Reviewerly/exHarmony huggingface dataset:
Large Language Models are Not Models of Natural Language: They are Corpus Models

Natural Language Processing (NLP) has become one of the leading application areas in the current Artificial Intelligence boom.Transfer learning has enabled large deep learning neural networks trained on the language modeling task to vastly improve performance in almost all language tasks.Interestingly, when the models are trained with data that includes software code, they demonstrate remarkable abilities in generating functioning computer code from natural language specifications.We argue that this creates a conundrum for claims that neural models provide an alternative theory to generative phrase structure grammars in explaining how language works.Since the acceptable syntax of programming languages is determined by phrase structure grammars, successful neural models are apparently uninformative about the theoretical foundations of programming languages, and by extension, natural languages.We argue that the term language model is misleading because deep learning models are not theoretical models of language and propose the adoption of corpus model instead, which better reflects the genesis and contents of the model.
FBI-Net: Frequency-Based Image Forgery Localization via Multitask Learning With Self-Attention

Image forgery is easily manufactured for illegal acts such as spreading misleading information, which can have unfortunate consequences for society. In this work, we propose a Discrete Cosine Transformation (DCT) based multi-task learning network named FBI-Net, for forgery localization. Our proposed network adopts a fully convolutional encoder-decoder architecture, consisting of three encoders sharing parameters, a bridge attention module, and two output streams in the decoder. The encoder takes three inputs: RGB images and high-/low- DCT-filtered images. High-frequency components help learn object characteristics that improve CNN accuracy; low-frequency components are essential frequency information to keep most of the energy found in the typical DCT. Subsequently, Dilated Frequency Self-Attention Module, DFSAM in the bridge layer, is incorporated into the network to recalibrate the fused features and enhance the representation. Finally, in the decoder stage, region and edge information of the label are learned through multi-task learning to provide more extensive supervision for forged region localization; the edge stream will give a deeper understanding of features between forged and authentic images and help learn how to predict exquisite representations in images. Simultaneously, the auxiliary features from the pre-trained segmentation model are fused to separate the segmented background and objects, drawing the segmentation result of the dense region obtained. Extensive experiments show that our proposed FBI-Net outperforms existing forgery localization methods on six benchmark splicing and copy-move image datasets, CASIA TIDE v 1.0, CASIA TIDE v 2.0, Carvalho, Columbia, Coverage, IMD2020, achieving the best performance in an average of IoU of 70.99% and F1-score of 76.98% which is 9.79%, 9.82% higher than the previous method, respectively.
Hate and Aggression Detection in Social Media Over Hindi English Language

In today’s time, everyone is familiar with social media platforms. It is quite helpful in connecting people. It has many advantages and some disadvantages too. Currently, in social media, hate and aggression have become a huge problem. On these platforms, many people make inflammatory posts targeting any person or society by using code mixed language, due to which many problems arise in the society. At the current time, much research work is being done on English language-related social media posts. The authors have focused on code mixed language. Authors have also tried to focus on sentences that do not use abusive words but contain hatred-related remarks. In this research, authors have used Natural Language Processing (NLP). Authors have applied Fasttext word embedding to the dataset. Fasttext is a technique of NLP. Deep learning (DL) classification algorithms were applied thereafter. In this research, two classifications have been used i.e. Convolutional Neural Network (CNN) and Bidirectional LSTM (Bi-LSTM).
Develop Then Rival: A Human Vision-Inspired Framework for Superimposed Image Decomposition

A single superimposed image containing two image views causes visual confusion for both human vision and computer vision. Human vision needs a “develop-then-rival” process to decompose the superimposed image into two individual images, which effectively suppresses visual confusion. However, separating individual image views from a single superimposed image has been an important but challenging task in computer vision area for a long time. In this paper, we propose a human vision-inspired framework for single superimposed image decomposition. We first propose a network to simulate the development stage, which tries to understand and distinguish the semantic information of the two layers of a single superimposed image. To further simulate the rivalry activation/suppression process in human brains, we carefully design a rivalry stage, which incorporates the original mixed input (superimposed image), the activated visual information (outputs of the development stage) together, and then rivals to get images without ambiguity. Experimental results show that our novel framework effectively separates the superimposed images and significantly improves the performance with better output quality compared with state-of-the-art methods. The proposed method also achieves state-of-the-art results on related applications including single image reflection removal, single image rain removal, single image shadow removal, and illumination correction, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> , which validates the generalization of the framework.
Efficient Point-of-Interest Recommendation Services With Heterogenous Hypergraph Embedding

Point-of-interest (POI) recommendation service has drawn growing attention with the widespread popularity of location- based social networks (LBSNs). Recent research methods on POI recommendation based on graph embedding have mainly focused on explicit interactions of LBSN objects such as user's check-ins on POIs and social relationships, while neglecting implicit relationship that cannot be directly observed but may notably contribute to the POI recommendation. This paper presents VirHpoi, a heterogeneous hypergraph embedding method for POI recommendation in LBSNs with three original contributions. First, we model the LBSNs as a hypergraph to capture the complex interactions in LBSNs and learn the hypergraph by preserving homophily and interaction attribute affinity of the LBSNs. Second, we introduce the notion of “virtual hyperedges” to capture the intrinsic correlations of POIs. Virtual hyperedges incorporate implicit yet informative connections of the check-in patterns in LBSNs in terms of geographical and semantic characteristics. Third, we propose techniques to learn heterogenous hypergraph embedding on the complex LBSN graph with both homogenous edges and heterogenous hyperedges with dual objectives: we aim to preserve the homophily of objects intra domain by maximizing the co-occurrence probability of all homogenous edges, and we want to learn the interaction attribute affinity across domains by maximizing the probability of predicting the target object in the hyperedges. As a result, our approach can preserve both the intra domain homophily of objects and the interaction attribute affinity across domains by learning low-dimensional embeddings of LBSN objects and then make more effective recommendations based on the embeddings. Extensive experiments on four real-world datasets show the effectiveness and superiority of VirHpoi compared with the state-of-the-art methods.
Microseismic Source Location Using Deep Reinforcement Learning

Locating microseismic sources in time is a challenging problem in microseismic monitoring. In order to improve the accuracy and efficiency of locating sources, this paper presents a method for locating microseismic sources using deep reinforcement learning. We first construct and train a convolutional autoencoder to preprocess the seismic records in the microseismic waveform database. Then, the problem of locating the source is described as a Markov decision process for the application of deep reinforcement learning. We decompose the task of locating the source into three subtasks and design the critical elements of deep reinforcement learning. Three agents independently learn optimal policies for their respective subtasks in the framework of a deep Q-network (DQN) and jointly determine the precise location of the microseismic source. Finally, we evaluate the proposed method using synthetic data generated from the Marmousi model and the 3D velocity model. The experiment results indicate that the proposed method can locate microseismic sources efficiently and accurately.
The Performance Analysis of Time Series Data Augmentation Technology for Small Sample Communication Device Recognition

Communication device recognition is a key problem of electromagnetic space perception. At present, the traditional recognition technology is difficult to adapt to the complex signal situation. Thanks to the deep learning's superior capability of processing complex and massive data, it has been a hot topic in the field of communication area. However, it needs a large amount and high-quality signal dataset, which will pay for much cost. Therefore, small sample and data augmentation technology should be given much attention. In this article, we propose a novel time series data augmentation technology for small sample recognition. First, a complex neural network is designed to recognize the communication device based on the in-phase/quadrature time series. Second, based on signal data characteristic, several simple and effective methods of time series data augmentation are analyzed, which include noise disturbance, amplitude and time-delay transformation, frequency offset, and phase shift transformation. Third, in order to get a better augmentation result, based on complex neural network model characteristic, a novel data augmentation method of virtual adversarial training is presented for the small sample device recognition. Finally, a series of experimental simulations in real ADS-B signal indicates that the proposed method is suitable for the time series analysis and recognition of communication device with a small sample.
GraphNAS++: Distributed Architecture Search for Graph Neural Networks

Graph neural networks (GNNs) are popularly used to analyze non-Euclidean graph data. Despite their successes, the design of graph neural networks requires heavy manual work and rich domain knowledge. Recently, neural architecture search algorithms are widely used to automatically design neural architectures for CNNs and RNNs. Inspired by the success of neural architecture search algorithms, we present a graph neural architecture search algorithm GraphNAS that enables automatic design of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS uses a recurrent network as the controller to generate variable-length strings that describe the architectures of graph neural networks, and trains the recurrent network with policy gradient to maximize the expected accuracy of the generated architectures on a validation data set. Moreover, based on GraphNAS, we design a new GraphNAS++ model using distributed neural architecture search. Compared with GraphNAS that generates and evaluates only one candidate architecture at each iteration, GraphNAS++ generates a mini-batch of candidate architectures and evaluates them in a distributed computing environment until convergence. Experiments on real-world datasets demonstrate that GraphNAS can design a novel network architecture that rivals the best human-invented architecture. Moreover, GraphNAS++ can speed up the design process at least five times by using the distributed training framework with GPUs.
Iterative Learning in Support Vector Regression With Heterogeneous Variances

The presence of heterogeneous variances is the norm in practice, which makes machine learning predictions less reliable when noise variances are implicitly assumed to be equal. To this end, we extend support vector regression by allowing a range of variance functions in the model training. Specifically, we model the variance as a function of the mean and other variables as traditionally used in statistical modeling. This leads to iterative learning between support vector regression training and heterogeneous variance modeling. Extensive simulations are implemented to validate the effectiveness of the proposed framework in both linear and nonlinear regressions. Finally, two real data sets are used to demonstrate the superiority of the proposed algorithm in the presence of heterogeneity.
Mapping the Knowledge on Blockchain Technology in the Field of Business and Management: A Bibliometric Analysis

Blockchain technology is gaining momentum in business and management (B&M) field for its decentralization, automation, unalterability and traceability.Yet, despite such a wide range of applications, there is little literature that integrates the history and evolution of blockchain.To this end, this study combines science mapping and bibliometric analysis of 696 articles published between 2015 and 2021 obtained from the Web of Science Core Collection database.By drawing the reference co-citation map, bibliographic coupling map and co-word map with CiteSpace and VOSviewer tools, we identify the foundational themes, current status, and future research directions of blockchain literature in B&M.The results indicate that the blockchain research evolves from decentralized transaction and smart contract, and future research is focusing on the role of blockchain in financial risk management, organizational structure and the digital transformation of society.
A Parallel Approach to Partition-Based Frequent Pattern Mining Algorithm

Association rule mining is one of the common way to analyze market basket data, and it provides knowledge to the decision-makers that help them to make strategic decisions. In the literature, many techniques have been studied for association rule mining but the exponential growth of data from various sources and the changing nature of data with respect to time and zone makes the analysis task trivial. In this paper, we propose a novel partition-based frequent pattern mining algorithm in order to generate robust and useful patterns from dataset in a more efficient way. The frequent patterns found from the proposed algorithm are used to generate interesting association rules. This paper provides an optimized method, which split the dataset into multiple loads on the basis of a particular attribute depending upon the number of cores available in the system, and these individual loads will get executed in parallel using our proposed algorithm. We show experimental results using datasets from retail sector to validate the capability and usefulness of our proposed algorithm.
A Novel Verification Scheme to Resist Online Password Guessing Attacks

User name and password are the most commonly used user authentication scheme in information systems. Strong passwords are secure but difficult to remember, so many users prefer easy-to-remember passwords. These weak passwords are also easily guessed by attackers, leading to online password guessing attacks, posing a serious security threat to information systems. Providing a reliable user authentication scheme to allow legitimate users to login while preventing online password guessing attacks is a challenge. We define a formal statistical model for the behavior of users and attackers, and differentiate users and attackers according to this model. The proposed solution computes the entropy of the passwords entered by the user and considers the user legitimate only if the entropy does not exceed a threshold. We show that entropy is an effective feature to distinguish legitimate users from attackers. We also show that the proposed user authentication scheme is effective in identifying password guessing attacks, even if the user chooses a common password. The new scheme adds an extra layer of protection to passwords, which is especially important for weak passwords. The scheme is a slight modification of the existing scheme, so it can be easily integrated into existing systems.
A Novel Algorithm for Quantized Particle Filtering With Multiple Degrading Sensors: Degradation Estimation and Target Tracking

This article addresses the particle filtering problem for a class of nonlinear/non-Gaussian systems with quantized measurements and multiple degrading sensors. A degradation variable described by the Wiener process is proposed to describe the phenomenon of sensor degradation that is often encountered in engineering practice. The measurement output of each sensor is quantized by a uniform quantizer before being sent to the remote filter. An augmented system is constructed, which aggregates the original system state and the degradation variables. In the presence of the sensor degradation and the quantization errors, a new likelihood function at the remote filter is calculated by resorting to all the transmitted measurements. According to the mathematical characterization of the likelihood function, a novel particle filtering algorithm is developed, where the parameters of both the degradation processes and the quantization functions are exploited to obtain the modified importance weights. Finally, the effectiveness of the proposed method is shown via a target tracking example with bearing measurements.
Gemini: Elastic SNARKs for Diverse Environments

We introduce a new class of succinct arguments, that we call elastic. Elastic SNARKs allow the prover to allocate different resources (such as memory and time) depending on the execution environment and the statement to prove. The resulting output is independent of the prover's configuration. To study elastic SNARKs, we extend the streaming paradigm of [Block et al., TCC'20]. We provide a definitional framework for elastic polynomial interactive oracle proofs for R1CS instances and design a compiler which transforms an elastic PIOP into a preprocessing argument system that supports streaming or random access to its inputs. Depending on the configuration, the prover will choose different trade-offs for time (either linear, or quasilinear) and memory (either linear, or logarithmic). We prove the existence of elastic SNARKS by presenting Gemini, a novel FFT-free preprocessing argument. We prove its security and develop a proof-of-concept implementation in Rust based on the arkworks framework. We provide benchmarks for large R1CS instances of tens of billions of gates on a single machine.
Emoji, Sentiment and Emotion Aided Cyberbullying Detection in Hinglish

The advent of the Internet is a boon to society. However, many of its banes cannot be undermined, cyberbullying being one of them. The emotional state and sentiment of a person have a significant influence on the intended content. The current work is the first attempt in investigating the role of sentiment and emotion information for identifying cyberbullying in the Indian scenario. From Twitter, a benchmark Hind–English code-mixed corpus called BullySentEmo has been developed as there is no dataset available labeled with bully, sentiment, and emotion. Moreover, emoji information available with tweet texts can provide better understanding of user intention. The developed dataset consists of both modalities, tweet text, and emoji. In India, the majority of communication on different social media platforms is based on Hindi and English and language switching is a common practice in digital communication. An attention-based multimodal, adversarial multitasking framework is proposed for cyberbully detection (CBD) considering two auxiliary tasks: sentiment analysis (SA) and emotion recognition (ER). Experimental results indicate that compared to unimodal and single-task variants, the proposed framework improves the performance of the main task, i.e., CBD, by 3.59% and 2.56% in terms of accuracy and F1-Score, respectively. Furthermore, two different benchmark datasets (Twitter dataset and Aggression dataset) have been considered to show the robustness of our proposed model.
Efficient Image Captioning Based on Vision Transformer Models

Image captioning is an emerging field in machine learning. It refers to the ability to automatically generate a syntactically and semantically meaningful sentence that describes the content of an image. Image captioning requires a complex machine learning process as it involves two sub models: a vision sub-model for extracting object features and a language sub-model that use the extracted features to generate meaningful captions. Attention-based vision transformers models have a great impact in vision field recently. In this paper, we studied the effect of using the vision transformers on the image captioning process by evaluating the use of four different vision transformer models for the vision sub-models of the image captioning The first vision transformers used is DINO (self-distillation with no labels). The second is PVT (Pyramid Vision Transformer) which is a vision transformer that is not using convolutional layers. The third is XCIT (cross-Covariance Image Transformer) which changes the operation in self-attention by focusing on feature dimension instead of token dimensions. The last one is SWIN (Shifted windows), it is a vision transformer which, unlike the other transformers, uses shifted-window in splitting the image. For a deeper evaluation, the four mentioned vision transformers have been tested with their different versions and different configuration, we evaluate the use of DINO model with five different backbones, PVT with two versions: PVT_v1and PVT_v2, one model of XCIT, SWIN transformer. The results show the high effectiveness of using SWIN-transformer within the proposed image captioning model with regard to the other models.
Detection Enhancement for Various Deepfake Types Based on Residual Noise and Manipulation Traces

As deepfake techniques become more sophisticated, the demand for fake facial image detection continues to increase. Various deepfake detection techniques have been introduced but detecting all types of deepfake images with a single model remains challenging.We propose a technique for detecting various types of deepfake images using three common traces generated by deepfakes: residual noise, warping artifacts, and blur effects. We adopted a network designed for steganalysis to detect pixel-wise residual-noise traces. We also consider landmarks, which are the primary parts of the face where unnatural deformations often occur in deepfake images, to capture high-level features. Finally, because the effect of a deepfake is similar to that of blurring, we apply features from various image quality measurement tools that can capture traces of blurring. The results demonstrate that each detection strategy is efficient, and that the performance of the proposed network is stable and superior to that of existing detection networks on datasets of various deepfake types.