report demonstrates a simple endodontic solution to an interdisciplinary case of a patient with multiple gene deficiencies. An adolescent patient presented with an impacted premolar that could not be extracted due to high-risk conditions. A suspicious degree of radiolucency around the crown was clinically diagnosed as dentigerous cyst. The treatment dilemma and implementation are discussed. This case report offers a different treatment option for impacted teeth when significant differential diagnosis of a pericoronal lesion dictates treatment.

Suspiciousness, Mental Simulation, and Norm Theory

Seven studies were conducted to replicate the work of Miller, Turnbull, and McFarland (1989), who tested predictions from norm theory (Kahneman & Miller, 1986). The first three studies with stimulus materials identical to those used by Miller et al. failed to confirm that the ease with which the event might be mentally simulated affected the degree of suspiciousness. In Studies 4, 5, and 6, the improbable events were made objectively more probable, but this did not produce significant results. In the 7th study, the objective probability and attitude toward the target were varied. Although there was a main effect for ease of mental simulation, this effect was produced by only 1 of the 3 vignettes.

Intravenous cocaine: Psychiatric effects, biological mechanisms

Volunteer addicts were administered iv loading doses of cocaine, followed by 4-hr cocaine infusions that maintained steady-state conditions. The loading doses were followed by the “rush” and “high” subjective effects that users typically experience; cocaine infusions maintained the experience of drug “high,” but not “rush.” In a subsequent experiment, haloperidol pretreatment did not alter cocaine “rush” but partially attenuated cocaine “high.” During cocaine infusions, we also noted suspicious and paranoid behavior, which were blindly rated by nurses. During one of the infusion conditions, the degree of suspiciousness observed was related to the amount of cocaine previously administered. Although cardiovascular responses to cocaine were marked, we found no alterations in plasma catecholamines following cocaine administrations. Baseline homovanillic acid (HVA) levels, however, were related to the degree of suspiciousness observed following cocaine dosing. The potential contributions of dopaminergic systems and physiological sensitization to the development of the psychiatric toxicity of cocaine are discussed. faRecipient of 1987 A.E. Bennelt Award in Clinical Science.

Automated classification of cytological specimens based on features extracted from nuclei images

The paper describes the pattern recognition subsystem of an automated specimen analyser being developed for pre-screening applications with a high resolution system. The complete recognition system is divided into a hierarchy of two different recognition systems: the single cell classifier, and the specimen classifier. The single cell image classifier operates on the cell data of isolated cell images based on features which are extracted from the nucleus only. For each specimen a large number of single cells are processed by the single cell classifier. The collection of the resulting discriminant vectors-and not just the decisions only-are used as measurements for the subsequent specimen classifier, which has to produce a real-valued discriminant function indicating the degree of suspiciousness of the specimen analysed. By proper thresholding of this figure of malignancy, the final decision can be made. The approach offers the opportunity of detecting suspicious cell modifications in an early stage.

Insanity among the Bemba of Northern Rhodesia

Opening ParagraphThis article is concerned with tribal ideas on insanity among the Bemba of the Northern Province of Northern Rhodesia.Dr. Alan Gregg, the Director of Medical Services Division of the Rockefeller Foundation, states that ‘Psychiatry as the study of disordered conduct is intimate to an almost suspicious degree with ethics, with cultural anthropology, with sociology, with metaphysics, with religion, with artistic activities . . . ’, and adds that many mental disorders are ‘often discernible only in terms of the patient's relationships with other human beings in some given intellectual or cultural or social or moral system’. He thus definitely emphasizes the necessity of studying the sociological background of the insane patient when cures are attempted.