Fake Rate Research Articles

Fake It Till You Make It—A Statistical Assessment of the Proportion of Fake Reviews in Closed Reputation Systems

ABSTRACT Fake reviews have become an increasingly relevant issue in electronic commerce. Regulators and platform operators have begun to take action against fake reviews through regulation and litigation. While there has emerged an ample body of research on fake reviews in open reputation systems (i.e. where everyone can write reviews), little is known about the frequency and nature of fake reviews in closed reputation systems (i.e. where the completion of a transaction is a necessary precondition for reviewing). This article assesses the frequency of fake reviews in this particular domain of the platform economy. To do so, we draw on data from Airbnb and investigate systematic differences between first and second rating score distributions, estimating the proportion of potentially fake five-star ratings using KL-divergence. In doing so, we advance the understanding of fake reviews in closed reputation systems, offering theoretical insights into review dynamics and practical strategies for detecting fraudulent behavior using score distributions and user attributes. This shows that early-on reputation is crucial for complementors to overcome the cold-start problem, and platform operators will have to think of ways to allow them to do so (e.g. reputation imports) in order not to jeopardize the overall credibility of their reputation systems.

A recommendation attack detection approach integrating CNN with Bagging

Due to their open architecture, collaborative filtering recommender systems are susceptible to recommendation attacks, in which attackers inject fake rating data into the system to affect the accuracy of recommendation results. To detect these attacks, numerous detection methods have been designed and proven effective. However, in recent years, deep learning-based recommendation attack models such as GSA-GAN have shown higher concealment, posing new challenges to existing detection methods. Motivated by the need for improved detection, in this paper we propose a new approach called CNN-BAG, which integrates convolutional neural network (CNN) and Bagging (BAG) techniques. CNN-BAG can enhance the detection performance by simultaneously leveraging the deep learning capabilities of CNN and the ensemble learning strengths of Bagging. Firstly, we construct a deep neural network based on CNN as the base learner to automatically extract and learn features of recommendation attacks. Secondly, we use the Bagging algorithm to perform bootstrap sampling on the training data to generate multiple diverse training subsets. The above constructed base learners are then trained on these generated training subsets to produce multiple base classifiers for classifying recommendation attacks. Finally, we combine the base classifiers’ outputs using a majority voting method to obtain the final detection results. To assess the performance of CNN-BAG in detecting recommendation attacks, we compared it against several well-established detection methods on the Movielens-10M and Amazon datasets. Our experiments revealed that CNN-BAG is adept at identifying various attack types, including the deep learning-based recommendation attack models.

The New Small Wheel Trigger for the ATLAS experiment

The ATLAS New Small Wheel (NSW) Muon spectrometer upgrade, completed in 2022, was the largest Phase I detector upgrade among LHC experiments. The NSW enhances triggering in the endcap region (1.3<|η|<2.4) by confirming muons from the Interaction Point (IP) and rejecting fake contributions. It also improves muon tracking with 2.5 million high-resolution channels across 16 layers. The NSW Trigger, based on small-strip Thin Gap Chambers (sTGC) and Micromegas (MM) technologies, provides Level-1 triggers at every Bunch Crossing (BC) with a fixed low latency (44 BC). Integrated into ATLAS in 2023, the NSW Trigger significantly reduces fake rates and overall readout deadtime. The custom electronics of the NSW Trigger system efficiently collect, process, and trigger on IP muons, focusing on the fully operational sTGC pad-only path based on the Pad Trigger (PT) and Trigger Processor (TP) FPGA based boards. Performance studies using 13.6TeV pp collisions are presented, demonstrating NSW’s readiness for the High Luminocity LHC (HL-LHC) era and outlining Phase II upgrade perspectives.

MRFS: Mining Rating Fraud Subgraph in Bipartite Graph for Users and Products

Fraud in e-commerce fields (e.g., Amazon, Taobao, and so on) and social networks (e.g., Twitter and Weibo) has recently brought a very bad user experience. Rating fraud detection is an urgent issue for improving user experiences. However, existing methods have lots of limitations in some respects, because it is always very hard to acquire sufficient labeled data for fraud detection and detect new fraud patterns. Fortunately, the relationship for users rating (e.g., purchasing and following) products can be represented as a bipartite graph. So the problem of rating fraud detection can be transformed into the problem of abnormal subgraph detection in the bipartite graph. The major challenge of fraud detection is to distinguish fake rates from real user rates. In this article, we focus on mining rating fraud-connected subgraphs in a bipartite graph. The motivation for this work is fraud detection tasks, which can usually be formulated as mining a bipartite graph formed by source nodes (followers and users) and target nodes (followees and products) for malicious patterns. Now, smart fraudsters evade existing detection methods by buying a large pool of users and hijacking honest users, making them look “normal”-this behavior is called “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">camouflage</i> .” Accordingly, we propose a fraud detection approach for mining rating fraud subgraph (MRFS), which addresses the problem from the intrinsic metric (e.g., fraudulence, badness and unreliability). The proposed MRFS mines the intrinsic characteristics of nodes and edges from node behavior information, which is an effective and scalable (linear on the input size) algorithm. A large number of comparative experimental results on real-world rating networks show that our proposed MRFS is efficient and universal.

An intrinsic integrity-driven rating model for a sustainable reputation system

In the era of digital markets, the challenge for consumers is discerning quality amidst information asymmetry. While traditional markets use brand mechanisms to address this issue, transferring such systems to internet-based P2P markets – where misleading practices like fake ratings are rampant – remains challenging. Current internet platforms strive to counter this through verification algorithms, but these efforts find themselves in a continuous tug-of-war with counterfeit actions.Exploiting the transparency, immutability, and traceability of blockchain technology, this paper introduces a robust reputation voting system grounded in it. Unlike existing blockchain-based reputation systems, our model harnesses an intrinsically economically incentivized approach to bolster agent integrity. We optimize this model to mirror real-world user behavior, preserving the reputation system’s foundational sustainability. Through Monte-Carlo simulations, using both uniform and power-law distributions enabled by an innovative inverse transform method, we traverse a broad parameter landscape, replicating real-world complexity. The findings underscore the promise of a sustainable, transparent, and formidable reputation mechanism. Given its structure, our framework can potentially function as a universal, sustainable oracle for offchain-onchain bridging, aiding entities in perpetually cultivating their reputation. Future integration with technologies like Ring Signature and Zero Knowledge Proof could amplify the system’s privacy facets, rendering it particularly influential in the ever-evolving digital domain.

Silicon vertex detector of the Belle II experiment

The silicon vertex detector (SVD) is installed at the heart of the Belle II experiment, taking data at the high-luminosity B-Factory SuperKEKB since 2019. The SVD is a four-layer double-sided strip detector with tracking and particle-identification capabilities. In this paper, we report on the performance of the reconstruction of SVD hits. The detector has shown a stable and above-99% hit efficiency, with a large signal-to-noise in all sensors since the beginning of data taking. Cluster position and time resolution have been measured with 2020 and 2022 data and show excellent performance and stability. In particular, the cluster-position resolution is between 7 and 12 μm for the small-pitch sensors, in reasonable agreement with the expectations, while the cluster time resolution is measured to be below 3 ns. The effect of radiation damage is visible, but not affecting the performance. As the luminosity increases, higher machine backgrounds are expected and the excellent hit-time information in SVD can be exploited for background rejection. In particular, we have recently developed a novel procedure to select hits by grouping them event-by-event based on their time. This new procedure allows a significant reduction of the fake rate, while preserving the tracking efficiency, and it has therefore replaced the previous cut-based procedure. We have developed a method that uses the SVD hits to estimate the track time (previously unavailable) and the collision time. It has a similar precision to the estimate based on the drift chamber readout but its execution time is three orders of magnitude smaller, allowing a faster online reconstruction that is crucial in a high luminosity regime. The track time is a powerful information that allows, together with the aforementioned grouping selection, to raise the occupancy limit above that expected at nominal luminosity, leaving room for a safety factor. Finally, in June 2022 the data taking of the Belle II experiment was stopped to install a new two-layer DEPFET detector (PXD) and upgrade components of the accelerator. The whole silicon tracker (PXD+SVD) has been extracted from Belle II, the new PXD installed, the detector closed and commissioned. We briefly describe the SVD results of this upgrade.

A Sampling-Based Method for Detecting Data Poisoning Attacks in Recommendation Systems

The recommendation algorithm based on collaborative filtering is vulnerable to data poisoning attacks, wherein attackers can manipulate system output by injecting a large volume of fake rating data. To address this issue, it is essential to investigate methods for detecting systematically injected poisoning data within the rating matrix. Since attackers often inject a significant quantity of poisoning data in a short period to achieve their desired impact, these data may exhibit spatial proximity. In other words, poisoning data may be concentrated in adjacent rows of the rating matrix. This paper capitalizes on the proximity characteristics of poisoning data in the rating matrix and introduces a sampling-based method for detecting data poisoning attacks. First, we designed a rating matrix sampling method specifically for detecting poisoning data. By sampling differences obtained from the original rating matrix, it is possible to infer the presence of poisoning attacks and effectively discard poisoning data. Second, we developed a method for pinpointing malicious data based on the distance of rating vectors. Through distance calculations, we can accurately identify the positions of malicious data. After that, we validated the method on three real-world datasets. The results demonstrate the effectiveness of our method in identifying malicious data within the rating matrix.

Abnormal and Maltreatment Intrusion Detection in Big and Small Data Storage Blue Brain Network

In modern network environment users facing several network attacks that creates harm to entire network environment. An efficient intrusion recognition approaches is obligatory to diminish such harm and to defend the reliability and accessibility of network environment. In this research work a novel approach for intrusion recognition in network environment depends on awareness method that discover by propagating Intrusion Detection System information to several layers. To enhance accurateness of intrusion recognition high dimensional data characteristic depiction is applied in IoT network. For illustration public data set KDD-Cup 99 is utilized. The outcomes demonstrate that the Maltreatment Intrusion Detection in Big and Small Data Storage (M-BSDS) can efficiently identify irregular malicious activities in IoT network environment, enhanced recognition accurateness and diminish fake optimistic rate evaluated with established intrusion detection approaches.

New vertex reconstruction algorithm for measuring gamma-rays with the AMS tracker

Gamma-ray polarization is one of the most promising tools to probe the universe at extreme conditions and search for new physics. The Alpha Magnetic Spectrometer (AMS) is a precision particle physics detector on the International Space Station. With its high accuracy thin silicon strip detectors and permanent magnet, AMS is unique to study the polarization of high energy gamma-rays. The detection of gamma-rays above a few hundreds of MeV typically utilize the electron–positron pair production process, and the key step is to reconstruct the vertex where gamma-ray converts into the electron–positron pair. We present a new vertex reconstruction algorithm that is based on an extended cellular automaton to determine the direction and energy information of gamma-rays from the electron and positron trajectories in the AMS tracker. The vertex reconstruction efficiency is significantly improved with the presented algorithm over the entire energy range above 100 MeV and reaches >90% above 1 GeV. The fake rate is drastically reduced over the entire energy range and is reduced from ∼10% to ∼3% at 200 MeV. The presented algorithm will thus provide the basis for measuring gamma-ray polarization with AMS.

Mass testing of SiPMs for the CMVD at IICHEP

A Cosmic Muon Veto Detector (CMVD) is being built around the mini-Iron Calorimeter (mini-ICAL) detector at the transit campus of the India based Neutrino Observatory, Madurai. The CMV detector will be made using extruded plastic scintillators with embedded wavelength shifting (WLS) fibres which propagate re-emitted photons of longer wavelengths to silicon photo-multipliers (SiPMs). The SiPMs detect these scintillation photons, producing electronic signals. The design goal for the cosmic muon veto efficiency of the CMV is &gt;99.99% and fake veto rate less than 10^{-5}−5. A testing system was developed, using an LED driver, to measure the noise rate and gain of each SiPM, and thus determine its overvoltage (V_{ov}ov). This paper describes the test results and the analysed characteristics of about 3.5k SiPMs.

Α ranking model based on user generated content and fuzzy logic

User Generated Comments and ranking based on it, has been studied extensively due to its impact on consumers and businesses. Literature highlights that platforms’ rankings might be deceiving due to fake reviews or rates that do not correspond to reality, but existing ranking models are a ‘black box’ thus, they cannot be assessed. This study fills in this gap by proposing a step-by-step ranking model which is based on comments rather than rates or the theoretically established criteria (e.g., food, service, atmosphere), which are not capable to portray the complexity of the dining experience. The proposed ranking model extracts topics from qualitative data (documents) and converts them into quantitative values through the lens of fuzzy logic. The topics are used as the ranking space while, pairwise comparisons between items (restaurants) are performed to extract a ranking ladder. We applied our model to TripAdvisor’s documents from restaurants in Athens, Greece.

Ready for LHC Run III: the ATLAS New Small Wheel and the MicroMegas chambers performances

The two New Small Wheels (NSW) for the upgrade of the ATLAS Muon Spectrometer are now installed in the experiment and ready to collect data in LHC Run III, started in July 2022. The NSW is the largest Phase-1 upgrade project of ATLAS. Its challenging completion and readiness for data taking is a remarkable achievement of the Collaboration. The two wheels (10 meters in diameter) replace the first muon stations in the high-rapidity regions of ATLAS and are equipped with multiple layers of two new detector technologies: the small strips Thin Gap Chambers (sTGC) and the MicroMegas (MM). The latter, belong to the family of Micro Pattern Gaseous Detectors (MPGD), for the first time used in such a large scale in HEP experiments. Each detector technology will cover more than 1200 m2 of active area. The new system is designed to assure high tracking efficiency, reduction of fake trigger rates and precision measurement of muon tracks, also in view of the higher background environment foreseen for Hi-Lumi LHC. In this presentation, the motivation of the NSW upgrade and the steps from the commissioning to the data taking together with the first results using the Run III data will be presented, focusing on the MicroMegasperformances.

SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease.

Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving artificial intelligence-enabled diagnosis of IRDs using generative adversarial networks (GANs). Diagnostic study of gene-labeled fundus autofluorescence (FAF) IRD images using deep learning. Moorfields Eye Hospital (MEH) dataset of 15692 FAF images obtained from 1800 patients with confirmed genetic diagnosis of 1 of 36 IRD genes. A StyleGAN2 model is trained on the IRD dataset to generate 512× 512 resolution images. Convolutional neural networks are trained for classification using different synthetically augmented datasets, including real IRD images plus 1800 and 3600 synthetic images, and a fully rebalanced dataset. We also perform an experiment with only synthetic data. All models are compared against a baseline convolutional neural network trained only on real data. We evaluated synthetic data quality using a Visual Turing Test conducted with 4 ophthalmologists from MEH. Synthetic and real images were compared using feature space visualization, similarity analysis to detect memorized images, and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score for no-reference-based quality evaluation. Convolutional neural network diagnostic performance was determined on a held-out test set using the area under the receiver operating characteristic curve (AUROC) and Cohen's Kappa (κ). An average true recognition rate of 63% and fake recognition rate of 47% was obtained from the Visual Turing Test. Thus, a considerable proportion of the synthetic images were classified as real by clinical experts. Similarity analysis showed that the synthetic images were not copies of the real images, indicating thatcopied real images, meaning the GAN was able to generalize. However, BRISQUE score analysis indicated that synthetic images were of significantly lower quality overall than real images (P < 0.05). Comparing the rebalanced model (RB) with the baseline (R), no significant change in the average AUROC and κ was found (R-AUROC=0.86[0.85-88], RB-AUROC=0.88[0.86-0.89], R-k=0.51[0.49-0.53], and RB-k=0.52[0.50-0.54]). The synthetic data trained model (S) achieved similar performance as the baseline (S-AUROC=0.86[0.85-87], S-k=0.48[0.46-0.50]). Synthetic generation of realistic IRD FAF images is feasible. Synthetic data augmentation does not deliver improvements in classification performance. However, synthetic data alone deliver a similar performance as real data, and hence may be useful as a proxy to real data. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references.

An Analysis of House Price Prediction Using Ensemble Learning Algorithms

It is very important to understand the market drifts in the wake of booming civilization and ever-changing market requirements. The principal purpose of the study is the prediction of house prices based on current conditions. From historical data on property markets, literature attempts to draw useful insights. Business trends must be understood so that individuals may prepare their budgetary needs accordingly. A society that is ever-expanding is driven by the growing real estate industry. A lot of clients have been duped by agents setting up a fake market rate. As a result, the real estate industry has become less transparent in recent years. Due to decreased accuracy and overfitting of data, the previous model reduced efficiency, whereas the newly developed model resolves such issues and provides a rich user interface with a better model. An important part of this study is to develop an extensive model that is beneficial to both business societies and individuals. This is the main objective of this study. In order to simplify the client’s fieldwork and free up his time and money, this software is intended to assist him. Machine learning algorithms enable models to be enlightened such as root mean square error, random forest, support vector machine, k-nearest neighbors, mean squared error, extreme gradient boost, mean absolute error, R-squared score, linear regression, AdaBoost, CatBoost.

Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems

Peer-to-peer sharing platforms become increasingly important in the platform economy. From an HCI-perspective, this development is of high interest, as those platforms mediate between different users. Such mediation entails dealing with various social issues, e.g., building trust between peers online without any physical presence. Peer ratings have proven to be an important mechanism in this regard. At the same time, scoring via car telematics become more common for risk assessment by car insurances. Since user ratings face crucial problems such as fake or biased ratings, we conducted a design case study to determine whether algorithm-based scoring has the potential to improve trust-building in P2P-carsharing. We started with 16 problem-centered interviews to examine how people understand algorithm-based scoring, we co-designed an app with scored profiles, and finally evaluated it with 12 participants. Our findings show that scoring systems can support trust-building in P2P-carsharing and give insights how they should be designed.

A prosocial fake news intervention with durable effects

The present online intervention promoted family-based prosocial values—in terms of helping family members—among young adults to build resistance against fake news. This preregistered randomized controlled trial study is among the first psychological fake news interventions in Eastern Europe, where the free press is weak and state-sponsored misinformation runs riot in mainstream media. In this intervention, participants were endowed with an expert role and requested to write a letter to their digitally less competent relatives explaining six strategies that help fake news recognition. Compared to the active control group there was an immediate effect (d = 0.32) that persisted until the follow-up four weeks later (d = 0.22) on fake news accuracy ratings of the young, advice-giving participants. The intervention also reduced the bullshit receptivity of participants both immediately after the intervention and in the long run. The present work demonstrates the power of using relevant social bonds for motivating behavior change among Eastern European participants. Our prosocial approach with its robust grounding in human psychology might complement prior interventions in the fight against misinformation.

Analysing supervised learning approaches for detecting shilling attacks in collaborative recommendations

Collaborative recommendation systems offer users personalized recommendations based on their past interactions and the actions of other users. However, these systems can be compromised by shilling attacks, in which fake feedback and ratings are introduced in order to manipulate the recommendations made by the system. It is important to identify and mitigate these attacks to maintain the reliability and accuracy of the recommendations. There are different ways to deal with shilling attacks, which involve using technology to detect fake ratings and assess the trustworthiness of users. Some solutions include using machine learning to spot patterns, applying filters to weed out fake ratings, using a combination of different filtering techniques to make recommendations, and establishing reputation systems to evaluate the reliability of users and their ratings. This work provides a comprehensive overview of the current methods for detecting shilling attacks in collaborative recommendation systems, including different types of attacks and various detection approaches. It also discusses the limitations and challenges of these approaches and compares their performance.

Deep Learning Hybrid Approaches to Detect Fake Reviews and Ratings

Nowadays, online reviews and ratings are the most valuable source of word-of-mouth, voice-of-customer, and feedback, also customers can make purchasing decisions on what to buy, where to buy, and what to select. Genuine online reviews are becoming popular, but unfortunately, we have an issue that might only sometimes be unbiased or accurate. Because most of the reviews are fake reviews and ratings, these could mislead innocent customers and highly influence customers' purchasing decisions in the wrong manner. This paper's primary goal is to accurately detect fake reviews and what is the main difference between them. The secondary goal is to detect fake ratings and actual ratings-based reviews across the online platform, especially Amazon datasets. The Paper proposes two novel deep-learning Hybrid techniques: CNN-LSTM for detecting fake online reviews, and LSTM-RNN for detecting fake ratings in the e-commerce domain. Both Hybrid models can outperform and achieve better performance with the most advanced word embedding techniques, Glove, and One hot encoding techniques. As per the experimental results, the first technique efficiently detects fake online reviews with the highest prediction accuracy. The second hybrid model is better than the existing models that detect fake online ratings with the most excellent precision of 93.8%. The experimental research efficiently revealed that the CNN-LSTM and LSTM-RNN methods are more efficient and practicable and might be better suited for optimal results and maximizing the efficiency of fake online review detection .

Development of the Readout Electronics for a MAPS-Based Beam Telescope

Along with the change of the Deutsches Elektronen-Synchrotron (DESY) II test beam facility operation mode, the existing beam telescope built with the Mimosa26 chips will be unable to meet the requirements of the much-increased beam particle rate. In this article, we present the readout electronics of a new beam telescope built with ALice PIxel Detector (ALPIDE) chips which are originally designed for the upgrade of A Large Ion Collider Experiment (ALICE). The gigabit Ethernet implemented in the high-speed transceiver of Kintex field programmable gate array (FPGA) enables individual data acquisition and control of each ALPIDE chip. The data from the multidetector plane are transmitted to the remote platform via an Ethernet switch. The global trigger is generated by an AIDA2020-trigger logic unit (TLU) by the coincidence of two pairs of plastic scintillators. The results of long-term beam tests show that the new beam telescope can achieve a lower fake hit rate and a trigger rate of 220 kHz, which increases beam utilization and reduces the difficulty of track reconstruction. The biased resolution of distribution using all six planes for tracking in a 6 GeV electron beam is measured to be about 5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> .

Unmasking the Deception: The Interplay between Fake Reviews, Rating Dispersion, and Consumer Demand

Unmasking the Deception: The Interplay between Fake Reviews, Rating Dispersion, and Consumer Demand