Reviewerly/exHarmony huggingface dataset:
Famous Companies Use More Letters in Logo: A Large-Scale Analysis of Text Area in Logo

This paper analyzes a large number of logo images from the LLD-logo dataset, by recent deep learning-based techniques, to understand not only design trends of logo images and but also the correlation to their owner company. Especially, we focus on three correlations between logo images and their text areas, between the text areas and the number of followers on Twitter, and between the logo images and the number of followers. Various findings include the weak positive correlation between the text area ratio and the number of followers of the company. In addition, deep regression and deep ranking methods can catch correlations between the logo images and the number of followers.
A New Method for Polygon Detection Based on Clustering

Polygon fitting is widely used in the field of image measurement. This paper proposes a polygon fitting method based on clustering algorithm. Image preprocessing is the first step, including bilateral filtering and binarization to extract a complete set of contour points. Secondly, the sliding window method is used to fit the point set in the window to a straight line to obtain the cyclic parameter set. We propose a variant of K-means algorithm. The algorithm only considers the transformation between adjacent clusters in each iteration, which ensures the order of the collection. In order to adapt to the cyclic point set, the boundary of the cluster will be sequentially changed after each iteration. Experimental results show that the algorithm can detect and fit polygons from contours with satisfactory accuracy and efficiency.
Optimization of MLP Neural Networks in 8-bit Microcontrollers using Program Memory

This work proposes a memory optimization technique for embedded Multi-Layer Perceptron (MLP) Artificial Neural Networks (ANNs) applications in a Microcontroller (µC) device as implementation platform. This platform has an attached general-purpose processor as one if its peripheral device and, as usual for this kind of hardware, the memory size is significantly lower when compared to other devices in which ANNs are implemented. This work demonstrate that Harvard architecture µCS such as ATmega family of µCS, NXP's MK20DX128VLF5, ESPRESSIF's ESP8266, ESP32 and the family of PIC32 µCS ease the storage of synaptic weights in program memory in such a way that these weights can be read at run time, without continuously occupying the data memory (RAM). This enables the application of larger and more complex ANN architectures on these low-power, low-cost and low-memory devices. The implementation here presented was developed in a ATmega-2560 µC and the embedded MLP neural network was trained to classify the digits from 0 to 9 of the MNIST Dataset.
A Semantic Matching Method Based on Dynamic Encoding and Dual-attention

Semantic matching is a critical task in the field of natural language processing (NLP). It aims to compare two sentences and identify the relationship between them. A new semantic matching method is proposed in this paper. First, the pre-trained model BERT is used to dynamically encode the input text into the corresponding encoding vectors. Then, the encoding vectors of the two sequences are respectively input to a bidirectional LSTM with attention mechanism in order to obtain the global vectors of two sequences. Finally, the two global vectors are concatenated and input to a linear layer for computing the final semantic result. This paper compares with other representative methods on the Chinese data set LCQMC. The experimental results show that the method proposed in this paper has a significant improvement.
Class-Decomposition and Augmentation for Imbalanced Data Sentiment Analysis

Significant progress has been made in the area of text classification and natural language processing. However, like many other datasets from across different domains, text-based datasets may suffer from class-imbalance. This problem leads to model's bias toward the majority class instances. In this paper, we present a new approach to handle class-imbalance in text data by means of unsupervised learning algorithms. We present class-decomposition using two different unsupervised methods, namely k-means and Density-Based Spatial Clustering of Applications with Noise, applied to two different sentiment analysis data sets. The experimental results show that utilizing clustering to find within-class similarities can lead to significant improvement in learning algorithm's performances as well as reducing the dominance of the majority class instances without causing information loss.
A Financial Advertisement Recognition Algorithm Model Based on Text

Acquisition and intelligent recognition of financial advertising is the premise of monitoring and managing it. This paper studies how to obtain and intelligently recognize financial advertisements, proposes an algorithm of financial search terms extension based on graph constructed from context, and a XGBoost multi model algorithm of financial advertisement recognition combined with multi text representation such as Word2Vec and ELMo and machine learning models such as CNN and RNN, constructs a model of financial advertisement recognition algorithm based on text, and realizes the specific recognition of financial advertisement. The intelligent acquisition and accurate identification of advertising provides data support for further multi-dimensional analysis, and lays a solid foundation for the subsequent governance of illegal financial advertising.
Privacy-Preserving Resource Sharing Using Permissioned Blockchains

In a resource sharing system users offer goods and services with specified conditions that if met, the access will be granted. Traditional resource sharing systems use a trusted intermediary that mediates users’ interactions. Our work is motivated by a decentralized resource sharing platform (proposed in WTSC’20) that uses a permissioned blockchain to allow users to share their digital items with their specified attributed-based access policies that are enforced through a set of smart contracts, and removes the need for a trusted intermediary. The system however allows users’ accesses to be traced, and has limited availability as access to a resource requires its owner to be on-line. We design a decentralized attribute-based access control system that achieves the same functionality while preserving the privacy of user’s access, and automating access which removes the need for the resource owner to be online. We use two cryptographic primitives, Ciphertext Policy Attribute-Based Encryption (CP-ABE), and ring signatures, and design smart contracts that allow specification and cryptographic enforcement of the users’ specified policies. We analyze the security and privacy of the system, provide the description of smart contracts and give a proof of concept implementation of the smart contracts and cryptographic algorithms that are used in the system. Our design and implementation are general and can be used for privacy-preserving resource sharing with fine-grained access control in other settings including data and information sharing among collaborating parties.
A News Image Locating Approach Based on Cross-Modal RNN with An Encoding Updating Mechanism

Image plays an important role in news articles. Appropriate image arrangements can well transmit scene information and help readers understand more clearly. This paper proposes a neural model based on the cross-modal RNN equipped with an encoding updating mechanism to insert images into proper positions of a news document sequentially. For each image, our model selects the position of which the surrounding sentences are most relevant to the image. Two methods are employed to score the relevance between the image and surrounding sentences: the one employs a layer of convolution network before scoring, and the other employs the pooling operation after the scoring. It is reasonable to believe that inserting an image in news will render the information of the news. We hence introduce an updating mechanism to update the encoding of the news document after the image is inserted. To the best of our knowledge, we are the first to focus on the news image locating problem, as well as provide a feasible solution. We create the dataset for the problem by extending the DailyMail corpora and carry out experiments. Experiments show that the proposed model outperforms four baselines including the pointer network according to two evaluation metrics. The code is released on Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/njupt-summ/ImageLocating.
A Mathematical Model of Load Distribution for Data Center Server Clusters

An approach to estimate the performance of a load distribution and balancing system for data center server clusters is proposed, which takes into account the current load (occupancy) of servers. This system is described in mathematical terms, focusing on the server states at each instant of receiving service requests in a steady-state mode. The capacity and response time to customer requests are used as the main performance indicators. These indicators are estimated using queuing theory and simulation modeling as mathematical apparatus. The flows of service requests are stationary and ordinary. The receipt and processing of service requests are described by Markov processes. The load distribution and balancing system is represented as a multi-channel queueing system with a limited queue length, which includes an unlimited buffer (disk memory) for all servers in the cluster and input buffers of limited capacity for each server. The effectiveness of the proposed model and the theoretical results are verified by simulation, and experimental data are obtained.
Verifiable Access Technology of Hybrid Database in Distributed System Under Big Data

Abstract Targeting at the problem that the existing hybrid database solutions can not simultaneously realize the client’s verification and access function to the encrypted data in the public cloud database and the cross database query function in the heterogeneous database environment, this paper designs an access and integration middleware to realize the client’s verification and access function to the heterogeneous hybrid database in the public cloud and the cross database query function. Access and integration middleware is composed of verification access component and heterogeneous database data integration method. The verification and access component reconstructs the protocol interaction process based on MySQL protocol, and encrypts and decrypts the encrypted data in the public cloud database through the existing hybrid database scheme, so as to realize the client’s verification and access to the encrypted data in the cloud database. The heterogeneous database data integration method unifies the data from different types of databases, and then integrates them into the same database, and uses this database for query operation to realize cross database query. This paper first analyzes the hybrid database system model, then designs from the aspects of MySQL protocol analysis, verification access, data integration, and finally analyzes the access and integration middleware from the aspects of functionality and security.
Privacy Preserving in Sequential Data Publishing

Nowadays, sequentially captured data, i.e., trajectory datasets, are growing attention for data analytics because of the massive use of location-based service devices. Sharing such trajectory datasets with the researcher is vital for location data mining, such as traffic jams and people’s places of interest. Before the publication of trajectory datasets, enough privacy must be provided such that the confidentiality of each trajectory is preserved. In our solution, we apply space shifting operation on spatiotemporal points to minimize the record linkage attack by an adversary who knows some locations that are visited by a trajectory. We proposed a trajectory spatiotemporal set algorithm that gathers k number of various trajectories locations. Finally, we applied generalization operation on each set to achieve k-anonymity property where each spatiotemporal trajectory location is identical from k-1 other trajectories within a set.An extensive experiment result shows that enough trajectory privacy is preserved while publishing sequential trajectory data.
Aspect-based sentiment analysis for German : analyzing 'talk of literature' surrounding literary prizes on social media

Since the rise of social media, the authority of traditional professional literary critics has been supplemented – or undermined, depending on the point of view – by technological developments and the emergence of community-driven online layperson literary criticism. So far, relatively little research (Allington 2016, Kellermann et al. 2016, Kellermann and Mehling 2017, Bogaert 2017, Pianzola et al. 2020) has examined this layperson user-generated evaluative “talk of literature” instead of addressing traditional forms of consecration. In this paper,1 we examine the professional and layperson literary criticism pertaining to a prominent German-language literary award: the Ingeborg-Bachmann-Preis, awarded during the Tage der deutschsprachigen Literatur (TDDL).We propose an aspect-based sentiment analysis (ABSA) approach to discern the evaluative criteria used to differentiate between ‘good’ and ‘bad’ literature. To this end, we collected a corpus of German social media reviews retrieved from Twitter, Instagram and Goodreads and enriched it with manual ABSA annotations: aspects and aspect categories (E.g., TEXT Motifs Themes, JURY Discussion Valuation), sentiment expressions and named entities. In a next step, the manual annotations are used as training data for our ABSA pipeline including 1) aspect term extraction, 2) aspect term category prediction and 3) aspect term polarity classification. Each pipeline ncomponent is developed using state-of-the-art pre-trained BERT models.Two sets of experiments were conducted for the aspect polarity detection: one where only the aspect embeddings were used and another where an additional context window of five adjoining words in either direction of the aspect was considered. We present the classification results for the aspect category and aspect sentiment prediction subtasks for the Twitter corpus as well as the next steps to tackle aspect term extraction. These preliminary experimental results show a good performance and accuracy for the aspect category classification, with an F1-score of 0.81, and for the aspect sentiment subtask, which uses an additional context window, with an F1-score of 0.72.
Correlation filter tracking based on multi-peak detection and adaptive coefficient

In order to further improve the robustness of the ambiguity suppression related filtering algorithm (ARCF) tracking in many complex scenes such as rapid motion and occlusion, an adaptive normal punishment coefficient is proposed based on the relevant filter algorithm of automatic space-time normalization. At the same time, in order to make the tracking results more accurate, a object peak detection and selection algorithm is proposed specifically for multi-peak situation, which can determine the peak condition of the response map of the current frame and select the peak that best meets the object in multi-peak situation. In the template update stage, in order to keep the updated template from the pollution of similarities and masks, this paper also proposes a situation to detect whether the object is obscured, as a condition for the template update, so that the object template can better match the object.
Analysis of Trainability of Gradient-based Multi -environment Learning from Gradient Norm Regularization Perspective

Adaptation and invariance to multiple environments are both crucial abilities for intelligent systems. Model-agnostic meta-learning (MAML) is a meta-learning algorithm to enable such adaptability, and invariant risk minimization (IRM) is a problem setting to achieve the invariant representation across multiple environments. We can formulate both methods as optimization problems with the environment-dependent constraint and this constraint is known to hamper optimization. Therefore, understanding the effect of the constraint on the optimization is important. In this paper, we provide a conceptual insight on how the constraint affects the optimization of MAML and IRM by analyzing the trainability of the gradient descent on the loss with the gradient norm penalty, which is easier to study but is related to both MAML and IRM. We conduct numerical experiments with practical datasets and architectures for MAML and IRM and validate that the analysis of the gradient norm penalty loss captures well the empirical relationship between the constraint and the trainability of MAML and IRM.
Explaining Arguments at the Dutch National Police

As AI systems are increasingly applied in real-life situations, it is essential that such systems can give explanations that provide insight into the underlying decision models and techniques. Thus, users can understand, trust and validate the system, and experts can verify that the system works as intended. At the Dutch National Police several applications based on computational argumentation are in use, with police analysts and Dutch citizens as possible users. In this paper we show how a basic framework of explanations aimed at explaining argumentation-based conclusions can be applied to these applications at the police.
Analysis Accuracy of Complementary Label for Multi-Class Classification: Case Study on Chest X- Ray Images

Collecting label data for multi-class data is always costly and time-consuming. To overcome this problem, we put forward an effective method, namely complementary labels for multi-class classification. As opposed to the regular classification in which the original labels are given to training data, learning with complementary labels specifies one of the classes that the pattern does not belong to. Collecting ordinary labels will be more exhausting than collecting complementary labels because users must find out the ordinary class from some of the candidate classes. Although, ordinary labels are more informative than complementary labels and thus an assisted approach is needed to learn from. This paper uses chest X-ray because it is an image dataset that need time and cost for the labeling. This paper tries to classify normal, viral pneumonia and COVID-19 images. It uses linear/multilayer perceptron models for complementary label and use ResNet-50 model for ordinary label. It also checks the result with ordinary labels and compares its accuracy with complementary labels. With ordinary labels, it gets accuracy around 96,97%. After implementing it with complementary labels, it gets accuracy around 88%.
Shot Boundary Detection Framework For Video Editing Via Adaptive Thresholds And Gradual Curve Point

Day to day huge volumes of extended videos entrained from documentaries, cinemas, athletics and surveillance cameras are evolving over video databases and in internet. Processing these videos manually is hard, costly and time-consuming. For extracting these long-duration videos an automatic procedure is desperately needed. As a vital factor the Shot boundary detection (SBD) is considered for lot of video analysis tasks, for example video editing, indexing, summarization and action recognition. In the analysis of video content SBD is considered to be one of the vital task. Based on this, we have presented an effective SBD approach. We have used the gradient and color information for abrupt transition detection. For Gradual transition detection the average edge information of the gradual curves in the sequence of frames are obtained. From the optimal edge detector an average edge frame is gained. The computational complexity is reduced by this approach by processing only the transition regions. The proposed approach when compared to the exiting work done have achieved improved results in terms of precision, recall and F1.
Design Scheme of Perceptual Hashing based on Output of CNN for Digital Watermarking

Perceptual hashing generates a message digest based on the image content of the human visual system, and differs from the cryptographic hash function that generates the hash value based on each bit of the image file. We apply perceptual hashing to digital watermarking to generate watermark information after each image modification/editing, and verify that modified/edited images and the original image are the same in copyright. To obtain a stable perceptual hash value robust to image modification/editing for digital watermarking, we previously developed a construction method for perceptual hashing using a convolutional neural network (CNN). This was necessary because the conventional perceptual hash algorithms are used for database retrieval, and the required characteristics are different from those used for digital watermarking. However, in this method we needed to fine-tune the CNN for each image used to calculate the perceptual hash value, which led to inefficiency. In order to make the calculation of the perceptual hash value more efficient, we propose a construction method for perceptual hashing based on CNN that does not require fine-tuning. In the proposed method, an image is input to the CNN and the perceptual hash value is calculated based on the response of the output layer of the trained CNN.
An empirical study on the benefits of augmented reality technology (ART): A Nigerian construction industry context

Augmented reality (AR) is the combination of real and computer-based images and scenes to bring about a unified and improved view of the world. This study assesses the benefits of augmented reality technology (ART) to the construction industry so that productivity in construction can be enhanced. A survey of construction professionals involved in the built environment was carried out in Lagos State, Nigeria. Architects, Project managers, Engineers, Quantity surveyors, and Builders were engaged in the survey, and they were sent a well-structured questionnaire. Convenience sampling techniques were adopted in choosing these professionals. The collected data were analyzed using both descriptive and inferential statistics. According to the findings, the five most important benefits of ART are improved team collaboration and communication; enhanced repairs, rehabilitation, and maintenance; improved communication / real-time information retrieval; accurate automated measurement; and low-cost virtual training in operating heavy construction equipment. The results showed that there nine (9) out of fifteen (15) benefits had no significant difference in terms of the opinions of professionals, while the other six (6) had a significant difference. A post-hoc test revealed that there is a significant difference in opinions between the Architects and Quantity surveyors or between Quantity surveyors and Project managers on the benefits of AR while there is no significant difference for other professionals. The study concluded that AR improves collaboration and communication between workers on construction sites. It is therefore recommended that the Nigerian construction industry should create awareness programmes and platforms to inform its current and upcoming professionals on the benefits of using AR in the industry.