Reviewerly/exHarmony huggingface dataset:
Kallima: A Clean-Label Framework for Textual Backdoor Attacks

Although Deep Neural Network (DNN) has led to unprecedented progress in various natural language processing (NLP) tasks, research shows that deep models are extremely vulnerable to backdoor attacks. The existing backdoor attacks mainly inject a small number of poisoned samples into the training dataset with the labels changed to the target one. Such mislabeled samples would raise suspicion upon human inspection, potentially revealing the attack. To improve the stealthiness of textual backdoor attacks, we propose the first clean-label framework Kallima for synthesizing $$mimesis$$ -style backdoor samples to develop insidious textual backdoor attacks. We modify inputs belonging to the target class with adversarial perturbations, making the model rely more on the backdoor trigger. Our framework is compatible with most existing backdoor triggers. The experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method.
Detecting review spammer groups based on generative adversarial networks

The detection of spammer groups has recently gained more attention. However, the existing spammer group detection approaches rely on manual feature engineering to design spam indicators or extract features to capture the aggregated behavioral patterns exhibited by spammer groups. This is costly and time-consuming. To address this limitation, we formulate spammer group detection as a problem of finding distribution differences between normal user groups and spammer groups, and propose a review spammer group detection approach based on generative adversarial networks. First, we perform multiview sequence sampling on the dataset and utilize the Word2Vec model to obtain low-dimensional vector representations of users. Second, we construct the N-nearest neighbor user relationship graph according to the vector similarities in the embedding space. In the closed neighborhood of each node, we use the DBSCAN algorithm to find candidate groups. Finally, we construct a generative adversarial network model for spammer group detection, which uses the sum of generator reconstruction loss and discriminator loss to evaluate the spamicities of candidate groups. The experimental results on three real-world review datasets indicate that the proposed approach outperforms the baseline approaches in detection performance.
Testing of Random Walk Hypothesis in the Cryptocurrency Market

Cryptocurrency as a financial asset has emerged as a fad among investors, academicians and policymakers alike. In a financial purview, this study intends to empirically test the behaviour of the cryptocurrency return, inferring its market efficiency. For this purpose, daily data of five cryptocurrencies (Bitcoin, Ethereum, Litecoin, Tether and Ripple) have been collected from 1 January 2016 to 31 March 2021 to investigate the well-known financial theory of random walk hypothesis for this young market. To provide statistical evidence and ensure the robustness of results, analysis is performed using the variance ratio test, augmented Dickey–Fuller test, Philip–Perron test, Breusch–Godfrey serial correlation LM test and ARIMA model. The statistical results illustrated strong evidence refuting the presence of the random walk hypothesis in this emerging market, thus implying inefficiency in the cryptocurrency market. Furthermore, the absence of random walk in the cryptocurrency makes this financial asset predictable, giving investors an arbitrage edge to earn abnormal gains using trading strategies, which is euphoria.
Research on the internet of vehicles assisted traffic management systems for observing traffic density

Computer vision (CV) in a modern smart city is to be relied upon to deliver interactive and user-satisfying applications. CV is responsible for identifying humans and objects, among others, through which interactive applications are designed. The interacting machine handles visual inputs and processes them to provide quality responses to users. An artificial intelligence (AI) field known as computer vision (CV) is used to train computers in real-world applications to interact with the visual world. The internet of vehicles assisted traffic management system based on computer vision ((IoVTMS-CV) roadside pedestrian process is discussed to monitor and evaluate human movements depending on their location and deviation from previous time intervals. Traffic density and observations were interactively shared with the vehicles through navigation assistance based on these detections. In this method, convolutional neural learning has been used to identify the deviations over different time intervals. The variations were identified as incoherent with the standard patterns defined for roadside traffic management. The experiment's results show it's at least 37% more effective than traditional methods.
Face morphing attack detection and attacker identification based on a watchlist

Face verification is widely used for Automated Border Control (ABC) in many countries. But such ABC systems are vulnerable to Morphing Attacks (MAs), where a morphed face image is submitted to apply for a passport. To counter face MAs, this paper proposes to detect morphed face images and identify morphing attackers by the use of a watchlist. It is carried out by comparing a suspect image with the biometric references contained in a watchlist, and its detection process is accomplished by analyzing the results of face comparison. Once a morphed image is detected, its morphing attacker is also identified. Meanwhile, a database with different morphing methods, image qualities, facial expressions, and face angles is collected. Experimental results and analysis show that it can achieve stable detection and attacker identification performance for 4 different face MAs, and it can well generalize to unseen morphing types and weights. Moreover, it has good robustness to variations of image qualities, facial expressions, and face angles. • A morphing attack detection and attacker identification with a watchlist. • A database for face morphing attacks is built. • Generalization to unseen morphs.
Traffic Violation Invigilation Using Transfer Learning

The modern era of evolution and urbanization in this populated world makes the abundance of vehicles on the roads. The plenty number of road accidents are increased as a consequence of violation of traffic rules. Even though many techniques are applied to invigilate the traffic violation, still it is difficult to supervise the traffic violation. An automated system for traffic invigilation is required to handle and track the people who are infringing the rules. By using Convolutional Neural Network (CNN), the right model is built to predict the class of violation. In this work, the classification of the vehicle, detection of helmet violation, wearing mask is also done using CNN model. Transfer Learning is applied here to use the learned weightage from the existing model to improve the detection.
Aligning random graphs with a sub-tree similarity message-passing algorithm

Abstract The problem of aligning Erdős–Rényi random graphs is a noisy, average-case version of the graph isomorphism problem, in which a pair of correlated random graphs is observed through a random permutation of their vertices. We study a polynomial time message-passing algorithm devised to solve the inference problem of partially recovering the hidden permutation, in the sparse regime with constant average degrees. We perform extensive numerical simulations to determine the range of parameters in which this algorithm achieves partial recovery. We also introduce a generalized ensemble of correlated random graphs with prescribed degree distributions, and extend the algorithm to this case.
A Novel Technique for Detecting Various Thyroid Diseases Using Deep Learning

Thyroid disease is a medical condition caused due to the excess release of thyroid hormone.It is released by the thyroid gland which is in front of the neck just below the larynx.Medical pictures such as X-rays and CT scans can, however, be used to diagnose it.In this proposed model, Deep Learning technology is used to detect thyroid diseases.A Convolution Neural Network (CNN) based modified ResNet architecture is employed to detect five different types of thyroid diseases namely 1. Hypothyroid 2. Hyperthyroid 3. Thyroid cancer 4. Thyroiditis 5. Thyroid nodules.In the proposed work, the training method is enhanced using dual optimizers for better accuracy and results.Keras, a Python library that is high level runs as the main part of the Tensor Flow framework.It is used in the proposed work to implement deep learning techniques.The comparative analysis of the proposed model and the existing work helps to show that there is a great improvement in the performance metrics in classifying the type of thyroid disease.By applying Adam and SGD (Stochastic Gradient Descent) optimizers in the training phase of the proposed model it was identified that these increase the operational efficiency of the modified ResNet model.After retraining the model with SGD, the modified ResNet provides more accuracy of about 97% whereas the basic ResNet architecture attains 94% accuracy.A web-based framework is also developed which yields the type of thyroid disease as the output for a given input scanned image of the system.
Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities

Enterprises are moving their business-critical workloads to public clouds at an accelerating pace. Multi-tenancy is a crucial tenet for cloud data service providers allowing them to provide services in cost-effective manner by sharing of resources among tenants of the service. In this tutorial we review architectures of today's cloud data services and identify trends and challenges that arise in multi-tenant cloud data services. We discuss techniques that have been developed for enabling elasticity, providing SLAs, ensuring performance isolation, and reducing cost. We conclude with open research problems in cloud data services.
Find potential partners: A GitHub user recommendation method based on event data

GitHub has attracted much popularity among a large number of software developers around the world and introduced the social function follow to strengthen the relationship among developers. Like other social networks, GitHub users usually follow others who are popular in the community, co-workers, or friends in real life. However, according to our investigation, more than half of GitHub users prefer to follow recently like-minded developers other than their traditional networks for communicating with timely feedback, discovering niche repositories, and attracting more active contributors to cooperate, while these users are hard to find.
Smoking behavior recognition based on a two-level attention fine-grained model and EfficientDet network

A new smoking behavior recognition algorithm based on a weak supervision fine-grained structure and the EficientDet network is proposed in this study to solve the poor recognition effect and lack of data samples of smoking behavior in complex situations. The proposed algorithm uses the framework of a fine-grained two-level attention model with weak supervision. First, the feature edge of the image block is detected by a structured method, and the edge is screened by non-maximum suppression to form a candidate region block. Smoking behavior can then be recognized effectively by combining the results of the object-level filter for specific objects and the local-level filter for locating discriminant parts. Second, the object-level filter uses an improved EfficientDet network to classify prospective objects and candidate regions with strong features. The present smoking behavior recognition algorithm and coarse- and fine-grained algorithms are compared to verify the effectiveness of the algorithm. Experimental results show that the accuracy of the proposed algorithm is 93.10%, which is higher than that of the optimal smoking behavior detection algorithm by 1.7%, and the error detection rate is 3.6%.
P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

Data clustering is crucial when it comes to data processing and analytics.The new clustering method overcomes the challenge of evaluating and extracting data from big data.Numerical or categorical data can be grouped.Existing clustering methods favor numerical data clustering and ignore categorical data clustering.Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods.However, these algorithms could not use the concept of categorical data for clustering.Following that, suggestions for expanding traditional categorical data processing methods were made.In addition to expansions, several new clustering methods and extensions have been proposed in recent years.ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them.This paper aims to modify the algorithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures.The parameterized ROCK algorithm is the name given to the modified algorithm (P-ROCK).The proposed modification makes the original algorithm more flexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also provided.Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is more flexible and scalable.
Comparative analysis of deep learning based Afaan Oromo hate speech detection

Abstract Social media platforms like Facebook, YouTube, and Twitter are banking on developing machine learning models to help stop the spread of hateful speech on their platforms. The idea is that machine learning models that utilize natural language processing will detect hate speech faster and better than people can. Despite numerous progress has been made for resource reach language, only a few attempts have been made for Ethiopian Languages such as Afaan Oromo. This paper examines the viability of deep learning models for Afaan Oromo hate speech recognition. Toward this, the biggest dataset of hate speech was collected and annotated by the language experts. Variations of profound deep learning models such as CNN, LSTMs, BiLSTMs, LSTM, GRU, and CNN-LSTM are examined to evaluate their viability in identifying Afaan Oromo Hate speeches. The result uncovers that the model dependent on CNN and Bi-LSTM outperforms all the other investigated models with an average F1-score of 87%.
Autonomous detection and ascent of a step for an electric wheelchair

With the number of individuals using a wheelchair on the rise, the issue of removing architectural barriers, or at least overcoming them, has to be faced to improve independence, inclusiveness, and participation of wheelchair users. Some electric wheelchairs can climb and descend stairs and obstacles, however, the actual operations required to do so safely may be complex and may require an experienced or trained user. To overcome this issue, a method to first detect and classify a step and then autonomously climb it safely is proposed here. The same method is then applied and tested on an actual stair-climbing wheelchair prototype to prove its reliability in different conditions.
A Holistic Approach for Role Inference and Action Anticipation in Human Teams

The ability to anticipate human actions is critical to many cyber-physical systems, such as robots and autonomous vehicles. Computer vision and sensing algorithms to date have focused on extracting and predicting visual features that are explicit in the scene, such as color, appearance, actions, positions, and velocities, using video and physical measurements, such as object depth and motion. Human actions, however, are intrinsically influenced and motivated by many implicit factors such as context, human roles and interactions, past experience, and inner goals or intentions. For example, in a sport team, the team strategy, player role, and dynamic circumstances driven by the behavior of the opponents, all influence the actions of each player. This article proposes a holistic framework for incorporating visual features, as well as hidden information, such as social roles, and domain knowledge. The approach, relying on a novel dynamic Markov random field (DMRF) model, infers the instantaneous team strategy and, subsequently, the players’ roles that are temporally evolving throughout the game. The results from the DMRF inference stage are then integrated with instantaneous visual features, such as individual actions and position, in order to perform holistic action anticipation using a multi-layer perceptron (MLP). The approach is demonstrated on the team sport of volleyball, by first training the DMRF and MLP offline with past videos, and, then, by applying them to new volleyball videos online. These results show that the method is able to infer the players’ roles with an average accuracy of 86.99%, and anticipate future actions over a sequence of up to 46 frames with an average accuracy of 80.50%. Additionally, the method predicts the onset and duration of each action achieving a mean relative error of 14.57% and 15.67%, respectively.
Discovering Interdisciplinary Research Based on Neural Networks

Interdisciplinary research promotes the emergence of scientific innovation. Researchers want to find interdisciplinary research in their research field. However, the number of scientific papers published today is increasing, and completing this task by hand is time-consuming and laborious. A neural network is a machine learning model that simulates the connection mode of neurons in the human brain. It is an important application of bionics in the artificial intelligence field. This paper proposes an approach to discovering interdisciplinary research automatically. The method generates an IRD-BERT neural network model for discovering interdisciplinary research based on the pre-trained model BERT. IRD-BERT is used to simulate the domain knowledge of experts, and author keywords can be projected into vector space by this model. According to the keyword distribution in the vector space, keywords with semantic anomalies can be identified. Papers that use these author keywords are likely to be interdisciplinary research. This method is applied to discover interdisciplinary research in the deep learning research field, and its performance is better than that of similar methods.
Design of cross-cultural teaching management system for international students based on cloud service platform

Recently online teaching resources management has become an interesting domain in China due to emerging technologies like Video Internet of Things (VIoT). We propose a novel integrated framework for efficient teaching resources management using VIoT and cloud computing services . The proposed model designs a VIoT system to analyze the need for front-end portals. We employ a cloud platform for international students based on business goals and functional requirements to optimize sensor design. The proposed model first builds the VIoT front-end web portal to elaborate the design schemes, architecture, and various design ideas. Then, we integrate the VIoT system with other cloud-based modules such as the teaching management module , application management module, and system-object module. We represent the proposed model from three perspectives like Secure Shell (SSH) framework implementation, interfaces, and certain function modules. The experimental outcomes demonstrate that the system developed in this study has a specific influence.
A Hierarchical Contraction Scheme for Querying Big Graphs

This paper proposes a scheme for querying big graphs with a single machine. The scheme iteratively contracts regular structures into supernodes and builds a hierarchy of contracted graphs, until the one at the top fits into the memory. For each query class Q in use, supernodes carry synopses SQ such that queries of Q are answered by using SQ if possible, and otherwise by drilling down to the next level with decontraction of a bounded size. Moreover, we show how to adapt a variety of existing sequential (single-machine) algorithms to the hierarchy by reusing their logic and data structures. We also provide a bounded incremental algorithm to maintain the contracted graphs in response to updates, such that its cost is determined by the sizes of changes to the input and output only. Using real-life and synthetic graphs, we experimentally verify that with a single machine, the hierarchy is able to compute exact query answers when memory is as small as 7.6% of graphs, speeds up various applications by 9.8 times on average, and is even 120.1 times faster than some parallel graph systems that use 6 machines.
Exploiting document graphs for inter sentence relation extraction

Most previous relation extraction (RE) studies have focused on intra sentence relations and have ignored relations that span sentences, i.e. inter sentence relations. Such relations connect entities at the document level rather than as relational facts in a single sentence. Extracting facts that are expressed across sentences leads to some challenges and requires different approaches than those usually applied in recent intra sentence relation extraction. Despite recent results, there are still limitations to be overcome.We present a novel representation for a sequence of consecutive sentences, namely document subgraph, to extract inter sentence relations. Experiments on the BioCreative V Chemical-Disease Relation corpus demonstrate the advantages and robustness of our novel system to extract both intra- and inter sentence relations in biomedical literature abstracts. The experimental results are comparable to state-of-the-art approaches and show the potential by demonstrating the effectiveness of graphs, deep learning-based model, and other processing techniques. Experiments were also carried out to verify the rationality and impact of various additional information and model components.Our proposed graph-based representation helps to extract ∼50% of inter sentence relations and boosts the model performance on both precision and recall compared to the baseline model.
Comparative study of text representation and learning for Persian named entity recognition

Transformer models have had a great impact on natural language processing (NLP) in recent years by realizing outstanding and efficient contextualized language models. Recent studies have used transformer-based language models for various NLP tasks, including Persian named entity recognition (NER). However, in complex tasks, for example, NER, it is difficult to determine which contextualized embedding will produce the best representation for the tasks. Considering the lack of comparative studies to investigate the use of different contextualized pretrained models with sequence modeling classifiers, we conducted a comparative study about using different classifiers and embedding models. In this paper, we use different transformer-based language models tuned with different classifiers, and we evaluate these models on the Persian NER task. We perform a comparative analysis to assess the impact of text representation and text classification methods on Persian NER performance. We train and evaluate the models on three different Persian NER datasets, that is, MoNa, Peyma, and Arman. Experimental results demonstrate that XLM-R with a linear layer and conditional random field (CRF) layer exhibited the best performance. This model achieved phrase-based F-measures of 70.04, 86.37, and 79.25 and word-based F scores of 78, 84.02, and 89.73 on the MoNa, Peyma, and Arman datasets, respectively. These results represent state-of-the-art performance on the Persian NER task.