Miracle-dz/UniFAR-FTUs huggingface dataset:
Hateful meme detection with multimodal deep neural networks

The modern advances of social media platforms and content sharing websites led to the popularization of Internet memes, and today’s Internet landscape contains websites that are predominantly dedicated to meme sharing. While at their inception memes were mostly humorous, this concept evolved and nowadays memes cover a wide variety of subjects, including political and social commentaries. Considering the widespread use of memes and their power of conveying distilled messages, they became an important method for spreading hate speech against individuals or targeted groups. Given the multimodal nature of Internet memes, our proposed approach is also a multimodal one, consisting of two parallel processing branches, one textual and one visual, that are joined in a final classification step, providing prediction results for the samples. We test our approach on the publicly available Memotion 7k dataset and compare our results with the baseline approach developed for the dataset.
MVAN: Multi-View Attention Networks for Fake News Detection on Social Media

Fake news on social media is a widespread and serious problem in today’s society. Existing fake news detection methods focus on finding clues from Long text content, such as original news articles and user comments. This paper solves the problem of fake news detection in more realistic scenarios. Only source shot-text tweet and its retweet users are provided without user comments. We develop a novel neural network based model, Multi-View Attention Networks (MVAN) to detect fake news and provide explanations on social media. The MVAN model includes text semantic attention and propagation structure attention, which ensures that our model can capture information and clues both of source tweet content and propagation structure. In addition, the two attention mechanisms in the model can find key clue words in fake news texts and suspicious users in the propagation structure. We conduct experiments on two real-world datasets, and the results demonstrate that MVAN can significantly outperform state-of-the-art methods by 2.5% in accuracy on average, and produce a reasonable explanation.
Synthesis of Elliptical Array Antenna with Flat Top Beam using Multi-Verse Optimization Algorithm

This article deals with the synthesis of uniformly spaced elliptical array antenna using multi-verse optimization algorithm. Two cases are performed here. In case 1 a uniform elliptical array antenna is optimized to obtain a flat top beam pattern with 20 numbers of array elements and a specific semimajor axis value. In case 2, a 40 element elliptical array is designed with optimized semi-major axis. For both the cases the current excitation amplitude is varied within a specific boundary value to get the flat top shape in the radiation pattern. Case 1 mainly focuses on minimizing the ripple in the main beam and case 2 focuses on reducing the side lobes. The challenge of this study is to produce a flat-top pattern with a negligible amount of ripple in the main beam and lower side lobes which can essentially be applied with benefit of constant intensity over a given cross-section. The study shows the robustness of the algorithm and the usefulness of the considered optimization technique.
Probabilistic Decision Model for Adaptive Task Planning in Human-Robot Collaborative Assembly Based on Designer and Operator Intents

In the manufacturing industry, the era of mass customization has arrived. Combining the complementary strengths of humans and robots will allow to cope with growing product diversity and fluctuating demands. When collaborating, humans and robots operate in a shared workspace to assemble a common product and it has to be decided how tasks are distributed between them. In previous work, human operators have often been modeled as controllable agents whose actions can be planned. To achieve a natural collaboration, we believe that the robot has to estimate the operator's assembly intentions and should behave accordingly. This work presents a framework for planning collaborative robot tasks in assembly, considering both the designer and operator intents. From the designer's CAD data, a set of potential assembly plans is automatically derived and translated into a state graph from which the operator intentions follow. To enable the robot to act optimally under a given belief of operator intentions, a partially observable Markov decision process (POMDP) is formulated, whose state space is represented by the intention graph. Experimental results obtained with our approach demonstrate the potential to estimate the true operator intent based on observations about the parts and tools manipulated during collaborative assembly.
Design and Modeling of Auxiliary Misalignment Detection Coils for Dynamic Wireless Electric Vehicle Charging Systems

Due to the inherent misalignment variations in dynamic wireless electric vehicle (EV) charging systems, it is essential to implement effective misalignment detection and correction techniques to maximize the amount of energy received by the EV. A misalignment detection system consists of detector coils or sensors, and the corresponding detection circuitry. In this work, the design details of the auxiliary misalignment detection coils are presented. In particular, the paper investigates the effect of the number of turns of the auxiliary coils, their turn-to-turn spacing, and their positions relative to the edges of the secondary coil, on their lateral misalignment detection capability. Based on the conducted simulations, it is observed that increasing the number of turns as well as the turn-to-turn spacing of the auxiliary coils both significantly improve their misalignment detection capability by improving the resultant mutual inductance variations with lateral misalignments. Also, the optimal position of the auxiliary coils is determined to be 200 mm from both edges of the secondary coil.
Arabic Scene Text Recognition in the Deep Learning Era: Analysis on a Novel Dataset

The problem of scene text recognition has recently gained extra attention, being an essential part of scene understanding systems. The broad scope of applications and the unresolved challenges has given this problem its popularity. However, the research focus has long been on languages with Latin characters while leaving behind other languages with different characteristics, such as the Arabic language. In this paper, we focus on Arabic scene text recognition and attempt to fill two main gaps regarding this research task. First, the Arabic language is lacking a publicly available benchmark dataset to compare different proposed methods on the same grounds. Therefore, we introduce a novel Arabic/English dataset: Everyday Arabic-English Scene Text dataset (EvArEST), to fill that need. Second, while deep learning methods have continuously evolved and pushed the state of the art in languages with Latin characters, their use for the Arabic language has been very limited. Therefore, we use our new dataset to evaluate the problem of Arabic scene text recognition from three perspectives: (1) using deep learning techniques and studying their suitability for Arabic scene text recognition, where we identify essential components required for the model to obtain good performance; (2) identifying Arabic text challenges that differ from Latin text and require special attention; (3) investigating a bilingual model that concurrently deals with Arabic and English words, since Arabic text is usually found along with other languages. We determine the best model to handle bidirectional text, its challenges, and possible ways to overcome them. We offer both Arabic and Bilingual text recognition results using EvArEST dataset for upcoming research to build upon and improve. We also point to directions for future research based on the analysis performed on the dataset. The dataset is publicly available at https://github.com/HGamal11/EvArEST-dataset.
SpartanSSD: a Reliable SSD under Capacitance Constraints

In this paper, we present an SSD design that is resilient to sudden power-off failures. Modern SSDs use a large number of capacitors that act as energy reserves to persist both host data and SSD metadata in the unforeseen event of a power outage. However, these capacitors take up a large footprint that limits the SSD’s density. We present a series of design choices that significantly reduce the SSD’s dependence on capacitors, all the while meeting the durability, consistency, and power-on time constraints. We demonstrate that at a modest performance overhead of 11%, the amount of required capacitance is reduced by 97.87%.
A Model-Driven Approach to align Heterogeneous Models of a Complex System

To understand and manipulate a complex system, it is necessary to apply the separation of concerns and produce separate models, called viewpoints models. These models represent views on the system that correspond to distinct business domains. They are generally heterogeneous, i.e. conform to different meta-models. The management of the system’s global model (a complete view of the system) requires the identiﬁcation of the existing correspondences among the viewpoints models. However, in practice these correspondences are either incompletely identiﬁed or not sufﬁciently formalized to be maintained when models evolve. This restricts their use and does not allow their full exploitation for managing the global model. To ﬁx this issue, we propose AHM (Alignment of Heterogeneous Models), an approach to organize the source models as a network of models through a virtual global model called M1C (Model of correspondences between models) that conforms to a Meta-Model of Correspondences (MMC). AHM proposes also a process, allowing for both the creation of the global model, and its consistency control. Partial automation of this process is done through a reﬁning mechanism supported by a semantics expression described in a Domain Speciﬁc Language (DSL). The application of AHM is illustrated by the example of a conference management system. A prototype of a tool called Heterogeneous Matching and Consistency management Suite (HMCS) has been developed to support this approach.
Deidentify

The increased adoption of Electronic Health Record (EHR) systems offers new opportunities for clinical research. The Health Insurance Portability and Accountability Act (HIPAA) mandates that medical records need to be stripped of personal identifiers in order to be shared. One particular challenge is how to handle free-text medical records. While many methods have been developed, there is a dearth of software tools than can be easily used by practitioners. We present deidentify, a new de-identification tool, which comes with a graphical user interface and runs on all operating systems. Evaluating its algorithm on a gold-standard corpus of nursing notes, we demonstrate its adequate performance with a recall of 0.919 and a precision of 0.645. Since deidentify comes with a pre-trained model, it can be used when no training data is available, but can also be manually configured. This way, it should be convenient to use for de-identification tasks.
ScAA: A Dataset for Automated Short Answer Grading of Children's free-text Answers in Hindi and Marathi

1 Automatic short answer grading (ASAG) techniques are designed to automatically assess short answers written in natural language. Apart from MCQs, evaluating free text answer is essential to assess the knowledge and understanding of children in the subject. But assessing descriptive answers in low resource languages in a linguistically diverse country like India poses significant hurdles. To solve this assessment problem and advance NLP research in regional Indian languages, we present the Science Answer Assessment (ScAA) dataset of children’s answers in the age group of 8-14. ScAA dataset is a 2-way (correct/incorrect) labeled dataset and contains 10,988 and 1,955 pairs of natural answers along with model answers for Hindi and Marathi respectively for 32 questions. We benchmark various state-of-the-art ASAG methods, and show the data presents a strong challenge for future research.
A Novel Optimization Method for Bipolar Chaotic Toeplitz Measurement Matrix in Compressed Sensing

In this paper, a bipolar chaotic Toeplitz measurement matrix optimization algorithm for alternating optimization is presented. The construction of measurement matrices is one of the key techniques for compressive sensing from theory to engineering applications. Recent studies have shown that bipolar chaotic Toeplitz matrices, constructed by combining the intrinsic determinism of bipolar chaotic sequences with the advantages of Toeplitz matrices, have significant advantages over other measurement matrices in terms of memory overhead, computational complexity, and hard implementation. However, problems such as strong correlation and large interdependence coefficients between measurement matrices and sparse dictionaries may still exist in practical applications. To address this problem, we propose a new bipolar chaotic Toeplitz measurement matrix alternating optimization algorithm. Firstly, by introducing the structure matrix, the optimization problem of the measurement matrix is transformed into the optimization problem of the generating sequence, thus ensuring that the optimization process does not destroy the structural properties of the matrix; then, constraints are added to the values of the generating sequence during the optimization process, so that the optimized measurement matrix still maintains the bipolar properties. Finally, the effectiveness of the optimization algorithm in this paper is verified by simulation experiments. The experimental results show that the optimized bipolar chaotic Toeplitz measurement matrix can effectively reduce the reconstruction error and improve the reconstruction probability.
Deep Neural Model for Manipuri Multiword Named Entity Recognition with Unsupervised Cluster Feature

The recognition task of Multi-Word Named Entities (MNEs) in itself is a challenging task when the language is inflectional and agglutinative. Having break-through NLP researches with deep neural network and language modelling techniques, the applicability of such techniques/algorithms for Indian language like Manipuri remains unanswered. In this paper an attempt to recognize Manipuri MNE is performed using a Long Short Term Memory (LSTM) recurrent neural network model in conjunction with Part Of Speech (POS) embeddings. To further improve the classification accuracy, word cluster information using K-means clustering approach is added as a feature embedding. The cluster information is generated using a Skip-gram based words vector that contains the semantic and syntactic information of each word. The model so proposed does not use extensive language morphological features to elevate its accuracy. Finally the model’s performance is compared with the other machine learning based Manipuri MNE models.
Impact of regional differences in stroke symptom awareness and low-income status on seeking emergency medical service in China

Abstract Background: Unawareness of stroke symptoms and low income are two barriers that affect the seeking of emergency medical service (EMS). This study aimed to assess the effect of unawareness and low income on seeking EMS and to investigate the regional distribution of the unawareness and low-income status and their associations with failing to call EMS in China. Methods: A total of 187,723 samples from the China National Stroke Screening Survey was interviewed cross-sectionally. Four status of awareness and annual income were identified: unaware and low-income, unaware-only, low-income-only, and aware and regular income. The outcomes were whether they intended to call EMS or not. The regional distribution of each status and their associations with not calling EMS were presented. Results: The status of unaware and low-income, unaware-only, and low-income-only accounted for 6.3% (11,806/187,673), 11.9% (22,241/187,673), and 21.5% (40,289/187,673) of the total sample, respectively. Not calling EMS was significantly associated with the status of unaware and low-income (odds ratio [OR]: 3.21, 95% confidence interval [CI]: 3.07–3.35), unaware-only (OR: 2.38, 95% CI: 2.31–2.46), and low-income-only (OR: 1.67, 95% CI: 1.63–1.71), compared with the aware and regular income status. The Midwest regions had higher percentages of people in the unaware and low-income status; the East, South, and Central had higher percentages of unaware-only status; the North and Northeast regions had a higher percentage of low-income-only status, compared with other regions. Conclusion: The existence of the regional difference in unawareness and low income justifies the specific stroke education strategies for the targeted regions and population.
Reconstruction and coil combination of undersampled concentric-ring MRSI data using a Graph U-Net

Geometric deep learning has recently gained inﬂuence, as it allows the extension of convolutional neural networks to non euclidean domains. In this paper graph neural networks (GNNs) are used for the image reconstruction and coil combination of undersampled concentric-ring k-space MRSI data. We show that graph U-nets perform better on under-sampled data than GNNs. Speciﬁcally, results suggest that the omission of self-connecting edges results in a more stable behavior and better training for graph U-nets
D EFUSE : D EBUGGING C LASSIFIERS T HROUGH D IS - TILLING U NRESTRICTED A DVERSARIAL E XAMPLES

With the greater proliferation of machine learning models, the imperative of diagnosing and correcting bugs in models has become increasingly clear. As a route to better discover and fix model bugs, we propose failure scenarios: regions on the data manifold that are incorrectly classified by a model. We propose an end-to-end debugging framework called Defuse to use these regions for fixing faulty classifier predictions. The Defuse framework works in three steps. First, Defuse identifies many unrestricted adversarial examples—naturally occurring instances that are misclassified—using a generative model. Next, the procedure distills the misclassified data using clustering into failure scenarios. Last, the method corrects model behavior on the distilled scenarios through an optimization based approach. We illustrate the utility of our framework on a variety of image data sets. We find that Defuse identifies and resolves concerning predictions while maintaining model generalization.
Accidental identification withtracking technology

: Economic casualties due to traffic accidents are rising in most countries. Although the number of road deaths has decreased since 2001, much change is needed. The facilities to provide emergency care following an accident is also not up to the mark due to lack of knowledge. This is why we are implementing robotics in the ambulance that can help the rescue squad hit time to save lives. The automobile industry is more concerned with human welfare. This document is a car automation in which the network can be used to monitor the vehicle after the crash and to report relevant details about the vehicle in order to include emergency assistance. Emergency facilities or an SOS system shall be used to illustrate the operation of this system was considered. When the car has a crash, the device sends spatial coordinates and uses these coordinates. Tracking the location of the vehicle on the Google map through the internet is provided to the hospitality services. The message shall contain the Information of the car and even the precise location of the vehicle. Where an ambulance or emergency medical services can be needed be hurried to the scene of the crash.
WEARABLE TACTUAL COMMUNICATORS: DESIGNING PRODUCTS WITH TECHNOLOGY-MEDIATED TOUCH

In some situations, it is not possible or desirable for communications to take place through voice- or screen-based interaction. People may want to act discreetly around other people;it may not be physically possible to interact with a screen-based product;or it may be inappropriate or ineffective to talk out loud. Furthermore, with the challenges of the COVID-19 pandemic, product touch has been discouraged. In the circumstances, alternative means of communication can be made using technologies that mediate touch. This paper reports on an eight-week graduate industrial design project that required students to explore the sense of touch in the context of user-product-user communication. The aim was for students to learn how a product user interface (UI) can extend beyond the conventional visual and audible modalities, into the relatively under-explored area of touch sensations. Working in pairs, students made research and generated product concepts for communication scenarios that they foresaw would benefit from technology-mediated touch. The result was eight diverse scenario and product solutions that make use of passive touch sensations: some focused on the communication of instructions, whilst others evoked meanings. Learning was demonstrated through the diversity in outcomes, with students proposing unconventional forms of interaction where touch is the primary modality. The study empowered students to showcase innovative uses of technology-mediated touch in product design. © PDE 2021.
University of Birmingham One-way functions and malleability oracles

The supersingular isogeny Diﬃe-Hellman (SIDH) is a post-quantum key exchange protocol based on the presumed hardness of computing an isogeny between two supersingular elliptic curves given some additional torsion point information. Unlike other isogeny-based protocols, SIDH has been widely believed to be immune to subexponential quantum attacks because of the non-commutative structure of the endo-morphism rings of supersingular curves. We contradict this belief in this paper. More precisely, we highlight the existence of an abelian group action on the SIDH key space, and we show that for suﬃciently unbalanced and overstretched SIDH parameters, this action can be eﬃciently computed using the torsion point information revealed in the protocol. This reduces the underlying hardness assumption to an instance of the hidden shift problem which can be solved in quantum subexponential time. We formulate our attack in a new framework allowing the inversion of one-way functions in quantum subexponential time provided a malleability oracle with respect to some commutative group action. This framework uniﬁes our new attack with earlier subexponential quantum attacks on isogeny-based protocols, and it may be of further interest for crypt-analysis.
Fast fixed-time synchronization of T–S fuzzy complex networks

In this paper, fast fixed-time (FDT) synchronization of T–S fuzzy (TSF) complex networks (CNs) is considered. The given control schemes can make the CNs synchronize with the given isolated system more fleetly than the most of existing results. By constructing comparison system and applying new analytical techniques, sufficient conditions are established to derive fast FDT synchronization speedily. In order to give some comparisons, FDT synchronization of the considered CNs is also presented by designing FDT fuzzy controller. Numerical examples are given to illustrate our new results.
Interest Rates, Leverage, and Business Cycles in Emerging Economies: The Role of Financial Frictions

Countercyclical country interest rates have been shown to be an important characteristic of business cycles in emerging markets. In this paper we provide a microfounded rationale for this pattern by linking interest rate spreads to the dynamics of corporate leverage. For this purpose we embed a financial accelerator into a business cycle model of a small open economy and estimate it on a novel panel dataset for emerging economies that merges macroeconomic and financial data. The model accounts well for the empirically observed countercyclicality of interest rates and leverage, as well as for other other stylized facts. JEL: E32; E44; F41