CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts

The powerful ability of ChatGPT has caused widespread concern in the academic community. Malicious users could synthesize dummy academic content through ChatGPT, which is extremely harmful to academic rigor and originality. The need to develop ChatGPT-written content detection algorithms call for large-scale datasets. In this paper, we initially investigate the possible negative impact of ChatGPT on academia,and present a large-scale CHatGPT-writtEn AbsTract dataset (CHEAT) to support the development of detection algorithms. In particular, the ChatGPT-written abstract dataset contains 35,304 synthetic abstracts, with Generation, Polish, and Mix as prominent representatives. Based on these data, we perform a thorough analysis of the existing text synthesis detection algorithms. We show that ChatGPT-written abstracts are detectable, while the detection difficulty increases with human involvement.Our dataset is available in https://github.com/botianzhe/CHEAT.Comment: 9 pages, 6 figure

Learning Second Order Local Anomaly for General Face Forgery Detection

In this work, we propose a novel method to improve the generalization ability of CNN-based face forgery detectors. Our method considers the feature anomalies of forged faces caused by the prevalent blending operations in face forgery algorithms. Specifically, we propose a weakly supervised Second Order Local Anomaly (SOLA) learning module to mine anomalies in local regions using deep feature maps. SOLA first decomposes the neighborhood of local features by different directions and distances and then calculates the first and second order local anomaly maps which provide more general forgery traces for the classifier. We also propose a Local Enhancement Module (LEM) to improve the discrimination between local features of real and forged regions, so as to ensure accuracy in calculating anomalies. Besides, an improved Adaptive Spatial Rich Model (ASRM) is introduced to help mine subtle noise features via learnable high pass filters. With neither pixel level annotations nor external synthetic data, our method using a simple ResNet18 backbone achieves competitive performances compared with state-of-the-art works when evaluated on unseen forgeries

Neural correlates of quantity processing of numeral classifiers.

ObjectiveClassifiers play an important role in describing the quantity information of objects. Few studies have been conducted to investigate the brain organization for quantity processing of classifiers. In the current study, we investigated whether activation of numeral classifiers was specific to the bilateral inferior parietal areas, which are believed to process numerical magnitude.MethodUsing functional MRI, we explored the neural correlates of numeral classifiers, as compared with those of numbers, dot arrays, and nonquantity words (i.e., tool nouns).ResultsOur results showed that numeral classifiers and tool nouns elicited greater activation in the left inferior frontal lobule and left middle temporal gyrus than did numbers and dot arrays, but numbers and dot arrays had greater activation in the middle frontal gyrus, precuneus, and the superior and inferior parietal lobule in the right hemisphere. No differences were found between numeral classifiers and tool nouns.ConclusionThe results suggest that quantity processing of numeral classifiers is independent of that of numbers and dot arrays, supporting the notation-dependent hypothesis of quantity processing

PLDP: Personalized Local Differential Privacy for Multidimensional Data Aggregation

The collection of multidimensional crowdsourced data has caused a public concern because of the privacy issues. To address it, local differential privacy (LDP) is proposed to protect the crowdsourced data without much loss of usage, which is popularly used in practice. However, the existing LDP protocols ignore users’ personal privacy requirements in spite of offering good utility for multidimensional crowdsourced data. In this paper, we consider the personality of data owners in protection and utilization of their multidimensional data by introducing the notion of personalized LDP (PLDP). Specifically, we design personalized multiple optimized unary encoding (PMOUE) to perturb data owners’ data, which satisfies ϵtotal-PLDP. Then, the aggregation algorithm for frequency estimation on multidimensional data under PLDP is developed, which is described in two situations. Experiments are conducted on four real datasets, and the results show that the proposed aggregation algorithm yields high utility. Moreover, case studies with four real datasets demonstrate the efficiency and superiority of the proposed scheme

Resistance of multi-layered UHPFRC against in-service projectile: experimental investigation and modelling prediction

The present paper studies the ballistic performance of Ultra-High Performance Fiber Reinforced Concrete (UHPFRC) applying multi-layered concept against the 7.62 mm projectile at 840 m/s. Coarse basalt aggregates are incorporated in the UHPFRC under the premise of reducing the cement powder consumption and taking advantages of their superior ballistic resistance. We found that the designed triple-layered UHPFRC 16a1s(40)-8a1s(10)-16a1s(40) achieves a superior impact resistance compared to the single-layered reference, with a 32% reduction of the penetration depth. The improved resistance of the triple-layered UHPFRC is associated with the multiple effects of the coarse aggregate, the layer interface, the fibers direction in the thin middle layer, and the edge confinement of the rear layer. Moreover, a new analytical model is proposed to predict the penetration depth in the multi-layered UHFRC, which can take the varying mechanical properties of the layered targets into consideration. The results from this study shed light on understanding the ballistic performance of layered UHPFRC, and promote its application in protective constructions

Conceptual design and performance evaluation of two-stage ultra-low binder ultra-high performance concrete

This study proposes a novel concept of two-stage ultra-high performance concrete (TS-UHPC), towards ultra-low binder consumption. The effects of grout and coarse aggregate are investigated and their compatibility is evaluated. Results show that TS-UHPC has a low binder amount (down to 364 kg/m3) and high binder efficiency (up to 0.417 MPa·m3/kg), possessing excellent compressive strength of up to 151.8 MPa at 91 days. Microstructural analysis reveals that grout with a sand-to-powder ratio of 1.0 shows a higher hydration degree, denser structure, and increased later strength. Coarser basalt aggregate tends to slightly lower compressive and splitting tensile strength, 14% and 12% reduction with the maximum size from 8 mm to 25 mm, respectively. The TS-UHPC has an excellent interfacial transition zone that induces a water-permeable porosity of 0.91%–1.32%. New formulas are proposed to describe correlation between compressive and splitting tensile strength of TS-UHPC, and to predict strength of TS-UHPC by grout

Functionally graded ultra-high performance cementitious composite with enhanced impact properties

This study develops functionally graded ultra-high performance cementitious composite beams by applying the composite concepts of Ultra-high Performance Concrete (UHPC), Two-stage Concrete (TSC) and Slurry-infiltrated Fibrous Concrete (SIFCON). The functionally graded composite beam (FGCB) is fabricated with a bottom layer of SIFCON and top layer of TSC, and the two layers are synchronously cast by using UHPC slurry. The novel concept of FGCB is proposed towards more economical and high performance structural systems, namely excellent flexural bearing capacity and impact resistance, low cement consumption and high steel fibre utilization efficiency. The fresh and hardened properties of UHPC slurry, flexural and impact properties of FGCB are measured. The results reveal that the designed FGCB has superior flexural properties and impact resistance, without showing any interfacial bond problem. The fibre utilization efficiency of the designed FGCB is very high compared to the traditional UHPC and SIFCON beams. The 30 mm medium hook-ended steel fibres show the best utilization efficiency compared to the 13 mm short straight and 60 mm long 5D steel fibres, and 3% medium fibres are optimum to design FGCB. The low-velocity impact resistance of FGCB is well linearly correlated with its static flexural toughness

Development and properties evaluation of sustainable ultra-high performance pastes with quaternary blends

This study aims to investigate the synergistic effect of quaternary blends applying supplementary cementitious materials on sustainable Ultra-high Performance Concrete (UHPC) pastes. The hydration kinetics, pore structures, fresh behaviour, strength, fibre-to-matrix bond, shrinkage and environmental sustainability of 14 UHPC pastes are determined and analysed. The results show that limestone powder contributes to better environmental sustainability and fresh behaviour, but enlarged shrinkage and diminished strength, and application of silica powder is an effective measure to overcome those disadvantages. Slag cement possessing a relatively lower Ca/Si ratio (2.45) is preferred to a lower amount but finer silica (3% nano silica) in the presence of limestone powder, compared to the Portland cement with a higher Ca/Si (3.22) that needs more silica even with coarser particle size (5% micro silica). Quaternary blends with cement-slag-limestone-silica in UHPC pastes have considerable advantage of reducing embedded CO2 emission and and improving sustainability efficiency. Furthermore, positive synergies in term of strength, fibre-to-matrix bond and total free shrinkage are observed in UHPC pastes with quaternary binders compared to binary and ternary ones

Development and properties evaluation of sustainable ultra-high performance pastes with quaternary blends

This study aims to investigate the synergistic effect of quaternary blends applying supplementary cementitious materials on sustainable Ultra-high Performance Concrete (UHPC) pastes. The hydration kinetics, pore structures, fresh behaviour, strength, fibre-to-matrix bond, shrinkage and environmental sustainability of 14 UHPC pastes are determined and analysed. The results show that limestone powder contributes to better environmental sustainability and fresh behaviour, but enlarged shrinkage and diminished strength, and application of silica powder is an effective measure to overcome those disadvantages. Slag cement possessing a relatively lower Ca/Si ratio (2.45) is preferred to a lower amount but finer silica (3% nano silica) in the presence of limestone powder, compared to the Portland cement with a higher Ca/Si (3.22) that needs more silica even with coarser particle size (5% micro silica). Quaternary blends with cement-slag-limestone-silica in UHPC pastes have considerable advantage of reducing embedded CO2 emission and and improving sustainability efficiency. Furthermore, positive synergies in term of strength, fibre-to-matrix bond and total free shrinkage are observed in UHPC pastes with quaternary binders compared to binary and ternary ones

Enhancing flexural performance of ultra-high performance concrete by an optimized layered-structure concept

The study aims to improve the flexural behaviors of ultra-high performance fiber reinforced concrete (UHPFRC) by applying the concept of layered-structure. Deterministic criteria for layer cracking and debonding are proposed, formulas to predict the critical load at the first failure stage are developed, and effects of the layer E-modulus and thickness are assessed. Subsequently, double-layered UHPFRC beams are designed and tested under the three-point bending. Mechanical and interfacial properties of the beams are studied. Influences of the bottom layer thickness on the peak flexural load and the flexural energy are then investigated, which presents that a layer thickness ratio of 0.6 gives the optimum load carrying ability and beam flexural energy. The subsequent section discusses the effects of fiber re-arrangement on the flexural performances, revealing that the designed double-layered UHPFRC beam is able to withstand higher flexural load and energy than its single-layered counterpart with the same total fiber content. Moreover, it is exhibited that the peak flexural load is dependent on the fibers in the bottom layer while the flexural energy enhancement is related to fibers in both layers. The layered UHPFRC beam composed of a 40 mm-thick top layer with 0.6% steel fibers and a 60 mm-thick bottom layer with 1.6% fibers is an optimal choice leading to the superior peak flexural load and energy