Reviewerly/exHarmony huggingface dataset:
A Neural Pairwise Ranking Model for Readability Assessment

Automatic Readability Assessment (ARA), the task of assigning a reading level to a text, is traditionally treated as a classification problem in NLP research.In this paper, we propose the first neural, pairwise ranking approach to ARA and compare it with existing classification, regression, and (non-neural) ranking methods.We establish the performance of our model by conducting experiments with three English, one French and one Spanish datasets.We demonstrate that our approach performs well in monolingual single/cross corpus testing scenarios and achieves a zero-shot cross-lingual ranking accuracy of over 80% for both French and Spanish when trained on English data.Additionally, we also release a new parallel bilingual readability dataset in English and French.To our knowledge, this paper proposes the first neural pairwise ranking model for ARA, and shows the first results of cross-lingual, zeroshot evaluation of ARA with neural models.
T6D-Direct: Transformers for Multi-object 6D Pose Direct Regression

6D pose estimation is the task of predicting the translation and orientation of objects in a given input image, which is a crucial prerequisite for many robotics and augmented reality applications. Lately, the Transformer Network architecture, equipped with multi-head self-attention mechanism, is emerging to achieve state-of-the-art results in many computer vision tasks. DETR, a Transformer-based model, formulated object detection as a set prediction problem and achieved impressive results without standard components like region of interest pooling, non-maximal suppression, and bounding box proposals. In this work, we propose T6D-Direct, a real-time single-stage direct method with a transformer-based architecture built on DETR to perform 6D multi-object pose direct estimation. We evaluate the performance of our method on the YCB-Video dataset. Our method achieves the fastest inference time, and the pose estimation accuracy is comparable to state-of-the-art methods.
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement

Compressed video action recognition has recently drawn growing attention, since it remarkably reduces the storage and computational cost via replacing raw videos by sparsely sampled RGB frames and compressed motion cues (e.g., motion vectors and residuals). However, this task severely suffers from the coarse and noisy dynamics and the insufficient fusion of the heterogeneous RGB and motion modalities. To address the two issues above, this paper proposes a novel framework, namely Attentive Cross-modal Interaction Network with Motion Enhancement (MEACI-Net). It follows the two-stream architecture, i.e. one for the RGB modality and the other for the motion modality. Particularly, the motion stream employs a multi-scale block embedded with a denoising module to enhance representation learning. The interaction between the two streams is then strengthened by introducing the Selective Motion Complement (SMC) and Cross-Modality Augment (CMA) modules, where SMC complements the RGB modality with spatio-temporally attentive local motion features and CMA further combines the two modalities with selective feature augmentation. Extensive experiments on the UCF-101, HMDB-51 and Kinetics-400 benchmarks demonstrate the effectiveness and efficiency of MEACI-Net.
The Critical Role Played by Big Data Management in Effectively Addressing the Security and Overall Privacy Concerns Through Correlation Analysis

Due to the widespread application of big data in a wide range of fields, there has been a significant rise in a diverse range of data assets, and numerous data analysis technologies, such as standardized data mining or quantitative analytical techniques, are speeding up the constant enlargement of the big data industry. The fact that information from several different resources has a life cycle from acquisition to disposal is a key property of big data, as is the fact that new information may be obtained via analysis, combining, and exploitation of existing data. Yet, information security & dependability challenges arise at every stage of the life cycle, rendering the protection of individually identifying data a vital goal throughout the whole process. It is possible to evaluate user behavior using different big data analytics techniques, and the collection of this information results in the violation of personal privacy. This study analyses dangers and security challenges that arise during the administration of large amounts of data by verifying the existing standards produced by international standardization bodies and doing an analysis of relevant research.
Smart Traffic Light Controller Using Edge Detection in Digital Signal Processing

Traffic is one of the significant concerns globally because the numbers of vehicles with new upcoming technology are increasing, especially in large urban areas. Advanced technologies are needed to resolve this issue. The best way to control a traffic light is to assign a timer for each light signal. Although the use of electronic sensors to detect traffic density is helpful, it is not effective since sensors do not working correctly. The MATLAB simulation model is proposed in this paper. This simulation model controls the traffic light with the help of image sequences. The proposed system will detect the vehicles through images. A camera is installed alongside the traffic light for capturing the signal (image) sequences. The image sequence is provided as an input to a digital-signal (image) processing algorithm; through it, the percentage of the match is found concerning an empty road whose image is already stored in the processor and based on the criteria of matching and the traffic light can be controlled.
Quantum steering as resource of quantum teleportation

Quantum steering, which lies between Bell nonlocality and entanglement, is a characteristic quantum correlation. Its application in quantum information tasks is worth pursuing. In this paper, we investigate the application of quantum steering as resource of quantum teleportation. It is found that two-qubit states violating the three-setting linear steering inequality are always useful for teleportation. Some steerable states which obey the Bell--Clauser-Horne-Shimony-Holt (CHSH) inequality could be used for teleportation. Two-qubit states that are optimal for quantum teleportation for a fixed amount of steering are present. The optimal states achieve the maximal average fidelity and also exhibit zero fidelity deviation.
Connection-Oriented and Connectionless Remote Entanglement Distribution Strategies in Quantum Networks

Quantum network is a promising platform for numerous quantum applications, including distributed quantum computing, secure communication, and improved sensing. Implementing entanglement distribution between remote quantum nodes plays a vital role in realizing the quantum network's capabilities, and it requires effective remote entanglement distribution protocols. To meet this requirement, we propose two strategies, namely connection-oriented and connectionless, to achieve end-to-end entanglement distribution inspired by the designs of classical communication. For the connection-oriented strategy, an exclusive quantum connection is pre-established between the source and destination nodes. For the connectionless strategy, entanglement swapping is performed hop-by-hop tentatively. In this way, the best-effort service is provided for the source node to establish long-distance entanglement with the destination node. Analogous to classical communication, the proposed strategies have their advantages and disadvantages. Our simulation results show that the connectionless strategy is more efficient and robust than the connection-oriented strategy in terms of throughput. However, the connection-oriented strategy provides reliable end-to-end entanglement distribution but at the expense of higher overhead. Moreover, the application scenarios of these two strategies are discussed. This work paves the road for designing end-to-end entanglement distribution protocols in quantum networks.
Analysis of Credit Card Fraud Detection using Machine Learning Techniques

This project primarily concentrates on detecting credit card scam in reality. Credit card scam is on increase as debit cards have become the preferred means of payment both for offline and online transactions. E-commerce has skyrocketed because of the increased usage of credit cards for online transactions that has also led to a quick rise of credit card scams recently. For training dataset, credit card datasets are collected. The user is then given credit card inquiries to test the dataset. After analyzing a prior dataset, a deep neural network technique is used to classify user's current dataset, which has been provided. Undoubtedly, a very efficient credit card scam detecting system has been achieved wherein strategies such as, Support Vector Machine (SVM), Deep Neural Network techniques, and Naive Bayes are all used in this research.
An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy

Abstract Whale Optimization Algorithm (WOA), as a newly proposed swarm-based algorithm, has gradually become a popular approach for optimization problems in various engineering fields. However, WOA suffers from the poor balance of exploration and exploitation, and premature convergence. In this paper, a new enhanced WOA (EWOA), which adopts an improved dynamic opposite learning (IDOL) and an adaptive encircling prey stage, is proposed to overcome the problems. IDOL plays an important role in the initialization part and the algorithm iterative process of EWOA. By evaluating the optimal solution in the current population, IDOL can adaptively switch exploitation/exploration modes constructed by the DOL strategy and a modified search strategy, respectively. On the other hand, for the encircling prey stage of EWOA in the latter part of the iteration, an adaptive inertia weight strategy is introduced into this stage to adaptively adjust the prey’s position to avoid falling into local optima. Numerical experiments, with unimodal, multimodal, hybrid and composition benchmarks, and three typical engineering problems are utilized to evaluate the performance of EWOA. The proposed EWOA also evaluates against canonical WOA, three sub-variants of EWOA, three other common algorithms, three advanced algorithms and four advanced variants of WOA. Results indicate that according to Wilcoxon rank sum test and Friedman test, EWOA has balanced exploration and exploitation ability in coping with global optimization, and it has obvious advantages when compared with other state-of-the-art algorithms.
Globalisation and economic growth in Africa: New evidence from the past two decades

Background: In the last two decades, the world experienced two overlapping global shocks – that is, the 2008–2009 financial crisis and the COVID-19 lockdowns – with severe social and economic consequences on African economies that have, once again, brought to the fore the intricate role that globalisation plays in economic growth because of grave risks that often accompany it.Aim: We consider three research questions about globalisation: (i) does globalisation heighten economic growth? (ii) is there a statistically significant threshold level of globalisation above which globalisation affects growth differently than at lower levels? (iii) what factors moderate the globalisation-economic growth nexus?Setting: A panel of 47 selected countries from Africa from 2001 to 2018 is under scrutiny.Method: To begin, it applies an overlapping five-year moving average (MA) to smoothen the data. In addition, we employ the revised globalisation index and the two-step systems generalised method of the moment (GMM) in its empirical strategy.Results: We find a largely insignificant relationship between globalisation and economic growth. We attribute these results to Africa’s infinitesimal share – less than 5% – in foreign direct investment (FDI) and global trade, acute infrastructure deficit and the lack of relevant skills that lead to productivity losses and weak performance within the international business ecosystem. We also find, among others, that globalisation is more effective in countries with more gross capital formation, higher population and urban growth rates.Conclusion: For Africa to maximise its growth potential from globalisation, sound policies should be put in place to promote trade, FDI, domestic capital formation and urbanisation. We suggest that future studies investigate the long-run equilibrium relationship between globalisation and economic growth.
Shallow and Deep Non-IID Learning on Complex Data

Non-IID (i.i.d.) data holds complex non-IIDness, e.g., couplings and interactions (non-independent) and heterogeneities (not IID drawn from a given distribution). Non-IID learning emerges as a major challenge to shallow and deep learning, including classic statistical learning, mathematical modeling, shallow machine learning, and deep neural learning. Here, we outline the problem, research map, main challenges and topics of shallow and deep non-IID learning.
Anomaly Detection in Aerial Videos With Transformers

Unmanned aerial vehicles (UAVs) are widely applied for purposes of inspection, search, and rescue operations by the virtue of low-cost, large-coverage, real-time, and high-resolution data acquisition capacities. Massive volumes of aerial videos are produced in these processes, in which normal events often account for an overwhelming proportion. It is extremely difficult to localize and extract abnormal events containing potentially valuable information from long video streams manually. Therefore, we are dedicated to developing anomaly detection methods to solve this issue. In this paper, we create a new dataset, named Drone-Anomaly, for anomaly detection in aerial videos. This dataset provides 37 training video sequences and 22 testing video sequences from 7 different realistic scenes with various anomalous events. There are 87,488 color video frames (51,635 for training and 35,853 for testing) with the size of 640 &#x00D7; 640 at 30 frames per second. Based on this dataset, we evaluate existing methods and offer a benchmark for this task. Furthermore, we present a new baseline model, ANomaly Detection with Transformers (ANDT), which treats consecutive video frames as a sequence of tubelets, utilizes a Transformer encoder to learn feature representations from the sequence, and leverages a decoder to predict the next frame. Our network models normality in the training phase and identifies an event with unpredictable temporal dynamics as an anomaly in the test phase. Moreover, To comprehensively evaluate the performance of our proposed method, we use not only our Drone-Anomaly dataset but also another dataset. We will make our dataset and code publicly available. A demo video is available at https://youtu.be/ancczYryOBY. We make our dataset and code publicly available<sup>1</sup>.
Overview of Touché 2022: Argument Retrieval

This paper is a condensed report on the third year of the Touché lab on argument retrieval held at CLEF 2022. With the goal to foster and support the development of technologies for argument mining and argument analysis, we organized three shared tasks in the third edition of Touché: (a) argument retrieval for controversial topics, where participants retrieve a gist of arguments from a collection of online debates, (b) argument retrieval for comparative questions, where participants retrieve argumentative passages from a generic web crawl, and (c) image retrieval for arguments, where participants retrieve images from a focused web crawl that show support or opposition to some stance.
Enhancement in Speaker Recognition using SincNet through Optimal Window and Frame Shift

Speech is the most natural mode of human communication. Speech extracts a lot of information, including language identification, gender, age, emotion, cognitive behavior, speaker identity, and exchanging thoughts and ideas. Speaker recognition identifies an individual's identity based on paralinguistic information in their speech signal. It finds many applications in biometrics, forensics, and access control systems. Convolutional Neural Networks (CNNs), which learn low-level speech representations from raw waveforms, have been frequently employed for speaker identification. SincNet is a novel neural architecture-based framework that has shown to be effective in performing the speaker recognition task. SincNet is used to analyze the raw audio samples and discover robust features. As a result, it takes the position of the DNN model's first layer, which performs convolution using parametrized sinc functions. As an initial step in processing a speech signal, it is separated into short (overlapping) temporal chunks called frames. Then, the input signal is windowed to reduce the artifacts due to sudden signal truncations at the boundaries of the frames before the Fast Fourier Transform has been applied. The proposed research aims to examine the performance of a few windowing functions over the sinclayer and preprocess the speech signal effectively for the Speaker Recognition task. Also, a variable-length frame overlapping approach for speaker recognition was presented in this research. The results obtained by the various windowing approaches over the sinclayer are plotted. Experimental results show that the Blackman windowing outperformed well among all windows, and frame size with 50% overlap between speech frames enhances the Sneaker Recognition accuracy by 5%.
Identification of cyber harassment and intention of target users on social media platforms

Due to Coronavirus diseases in 2020, all the countries departed into lockdown to combat the spread of the pandemic situation. Schools and institutions remain closed and students’ screen time surged. The classes for the students are moved to the digital platform which leads to an increase in social media usage. Many children had become sufferers of cyber harassment which includes threatening comments on young students, sexual torture through a digital platform, people insulting one another, and the use of fake accounts to harass others. The rising effort on automated cyber harassment detection utilizes many AI-related components Natural language processing techniques and machine learning approaches. Though machine learning models using different algorithms fail to converge with higher accuracy, it is much more important to use significant natural language processes and efficient classifiers to detect cyberbullying comments on social media. In this proposed work, the lexical meaning of the text is analysed by the conventional scheme and the word order of the text is performed by the Fast Text model to improve the computational efficacy of the model. The intention of the text is analysed by various feature extraction methods. The score for intention detection is calculated using the frequency of words with a bully-victim participation score. Finally, the proposed model’s performance is measured by different evaluation metrics which illustrate that the accuracy of the model is higher than many other existing classification methods. The error rate is lesser for the detection model.
Resource theory of causal connection

The capacity of distant parties to send signals to one another is a fundamental requirement in many information-processing tasks. Such ability is determined by the causal structure connecting the parties, and more generally, by the intermediate processes carrying signals from one laboratory to another. Here we build a fully fledged resource theory of causal connection for all multi-party communication scenarios, encompassing those where the parties operate in a definite causal order and also where the order is indefinite. We define and characterize the set of free processes and three different sets of free transformations thereof, resulting in three distinct resource theories of causal connection. In the causally ordered setting, we identify the most resourceful processes in the bipartite and tripartite scenarios. In the general setting, instead, our results suggest that there is no global most valuable resource. We establish the signalling robustness as a resource monotone of causal connection and provide tight bounds on it for many pertinent sets of processes. Finally, we introduce a resource theory of causal non-separability, and show that it is -- in contrast to the case of causal connection -- unique. Together our results offer a flexible and comprehensive framework to quantify and transform general quantum processes, as well as insights into their multi-layered causal connection structures.
Evolutionary natural-language coreference resolution for sentiment analysis

Communicating messages on social media usually conveys much implicit linguistic knowledge, which makes it difficult to process texts for further analysis. One of the major problems, the linguistic coreference resolution task involves detecting coreference chains of entities and pronouns that coreference them. It has mostly been addressed for formal and full-sized text in which a relatively clear discourse structure can be discovered, using Natural-Language Processing techniques. However, texts in social media are short, informal and lack a lot of underlying linguistic information to make decisions so traditional methods can not be applied. Furthermore, this may significantly impact the performance of several tasks on social media applications such as opinion mining, network analysis, sentiment analysis, text categorization. In order to deal with these issues, this research address the task of linguistic co-referencing using an evolutionary computation approach. It combines discourse coreference analysis techniques, domain-based heuristics (i.e., syntactic, semantic and world knowledge), graph representation methods, and evolutionary computation algorithms to resolving implicit co-referencing within informal opinion texts. Experiments were conducted to assess the ability of the model to find implicit referents on informal messages, showing the promise of our approach when compared to related methods.
Leukemia segmentation and classification: A comprehensive survey

Blood is made up of leukocytes (WBCs), erythrocytes (RBCs), and thrombocytes. The ratio of blood cancer diseases is increasing rapidly, among which leukemia is one of the famous cancer which may lead to death. Leukemia cancer is initiated by the unnecessary growth of immature WBCs present in the sponge tissues of bone marrow. It is generally analyzed by etiologists by perceiving slides of blood smear images under a microscope. The morphological features and blood cells count facilitated the etiologists to detect leukemia. Due to the late detection and expensive instruments used for leukemia analysis, the death rate has risen significantly. The fluorescence-based cell sorting technique and manual recounts using a hemocytometer are error-prone and imprecise. Leukemia detection methods consist of pre-processing, segmentation, features extraction, and classification. In this article, recent deep learning methodologies and challenges for leukemia detection are discussed. These methods are helpful to examine the microscopic blood smears images and for the detection of leukemia more accurately.