Reviewerly/exHarmony huggingface dataset:
CSABlock-based Cascade RCNN for Breast Mass Detection in Mammogram

Early screening and diagnosis of breast mass are essential for the prevention of breast cancer. There are some reasons to make mass detection be difficult and challenging. First, the resolution of mammography is very large and mass tissues are often subtle. Second, some mass overlap with the normal tissues which own similar texture. In this paper, we propose a novel attention module channel self-attention block (CSABlock), it can make better use of inter-layer features and strengthen the detection capability of the cascade R-CNN model. In order to further improve the detection quality, we also use a new domain-adaptive pre-training strategy. Experiments show that the proposed method achieves an average precision (AP) of 0.822 and average recall (AR) of 0.949, outperforms the state-of-the-art methods.
Challenges of Deep Learning-based Text Detection in the Wild

The reported accuracy of recent state-of-the-art text detection methods, mostly deep learning approaches, is in the order of 80% to 90% on standard benchmark datasets. These methods have relaxed some of the restrictions of structured text and environment (i.e., "in the wild") which are usually required for classical OCR to properly function. Even with this relaxation, there are still circumstances where these state-of-the-art methods fail. Several remaining challenges in wild images, like in-plane-rotation, illumination reflection, partial occlusion, complex font styles, and perspective distortion, cause exciting methods to perform poorly. In order to evaluate current approaches in a formal way, we standardize the datasets and metrics for comparison which had made comparison between these methods difficult in the past. We use three benchmark datasets for our evaluations: ICDAR13, ICDAR15, and COCO-Text V2.0. The objective of the paper is to quantify the current shortcomings and to identify the challenges for future text detection research.
Face Image Inpainting Algorithm Via Progressive Generation Network

Image inpainting is to fill the content and pixels of the missing area of the image so that it can achieve a semantic reasonable and realistic visual effect. Recently deep neural network has shown its significant advantages in filling large missing areas in image inpainting tasks. These methods can generate reasonable inpainting structures and textures, but they usually produce distorted structures or fuzzy textures inconsistent with the background. To solve these problems, we propose a new progressive generation network for image inpainting. The proposed model does not only generate new image structures but also effectively combine the context information of missing areas to predict the content better. The model is a feed-forward complete convolution neural network that divides the image inpainting task into a progressive generation process from coarse to fine. It introduces structural reconstruction loss and style loss in different stages to meet the requirements of different inpainting tasks. Extensive experiments show that the proposed method produces higher quality than the existing methods, especially for the broad area missing face image inpainting task.
Optimal Multi-Agent Map Coverage Path Planning Algorithm

Multi-agent path planning is one of the important research directions of multi-agent cooperative control, among which multi-agent covering path planning (mCPP) is a hot topic at present. Nowadays, many researchers have put forward relevant algorithms, but in some specific cases, problems such as low success rate of planning and low computational efficiency have appeared. In this paper, a hybrid algorithm based on the combination of the initial position partitioning algorithm and Two-pass algorithm is proposed for map pre-scanning and map full coverage path planning. Before the coverage path planning, this algorithm firstly scans the complex environment, calculates an evaluation coefficient, then divides the map according to the initial position of multiple agents, and finally carries out path planning in each area to complete the map's full coverage path planning. Compared with the traditional method, the designed algorithm can prescan the map without dividing the region through multiple iterations, which has higher computing efficiency and solves the shortcoming of low success rate of the traditional algorithm under special circumstances. It ensures the security, integrity and robustness of covering path planning without collision of multiple agents.
Thyroid Nodules Categorization Based On Margin Features Using Deep Learning

Thyroid cancer is a rare malignancy originated in the thyroid gland. It is often found incidentally in patients with thyroid nodules. One of the ways to evaluate its malignancy is by using ultrasonography of thyroid and neck. Many studies conducted experiments around thyroid nodules but none were focusing on thyroid nodules characteristics especially margin characteristic. This characteristic tends to be visibly obvious and can be the lead to another characteristics. We proposed a method to help experts in identifying the regular and irregular categories from margin characteristic. The proposed method consists of pre-processing, segmentation, feature extraction by using nine geometric features, data balancing by using synthetic minority oversampling technique (SMOTE), and classification by using deep learning method. For the training process we used the dataset collected from the Department of Radiology RSUP Dr. Sardjito Yogyakarta Indonesia. We successfully obtained accuracy of 94.79%, sensitivity of 94.9%, specificity of 94.1%, PPV of 98.68%, NPV of 80%, F-measure of 94.6% and ROC of 96%.
Security in IoT: Threats and Vulnerabilities, Layered Architecture, Encryption Mechanisms, Challenges and Solutions

In the future, every physical thing or object will be connected in scenarios that are divergent than merely connected human and make everything intelligent. As everything becomes connected and intelligent, it is driving digital transformation of all industries. However, the IoT brings with it, new security threats because the working IoT applications mechanisms are differ due to the heterogeneity nature of IoT environments. The comprehensive review of the security concerns with regards to the IoT layered architecture, encryption mechanisms, threats, and vulnerabilities is the major contribution of this paper. Furthermore, the IoT security challenges and solutions are emphasized by the layered architecture in order to get a greater understanding and knowledge on the addressing and implementing proper approaches to prevent the recent security threats in IoT on every layer.
Meta Back-Translation

Back-translation is an effective strategy to improve the performance of Neural Machine Translation~(NMT) by generating pseudo-parallel data. However, several recent works have found that better translation quality in the pseudo-parallel data does not necessarily lead to a better final translation model, while lower-quality but diverse data often yields stronger results instead. In this paper we propose a new way to generate pseudo-parallel data for back-translation that directly optimizes the final model performance. Specifically, we propose a meta-learning framework where the back-translation model learns to match the forward-translation model's gradients on the development data with those on the pseudo-parallel data. In our evaluations in both the standard datasets WMT En-De'14 and WMT En-Fr'14, as well as a multilingual translation setting, our method leads to significant improvements over strong baselines.
Improved Structure of Data Encryption Standard Algorithm

The Internet is providing vital communications between millions of individuals. It is also more and more utilized as one of the commerce tools; thus, security is of high importance for securing communications and protecting vital information. Cryptography algorithms are essential in the field of security. Brute force attacks are the major Data Encryption Standard attacks. This is the main reason that warranted the need to use the improved structure of the Data Encryption Standard algorithm. This paper proposes a new, improved structure for Data Encryption Standard to make it secure and immune to attacks. The improved structure of Data Encryption Standard was accomplished using standard Data Encryption Standard with a new way of two key generations. This means the key generation system generates two keys: one is simple, and the other one is encrypted by using an improved Caesar algorithm. The encryption algorithm in the first 8 round uses simple key 1, and from round 9 to round 16, the algorithm uses encrypted key 2. Using the improved structure of the Data Encryption Standard algorithm, the results of this paper increase Data Encryption Standard encryption security, performance, and complexity of search compared with standard Data Encryption Standard. This means the Differential cryptanalysis cannot be performed on the cipher-text.
Research-ready, Technology-set, Deployment-go: the role of blockchain in peer-to-peer energy trading

The source of end-user electricity demand is shifting from centrally located conventional power plants to a mix consisting of distributed renewable energy. Consequently, the grid infrastructure is undergoing a transformation to accommodate the intermittent injection of renewable energy, reduce losses and improve consumer engagement. Such a bidirectional exchange of energy and information, in a modern electricity grid, shows the appropriateness of data-driven and ICT based smart solutions for efficient resource utilization. One such intervention gaining world-wide traction with assorted objectives of cost minimization, utilization of locally generated renewable energy, peak reduction, etc. is peer-to-peer (P2P) energy trading. The research community and pilot-scale commercial deployments considerably focus on blockchain as an enabler of P2P energy trading. Therefore in this work, we analyze state-of-the-art from research publications and pilot/commercial deployments of P2P energy trading using blockchain. Further, the analysis is used with Technology Readiness Level (TRL) framework to classify the existing solutions, challenges and future directions in application of blockchain for P2P energy trading.
Developing a Conceptual Model for Crime Against Women using ISM &amp; MICMAC

Background: Crime against women is a major issue of society, resulted in physical, psychological, sexual or economic harm of women. Objective: The main objective of this research paper is to propose the conceptual model after finding the contextual relationship among all identified factors affecting crime against women. Methods: Interpretive structural modeling (ISM) technique has been used to develop the model. This is the mathematical approach or step-by-step procedure to deploy the unstructured items into a structured or hierarchical form to build a conceptual model. Further, MICMAC (Matriced' Impacts Croisés Multiplication Appliquée á un Classement) analysis has also been applied to segregate the factors into groups of independent, dependent and operational factors on the basis of their driving power and dependence power. Hence, driving power indicates on what extent an individual factor is contributing for driving the issue and dependence power indicates on what extent an individual factor-driven from other factors. Results: As resulted all 11 identified factors have been structured into a well-defined model with the groups of linkage factors, independent factors, and dependent factors. The model clearly defines the role and contribution of each factor which gives very good insights to take any kind of decision by the law firms, police departments and other criminal or crime organizations to control or prevent this major issue ‘crime against women’. Conclusion: Public and private crime organizations, law firms can use this article to reform its policies to take more security measures with their implementation related to it.
Popular Optimisation Algorithms with Diversity-Based Adaptive Mechanism for Population Size

An adaptive mechanism for population size which is a parameter of many optimisation algorithms was recently proposed. The mechanism is based on controlling the population diversity. It was very successful when implemented into the differential evolution algorithm and its adaptive versions. The mechanism is employed in several popular optimisation algorithms here. The goal of the research was to find whether the mechanism can increase also their performance or not. The experimental tests are carried out on CEC2014 benchmark set.
Chapter 30. Reasoning with Quantified Boolean Formulas

The implementation of effective reasoning tools for deciding the satisfiability of Quantified Boolean Formulas(QBFs) is an important research issue in Artificial Intelligence and Computer Science. Indeed, QBF solvers have already been proposed for many reasoning tasks in knowledge representation and reasoning, in automated planning and in formal methods for computer aided design. Even more, since QBF reasoning is the prototypical PSPACE problem, the reduction of many other decision problems in PSPACE are readily available. For these reasons, in the last few years several decision procedures for QBFs have been proposed and implemented, mostly based either on search or on variable elimination, or on a combination of the two. In this chapter, after a brief recap of the basic terminology and notation about QBFs, we briefly review various applications of QBF reasoning that have been recently proposed, and then we focus on the description of the main approaches which are at the basis of currently available solvers for prenex QBFs in conjunctive normal form (CNF). Other approaches and extensions to non prenex, non CNF QBFs are briefly reviewed at the end of the chapter.
A Productive Review on Sentimental Analysis for High Classification Rates

Mining of sentiments is the key aspect of Natural Language Processing. The analysis of sentiments has extended much consideration in recent years. In this paper, problem tackling of sentiment polarization is discussed, which deals with the high difficulties of analysis in terms of opinion/sentimental analysis. In this paper, the overall practice for sentiment polarity tagging is reviewed and also this paper discusses some recent approaches done on the sentimental analysis with detailed descriptions. Also the basic knowledge which is required to achieve effectual sentimental analysis is discussed in this review paper with their applications which deals with the sentimental analysis with various aspects. This paper discusses various tactics in brief to achieve computational behavior of sentimentalities and opinions. Several controlled practices in mining of the opinions in terms of the assets and disadvantages are discussed in this paper.
Introducing the Hidden Neural Markov Chain Framework

Nowadays, neural network models achieve state-of-the-art results in many areas as computer vision or speech processing. For sequential data, especially for Natural Language Processing (NLP) tasks, Recurrent Neural Networks (RNNs) and their extensions, the Long Short Term Memory (LSTM) network and the Gated Recurrent Unit (GRU), are among the most used models, having a term-to-term sequence processing. However, if many works create extensions and improvements of the RNN, few have focused on developing other ways for sequential data processing with neural networks in a term-to-term way. This paper proposes the original Hidden Neural Markov Chain (HNMC) framework, a new family of sequential neural models. They are not based on the RNN but on the Hidden Markov Model (HMM), a probabilistic graphical model. This neural extension is possible thanks to the recent Entropic Forward-Backward algorithm for HMM restoration. We propose three different models: the classic HNMC, the HNMC2, and the HNMC-CN. After describing our models' whole construction, we compare them with classic RNN and Bidirectional RNN (BiRNN) models for some sequence labeling tasks: Chunking, Part-Of-Speech Tagging, and Named Entity Recognition. For every experiment, whatever the architecture or the embedding method used, one of our proposed models has the best results. It shows this new neural sequential framework's potential, which can open the way to new models, and might eventually compete with the prevalent BiLSTM and BiGRU.
Devising a Prototype Model for Assessing Digital Competencies Based on the DigComp Proficiency Levels

The assessment of the current situation of digital skills and competencies in a given group is a fundamental issue nowadays. Hence, the purpose of the research study is to address this issue. The research methodology is quantitative, including also a case study experiment and pre and post-questionnaires. To obtain and make use of digital resources, one should own the necessary skills and digital competencies to do so. To specify the key elements of digital competence and the ways to assess it, the European Commission developed a Digital Competence Framework for Citizens – DigComp. The European Computer Driving Licence (ECDL), modules back up all competencies and areas of DigComp, which facilitate the structure in the digital skills area. Based on a developed prototype model which has been evaluated by an expert group, an experiment to assess the digital skills and competencies of high school and undergraduate students was conducted in the context of the ECDL Base modules. The experts examined the procedure and the list of selected categories, skill sets, and task items included in the prototype model, and assessed if they comply with the word processing syllabus. After the experiment, we used the statistical analyses to find cross-tabulation of self-evaluation questions within the level of education from tasks devised. The experiment revealed digital literacy levels of students and pointed out the areas that require improvement. It also compared the self-evaluated levels and the actual task experiment results of the students. This comparison showed that self-evaluation is not a proper way to evaluate the digital skills and competences of individuals in general. The study proved that there is a high discrepancy between the self-evaluation and actual evaluation results of the assessment groups.
Detection of Breast Cancer from Mammogram Images Using Deep Transfer Learning

Among all types of cancers found in women, breast cancer is having the second highest mortality rate and it is also considered to be the prime cause of high death rate. Mammographic images are often investigated by the experienced and trained radiologists to recognize the breast abnormalities like masses and micro-calcifications. This paper focuses on computer aided diagnosis to help the radiologists so that breast cancer can be detected with better accuracy. In this paper, our aim is to process the mammogram images for breast cancer affected patients using deep learning architectures. In this paper, two approaches are considered. In first approach, mammogram images are feed into various pre-trained models which are trained from scratch. In second approach, exactly same techniques are being replicated with the exception that fine tuning has been performed using transfer learning for all the models. For this work, we have considered variants of convolutional neural networks. Experimental works have performed on two different datasets and for both datasets, the results of the fine-tuned network outperforms all other approaches.
An Efficient FPGA Implementation of Izhikevich Neuron Model

This paper presents a modified Izhikevich neuron model replacing complex multiplication and division operations with simple binary-based shift operations. A counter-based adder circuit is designed to address the problem that multiple neurons fire spikes simultaneously to one neuron. The proposed model is implemented on FPGA. Results show that the hardware resource utilization of the proposed model is reduced by 87.2% compared with that of the original model and the highest operating frequency is increased from 123.8MHz to 291.8MHz.
Topic-Based Tweet Clustering for Public Figures Using Ant Clustering

The aspects of life on a public figure, discussed by the community, are often exploited by the news media as topic information to create an article that can attract the attention of the reader. Efficiently, the media only needs to pay attention to social media to get some of the information. The more information needed leads to a vast amount of data involved, so the process becomes hard. In this paper, a tweet clustering system to determine topics from many documents in the form of text through text mining method using an ant clustering (AC) technique. AC is one of swarm intelligence algorithms inspired by the behavior of the ant colony in sorting corpses. Evaluation of a small dataset of text documents shows that four topics are successfully concluded: economy, social, politics, and government. The developed AC-based tweet clustering system produces an average cluster quality of the Dunn Index up to 0,3455.
On the Effective Transfer Learning Strategy for Medical Image Analysis in Deep Learning

In this study, we focus on exploring different strategies of transfer learning for medical applications. Firstly, we report competitive results indicating that convolutional neural networks (CNNs) that were pre-trained with different annotations could have diverse effects on the performance of medical image analysis, especially for segmentation tasks. Then, we present our further explorations of transferring different components of the CNNs, which revealed the importance of the decoder on medical segmentation. Finally, we demonstrate the advantages and disadvantages of transfer learning methods based on model integration. These observations present novel aspects of transfer learning for visual tasks in the medical field, and we expect that these discoveries will encourage the exploration of more effective transfer learning strategies for CNN-based medical image analysis.