Miracle-dz/UniFAR-FTUs huggingface dataset:
FF-Net: An End-to-end Feature-Fusion Network for Double JPEG Detection and Localization

In the real-world, most images are saved in JPEG format, so many forged images are partially or totally composed of JPEG images and then saved in JPEG format again. In this case, exposing forged images can be accomplished by the detection of double JPEG compressions. Although the detection methods of double JPEG compressions have greatly improved, they rely on handcrafted features of image patches and cannot locate forgery at pixel-level. To break this limitation, we propose an end-to-end feature-fusion network (FF-Net) for double compression detection and forgery localization. We find that JPEG compression fingerprint primarily exists on the high-frequency component of an image, and the singly and doubly compression yield different fingerprints. Therefore, we design two encoders cooperatively to learn the compression fingerprint directly from the whole image. A decoder is deployed to locate the regions with different compression fingerprints at pixel-level based on the learned compression fingerprint. The experiment results verify that the proposed FF-Net can detect and locate the forged regions more accurately than these existing detection methods. Besides, it has a good generalization ability that the network trained on one compression case can work in numerous compression cases. Moreover, it can detect different local forgeries, including copy-move, splicing, and object-removal.
Precision Recall Cover: A Method For Assessing Generative Models

Generative modelling has seen enormous practical advances over the past few years. Evaluating the quality of a generative system however is often still based on subjective human inspection. To overcome this, very recently the research community has turned to exploring formal evaluation metrics and methods. In this work, we propose a novel evaluation paradigm based on a two way nearest neighbor test. We define a novel measure of mutual coverage for two probability distributions. From this, we derive an empirical analogue and show analytically that it exhibits favorable theoretical properties while it is also straightforward to compute. We show that, while algorithmically simple, our derived method is also statistically sound. In contrast to previously employed distance measures, our measure naturally stems from a notion of local discrepancy, which can be accessed separately. This provides more detailed information to practitioners for diagnosing where their generative models will perform well, or conversely where their models fail. We complement our analysis with a systematic experimental evaluation and comparison to other recently proposed measures. Using a wide array of experiments we demonstrate our algorithm’s strengths over other existing methods and and confirm our results from the theoretical analysis.
A Customizable Reinforcement Learning Environment for Semiconductor Fab Simulation

Reinforcement learning based methods are increasingly used to solve NP-hard combinatorial optimization problems. By learning from the problem structure, or the characteristics of instances, the approach has high potential compared to alternative techniques solving all instances from scratch. This work introduces a novel framework for creating (deep) reinforcement learning environments simulating up to real-world scale semiconductor fab scheduling problem instances. The highly configurable framework supports creating single- and multi-agent environments where the simulation factory is either partially or fully controlled by the learning agents. The action and observation spaces and the reward function are customizable based on pre-defined features. Our toolkit creates environments with a standard interface that can be integrated with various algorithms in a few minutes. The simulated datasets may involve challenging features like downtimes, batching, rework, and sequence-dependent setups. These can also be turned off and simulated datasets be automatically downscaled during the prototyping phase.
Phonological reanalysis is guided is guided by markedness: the case of Malagasy weak stems

A key goal in phonology is to understand the factors that affect phonological learning. This paper addresses the issue by examining how paradigms are reanalyzed over time. Malagasy has a class of stems, called weak stems, where final consonants alternate when suffixed. Comparison of historical and modern Malagasy shows that weak stem paradigms have undergone extensive reanalysis in a way that cannot be predicted by the probabilistic distribution of alternants. This poses a problem for existing quantitative models of reanalysis, where reanalysis is always towards the most probable alternant. I argue instead that reanalysis in Malagasy is driven by both distributional factors and a markedness bias. To capture the Malagasy pattern, I propose a maximum entropy learning model (Goldwater & Johnson, 2003), with a markedness bias implemented via the model’s prior probability distribution. This biased model successfully predicts the direction of reanalysis in Malagasy, outperforming purely distributional models.
Asymptotic Bianchi IX model: diagonal and general cases

We make comparison of the dynamics of the diagonal and nondiagonal Bianchi IX models in the asymptotic regime near the cosmological singularity. Apart from the original variables, we use the Hubble normalized ones commonly applied in the examination of the dynamics of homogeneous models. Applying the dynamical systems method leads to the result that in both cases the continuous space of critical points is higher dimensional and they are of the nonhyperbolic type. This is a generic feature of the dynamics of both cases and seems to be independent on the choice of phase space variables. However, the topologies of the corresponding critical spaces are quite different. We conjecture that the nondiagonal case may carry a new type of chaos different from the one specific to the usually examined diagonal case.
The Effect of the use of ICT on the Touristic Experience

The quick adoption of information and communication technologies (ICT) across the world, particularly in tourism, aiming to revolutionize the creation of the travel experience or improve it, happens without specifying the most appropriate ICT or its actual results. While visitors adopting personal ICTs in search of fluid, efficient and personalized trips, emerge; regardless of the tourist destination, empowering themselves with smart devices in pursuit of extraordinary experiences and value. The objective of this research was to comprehend the effect of the use of ICT by tourists on their travel experience. Mexican tourists traveling within the country for pleasure using smart devices with internet connection were evaluated. Data were obtained using snowball sampling on social media and analyzed with SEM to define the relationships and the factors that are affected and comprise the smart tourist experience. The results show that there is a preferential use of smartphones and tablets and search engines and maps rather than applications (apps). Also, a notable influence of ICT is experiencing something new and enjoying activities that the tourist wanted to do. The factors comprising the smart tourist experience are learning about oneself during the trip, making the trip more exciting and memorable, and feeling surprised.
CHARACTERIZATION OF A MULTIMODE PROPELLANT OPERATING IN A POROUS GLASS ELECTROSPRAY THRUSTER BY ANTHONY ADDUCI

Multimode space propulsion is an emerging technology that shows a promising increase in mission flexibility, adaptability, and mass savings for specific missions [1]. Finding a propellant that operates well in multiple propulsive modes is a key step in getting multimode propulsion systems flying in space. This study characterizes a novel multimode propellant named FAM-110A through operation in a porous glass electrospray thruster. Measurements acquired include current-voltage characteristic curves, plume ion potential via a retarding potential analyzer, and mass to charge ratio via a linear time of flight spectrometer. FAM-110A emitted more current at comparable emitter voltages as compared to EMIM-BF4. Currents of 422 μA and 305 μA were observed at voltages of +1760 V and -1730 V respectively. RPA measurements showed less field free fragmentation during thruster operation with FAM-110A and also a 0.9% average energy efficiency. During cation emission, it was found that the FAM-110A plume consists of primarily the EMIM cation with no observable evidence of the presence of HA+. Many species were found to exist in the negative emission plume including the two know anions: nitrate, and ethylsulfate. Performance parameters were calculated for each propellant. The calculations showed the thruster operating with FAM-110A produced 3.9 μN and 9.1 μN more thrust in cation and anion emission, respectively. The specific impulse of FAM-110A was 1412 seconds lower in positive mode and 1485 seconds lower in negative mode. The calculated propulsive efficiency for FAM-110A was 35.9% lower during positive mode and 21.2% lower during negative mode when compared to EMIM-BF4.
Design of a Landslide Prediction Model Based on Dual Attention and Dual LSTM Convolutional Neural Network

A large number of landslide disasters occur every year in the world, causing a large number of casualties and huge economic losses, landslides are particularly common during mine operation. It is essential to predict landslides correctly. The traditional landslide prediction model has problems such as a low utilization rate of information between historical data, and the correlation between multi-variables cannot be well mined, resulting in insufficient prediction accuracy. This paper proposes a landslide prediction model (CNN-Attention-Bi-LSTM-Attention Model) based on the dual attention mechanism of the convolutional neural network and double-layer LSTM for multi-feature landslide data. The dual attention mechanism mines the information between variables better. The CNN-Attention-Bi-LSTM-Attention Model is compared with RNN, double-layer LSTM, and single-layer LSTM based on attention mechanism to conduct the experimental comparison of landslide prediction model, and the results show that the RMSE values are increased by 9.84, 8.68 and 3.76, respectively, MAPE values were increased by 4.2266848%, 0.1880235% and 2.6510467%, respectively. The validity of the proposed model in landslide prediction is confirmed. The improvement of the prediction accuracy of the landslide displacement in the mining area has effectively helped us to realize the landslide stability monitoring and the safe and reliable operation of the mine.
Fast Block Coordinate Descent for Non-Convex Group Regularizations

Non-convex sparse regularizations with group structures are useful tools for selecting important feature groups. For optimization with these regularizations, block coordinate descent (BCD) is a standard solver that iteratively updates each parameter group. However, it suffers from high computation costs for a large number of parameter groups. The state-of-the-art method prunes unnecessary updates in BCD by utilizing bounds on the norms of the parameter groups. Unfortunately, since it computes the bound for each iteration, the computation cost still tends to be high when the updates are not sufﬁciently pruned. This paper proposes a fast BCD for non-convex group regularizations. Speciﬁcally, it selects a small subset of the parameter groups from all the parameter groups on the basis of the bounds and performs BCD on the subset. The subset grows step by step in accordance with the bounds during optimization. Since it computes the bounds only when selecting and growing the subsets, the total cost for computing the bounds is smaller than in the previous method. In addition, we theoretically guarantee the convergence of our method. Experiments show that our method is up to four times faster than the state-of-the-art method and 68 times faster than the original BCD without any loss of accuracy.
Score-based Quickest Change Detection for Unnormalized Models

Classical change detection algorithms typically require modeling pre-change and post-change distributions. The calculations may not be fea-sible for various machine learning models because of the complexity of computing the partition functions and normalized distributions. Additionally, these methods may suffer from a lack of robustness to model mismatch and noise. In this paper, we develop a new variant of the classical Cumulative Sum (CUSUM) change detection, namely Score-based CUSUM (SCUSUM), based on Fisher divergence and the Hyv¨arinen score. Our method allows the applications of the quickest change detection for unnormalized distributions. We provide a theoretical analysis of the detection delay given the constraints on false alarms. We prove the asymptotic optimality of the proposed method in some particular cases. We also provide numerical experiments to demonstrate our method’s computation, performance, and robustness advantages. 1
Etna: An Evaluation Platform for Property-Based Testing (Experience Report)

Property-based testing is a mainstay of functional programming, boasting a rich literature, an enthusiastic user community, and an abundance of tools — so many, indeed, that new users may have difficulty choosing. Moreover, any given framework may support a variety of strategies for generating test inputs; even experienced users may wonder which are better in a given situation. Sadly, the PBT literature, though long on creativity, is short on rigorous comparisons to help answer such questions. We present Etna, a platform for empirical evaluation and comparison of PBT techniques. Etna incorporates a number of popular PBT frameworks and testing workloads from the literature, and its extensible architecture makes adding new ones easy, while handling the technical drudgery of performance measurement. To illustrate its benefits, we use Etna to carry out several experiments with popular PBT approaches in both Coq and Haskell, allowing users to more clearly understand best practices and tradeoffs.
AN EFFECTIVE ALGORITHM FOR COMPUTING FRACTIONAL DERIVATIVES AND APPLICATION TO FRACTIONAL DIFFERENTIAL EQUATIONS

In recent years, fractional di(cid:11)erential equations have been extensively applied to model various complex dynamic systems. The studies on highly accurate and e(cid:14)cient numerical methods for fractional di(cid:11)erential equations have become necessary. In this paper, an e(cid:11)ective recurrence algorithm for computing both the fractional Riemann-Liouville and Caputo derivatives is proposed, and then spectral collocation methods based on the algorithm are investigated for solving fractional di(cid:11)erential equations. By the recurrence method, the numerical stability with respect to N , the number of collocation points, can be improved remarkably in comparison with direct algorithm. Its robustness ensures that a highly accurate spectral collocation method can be applied widely to various fractional di(cid:11)erential equations.
Pre-stack reverse-time noise suppressing method based on Laplacian operator

Low frequency noise is the main problem of the pre-stack reverse-time migration section.The figure noise removal mechanism based on Laplacian operator and the raising mechanism of the pre-stack reverse-time migration are analyzed.Laplacian operators are used to carry out the pre-stack reverse-time noise suppressing experiments in stacking image domain,common imaging domain and common shot domain respectively,and the results are compared with high-pass filter method.The numerical simulation results show that the low-frequency reverse-time migration noise is mainly caused by the inaccuracy of migration velocity,and other reasons of low-frequency reverse-time migration noise are the inconsistency of wavelet form between numerical simulating wavelet and numerical records and the application of the correlation type reverse-time imaging condition.The noise suppressing effects based on the Laplacian operator in the above different domains are obvious,and the result in common shot domain is the best.The above methods in three domains are better than that of the conventional high-pass filter method,the formation detail characteristics of the processed section are clearer,and the horizontal cophasal axes of the common imaging gather are correctly depicted.
Towards Decentralized Decisions for Managing Product Transitions in Semiconductor Manufacturing

Continuous renewal of the product portfolio through product transitions is crucial to semiconductor manufacturing firms. These decisions take place in a decentralized environment, where decisions by different functional units must be coordinated to optimize corporate performance. Starting from a centralized optimization model, we obtain decentralized models using Lagrangian relaxation, and explore the challenges encountered in formulating and solving these decentralized models. Although the Lagrangian approaches yield tight upper bounds on the optimal solution value, the structure of the dual solution renders the construction of a near-optimal feasible solution difficult, and fully separable decentralized models encounter significant problems in achieving convergence due to scaling issues. We present computational experiments that illustrate the difficulties involved, and discuss directions for future work.
The Application of Machine Learning on the Injury Prediction of Soccer Players

Soccer player lives with a high risk of injury since soccer is one of the sports activities with relatively high injury incidence compared with many other sports. Injuries can be a huge influence not only player’s career and financial situation but also it is the reason for soccer changing the coach’s game tactics as well as for directors of clubs have to find a new player. In order to reduce the risk of getting injured by predicting the probability of soccer players’ injuries for the new season, we conduct research in this paper. In this paper, we propose parameters that are connected each other and we collected using a non-technical way while most of the recent research provides technical ways such as GPS tracking technology or wearing devices. Moreover, we provide the accuracy of injury prediction and estimation of recovery time by using supervised classification machine learning models .
Investigation of Electrical and Wearing Properties of Wool Fabric Coated with PEDOT:PSS

The way to improve the properties (resistance to washing, delamination, and rubbing off) of the PEDOT:PSS coating applied on wool fabric without reduction of its electrical conductivity by introducing a commercially available combination of low formaldehyde content melamine resins into the printing paste is presented in this paper. Primarily, to improve the hydrophilicity and dyeability of wool fabric, the samples were modified using low-pressure nitrogen (N2) gas plasma. Two commercially available PEDOT:PSS dispersions were used to treat wool fabric by the exhaust dyeing and screen printing methods, respectively. Spectrophotometric measurements of the color difference (ΔE*ab) and visual evaluation of woolen fabric dyed and printed with PEDOT:PSS in different shades of the blue color showed that the sample modified with N2 plasma obtained a more intense color compared to the unmodified one. SEM was used to examine the surface morphology and a cross-sectional view of wool fabric that had undergone various modifications. SEM image shows that the dye penetrates deeper into the wool fabric after plasma modification using dyeing and coating methods with a PEDOT:PSS polymer. In addition, with a Tubicoat fixing agent, HT coating looks more homogeneous and uniform. The chemical structure spectra of wool fabrics coated with PEDOT:PSS were investigated using FTIR-ATR characterization. The influence of melamine formaldehyde resins on the electrical properties, resistance to washing, and mechanical effects of PEDOT:PSS treated wool fabric was also evaluated. The resistivity measurement of the samples containing melamine-formaldehyde resins as an additive did not show a significant decrease in electrical conductivity, while the electrical conductivity was maintained after the washing and rubbing test as well. The best results of electrical conductivity for investigated wool fabrics before and after washing and mechanical action were determined for samples subjected to the combined processing–surface modification by low-pressure N2 plasma, dyeing by exhaust with PEDOT:PSS, and coating by the screen-printing method of PEDOT:PSS and a 3 wt. % melamine formaldehyde resins mixture.
Short-Term Effects of Competitive Video Games on Aggression: An Event-Related Potential Study

Previous research on factors affecting video game player aggression has mainly reflected on the violent content of video games; in recent years, some researchers have focused on competitive factors in video games. However, little research has examined the sole impacts of competitive factors in video games without violent content on aggression, and the neurological processes of these effects are still unknown. The present study was the first to examine the electrophysiological characteristics of short-term competitive video game exposure and aggression. Thirty-five participants played a video game in either competitive or solo mode for 15 min, followed by an ERP experiment based on the oddball paradigm and the hot sauce paradigm to measure aggressive behavior. Results showed that playing competitive game mode was associated with faster judgment of aggressive words, larger P300 amplitudes, and selection of more chili powder than in solo mode. Mediation analysis further revealed that the P300 amplitude evoked by the aggressive words partially mediated the relationship between competitive game exposure and aggressive behavior. These findings support the general aggression model. However, this study has limitations, such as a single form of competitive game examined and single blindness, which need further improvement in future studies.
Student Achievement in Relation to Time Spent Studying and Playing Video Games: A Gender Perspective

– The results of previous studies showed that male students have a lower average grade than female students. Men are also reported to play more video games than women. We were interested in whether male students maintain the same pattern of behavior in relation to playing games during the exam period and how this affects their average grade compared to female students. If male students spend more time playing video games compared to female students, their academic performance may decrease as time spent on academic activities is replaced by video game play. In a survey of 400 university students, the authors focused on the gender characteristics of students and their achievements. Our results showed that female students studied more and played video games less during the exam periods compared to male and non-binary students, which is the reason for a higher total average grade in contrast to others