Reviewerly/exHarmony huggingface dataset:
Splicing Image Forgery Detection and Localization Based on Color Edge Inconsistency using Statistical Dispersion Measures

Nowadays, due to the availability of low-cost and high-resolution digital cameras, and the rapid growth of user-friendly and advanced digital image processing tools, challenges for ensuring authenticity of digital images have been raised. Therefore, development of reliable image authenticity verification techniques has high importance in digital life. In this paper, we proposed a blind image splicing detection method based on color distribution in the neighborhood of edge pixels. First, we extracted edge pixels using contourlet transform. Then, to accurately distinguish the authentic edges from tampered ones, Interquartile Range (IQR) criteria are utilized to illustrate the distribution of Cr and Cr histograms of the spliced boundaries in YCbCr color space. Finally, a segmentation method is used to improve the localization performance and to reduce especially the computational time. The effectiveness of the method has been demonstrated by our experimental results obtained using the Columbia Image Splicing Detection Evaluation (CISED) dataset in terms of specificity and accuracy. It is observed that the proposed method outperforms some state-of-the-art methods. The detection accuracy is approximately 97 with 100% specificity.
Modern Applications and Challenges for Rare Itemset Mining

Data mining is the process of extracting useful unknown knowledge from large datasets. Frequent itemset mining is the fundamental task of data mining that aims at discovering interesting itemsets that frequently appear together in a dataset. However, mining infrequent (rare) itemsets may be more interesting in many real-life applications such as predicting telecommunication equipment failures, genetics, medical diagnosis, or anomaly detection. In this paper, we survey up-to-date methods of rare itemset mining. The main goal of this survey is to provide a comprehensive overview of the state-of-the-art algorithms of rare itemset mining and its applications. The main contributions of this survey can be summarized as follows. In the first part, we define the task of rare itemset mining by explaining key concepts and terminology, motivation examples, and comparisons with underlying concepts. Then, we highlight the state-of-art methods for rare itemsets mining. Furthermore, we present variations of the task of rare itemset mining to discuss limitations of traditional rare itemset mining algorithms. After that, we highlight the fundamental applications of rare itemset mining. In the last, we point out research opportunities and challenges for rare itemset mining for future research.
Preregistering NLP research

Preregistration refers to the practice of specifying what you are going to do, and what you expect to find in your study, before carrying out the study.This practice is increasingly common in medicine and psychology, but is rarely discussed in NLP.This paper discusses preregistration in more detail, explores how NLP researchers could preregister their work, and presents several preregistration questions for different kinds of studies.Finally, we argue in favour of registered reports, which could provide firmer grounds for slow science in NLP research.The goal of this paper is to elicit a discussion in the NLP community, which we hope to synthesise into a general NLP preregistration form in future research.
Remotely Perceived: Investigating the Influence of Valence on Self-Perception and Social Experience for Dyadic Video-Conferencing With Personalized Avatars

Avatar use on video-conference platforms has found dual purpose in recent times as a potential method for ensuring privacy and improving subjective engagement with remote meeting, provided one can also ensure a minimal loss in the quality of social interaction and sense of personal presence. This work focuses on interactions of this sort through real-time motion captured 3D personalized virtual avatars in a 2D video-conferencing context. Our experiments were designed with the intention of exploring previously defined perceptual illusions that occur with avatar-use in Virtual and Augmented Reality settings, outside of the immersive technological domains where they are normally measured. The research described here was aimed at empirically evaluating three separate dimensions of human-avatar interaction. The first was humans-as-avatars, with experimental conditions that were designed to measure changes to subjective perceptions of self-face ownership and self-concept. The second focus was other-perception, with the unique design of the studies outlined below among the first to measure social presence in a video-call between two human-driven avatars. The third emphasis was on the experiential content involved in avatar use, as there were measurements for emotion induction, fatigue and behavior change included in the data collection. The results describe some evidence for face and body ownership, while participants also reported high levels of social presence with the other avatar, indicating that avatar cameras could be a favorable alternative to non-camera feeds in video conferencing. There were also some useful insights gained regarding emotion elicitation in non-video vs. avatar conditions, as well as avatar-induced behavior change.
Delay Analysis in IoT Sensor Networks

Internet of Things (IoT) devices, particularly those used for sensor networks, are often latency-sensitive devices. The topology of the sensor network largely depends on the overall system application. Various configurations include linear, star, hierarchical and mesh in 2D or 3D deployments. Other applications include underwater communication with high attenuation of radio waves, disaster relief networks, rural networking, environmental monitoring networks, and vehicular networks. These networks all share the same characteristics, including link latency, latency variation (jitter), and tail latency. Achieving a predictable performance is critical for many interactive and latency-sensitive applications. In this paper, a two-stage tandem queuing model is developed to estimate the average end-to-end latency and predict the latency variation in closed forms. This model also provides a feedback mechanism to investigate other major performance metrics, such as utilization, and the optimal number of computing units needed in a single cluster. The model is applied for two classes of networks, namely, Edge Sensor Networks (ESNs) and Data Center Networks (DCNs). While the proposed model is theoretically derived from a queuing-based model, the simulation results of various network topologies and under different traffic conditions prove the accuracy of our model.
Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation

Non-Autoregressive machine Translation (NAT) models have demonstrated significant inference speedup but suffer from inferior translation accuracy.The common practice to tackle the problem is transferring the Autoregressive machine Translation (AT) knowledge to NAT models, e.g., with knowledge distillation.In this work, we hypothesize and empirically verify that AT and NAT encoders capture different linguistic properties of source sentences.Therefore, we propose to adopt multi-task learning to transfer the AT knowledge to NAT models through encoder sharing.Specifically, we take the AT model as an auxiliary task to enhance NAT model performance.Experimental results on WMT14 English⇔German and WMT16 English⇔Romanian datasets show that the proposed MULTI-TASK NAT achieves significant improvements over the baseline NAT models.Furthermore, the performance on large-scale WMT19 and WMT20 English⇔German datasets confirm the consistency of our proposed method.In addition, experimental results demonstrate that our MULTI-TASK NAT is complementary to knowledge distillation, the standard knowledge transfer method for NAT.
Design and Evaluation of a Graphical Deep Learning Approach for RF Fingerprinting

The widespread deployment of wireless sensor networks and the burgeoning internet of things (IoT) are enabling devices to be connected in wider and denser ecosystems in myriad of applications, making wireless security of paramount importance. Radio Frequency (RF) fingerprinting has the potential to enhance the security and with increasing popularity of deep learning, RF fingerprinting approaches have attracted increased attention. In this paper we propose a graphical deep learning approach for RF fingerprinting which can be paired with either convolutional or dense neural network architectures for device identification. With the proposed approach captured raw sample sequences are first transformed into image inputs, which are then used to train and test the deep learning models. The use of images, as opposed to raw samples sequences, provides a compact way to capture signal characteristics without impacting the structure and the complexity of the subsequent deep learning processing. We evaluate the performance using a two-stage process consisting of coarse and fine evaluation, carried out using synthetic and over-the-air captured datasets respectively. The coarse evaluation enables us to swiftly verify the performance without involving hardware implementation for data collection, whereas signals captured over-the-air are used for fine evaluation, with the proposed approach achieving near 100% identification accuracy.
Non-uniform motion deblurring with blurry component divided guidance

Blind image deblurring is a fundamental and challenging computer vision problem, which aims to recover both the blur kernel and the latent sharp image from only a blurry observation. Despite the superiority of deep learning methods in image deblurring have displayed, there still exists major challenge with various non-uniform motion blur. Previous methods simply take all the image features as the input to the decoder, which handles different degrees (e.g. large blur, small blur) simultaneously, leading to challenges for sharp image generation. To tackle the above problems, we present a deep two-branch network to deal with blurry images via a component divided module, which divides an image into two components based on the representation of blurry degree. Specifically, two component attentive blocks are employed to learn attention maps to exploit useful deblurring feature representations on both large and small blurry regions. Then, the blur-aware features are fed into two-branch reconstruction decoders respectively. In addition, a new feature fusion mechanism, orientation-based feature fusion, is proposed to merge sharp features of the two branches. Both qualitative and quantitative experimental results show that our method performs favorably against the state-of-the-art approaches.
Signed Graph Neural Network with Latent Groups

Signed graph representation learning is an effective approach to analyze the complex patterns in real-world signed graphs with the co-existence of positive and negative links. Most previous signed graph representation learning methods resort to balance theory, a classic social theory that originated from psychology as the core assumption. However, since balance theory is shown equivalent to a simple assumption that nodes can be divided into two conflicting groups, it fails to model the structure of real signed graphs. To solve this problem, we propose Group Signed Graph Neural Network (GS-GNN) model for signed graph representation learning beyond the balance theory assumption. GS-GNN has a dual GNN architecture that consists of the global and the local module. In the global module, we adopt a more generalized assumption that nodes can be divided into multiple latent groups and that the groups can have arbitrary relations and propose a novel prototype-based GNN to learn node representations based on the assumption. In the local module, to give the model enough flexibility in modeling other factors, we do not make any prior assumptions, treat positive links and negative links as two independent relations, and adopt a relational GNN to learn node representations. Both modules can complement each other, and the concatenation of two modules is fed into downstream tasks. Extensive experimental results demonstrate the effectiveness of our GS-GNN model on both synthetic and real-world signed graphs by greatly and consistently outperforming all the baselines and achieving new state-of-the-art results. Our implementation is available in PyTorch.
Inferring the dynamics of “black-box” systems using a learning machine

Given a segment of a time series of a system at a particular set of parameter values, is it possible to infer the dynamic behavior of the system in its parameter space? Here, we show that this goal can be achieved to a certain extent using a self-evolution learning machine. It is found that following an appropriate training strategy that monotonously decreases the cost function, the learning machine in different training stages is just like the system at different parameter sets. Consequently, the dynamic properties of the system are, in turn, usually revealed in the simple-to-complex order. The underlying mechanism can be attributed to the training strategy, which results in the learning machine collapsing to a qualitatively equivalent system of the system behind the time series. Thus, the learning machine enables a novel way of probing the dynamic properties of a “black-box” system without artificially establishing the equations of motion. The given illustrative examples include a representative model of low-dimensional nonlinear dynamical systems and a spatiotemporal model of reaction-diffusion systems.
Liver vessel segmentation based on inter-scale V-Net

<abstract> Segmentation and visualization of liver vessel is a key task in preoperative planning and computer-aided diagnosis of liver diseases. Due to the irregular structure of liver vessel, accurate liver vessel segmentation is difficult. This paper proposes a method of liver vessel segmentation based on an improved V-Net network. Firstly, a dilated convolution is introduced into the network to make the network can still enlarge the receptive field without reducing down-sampling and save detailed spatial information. Secondly, a 3D deep supervision mechanism is introduced into the network to speed up the convergence of the network and help the network learn semantic features better. Finally, inter-scale dense connections are designed in the decoder of the network to prevent the loss of high-level semantic information during the decoding process and effectively integrate multi-scale feature information. The public datasets 3Dircadb were used to perform liver vessel segmentation experiments. The average dice and sensitivity of the proposed method reached 71.6 and 75.4%, respectively, which are higher than those of the original network. The experimental results show that the improved V-Net network can automatically and accurately segment labeled or even other unlabeled liver vessels from the CT images. </abstract>
An Improved Recommendation Method Based on Content Filtering and Collaborative Filtering

With the popularization of the Internet and the prevalence of online marketing, e-commerce systems provide enterprises with unlimited display space and provide customers with more product choices, while its structure is becoming increasingly complex. The emergence and application of the network marketing recommendation system have greatly improved this series of problems. It can effectively retain customers, prevent customer loss, and increase the cross-selling volume of the e-commerce system. However, the current network marketing recommendation system is still immature in practical applications, and the problem of data sparseness is serious. The problem of user interest drift is not well dealt with, resulting in poor recommendation quality and poor real-time recommendation. Therefore, this paper proposes an online marketing recommendation algorithm based on the integration of content and collaborative filtering. First, content-based methods are used to discover users’ existing interests. After that, the mixed similarity model of content and behaviour is used to find the similar user group of the target user, predict the user’s interest in the feature words, and discover the user’s potential interest. Then, the user’s existing interest and potential interest are merged to obtain a user interest model that is both personalized and diverse. Finally, the similarity between the marketing content and the fusion model is calculated to form a set of user ratings combined with characteristics and then clustered through K-means to finally achieve recommendation. Experiments have proved that this method has good recommendation performance.
A Personalized English Learning Material Recommendation System Based on Knowledge Graph

The world has rushed into the information age. As the lingua franca, English shapes the global landscape of information transmission and exchange. Mastering English is equivalent to possessing an important tool for acquiring precious information. Therefore, it is very necessary to improve English teaching. This paper analyzes the problems in traditional classroom teaching and online learning of English, and discusses how to keep students unbaffled in online learning, improve their English learning efficiency, and satisfy their personalized demands. Specifically, the relevant data were characterized by knowledge points in English teaching, and used to formulate a knowledge graph. Then, the related knowledge points were labeled in online learning data on learning platforms. After that, user portrait was created by analyzing the data on daily learning behaviors. Finally, collaborative filtering was coupled with content-based recommendation to push English learning resources to students, which meet their personalized demands.
Teacher-Student Learning for a Binary Perceptron with Quantum Fluctuations

We analyse the generalisation performance of a binary perceptron with quantum fluctuations using the replica method. An exponential number of local minima dominate the energy landscape of the binary perceptron. Local search algorithms often fail to identify the ground state of the binary perceptron. In this study, we consider the teacher-student learning and compute the generalisation error of the binary perceptron with quantum fluctuations. Due to quantum fluctuations, we can efficiently find robust solutions that have better generalisation performance than the classical model. We validate our theoretical results through quantum Monte Carlo simulations. We adopt the replica symmetry (RS) ansatz and static approximation. The RS solutions are consistent with our numerical results, except for the relatively low strength of the transverse field and high pattern ratio. These deviations are caused by the violation of ergodicity and static approximation. After accounting for the deviation between the RS solutions and numerical results, the enhancement of generalisation performance with quantum fluctuations holds.
Does goodness of governance dissuade citizens from using cryptocurrencies?

Cryptocurrencies are an increasing concern for national governments and international institutions.Anonymity, real-time transactions, together with the lack of intermediaries and control from financial and economic authorities make them appealing to criminals.Despite of the global efforts for standardizing the approach to cryptocurrencies, there are several risks identified and the way governments are facing them is not uniform.This paper analyzes the relation between goodness of governance and the use of cryptocurrencies on a sample of 33 countries.The results confirm that goodness of governance, by itself, dissuades people from holding and using cryptocurrencies.Additionally, our findings show that this effect relies on different governance factors, depending on how extended the use of cryptocurrencies is.
MoSIoT: Modeling and Simulating IoT Healthcare-Monitoring Systems for People with Disabilities

The need to remotely monitor people with disabilities has increased due to growth in their number in recent years. The democratization of Internet of Things (IoT) devices facilitates the implementation of healthcare-monitoring systems (HMSs) that are capable of supporting disabilities and diseases. However, to achieve their full potential, these devices must efficiently address the customization demanded by different IoT HMS scenarios. This work introduces a new approach, called Modeling Scenarios of Internet of Things (MoSIoT), which allows healthcare experts to model and simulate IoT HMS scenarios defined for different disabilities and diseases. MoSIoT comprises a set of models based on the model-driven engineering (MDE) paradigm, which first allows simulation of a complete IoT HMS scenario, followed by generation of a final IoT system. In the current study, we used a real scenario defined by a recognized medical publication for a patient with Alzheimer’s disease to validate this proposal. Furthermore, we present an implementation based on an enterprise cloud architecture that provides the simulation data to a commercial IoT hub, such as Azure IoT Central.
Using Blockchain Technology to Foster Collaboration among Shippers and Carriers in the Trucking Industry: A Design Science Research Approach

In the global trucking industry, vertical collaboration between shippers and carriers is attained by intermediaries, called brokers. Brokers organize carriers for a shipper in accordance with its quality and price requirements, and support carriers to collaborate horizontally by sharing a large distribution order from a shipper. Brokers also act as trustees, preventing the passing of private information of any party to the others. Despite these benefits, intermediaries in the trucking industry are involved in several sustainability problems, including high costs, high levels of carbon emissions, high percentages of empty miles, low-capacity utilizations, and driver shortages. Several studies have acknowledged the importance of improving collaboration to address these problems. Obviously, the major concern of brokers is not collaboration, but rather to optimize their own gains. This paper investigates the potential of blockchain technology to improve collaboration in the trucking industry, by eliminating brokers while preserving their responsibilities as organizers and trustees. This paper extends the transportation control tower concept from the logistics literature, and presents a system architecture for its implementation through smart contracts on a blockchain network. In the proposed system, the scalability and privacy of trucking operations are ensured through integration with privacy-preserving off-chain computation and storage solutions (running outside of the blockchain). The potential of this design artifact for fostering collaboration in the trucking industry was evaluated by both blockchain technology experts and trucking industry professionals.