Reviewerly/exHarmony huggingface dataset:
Exposing DeepFake Videos Using Facial Decomposition-Based Domain Generalization

Existing deepfake detection methods have achieved high accuracy under the intra-database scenario, but most of them suffer from a significant performance drop when evaluated on cross-database experiments. In this paper, we regard the deepfake detection task as domain generalization problem and design a Facial Decomposition-based Domain Generalization framework to learn a more generalized feature representation. To optimize our framework, we experimentally analysis that current CNN-based detectors tend to overfit the facial semantic content but neglect the common shared traces of deepfake, thus a facial semantic content decomposition based dual-branch network is designed to reduce the representation discrepancies among multiple source domains in a specific feature space to be domain invariant. Besides, based on the consideration that the distribution discrepancies are much larger among the fake faces than the real ones, a hybrid loss function is designed to enlarge the distance among samples from different categories even in cross-domain scene. Extensive experiments demonstrate that the proposed method can achieve better performance compared with state-of-the-art methods, especially on cross-database evaluation.
Redacting Blockchain Without Exposing Chameleon Hash Collisions

A new implementation of a redactable blockchain is proposed: a construction where trusted authorities can change past blocks without invalidating the blockchain and without needing a hard fork. Like previous works, it uses a chameleon hash function—a special kind of hash function where it is possible to compute collisions efficiently if a trapdoor is known. One problem with most chameleon hash functions is that collision-resistance and security are invalidated or severely weakened if a collision is publicized. It turns out that redacting a blockchain in existing constructions reveals collisions. Previous works dealt with this using public-key encryption schemes and non-interactive zero-knowledge proofs to achieve what is known as Enhanced or Full Collision-Resistance. Here instead, we propose a simpler and hopefully faster construction. We use a subset of chameleon hash schemes where the trapdoor also allows for first-preimage computation. We build a redactable blockchain using this stronger property where redactions do not expose collisions.
Real-Time Vehicle Counting by Deep-Learning Networks

In order to improve the driving safety and reduce traffic congestion during holidays and work hours, a real-time vehicle detection and counting system is a very urgently needed system. In this paper, a lane-based vehicle counting system using deep-learning networks is proposed. Our method includes YOLO vehicle detection and lane-based vehicle counting. From the vehicle detection experimental results, YOLOv3-spp has the highest Precision, Recall, and F1 score, which achieve all 100% among three YOLOv3 methods and two YOLOv2 methods. From the vehicle counting experimental results, YOLOv3-608 has the highest Accuracy, Precision and F1 scores, which achieve 91.4%, 99.3%, and 95.3% among three YOLOv3 methods, two YOLOv2 methods, and one SSD method.
A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts

Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system's dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham's historical manuscript collections to obtain some really promising results in this direction.
Parallel Scientific Power Calculations in Cloud Data Center Based On Decomposition-Coordination Directed Acyclic Graph

With the expansion scale of interconnected power systems and refined state perception, scientific power calculations become more complex and diverse. They need faster computation speed and better scalability to support power flow calculation, reactive power optimization, and static/transient stability analysis for unit scheduling. Therefore, this paper proposes a novel cloud data center task mapping algorithm of the Stoer-Wagner binary tree (SWBT) to support accelerated executions of these calculations. Firstly, based on the block bordered-diagonal form of the admittance matrix, high-time complexity scientific power calculations are transformed into a unified multi-task decomposition-coordination directed acyclic graph (DC-DAG). And then, the critical tasks in this DC-DAG are found and the virtual machines encapsulating them are matched with physical machines in the data center preferentially. Finally, on CloudSim, a cloud computing platform, the multi-job mixed experiments of 118-13659 bus power systems are carried out. In addition, real-time workload performance is enhanced in two very large real-world power systems. Studies illustrate that SWBT can improve the underlying physical machine resource utilization and reduce data interaction transmission hops to achieve better computing acceleration performance.
Multi-level Latent Fusion in Learning-based Image Coding

Learning-based image coding has shown promising results for coding of natural images compared to traditional block-based coding schemes. However, improvements are needed for screen content coding. Most of the popular learning-based coding approaches are based on variational autoencoders employing Convolutional Neural Networks (CNNs) which are end-to-end trained on a training dataset. The receptive field area of the latents in these architectures increase based on the down-sampling ratio and the kernel size used in each convolution layer. The latents coded from the last layer therefore have a large receptive field size which may not be optimal to code image sources such as screen content or mixed content containing text, logos and small edges. This paper proposes new methods to adaptively fuse and code the latents from different layers. It enables a novel multi-level receptive field based latent coding architecture to achieve better coding performance for a diverse set of contents. Additionally, Multi-Mixture distribution based entropy modeling of latent features and content adaptive latent refinements in the encoder is proposed to bring more coding gains. The experimental results show that the approach can significantly improve the coding efficiency for screen content with average bitrate savings of36%.
A Measurement Study of RFID-based Face Recognition

Conventional vision-based face recognition systems has the advantages of high accuracy and fast recognition speed, however, it also suffers from the limitation of poor privacy protection and lower accuracy under insufficient background lighting conditions and limited computing resources. In contrast, recently proposed RFID-based face recognition enjoys much better privacy protection and is independent on the lighting conditions of the environment. In this paper, we investigate the emerging RFID-based face recognition technologies and assume that tag configurations, input data sources, filtering algorithms and dynamic background are all factors that may impact the accuracy of RFID-based face recognition. We set up the testbed for performance measurement on RFID-based face recognition and proposes a measurement study to verify the impact of different factors on the recognition accuracy of RFID-based face recognition.
A Comparative Study of Prototyping Methods for HCI Design Using Cognitive Load-Based Measures

Increased information complexity in HCI designs causes cognitive load on users. HCI design prototypes have been used in various stages of design process to assess the design quality and enable course correction. However, there are only a few studies reported on suitability of prototyping methods for HCI design process in testing. Also, there is a dearth of literature on cognitive load (CL) based measurement for different prototyping methods. This paper reports a comparative study of prototyping methods for HCI based control panel design from CL perspective. Comparisons of prototyping methods have been reported based on three CL measurement methods namely, subjective measure, task performance and physiological measure. Results of three CL methods were congruent and shows that, software prototype caused significantly lower CL compared to paper prototype testing. Also, it is concluded that software prototype is more suitable prototyping method in cyber physical production system scenario.
Material Turn: Material Thinking-Oriented Sustainable Artefact Design Education Mode

The increasingly decayed eco-system, especially the sudden outbreak of Covid-19, stresses the need to transform human-centred design ethics to non/de/post-human and object-oriented ontology, indicating the mainstream Material Turn in modern philosophy. A circular economy is an ideal economic strategy for sustainable development aiming at achieving the goal of Cradle to Cradle. Realizing material flow in a closed-loop circulating system can be seen as the critical point of a circular economy where the material is playing a crucial role. However, design thinking-oriented artefact design education mode is essentially immaterial and human-centred, separating designers from physical materials and manufacturing, and resulting in a gap between students and the real physical world. In this paper, I suggest an alternative design education mode directed by material thinking which regards material as a new “living” species to guide designers to have a dialogue with materials to bridge the gap between designers and the real physical world.
Data Imbalance in Immunity Bone Age Assessment System Using Independent Autoencoders

Bone age assessment is an important indicator of child maturity. Generally, a person evaluated for bone age is mostly in the puberty stage. Compared to toddlers and post-puberty stages, the data of bone age at the puberty stage are hence much easier to obtain. As a result, the amounts of bone age data collected at the toddlers and post-puberty stages are often less than that of bone age data at the pulpy stage. This causes a data imbalance problem that affects the prediction accuracy. To deal with this problem, a data imbalance immunity bone age assessment (DIIBAA) system is proposed which integrates a CNN-based autoencoder and CNN-based scoring network. The DIIBAA system builds three autoencoders for the bone age data of toddlers, puberty, and post-puberty stages. Since the three types of autoencoders do not interfere with each other, there is no data imbalance problem in the DIIBAA system. After that, the outputs of the three autoencoders are input to the scoring network, and the auto-encoder produced the image with the highest score is regarded as the final prediction result. In the experiments, imbalanced training data with a positive and negative sample ratio of 1:2 were used, which has been alleviated compared to the original very imbalanced data. Meanwhile, the DenseNet169-based autoencoders are employed in the experiments, and the obtained evaluation accuracies are improved.
On Computerizing the Ancient Game of Ṭāb

The ancient game of ṭāb is a war and race game. It is played by two teams, each consisting of at least one player. In addition to presenting the game and its rules, the authors develop three versions of the game: human versus human, human versus computer, and computer versus computer. The authors employ a Genetic Algorithm (GA) to help the computer to choose the ‘best' move to play. The computer game is designed allowing two degrees of difficulty: Beginners and Advanced. The results of several experiments show the strategic properties of this game, the strength of the proposed method by making the computer play the game intelligently, and the potential of generalizing their approach to other similar games.
A Fast Compressed Hardware Architecture for Deep Neural Networks

Hardware acceleration of Deep Neural Networks (DNNs) is very critical to many edge applications. The acceleration solutions available today are typically for GPU, CPU, FPGA and ASIC platforms. The Single Partial Product 2-D Convolution known as SPP2D is a hardware architecture for fast 2-D convolution which can be used for implementing a convolutional neural network (CNN). The SPP2D based CNNs prevent the re-fetching of input pixels for the calculation of partial products and it computes the output for any input size and kernel with low latency and low power consumption compared to some other popular techniques. SPP2D based VGGNet-16 rivals the performance of other existing implementations as well as a FFO based CNN architecture which takes a novel approach to compute convolution results using row-wise inputs as opposed to traditional tile-based processing. In this paper, we present an SPP2D based hardware accelerator for a channel compressed network. We find that the low power SPP2D implementation gives a better performance in terms of low power and low execution time compared to other compressed contemporary designs. A compressed network requires less on-chip memory thus reducing the most power consuming task of moving data from off-chip to on-chip. This results in a considerable reduction in power consumption due to the reduction in memory traffic. Channel-pruned SPP2D accelerator is a low power design of 298 mW which is about $0.01\times$ to $0.37\times$ of the existing works while also having a low execution time of 0.9 secs.
Feature Selection Using Information Gain for Software Effort Prediction Using Neural Network Model

An important phase in software development is the prediction of effort. It has its significance in project planning, control, and budgeting. Many researchers developed common software effort estimation models known as algorithmic models. These models need accurately estimated input parameters, namely lines of code and complexity. Accurate estimation of these features during the initial phases of the software life cycle is quite difficult. This issue of algorithmic models can be handled with non-algorithmic models. These non-algorithmic models are based on soft computing techniques such as Genetic Programming, Fuzzy Sets, and Artificial Neural Network (ANN). Many researchers proposed various models based on ANN but we did not find any estimation method focused on feature selection to remove the negative impact of irrelevant information. In this study, features with high information gain are selected using information gain to train the multilayer perceptron network FITNET. Experiment with two- and threefold cross-validation on 3 benchmark datasets shows that New ANN (NANN) trained on selected features makes effective prediction as compared with the ANN trained on all features. Our approach compared the performance using 5 performance metrics MAR, MMRE, MdMRE, PRED (25), and MSE to show that it will perform better for different metrics.
A Novel GASEN-PSOELM-based Ensemble Method for Quality Variable Prediction in Chemical Processes

Data-driven soft sensor technology is an effective way to realize the online prediction of those important yet difficult-to-measure quality and component variables in chemical processes. Considering the characteristics of strong nonlinearity, time-variability and multi-working conditions, the soft sensor models based on a single network are difficult to guarantee global-scale prediction accuracy, while the traditional integration methods usually lead to a decrease in prediction speed and an increase in storage space. To cope with these issues, this paper proposes a novel ensemble soft sensor method using genetic algorithm to selectively integrate an extreme learning machine optimized by particle swarm algorithm, named GASEN-PSOELM in short. Firstly, the sampling data are used to generate the training samples of the sub-models by Bootstrap method. Secondly, the candidate sub-models are established via the extreme learning machine optimized by particle swarm algorithm, and then selected by using genetic algorithm. Finally, the prediction outputs are obtained by simple averaging integration. The validity and effectiveness of the proposed method is verified by the chemical equipment and chemical process, separately. The experimental results show that the proposed GASEN-PSOELM method can not only improve the prediction speed, but also ensure the good prediction accuracy and generalization performance.
Westerly Granite Anisotropy Study

ABSTRACT: Westerly granite (WG) is well known rock, believed to be isotropic. We studied four samples of WG heated between 100°C and 600°C, by ultrasonic sounding on spherical samples under hydrostatic pressure up to 400 MPa, neutron diffraction on identical samples and scanning electron microscopy (SEM). Thermal treatment studies are important for localities like nuclear waste storages, geothermal projects, rock and earthquake mechanics. All measurements were done at room temperature. The 3D distribution of P-wave velocities at high pressures reflects intrinsic structure and even though the anisotropy is low, the orientation of the minimum velocity corresponds to the highly preferred orientation of plagioclase (010) and biotite (001). Image analyses showed that there is also preferred orientation of microcracks regardless of their size and thermal treatment level. Neutron diffraction measurements of the samples heated to 100°C and 600°C confirm weak intrinsic elastic anisotropy, which remain unchanged due to the thermal treatment. We can assume that in Westerly granite there are two types of anisotropy: crystal preferred orientation which was formed during igneous crystallization and second one is due to the oriented microcracks which have been formed during tectonic exhumation or during sample excavation in the quarry. Both seems to be unrelated. 1. INTRODUCTION Westerly granite has been studied for decades and its properties are very well known. There were studied mechanical properties, elastic properties, development of cracks introduced by uniaxial or triaxial loading, thermal heating, study of permeability, study of fracturing process by acoustic emission, modelling of crack systems and plenty of others. Westerly granite is considered as fine grained, homogeneous material, isotropic and therefore it is often discussed or even used as a standard for comparison with other granitic rocks. Quantification of elastic properties of granites is important to determine crustal seismic velocities and stress orientation. Generally, it is assumed that granitic rocks are elastically isotropic. In this paper, we study influence of thermal cracks and crack induced anisotropy on P-wave propagation in spherical samples of Westerly granite at different confining pressures. Experimental elastic wave velocity distributions in Westerly granite are compared to the model based on neutron diffraction data on mineral composition and mineral preferred orientations. Due to high penetration depth of thermal neutrons, information on a large representative volume of geomaterial is obtained; and the method of neutron diffraction allows to investigate same bulk samples that were used for elastic wave propagation study. Thus, ultrasonic sounding (US) and neutron diffraction form a pair of complementary methods suitable for in-depth analysis of elastic anisotropy of rocks.
Transfer Learning Realized With Nonlinearly Transformed Input Space

This study is concerned with the design of a nonlinear transformation strategy to address the issues caused by the mismatch of marginal probability distributions between the source and target domains in transfer learning. In the process of transfer learning, the existing model (which is regarded as the available source of knowledge) is constructed in the source domain with the assumption that data points are independent and identically distributed. However, this assumption does not hold when the existing model is transferred to a new target domain. As a consequence, the performance of the existing model in the target domain deteriorates. Through mapping the original target space to a new space of the same dimensionality by using nonlinear transformations, the distributions of the data in the source and target domains could well matched each other, which significantly facilitates the transfer of accumulated knowledge to the new domain. No prior knowledge of the data distributions or the detailed information of the existing model is required. Experiments involving both synthetic dataset and real-world datasets are performed to demonstrate the effectiveness of the proposed approach in improving classification accuracy of the exiting model in the target domain.
Neural Attention Networks for Recommendation With Auxiliary Data

With the rapid development of Internet technologies, an increasing amount of auxiliary data can be readily obtained through Web services. To alleviate the data sparsity issue, auxiliary data based recommendation has emerged for better recommendation performance. However, existing auxiliary data based methods suffer from two problems. First, only the relation features related to the meta-paths are extracted from auxiliary data, which may lead to features useful for recommendation being lost irreversibly. Second, an assumption is made that an individual has the same preference over the identical characteristic of different items, which is often invalid and may lead to misleading recommendations. Actually, a user may place different importance on the same feature of different items, and an item may get different attention from the same feature of different users. In this paper, we propose a neural network framework, named Neural Attention Recommendation model (NARec), for auxiliary data based collaborative filtering. For the first problem, we characterize users and items from three aspects, namely latent features, attribute features, and meta-path based relation features, which can comprehensively extract the useful recommendation features from auxiliary data. Regarding the second problem, we integrate different user features and item features into an attention mechanism based rating prediction model for recommendation, which can adaptively characterize the personalized features of users and items. Extensive experiments on three real-world datasets demonstrate that NARec significantly outperforms the state-of-the-art recommendation methods in the rating prediction task.
Bayesian Methods, Regularization and Expectation-Maximization

Abstract This chapter summarizes some techniques that use Bayes’ theorem. These are classical Bayesian statistical models using, e.g., the Markov chain Monte Carlo (MCMC) method for model fitting. We discuss regularization of regression models such as ridge and LASSO regularization, which has a Bayesian interpretation, and we consider the Expectation-Maximization (EM) algorithm. The EM algorithm is a general purpose tool that can handle incomplete data settings. We illustrate this for different examples coming from mixture distributions, censored and truncated claims data.