Reviewerly/exHarmony huggingface dataset:
An MPI-based Algorithm for Mapping Complex Networks onto Hierarchical Architectures

Processing massive application graphs on distributed memory systems requires to map the graphs onto the system’s processing elements (PEs). This task becomes all the more important when PEs have non-uniform communication costs or the input is highly irregular. Typically, mapping is addressed using partitioning, in a two-step approach or an integrated one. Parallel partitioning tools do exist; yet, corresponding mapping algorithms or their public implementations all have major sequential parts or other severe scaling limitations.In this paper, we propose a parallel algorithm that maps graphs onto the PEs of a hierarchical system. Our solution integrates partitioning and mapping; it models the system hierarchy in a concise way as an implicit labeled tree. The vertices of the application graph are labeled as well, and these vertex labels induce the mapping. The mapping optimization follows the basic idea of parallel label propagation, but we tailor the gain computations of label changes to quickly account for the induced communication costs. Our MPI-based code is the first public implementation of a parallel graph mapping algorithm; to this end, we extend the partitioning library ParHIP. To evaluate our algorithm’s implementation, we perform comparative experiments with complex networks in the million- and billion-scale range. In general our mapping tool shows good scalability on up to a few thousand PEs. Compared to other MPI-based competitors, our algorithm achieves the best speed to quality trade-off and our quality results are even better than non-parallel mapping tools.
An Optimal and Lightweight Convolutional Neural Network for Performance Evaluation in Smart Cities based on CAPTCHA Solving

Multimedia Internet of Things (IoT) devices, especially, the smartphones are embedded with sensors including Global Positioning System (GPS), barometer, microphone, accelerometer, etc. These sensors working together, present a fairly complete picture of the citizens' daily activities, with implications for their privacy. With the internet, Citizens in Smart Cities are able to perform their daily life activities online with their connected electronic devices. But, unfortunately, computer hackers tend to write automated malicious applications to attack websites on which these citizens perform their activities. These security threats sometime put their private information at risk. In order to prevent these security threats on websites, Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) are generated, as a form of security mechanism to protect the citizens' private information. But with the advancement of deep learning, text-based CAPTCHAs can sometimes be vulnerable. As a result, it is essential to conduct performance evaluation on the CAPTCHAs that are generated before they are deployed on multimedia web applications. Therefore, this work proposed an optimal and light-weight Convolutional Neural Network (CNN) to solve both numerical and alpha-numerical complex text-based CAPTCHAs simultaneously. The accuracy of the proposed CNN model has been accelerated based on Cyclical Learning Rates (CLRs) policy. The proposed CLR-CNN model achieved a high accuracy to solve both numerical and alpha-numerical text-based CAPTCHAs of 99.87% and 99.66%, respectively. In real-time, we observed that the speed of the model has increased, the model is lightweight, stable, and flexible as compared to other CAPTCHA solving techniques. The result of this current work will increase awareness and will assist multimedia security Researchers to continue and develop more robust text-based CAPTCHAs with their security mechanisms capable of protecting the private information of citizens in Smart Cities.
Impact of The Use of Telegram and WhatsApp at the University in Pandemic Times

At present, we are going through a situation global pandemic that has forced a change in drastically the way universities are ingeded, opting for a distance education of which we are not used. Therefore, they had to adapt to change and rely on media such as Telegram and WhatssApp. The methodology used was a survey of teachers and students. This research details that these media have made distance education an effective means of learning without the need to be physically present. The objective of the article is to analyze the impact of Telegram and WhatsApp on virtual university teaching. The result of the research is the satisfaction on the part of students and teachers in the appropriate use of Telegram and Whatsapp. In conclusion, teachers and students stated that the use of Telegram and WhatsApp media in university education were of great help in teaching and learning as it allowed the interrelationship between them.
Shopee Price Match Guarantee Algorithm based on multimodal learning

Shopee has been a popular online shopping website in the Southeast Asia. Customers appreciate its easy, secure, and fast online shopping experience tailored to their region. At the same time, it allows customers to choose the one with the lower price of the same product. It relies on the product matching, that is the same product with the same description image must be removed. The base technology to achieve this function is multimodal learning, in which we focus on the images and text. In our article, we proposed a new multimodal learning model mainly based on transformer and BERT. For image matching, we use NFNet, Swin_Transformer and Efficientnet to get image embeddings. For text matching, we use Distil-Bert, Albert, Multilingual Bert and TF-IDF to get text embeddings. After we get the embedding vector, we choose KNN to classify. We use cosine and distance to measure the similarity of the different models. It is worth mentioning that the loss function is Arcface, not the traditional Softmax, which improve the difficulty of training to ensure the final performance in the test periods. In addition, 7 models vote for the final results ensuring the effect of prediction. To avoid the bad matching result, we add some postprocessing process.
Journalist Safety and Self-Censorship

Journalist Safety and Self-Censorship, edited by Anna Grondahl Larsen, Ingrid Fadnes, and Roy Krovel, examines the reciprocal impact of journalist safety and self-censorship within different global...
Computer Vision System for Expressing Texture Using Sound-Symbolic Words

The major goals of texture research in computer vision are to understand, model, and process texture and ultimately simulate human visual information processing using computer technologies. The field of computer vision has witnessed remarkable advancements in material recognition using deep convolutional neural networks (DCNNs), which have enabled various computer vision applications, such as self-driving cars, facial and gesture recognition, and automatic number plate recognition. However, for computer vision to "express" texture like human beings is still difficult because texture description has no correct or incorrect answer and is ambiguous. In this paper, we develop a computer vision method using DCNN that expresses texture of materials. To achieve this goal, we focus on Japanese "sound-symbolic" words, which can describe differences in texture sensation at a fine resolution and are known to have strong and systematic sensory-sound associations. Because the phonemes of Japanese sound-symbolic words characterize categories of texture sensations, we develop a computer vision method to generate the phonemes and structure comprising sound-symbolic words that probabilistically correspond to the input images. It was confirmed that the sound-symbolic words output by our system had about 80% accuracy rate in our evaluation.
Blockchain and IoT for Auto Leak Unearthing

Blockchain and Internet of Things (IoT) are important constituents of Internet-enabled era of information technology. Both technologies are distributed, autonomous, and decentralized systems. IoT devices require the strengthening of its security features, and security is an intrinsic aspect of blockchain due to cryptographic mechanisms. On the other hand, blockchain needs contribution from the distributed nodes and IoT includes within its architecture. So, blockchain can aid in the settlement of major security requirements in IoT. Blockchain features such as decentralization, immutability and transparency (DIT), auditability, and data encryption help to solve various IoT architectural problems. The main goal of water supply sector is to provide a solution to get shielded, authentic, and cost-effective water supply through well-regulated arrangements. It is very hard to achieve these goals. This paper introduces an algorithm for implementing a smart water management system that identifies and quantifies the water requirement by an individual consumer within a given locus and also identifies leaks (if any) in the plumbing system. The system proposed monitors both water quality and water scarcity aspects within the supplied vicinity. The smart water management system is collateral to a decentralized system implemented using smart tanks that uses the Internet of Things (IoT) for implementation and blockchain technology for providing a more rooted mechanism.
A Survey on Representation Learning in Visual Question Answering

Visual question answering stands among the most researched computer vision problems, pattern recognition, and natural language processing. VQA extends the computer vision world’s challenges and directs us toward developing some basic reasonings on visual scenes to answer questions on the specific elements, actions, and relationships between different objects in the image. Developing reasonings on the image has always been popular among computer vision and natural language processing researchers. It is directly dependent on the expressivity of the representations learned from the datasets. In the past decade, with advancements in computing machinery, neural networks, and the introduction of highly optimized and efficient software, a substantial amount of research has been done to solve VQA efficiently. In this survey, we present an in-depth examination of representation learning of state-of-the-art methods proposed in the literature of VQA and compare them to discuss the future directions in the field.
An Incremental Model For Multi-Label Classification Of Tweets Based On Label Co-Occurrence

The tremendous growth in the field of ICT encourages use of social media like Twitter as an effective platform for communication between local government and resident of the city. Citizens share numerous urban issues which need to be categorized properly as it decides helps to decide priority of the issue. Multiclass classification which is the traditional algorithm to classify the complaints into multiple classes loses some important information as people tend to write their inconveniences spreaded across multiple domains in a same complaint. Hence, this research work has developed a framework to identify multiple issues discussed in a tweet by performing multi-label classification. One of the most used multi-label algorithms is ‘Classifier chain’ method. However, the performance of the classifier chain algorithm varies with the label order used during classification. So our approach used the concept of label correlation to derive the optimal label sequence and implement the classifier chain algorithm for multi-label classification. In this study, 14 types of most important issues faced by the citizens are considered like potholes, traffic, illegal parking etc. Our experiments clearly indicate that the adoption of label correlation concept has a positive influence on the results.
Detection for Dangerous Goods Vehicles in Expressway Service Station Based on Surveillance Videos

To improve the safety capabilities of expressway service stations, this study proposes a method for detecting dangerous goods vehicles based on surveillance videos. The information collection devices used in this method are the surveillance cameras that already exist in service stations, which allows for the automatic detection and position recognition of dangerous goods vehicles without changing the installation of the monitoring equipment. The process of this method is as follows. First, we draw an aerial view image of the service station to use as the background model. Then, we use inverse perspective mapping to process each surveillance video and stitch these videos with the background model to build an aerial view surveillance model of the service station. Next, we use a convolutional neural network to detect dangerous goods vehicles from the original images. Finally, we mark the detection result in the aerial view surveillance model and then use that model to monitor the service station in real time. Experiments show that our aerial view surveillance model can achieve the real-time detection of dangerous goods vehicles in the main areas of the service station, thereby effectively reducing the workload of the monitoring personnel.
ICDAR 2021 Competition on Scene Video Text Spotting

Scene video text spotting (SVTS) is a very important research topic because of many real-life applications. However, only a little effort has been put to spotting scene video text, in contrast to massive studies of scene text spotting in static images. Due to various environmental interferences like motion blur, spotting scene video text becomes very challenging. To promote this research area, this competition introduces a new challenge dataset containing 129 video clips from 21 natural scenarios in full annotations. The competition contains three tasks, that is, video text detection (Task 1), video text tracking (Task 2) and end-to-end video text spotting (Task3). During the competition period (opened on 1st March, 2021 and closed on 11th April, 2021), a total of 24 teams participated in the three proposed tasks with 46 valid submissions, respectively. This paper includes dataset descriptions, task definitions, evaluation protocols and results summaries of the ICDAR 2021 on SVTS competition. Thanks to the healthy number of teams as well as submissions, we consider that the SVTS competition has been successfully held, drawing much attention from the community and promoting the field research and its development.
Decoding of Interleaved Alternant Codes

Interleaved Reed–Solomon codes admit efficient decoding algorithms which correct burst errors far beyond half the minimum distance in the random errors regime, e.g., by computing a common solution to the Key Equation for each Reed–Solomon code, as described by Schmidt et al. If this decoder does not succeed, it may either <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fail</i> to return a codeword or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">miscorrect</i> to an incorrect codeword, and good upper bounds on the fraction of error matrices for which these events occur are known. The decoding algorithm immediately applies to interleaved alternant codes as well, i.e., the subfield subcodes of interleaved Reed–Solomon codes, but the fraction of decodable error matrices differs, since the error is now restricted to a subfield. In this paper, we present new general lower and upper bounds on the fraction of error matrices decodable by Schmidt et al.’s decoding algorithm, thereby making it the only decoding algorithm for interleaved alternant codes for which such bounds are known.
An Evolutionary Multi-Layer Extreme Learning Machine for Data Clustering Problems

Clustering technology such as K-means is a critical method in data analysis, and it occupies an even more important place with the explosion of information. Due to the complicated distribution of data in the original feature space, it is difficult to find suitable clusters of the data. To address this problem, Existing extreme learning machines (ELM) based clustering methods construct an embedding space to reveal the clusters more easily. In ELM and K-means, some parameters are randomly assigned, so it may lead to unstable or non-optimal results. In this paper, an evolutionary extreme learning machine method (LBH-MLELM) based on a black hole algorithm is proposed, in which a more effective embedding space is constructed by a multi-layer extreme learning machine, and the final results are subsequently derived by K-means algorithm. What's more, by optimizing not only the input weights, hidden biases of ELM, but also the initial center of K-means, the accuracy and robustness of clustering are improved. The experimental results on several public datasets demonstrated the effectiveness of the proposed method, and further analyzed the properties of the proposed method.
Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing

Object type classification for automotive radar has greatly improved with recent deep learning (DL) solutions, however these developments have mostly focused on the clas-sification accuracy. Before employing DL solutions in safety-critical applications, such as automated driving, an indispensable prerequisite is the accurate quantification of the classifiers&#x0027; reliability. Unfortunately, DL classifiers are characterized as black-box systems which output severely over-confident pre-dictions, leading downstream decision-making systems to false conclusions with possibly catastrophic consequences. We find that deep radar classifiers maintain high-confidences for ambiguous, difficult samples, e.g. small objects measured at large distances, under domain shift and signal corruptions, regardless of the correctness of the predictions. The focus of this article is to learn deep radar spectra classifiers which offer robust real-time uncertainty estimates using label smoothing during training. Label smoothing is a technique of refining, or softening, the hard labels typically available in classification datasets. In this article, we exploit radar-specific know-how to define soft labels which encourage the classifiers to learn to output high-quality calibrated uncertainty estimates, thereby partially resolving the problem of over-confidence. Our investigations show how simple radar knowledge can easily be combined with complex data-driven learning algorithms to yield safe automotive radar perception.
Attack-Guided Efficient Robustness Verification of ReLU Neural Networks

Nowadays the robustness of Deep Neural Networks (DNN) is gaining much more attention than ever. That is because DNNs are intensively adopted in safety-critical AI-enabled applications such as autonomous driving and authentication control. Formal methods have been proved to be effective to provide provable guarantee to the robustness of DNNs. However, they are suffering from bad scalability due to intrinsic high computational complexity of the verification problem. In this paper, we propose a novel attack-guided approach for efficiently verifying the robustness of neural networks. The novelty of our approach is that we use existing attack approaches to generate coarse adversarial examples, by which we can significantly simply final verification problem. In particular, we are focused on the neural networks that take ReLU activation functions, which are widely adopted for solving classification problems. The experimental results show that our approach outperforms those verification tools based on constraint solving by up to 69 times speedup, while it can compute minimum adversarial examples. The improvement is particularly significant on those adversarially trained networks.
PAC Bayesian Performance Guarantees for Deep (Stochastic) Networks in Medical Imaging

Application of deep neural networks to medical imaging tasks has in some sense become commonplace. Still, a "thorn in the side" of the deep learning movement is the argument that deep networks are prone to overfitting and are thus unable to generalize well when datasets are small (as is common in medical imaging tasks). One way to bolster confidence is to provide mathematical guarantees, or bounds, on network performance after training which explicitly quantify the possibility of overfitting. In this work, we explore recent advances using the PAC-Bayesian framework to provide bounds on generalization error for large (stochastic) networks. While previous efforts focus on classification in larger natural image datasets (e.g., MNIST and CIFAR-10), we apply these techniques to both classification and segmentation in a smaller medical imagining dataset: the ISIC 2018 challenge set. We observe the resultant bounds are competitive compared to a simpler baseline, while also being more explainable and alleviating the need for holdout sets.
A New Hierarchical Reinforcement Learning Framework for Relation Extraction

Relationships extraction from unstructured text is essential for building wholesale reusable information in social computing applications, such as knowledge base construction, intelligent understanding, and biomedical text mining. Most existing approaches only ascertain the relationship type after all entities are identified, so the interaction between the relationship type and entity extraction is not entirely modeled. Recently, HRL-RL is proposed to deal with relationship extraction by adopting joint extraction paradigms in the fralanguage of hierarchical reinforcement learning. Specifically, HRL-RL recognizes relationship indicators through a high-level reinforcement learning subtask and predicts the participating entities for the relationship through a low-level RL subtask. However, HRL-RL uses the Monte Carlo sampling method to learn low-level subtasks for entity extraction, which leads to inconsistencies between the learned entities and the relations detected in the high-level tasks because of high variance estimation. In this paper, we propose a new hierarchical reinforcement learning framework to reinforce interactions between entity extraction and relationship types. Specifically, in the low-level subtask, our algorithm uses an advantage function to learn low-level strategies and updates the value estimate of a current entity state through bootstrap operations, thereby reducing the variance of the model and speeding up learning.
Existence of solutions for neural networks involving boundary-value condition

A class of neural networks with boundary-value condition is considered here. Sufficient conditions are given and the system has at least one solution by a standard fixed point theorem. An example demonstrates the effectiveness of theoretical results.
A Concise Review of Acute Myeloid Leukemia Recognition Using Machine Learning Techniques

Acute myeloid leukemia is one of the fatal cancers and requires efficient as well as accurate solution for early detection of the disease. Analyses of blood and bone marrow smear images are standard and efficient method in field of leukemia detection. Various machine learning techniques are proposed to develop methods, models, and decision support systems to assist pathologist in decision making. In recent years, there is lot of research contribution toward building deep learning models. This paper reviews various literatures corresponding to recognition of acute myeloid leukemia in microscopic blood and bone marrow smear images using machine learning techniques. This review presents summary of all the methodologies, datasets and results of the reviewed research articles and also highlights some of the limitation, issues, and challenges faced by researchers in this field.