Reviewerly/exHarmony huggingface dataset:
DNA Feature Selection for Discriminating WirelessHART IIoT Devices

The proliferation of Wireless Highway Addressable Remote Transducer (WirelessHART) communications in support of Industrial Internet of Things (IIoT) applications is accompanied by increased vulnerability concerns that amplify the need for improved pre-attack security and post-attack forensic methods.This paper summarizes demonstration activity aimed at applying Time Domain Distinct Native Attribute (TD-DNA) fingerprinting and improving feature selection to increase computational efficiency and the potential for near-real time operational application.Assessments include both pre-classification and post-classification dimensional reduction using TD-DNA fingerprint features extracted from experimentally collected WirelessHART signals.Results show that pre-classification selection methods are superior, with average percent correct classification differential of 8% < %CD < 1% being maintained using selected feature subsets containing only 24 (10%) of the 243 full-dimensional features.
Blockchain Ready Port Supply Chain Using Distributed Ledger

Blockchain technology, as a bedrock for distributed ledgers, offers a platform for innovation for a new decentralized and transparent transaction machinery in industries and businesses, and the port is not an exception. The inbuilt features of this technology enrich trust through transparency and traceability within any transaction of data, goods, services, and financial resources. Notwithstanding initial doubts about this technology, lately government and large corporations have offered to adopt and enhance this technology in various fields of applications, from social and legal industries, and finance to design, maritime and port networks. In this paper, the authors review the current status of the Blockchain technology and some of its applications. The potential benefit of such a technology in port and maritime supply chain is then discussed, and a vision for the future Blockchain ready port and maritime supply chain are proposed. The importation of containerized vehicles is used as an example to demonstrate how such technology can be deployed in a global port industry. Finally, the requirements and challenges to adopt this technology in the future in relation to, port and maritime systems are discussed.
Protecting Your Shopping Preference With Differential Privacy

Online banks may disclose consumers’ shopping preferences due to various attacks. With differential privacy, each consumer can disturb his consumption amount locally before sending it to online banks. However, directly applying differential privacy in online banks will incur problems in reality because existing differential privacy schemes do not consider handling the noise boundary problem. In this paper, we propose an Optimized Differential prIvate Online tRansaction scheme (O-DIOR) for online banks to set boundaries of consumption amounts with added noises. We then revise O-DIOR to design a RO-DIOR scheme to select different boundaries while satisfying the differential privacy definition. Moreover, we provide in-depth theoretical analysis to prove that our schemes are capable to satisfy the differential privacy constraint. Finally, to evaluate the effectiveness, we have implemented our schemes in mobile payment experiments. Experimental results illustrate that the relevance between the consumption amount and online bank amount is reduced significantly, and the privacy losses are less than 0.5 in terms of mutual information.
CREGEX: A Biomedical Text Classifier Based on Automatically Generated Regular Expressions

High accuracy text classifiers are used nowadays in organizing large amounts of biomedical information and supporting clinical decision-making processes.In medical informatics, regular expressionbased classifiers have emerged as an alternative to traditional, discriminative classification algorithms due to their ability to model sequential patterns.This article presents CREGEX (Classifier Regular Expression), a biomedical text classifier based on an automatically generated regular-expressions-based feature space.We conceived an algorithm for automatically constructing an informative and discriminative regularexpressions-based feature space, suitable for binary and multiclass discrimination problems.Regular expressions are automatically generated from training texts using a coarse-to-fine text aligning method, which trades off the lexical variants of words, in terms of gender and grammatical number, and the generation of a feature space containing a large number of noisy features.CREGEX carries out feature selection by filtering keywords and also computes a confidence metric to classify test texts.Three de-identified datasets in Spanish, with information on smoking habits, obesity, and obesity types, were used here to assess the performance of CREGEX.For comparison, Support Vector Machine (SVM) and Naïve Bayes (NB) supervised classifiers were also trained with consecutive sequences of tokens (n-grams) as features.Results show that, in all the datasets used for evaluation, CREGEX not only outperformed both the SVM and NB classifiers in terms of accuracy and F-measure (p-value<0.05) but also used a fewer amount of training examples to achieve the same performance.Such a superior performance is attributed to the regular expressions' ability to represent complex text patterns.
E-Learning for Society: A Great Potential to Implement Education for All (EFA) Movement in Indonesia

UNESCO has designed the education concept through the EFA movement; thus, the SDGs can be achieved optimally. This movement is expected to create quality education that can be reached by all people, mainly to whom live in villages far from big cities. The effort to equalize education must be supported by recent technology, especially since the penetration of technology into various fields had been introduced simultaneously. E-learning has many benefits, and it can support wide-scale learning. The e-learning concept in Indonesia has been adopted through a digital platform, namely Massive Open Online Courses (MOOCs). There are various publications related to e-learning application for formal education, while the discussion of e-learning for non-formal education is still limited and has not much explored. Following are the results of this literature study (1) e-learning is a digital-based learning tool that has been implemented in most of the universities and higher educations; (2) e-learning application for society cannot be realized because there are many problems (such as low technical skill of the society, so they have difficulty in accessing technology and the facilities support have not been distributed well. Hence it can not be reached massively by the society), resulting the technology utilization for training or counseling have not been implemented optimally yet; and (3) MOOCs are part of e-learning that can be employed by everyone, not only for academics but also for the general public, providing various features like formal education.
The Dialectics of Humanism and Pragmatism as a Basis for the Formation of Higher Education Strategy Development

This article is an attempt to analyze the key contradictions between humanistic and pragmatic orientations in the modern space of higher education.The authors use a philosophical approach in understanding the value of knowledge as a powerful tool for social and economic development.It has been proved that politicians and teachers play almost the same role in overcoming the global poverty and violence.Instead of theorizing about humanistic ideals, they should direct the ideas of humanism in a practical way -through the training of students and the creation of a favorable climate for the life of citizens.The ideas of democracy and human respect must also go beyond the audience and be implemented in the practical dimensions of society.Science in universities must be subordinated to humanistic values, just as philosophy was subordinated to Theology in the Middle ages.While most universities are trying to mobilize efforts to increase economic performance, it is necessary to work on the development of intellectual capital in society.In addition, it is important for policy makers to understand the difference between the training of a competitive specialist and the education of a harmonious personality in the context of the formation of a higher education strategy.These processes must occur synchronously and not contradict each other.That is why the study of philosophy or cultural studies is no less important than the study of mathematics and physics.In addition, the language of the world is defined not only as a certain lexical set that a person
Constrained stochastic state estimation of deformable 1D objects: Application to single-view 3D reconstruction of catheters with radio-opaque markers

Minimally invasive fluoroscopy-based procedures are the gold standard for diagnosis and treatment of various pathologies of the cardiovascular system. This kind of procedures imply for the clinicians to infer the 3D shape of the device from 2D images, which is known to be an ill-posed problem. In this paper we present a method to reconstruct the 3D shape of the interventional device, with the aim of improving the navigation. The method combines a physics-based simulation with non-linear Bayesian filter. Whereas the physics-based model provides a prediction of the shape of the device navigating within the blood vessels (taking into account non-linear interactions between the catheter and the surrounding anatomy), an Unscented Kalman Filter is used to correct the navigation model using 2D image features as external observations. The proposed framework has been evaluated on both synthetic and real data, under different model parameterizations, filter parameters tuning and external observations data-sets. Comparing the reconstructed 3D shape with a known ground truth, for the synthetic data-set, we obtained average values for 3D Hausdorff Distance of 0.81 ± 0.53 mm, for the 3D mean distance at the segment of 0.37 ± 0.17 mm and an average 3D tip error of 0.24 ± 0.13 mm. For the real data-set,we obtained an average 3D Hausdorff distance of 1.74 ± 0.77 mm, a average 3D mean distance at the distal segment of 0.91 ± 0.14 mm, an average 3D error on the tip of 0.53 ± 0.09 mm. These results show the ability of our method to retrieve the 3D shape of the device, under a variety of filter parameterizations and challenging conditions: uncertainties on model parameterization, ambiguous views and non-linear complex phenomena such as stick and slip motions.
An Architectural Approach to Cognitive Information Systems

The fast changes in information technology and business needs have led to the evolution and development of Cognitive Information Systems (CIS).There have been few pieces of research on the general model for the analysis and design of CIS.This paper attempts to create a design scheme for incorporating the various models for CISs and Understanding-based management systems (UBMSS).The components that have been examined, create elements of CIS analysis and design, however, they were not described as modeling elements and not described as enabling tools needed to create a consistent and integrated system.The most significant components for modeling are: semi-structured documents, business processes, constituents of knowledge management, the enterprise and the information architecture, including self-directing software components -Artificial Intelligence (AI) -that yield functions.For CIS modeling, the above-mentioned elements were combined into a unified framework, that follows the object-oriented paradigm and architecture approach.The aim of the research is to describe a framework that presents an overarching model and assists our understanding of the properties of CIS and UBMSS, allowing the formulation of a practical development method for CIS and cognitive management systems.
Development of Flipbook using Web Learning to Improve Logical Thinking Ability in Logic Gate

The multimedia-based learning process has great potential to change the way of learning.One of them is a growing multimedia Flipbook from textbooks, with ease of reading and learning without carrying a thick book.The purpose of this study is to produce a Flipbook-assisted Web Learning development product and to improve the ability to logical thinking by using those products.This type of research using the 4D Models model consisting of define, design, develop, and disseminate.The results of the development with expert data validation analysis obtained an overall percentage of 83.92% included in the excellent category.Analysis of the validation of media expert's overall percentage obtained 80% included in the excellent category.And the validation analysis of peers the overall percentage found 84.78% included in the excellent category.From all analysis shows that flipbook-assisted teaching materials web learning is appropriate to be used.The results of the t-test calculations show 10.25 higher than 2.045, the use of flipbook logic gates assisted with web learning increases the ability of logical thinking.And the analysis of N-Gain 0.39 in including the medium criteria, the flipbook gate logic assisted with web learning to increase the ability to logical thinking.
Collecting Vulnerable Source Code from Open-Source Repositories for Dataset Generation

Different Machine Learning techniques to detect software vulnerabilities have emerged in scientific and industrial scenarios. Different actors in these scenarios aim to develop algorithms for predicting security threats without requiring human intervention. However, these algorithms require data-driven engines based on the processing of huge amounts of data, known as datasets. This paper introduces the SonarCloud Vulnerable Code Prospector for C (SVCP4C). This tool aims to collect vulnerable source code from open source repositories linked to SonarCloud, an online tool that performs static analysis and tags the potentially vulnerable code. The tool provides a set of tagged files suitable for extracting features and creating training datasets for Machine Learning algorithms. This study presents a descriptive analysis of these files and overviews current status of C vulnerabilities, specifically buffer overflow, in the reviewed public repositories.
Improve Language Modeling for Code Completion Through Learning General Token Repetition of Source Code with Optimized Memory

In last few years, applying language model to source code is the state-of-the-art method for solving the problem of code completion. However, compared with natural language, code has more obvious repetition characteristics. For example, a variable can be used many times in the following code. Variables in source code have a high chance to be repetitive. Cloned code and templates, also have the property of token repetition. Capturing the token repetition of source code is important. In different projects, variables or types are usually named differently. This means that a model trained in a finite data set will encounter a lot of unseen variables or types in another data set. How to model the semantics of the unseen data and how to predict the unseen data based on the patterns of token repetition are two challenges in code completion. Hence, in this paper, token repetition is modelled as a graph, we propose a novel REP model which is based on deep neural graph network to learn the code toke repetition. The REP model is to identify the edge connections of a graph to recognize the token repetition. For predicting the token repetition of token [Formula: see text], the information of all the previous tokens needs to be considered. We use memory neural network (MNN) to model the semantics of each distinct token to make the framework of REP model more targeted. The experiments indicate that the REP model performs better than LSTM model. Compared with Attention-Pointer network, we also discover that the attention mechanism does not work in all situations. The proposed REP model could achieve similar or slightly better prediction accuracy compared to Attention-Pointer network and consume less training time. We also find other attention mechanism which could further improve the prediction accuracy.
Distractor-Aware Long-Term Correlation Tracking Based on Information Entropy Weighted Feature

Correlation filter based tracking algorithms have received significant attention in visual tracking due to their accuracy and speed.However, it is susceptible to distractors and performs poor when it is faced with occlusion and severe deformation.In this paper, we introduce a distractor-aware long-term correlation tracking to distinguish the target and the background.Firstly, the information entropy weighted feature is utilized to deal with different scenarios dynamically.Secondly, in order to facilitate the tracker to distinguish the target from the distractors which lead to inaccurate localization, we propose a novel distractoraware mask processing.We sample distractor patches and incorporate a binary mask to attenuate the possible negative influences.Thirdly, an improved fast scale searching scheme based on dichotomy is incorporated to provide a tradeoff between the accuracy and efficiency.Finally, the localization ambiguity is detected by using a new criterion based on the variance.It is concluded that introducing the re-detection technique and solving the occlusion play a critical role to facilitate the tracking progress.Furthermore, an adaptive model update strategy is utilized to update the long-term model for alleviating the model drift.Experiments on benchmark database demonstrate that the proposed method achieves good performance compared with other trackers and the strategy can be applied to other trackers.
Mask Guided GAN for Density Estimation and Crowd Counting

Density estimation aims to predict the spatial distribution of a crowd scene, and crowd counting aims to automatically check the number of heads as close as the ground truth. We propose a mask guided GAN (Generative Adversarial Network) architecture to solve these two problems synthetically. Step one is generating a segmentation mask, separating the crowd region from the background and redundant information. Step two is predicting the density map with an adversarial learning process guided by the former mask information. Moreover, we branch out from the base network and feed into a counting regressor, which is solely to provide more accurate counting results. The whole scheme is trained collaboratively by compositing density loss (a weighted loss to balance the influence of different data) and counting loss (an MSE loss for counting regression branch). Through the mask information guidance, GAN likely finds its way training to capture more distinguishing features. Therefore, we also achieve more accurate prediction results. Experimental results on different datasets including collected from the internet and the actual scene in Shanghai, the second-most populous city located on China's southeast coast, indicate the validity and robustness by comparable counting numbers and high-quality density maps focusing on crowd area.
Reinforce Memory Error Protection by Breaking DRAM Disturbance Correlation Within ECC Words

Row-hammering attack is increasingly a concern to computer security and reliability, particularly for computer servers using chipkill correct memories, which are vulnerable to row-hammering attack because of disturbance correlation within ECC words. Probabilistic Adjacent Row Activation (PARA) can effectively lower the probability of successful row-hammering attack, but its strength can be significantly weakened by simultaneous attacks on many rows. This paper presents a novel hardware design to reinforce the chipkill correct memory by breaking the disturbance correlation within individual ECC words. It uses an XOR-based, device-specific row remapping such that a single PARA breach can only disturb any ECC word on at most one memory device. A row-hammering attack has to breach the PARA defense twice to cause uncorrectable or undetectable errors, which is much more challenging than before. A case of evaluation shows that the solution increases the memory MTTF from 0.144 to over 300,000 years. The design incurs negligible performance and energy overheads.
Robust Plane Clustering Based on L1-Norm Minimization

Plane clustering methods, typically, k plane clustering (kPC), play conclusive roles in the family of data clustering. Instead of point-prototype, they aim to seek multiple plane-prototype fitting planes as centers to group the given data into their corresponding clusters based on L2 norm metric. However, they are usually sensitive to outliers because of square operation on the L2 norm. In this paper, we focus on robust plane clustering and propose a L1 norm plane clustering method, termed as L1kPC. The leading problem is optimized on the L1 ball hull, a non-convex feasible domain. To handle the problem, we provide a new strategy and its related mathematical proofs for L1 norm optimization. Compared to state-of-the-art methods, the advantages of our proposed lie in 4 folds: 1) similar to kPC, it has clear geometrical interpretation; 2) it is more capable of resisting to outlier; 3) theoretically, it is proved that the leading non-convex problem is equivalent to several convex sub-problems. To our best knowledge, this opens up a new way for L1 norm optimization; 4) the k fitting planes are solved by k individual linear programming problems, rather than higher time-consuming eigenvalue equations or quadratic programming problems used in the conventional plane clustering methods. Experiments on some artificial, benchmark UCI and human face datasets show its superiorities in robustness, training time, and clustering accuracy.
Data Augmentation Technology Driven By Image Style Transfer in Self-Driving Car Based on End-to-End Learning

With the advent of deep learning, self-driving schemes based on deep learning are becoming more and more popular. Robust perception-action models should learn from data with different scenarios and real behaviors, while current end-to-end model learning is generally limited to training of massive data, innovation of deep network architecture, and learning in-situ model in a simulation environment. Therefore, we introduce a new image style transfer method into data augmentation, and improve the diversity of limited data by changing the texture, contrast ratio and color of the image, and then it is extended to the scenarios that the model has been unobserved before. Inspired by rapid style transfer and artistic style neural algorithms, we propose an arbitrary style generation network architecture, including style transfer network, style learning network, style loss network and multivariate Gaussian distribution function. The style embedding vector is randomly sampled from the multivariate Gaussian distribution and linearly interpolated with the embedded vector predicted by the input image on the style learning network, which provides a set of normalization constants for the style transfer network, and finally realizes the diversity of the image style. In order to verify the effectiveness of the method, image classification and simulation experiments were performed separately. Finally, we built a small-sized smart car experiment platform, and apply the data augmentation technology based on image style transfer drive to the experiment of automatic driving for the first time. The experimental results show that: (1) The proposed scheme can improve the prediction accuracy of the end-to-end model and reduce the model’s error accumulation; (2) the method based on image style transfer provides a new scheme for data augmentation technology, and also provides a solution for the high cost that many deep models rely heavily on a large number of label data.