Reviewerly/exHarmony huggingface dataset:
A decade: Review of scene text detection methods

The rapid development of scene text detection shows us the need for text recognition in a scene image. Road signs recognition, reading the scene image for machine translation, text recognition on commercial products, billboards, and vehicle plates are examples of text recognition on natural images. This review discussed the related research of scene text detection in the last ten years. We analysed the past strategies in scene text detection, the strengths and the weaknesses of each method. Additionally, we showed the relationship between text detection methods before and after using deep learning. Scene text detection has evolved to detect horizontal text, multi-orientation, multilingual, curved text, and arbitrary-shaped text. Researchers have proposed various methods to address this need. We evaluate the capability of the proposed framework based on the testing results of several representative benchmark datasets. This review aims to obtain opportunities or proposals to improve the existing accuracy, speed, or generalization cases (the various condition of the text appearances). We present future trends for scene text detection research to complete the review.
Deep Reinforcement Learning Based Controller for Active Heave Compensation

Heave compensation is an essential part in various offshore operations. It is used in various applications, which include on-loading or offloading systems, offshore drilling, landing helicopter on oscillating structures, and deploying and retrieving manned submersibles. In this paper, a reinforcement learning (RL) based controller is proposed for active heave compensation using a deep deterministic policy gradient (DDPG) lgorithm. A DDPG algorithm which is a model-free, online reinforcement learning method, is adopted to capture the experience of the agent during the training trials. The simulation results demonstrate up to 10 % better heave compensation performance of RL controller as compared to a tuned Proportional-Derivative Control. The performance of the proposed method is compared with respect to heave compensation, offset tracking, disturbance rejection, and noise attenuation.
Digitally saving face: An experimental investigation of cross-cultural differences in the use of emoticons and emoji

Across three experiments we examined cross-cultural differences in the use of emoticons (Experiment 1) and emoji (Experiments 2 and 3) when sending text messages. In all experiments, participants wrote text messages to another person based on different hypothetical situations (varying in valence or face-threat). We assume that digital CMC cues can serve a face-management function. Hence, based on the assumption that East Asians, relative to Americans, tend to be more concerned with face-management, we expected East Asians to use more emoji and emoticons than Americans, especially in threatening situations. Our results supported our hypotheses: East Asians used significantly more CMC cues overall, and displayed greater situational sensitivity (i.e., used more situationally-congruent CMC cues). Moreover, there were significant, positive correlations between emoji use and scores on collectivism and interdependent self-construal. The preference for emoji use by East Asians does not appear to be a function of poorer English language proficiency; in Experiment 3 there was a significant positive correlation between ESL proficiency and emoji use. We discuss these findings in the context of past cross-cultural communication research as well as politeness theory and face-negotiation theory.
AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy

For protecting users' private data, local differential privacy (LDP) has been leveraged to provide the privacy-preserving range query, thus supporting further statistical analysis. However, existing LDP-based range query approaches are limited by their properties, ie, collecting user data according to a pre-defined structure. These static frameworks would incur excessive noise added to the aggregated data especially in the low privacy budget setting. In this work, we propose an Adaptive Hierarchical Decomposition (AHEAD) protocol, which adaptively and dynamically controls the built tree structure, so that the injected noise is well controlled for maintaining high utility. Furthermore, we derive a guideline for properly choosing parameters for AHEAD so that the overall utility can be consistently competitive while rigorously satisfying LDP. Leveraging multiple real and synthetic datasets, we extensively show the effectiveness of AHEAD in both low and high dimensional range query scenarios, as well as its advantages over the state-of-the-art methods. In addition, we provide a series of useful observations for deploying \myahead in practice.
Solving the Traveling Salesman Problem on the D-Wave Quantum Computer

The traveling salesman problem is a well-known NP-hard problem in combinatorial optimization. This paper shows how to solve it on an Ising Hamiltonian based quantum annealer by casting it as a quadratic unconstrained binary optimization (QUBO) problem. Results of practical experiments are also presented using D-Wave’s 5,000 qubit Advantage 1.1 quantum annealer and the performance is compared to a classical solver. It is found the quantum annealer can only handle a problem size of 8 or less nodes and its performance is subpar compared to the classical solver both in terms of time and accuracy.
Reconstructing Solar Wind Profiles Associated With Extreme Magnetic Storms: A Machine Learning Approach

Abstract The lack of data on solar wind have prevented a detailed understanding of extreme magnetic storms. To address this issue, we apply a machine learning technique in the form of an Echo State Network (ESN) to reconstruct solar wind data for several extreme magnetic storms for which little or no solar wind data were previously available. Multiple geomagnetic activity indices are used as the input data for the ESN, which produces a continuous time series of solar wind parameters as output. As a result, the solar wind parameters for the largest storm event in March 1989 are obtained, and the minimum Bz is estimated to be −95 nT ±10 nT. Two different types of solar wind profiles are discussed for the extreme magnetic storms, a sheath‐driven profile and a magnetic cloud‐driven profile. The results reported here will be highly useful as input data for future simulation studies modeling extreme magnetic storms.
Managing the Cyber World: Hacker Edition

When surfing the internet, your input data can be put into one website, but do we have any concrete idea that who can see it. The hacking community lies within the bottom layers of the internet and can be fishing for people's data with intentions to fix the security flaw or to steal the person's data for financial gain. It is important to classify these hackers and figure out where the line is between good and bad to further create restrictions and stop hackers from harming people's data.
MTCNet: Multi-task collaboration network for rotation-invariance face detection

Detecting rotated faces is a challenging task with images from uncontrolled environments. The use of deep convolutional neural networks have greatly improved detection performance, but these methods still do not fully exploit face structure information. This leaves faces with more extreme rotation angles undetectable. In this paper, we present a novel Multi-Task Collaboration Network (MTCNet) for rotation-invariance face detection that fully uses facial landmarks to improve the detection performance by means of collaboration between face detection and face alignment. Differing from previous methods that predict rotation angles in a single step, MTCNet employs a cascaded architecture with three stages to predict faces with gradually decreasing rotation-in-plane ranges in a coarse-to-fine process. Accurate facial landmarks further facilitate face detection. We also introduce a new training loss by integrating the geometric angle into the penalization process, which is much more reasonable than measuring the differences of training samples roughly. Our approach also explores contextual information to distinguish challenging faces from unconstrained scenarios. Extensive experimental results were conducted to demonstrate the effectiveness of MTCNet on both the multiple orientation and rotation datasets. Empirical studies show that MTCNet achieves results competitive with state-of-the-art face detectors while being time-efficient.
Multi-view Teacher–Student Network

Multi-view learning aims to fully exploit the view-consistency and view-discrepancy for performance improvement. Knowledge Distillation (KD), characterized by the so-called "Teacher-Student" (T-S) learning framework, can transfer information learned from one model to another. Inspired by knowledge distillation, we propose a Multi-view Teacher-Student Network (MTS-Net), which combines knowledge distillation and multi-view learning into a unified framework. We first redefine the teacher and student for the multi-view case. Then the MTS-Net is built by optimizing both the view classification loss and the knowledge distillation loss in an end-to-end training manner. We further extend MTS-Net to image recognition tasks and present a multi-view Teacher-Student framework with convolutional neural networks called MTSCNN. To the best of our knowledge, MTS-Net and MTSCNN bring a new insight to extend the Teacher-Student framework to tackle the multi-view learning problem. We theoretically verify the mechanism of MTS-Net and MTSCNN and comprehensive experiments demonstrate the effectiveness of the proposed methods.
Comparing Bitcoin's Prediction Model Using GRU, RNN, and LSTM by Hyperparameter Optimization Grid Search and Random Search

Being the most expensive and most popular cryptocurrency, both the business world and the research community have started to study bitcoin development. However, due to the absence of most government regulation, the price of bitcoin has become uncontrollable, resulting in frequent large fluctuations. Using a dataset from 17 August 2017 to 13 April 2021 the GRU, RNN, and LSTM methods will be compared and implement Grid Search and Random Search to find out which one will do better in this research. Those three methods are considered to be the best method to get a prediction, but it also depends on the model that computer could have. The best result is the GRU with Grid Search method with MAE (Mean Absolute Error) of training 0.0043 and testing about 0.0594.
Combining embedding-based and symbol-based methods for entity alignment

The objective of entity alignment is to judge whether entities refer to the same object in the real world. Methods for entity alignment can be grossly divided into two groups: conventional symbol-based entity alignment methods and embedding-based entity alignment methods. Both groups of methods have advantages and disadvantages (which are detailed in Section 1). Therefore, combining the advantages of both methods might be a promising strategy. However, to the best of our knowledge, only the RTEA algorithm that was proposed in our previous conference paper (Proceeding of Pacific Rim International Conference on Artificial Intelligence, pp. 162–175, 2019) utilizes this strategy for entity alignment. This manuscript is an extended version of that conference paper, in which an improved algorithm, namely, ESEA (combining embedding-based and symbol-based methods for entity alignment), is proposed based on the following steps. First, a novel method for combining embedding models with symbol-based models is proposed. Entities with high vector similarities are obtained through a hybrid embedding model, and the final aligned entity pairs are calculated via symbol-based methods. Second, a series of symbol-based methods, instead of only the edit distance method in the original version, are combined with embedding-based methods for relation alignment. Third, we combine symbol-based and embedding-based methods in a more complicated framework with the objective of better exploiting the advantages of both methods. The experimental results on real-world datasets demonstrate that the proposed method outperformed several state-of-the-art embedding-based entity alignment approaches and outperformed our previous RTEA method.
Merkle Tree: A Fundamental Component of Blockchains

With the increasing popularity of blockchain technology, Merkle trees (or hash trees) are playing significant roles in verifying and retrieving data for the implementation of blockchain for allowing efficient and secure verification of the contents of large data structures. This article gives systematical insights into Merkle trees with respect to their principles, properties, advantages, and applications.
Virtual reality for synergistic surgical training and data generation

Surgical simulators not only allow planning and training of complex procedures, but also offer the ability to generate structured data for algorithm development, which may be applied in image-guided computer assisted interventions. While there have been efforts on either developing training platforms for surgeons or data generation engines, these two features, to our knowledge, have not been offered together. We present our developments of a cost-effective and synergistic framework, named Asynchronous Multibody Framework Plus (AMBF+), which generates data for downstream algorithm development simultaneously with users practicing their surgical skills. AMBF+ offers stereoscopic display on a virtual reality (VR) device and haptic feedback for immersive surgical simulation. It can also generate diverse data such as object poses and segmentation maps. AMBF+ is designed with a flexible plugin setup that allows for unobtrusive extension for simulation of different surgical procedures. We show one use case of AMBF+ as a virtual drilling simulator for lateral skull-base surgery, where users can actively modify the patient anatomy using a virtual surgical drill. We further demonstrate how the data generated can be used for validating and training downstream computer vision algorithms.
Compressed Oblivious Encoding for Homomorphically Encrypted Search

Fully homomorphic encryption (FHE) enables a simple, attractive framework for secure search. Compared to other secure search systems, no costly setup procedure is necessary; it is sufficient for the client merely to upload the encrypted database to the server. Confidentiality is provided because the server works only on the encrypted query and records. While the search functionality is enabled by the full homomorphism of the encryption scheme. For this reason, researchers have been paying increasing attention to this problem. Since Akavia et al. (CCS 2018) presented a framework for secure search on FHE encrypted data and gave a working implementation called SPiRiT, several more efficient realizations have been proposed. In this paper, we identify the main bottlenecks of this framework and show how to significantly improve the performance of FHE-base secure search. In particular, To retrieve l matching items, the existing framework needs to repeat the protocol l times sequentially. In our new framework, all matching items are retrieved in parallel in a single protocol execution. The most recent work by Wren et al. (CCS 2020) requires O(n) multiplications to compute the first matching index. Our solution requires no homomorphic multiplication, instead using only additions and scalar multiplications to encode all matching indices. Our implementation and experiments show that to fetch 16 matching records, our system gives an 1800X speed-up over the state of the art in fetching the query results resulting in a 26X speed-up for the full search functionality.
How African countries respond to fake news and hate speech

While scholars have already identified and discussed some of the most urgent problems in content moderation in the Global North, fewer scholars have paid attention to content regulation in the Global South, and notably Africa. In the absence of content moderation by Western tech giants themselves, African countries appear to have shifted their focus towards state-centric approaches to regulating content. We argue that those approaches are largely informed by a regime’s motivation to repress media freedom as well as institutional constraints on the executive. We use structural topic modelling on a corpus of news articles worldwide (N = 7′787) mentioning hate speech and fake news in 47 African countries to estimate the salience of discussions of legal and technological approaches to content regulation. We find that, in particular, discussions of technological strategies are more salient in regimes with little respect for media freedom and fewer legislative constraints. Overall, our findings suggest that the state is the dominant actor in shaping content regulation across African countries and point to the need for a better understanding of how regime-specific characteristics shape regulatory decisions.
Bit-Slicing the Hilbert Space: Scaling Up Accurate Quantum Circuit Simulation

Recent advancements in quantum technologies shed light on viable quantum computation in near future. Quantum circuit simulation plays a key role in the toolchain of quantum hardware and software development. Due to the enormous Hilbert space of quantum states, simulating quantum circuits with classical computers is notoriously challenging. This work enhances quantum circuit simulation in two dimensions: accuracy (by representing complex numbers algebraically) and scalability (by bit-slicing number representation and achieving matrix-vector multiplication with symbolic Boolean manipulation). Experiments demonstrate the superiority of our method to the state-of-the-art tools over various quantum circuits with up to tens of thousands of qubits.
Enhanced Inventory Management Using Blockchain Technology Under Cloud Sector Enabled by Hybrid Multi-Verse with Whale Optimization Algorithm

Inventory cost control is an essential factor in supply chain management. If the supplier’s inventory is insufficient, then the chance to trade the product will be reduced. The manufacturer’s inadequate material inventory will have an effect in termination of production, delays, and a waste of resources and time. On the other hand, postponed transportation will certainly raise costs such as transportation costs and cancellation of orders. Therefore, the operation costs of enterprises will be more, which will lower profits. In conventional supply chains, inventory costs control is not feasible for the view of the entire supply chain. The main intent of this paper is to plan for intelligent inventory management using blockchain technology under the cloud sector. The inventory management of the supply chain includes “multiple suppliers, a manufacturer, and multiple distributors”. The proposed inventory management models consider some significant costs like “transaction cost, inventory holding cost, shortage cost, transportation cost, time cost, setup cost, backordering cost, and quality improvement cost”. This multi-objective cost function is minimized by a novel hybrid optimization algorithm; the concept of WOA is integrated to produce the new algorithm which is termed as Whale-based Multi Verse Optimization (W-MVO) algorithm. For securing the data of distributors, using blockchain technology in a cloud environment helps from the leakage of data to other unauthorized users. Once the cost is reduced in all aspects based on the proposed hybrid optimization algorithm, the distributer will store the concerning data in the blockchain under the cloud sector, where each distributer holds a hash function to store its data, which cannot be restored by the other distributers. The valuable performance analysis over the conventional optimization algorithms proves the effective and reliable performance of the proposed model over the conventional models.
On the Rényi Differential Privacy of the Shuffle Model

The central question studied in this paper is Rényi Differential Privacy (RDP) guarantees for general discrete local randomizers in the shuffle privacy model. In the shuffle model, each of the n clients randomizes its response using a local differentially private (LDP) mechanism and the untrusted server only receives a random permutation (shuffle) of the client responses without association to each client. The principal result in this paper is the first direct RDP bounds for general discrete local randomization in the shuffle privacy model, and we develop new analysis techniques for deriving our results which could be of independent interest. In applications, such an RDP guarantee is most useful when we use it for composing several private interactions. We numerically demonstrate that, for important regimes, with composition our bound yields an improvement in privacy guarantee by a factor of $8\times$ over the state-of-the-art approximate Differential Privacy (DP) guarantee (with standard composition) for shuffle models. Moreover, combining with Poisson subsampling, our result leads to at least $10\times$ improvement over subsampled approximate DP with standard composition.
Multiple Instance Detection Networks With Adaptive Instance Refinement

Weakly supervised object detection (WSOD) aims to train object detectors by using only image-level annotations. Many recent works on WSOD adopt multiple instance detection networks (MIDN), which usually generate a certain number of proposals and regard proposal classification as a latent model learning within image classification. However, these methods tend to detect salient object, salient object parts and clustered objects due to lack of instance-level annotations during training. Thus a core issue is how to guarantee that the network learn as many objects with precise bounding boxes as possible. In this paper, we address this issue by exploiting the potential of proposal scores during training. We propose an adaptive instance refinement (AIR) framework with three novel designs, which can be integrated with MIDN into a single network. Specifically, adaptive instance mining attempts to discover all positive instances according to the score distribution of proposals and their spatial similarity. Adaptive score modulation dynamically adjusts proposal scores to make the network focus more on instances with different difficulties in different training iterations. Adaptive knowledge refinement distills important information from all previous stages by the weighted average of proposal scores. The experimental results on the PASCAL VOC 2007 and 2012 benchmarks and the MS COCO benchmark demonstrate that AIR significantly improves the performance of the original MIDN and achieves the state-of-the-art results.
Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-19 pandemic. The study of people’s emotions in social media is vital to understand the effect of this pandemic on mental health, in order to protect societies. This work aims to investigate to what extent deep learning models can assist in understanding society’s attitude in social media toward COVID-19 pandemic. We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet. We also show how the textual representation of emojis can boost the performance of sentiment analysis. In addition, we propose a dynamically weighted loss function (DWLF) to handle the issue of imbalanced datasets. The proposed approach has been evaluated on two datasets and the attained results demonstrate that the proposed BERT-based models with emojis replacement and DWLF technique can improve the sentiment detection of multi-dialect Arabic tweets with an F1-Micro score of 0.72.