Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Tree-based Machine Learning Algorithms for Breast Cancer Detection

Breast cancer prediction plays an essential role in medical decision making. This paper presents various machine learning algorithm based on three categories of decision tree learners to predict the presence of breast cancer based on routine blood analysis. Prediction, the presence of breast cancer, is performed in two scheme: non-feature selection and feature selection in the pre-processing stage. Machine learning that we used in this study is a basic decision tree, random forest, and gradient boosting. The performance of these classifiers is evaluated concerning sensitivity and specificity. The result shows that gradient boosting reached 85% of sensitivity and value of specificity is 80%. From this result, we can conclude that the combination of feature selection in the pre-processing step and gradient boosting classifier performs better than other schemas.
Accelerating Homomorphic Full Adder Based on FHEW Using Multicore CPU and GPUs

The latest implementation of the fully homomorphic encryption algorithm FHEW, FHEW-V2, takes about 0.12 seconds for a bootstrapping on a single-node computer. It seems much faster than the previous implementations. However, a 30-bit homomorphic full adder requires 270 times of bootstrapping, plus the time spent on key generation, the total elapsed time will be 55 seconds, which is unacceptable. In this paper, we reveal how to improve further the performance of FHEW-V2 by specifically focusing on the optimization of a homomorphic full adder. We strive to tackle the inefficiency in FHEW-V2 by massive efforts: first, we explore the reference codes for FHEW-V2 and find out hotspot codes for performance optimization; second, we leverage the computing power of multicore CPU and GPUs to remove the hotspots to improve performance. The empirical results so far show that a 30-bit homomorphic full adder is completed in 24 seconds after optimization, gaining an overall speedup of 2.2845. The 2.2845 speedup is the integration of a 13.248 speedup for the key generation and a 1.672 speedup for the bootstrapping.
Building a Database of Recurring Text in the Quran and its Translation

To understand the Quran better, correct interpretation requires paying more attention to verses in the Quran. From 114 surahs of the Quran, according to al-Khatib al-Iskafi (dead 420 H./1026 M.), only 28 or about 25% do not contain similar or repeated verses. Currently, to get longest recurring verses in the Quran requires manual effort, by searching the Quran verse by verse from beginning to end which takes a long time. This is a problem of longest common substring (LCS) in the Quran. We propose a system using Ukkonen’s algorithm to build a database of repetitive text sets. Ukkonen’s algorithm is chosen because this method of solving the longest common substring (LCS) problem has better efficiency than previous methods. We use two language translations, Indonesian and Arabic. We use 789 verses or 5 surahs in the Quran. We limit longest repeated text sets containing at least three words. For Indonesian we produced 2341 repetitive text sets, for Arabic we produced 2567 repetitive text sets, which are then stored in the database. After evaluating this system, for Indonesian we obtained precision of 45.9%. Whereas for Arabic, we obtained precision of 55.5%. This research produced a new database that did not exist previously in scientific papers. The resulting database can be used for various studies on the Quran and can also be used to compare with other studies about the Quran. The code and resulting database is available at: https://github.com/deviokta27/Al-Quran.git.
Smart Geoportal for Efficient Governance: A Case Study Municipality of M’diq

The land management has experienced the implementation of innovative new digital tools to enhance urban development in cities and improve their governance. The Smart governance is based on the federation of new technologies while allowing interoperability between different urban services, and offering free access to data and information. It is based on a close and real-time interactions between citizens and decision-makers to ensure efficient urban services while allowing the capitalization of skills. To do that, digital tools like GIS platforms and Geoportals, have been widely used to improve the performance of public services and the degree of citizen satisfaction. In this paper we consider a smart Geoportal project of the city of M’diq as a decision-making platform covering: Territorial data layers, 3D model and BI management tools for municipal decision-making. This platform is a demonstration of the use of Geoportals for smart governance in the implementation of smart city projects that requires complex interactions between government, citizens and public services, which are simplified through those tools.
Blockchain Technology Solutions for Supply Chains

Blockchain has become one of the most promising technologies in various industries. Within the supply chain management (SCM) domain, many researchers and practitioners argue that blockchain technologies can transform the supply chain for the better and shape its future. This review seeks to identify, summarize and analyze the extant literature in order to pinpoint the potential key benefits that the blockchain technologies can introduce into supply chains. Thus, this review aims at presenting the possible solutions that can address common supply chains issues when integrated with blockchain technologies. Six benefits are identified in literature; increased transparency, enhanced security, high veracity of data, cost-reductions, trust and better contract execution. In addition, several challenges were also discussed. Moreover, this paper may provide insights for those who seek pinpointing further research avenues in the blockchain within the SCM context.
Synthesis of Automatic Systems with Standard Dynamics on the Basis of Formalism Linear-Quadratic Optimization

In the present article the new method of the synthesis of automatic regulation problems based on the dynamic correction of the control object which is realized by means of use of formalism of LQ optimization problems is suggested. The basis of the solved problem of the correction is the idea of postulating of the desired dynamic properties of the synthesized system in the form of the given standard model of the corrected object. The al-gorithmization of correction problems is based on the formalism of LQ problems, and the optimized integral quadratic functional serve as a measure of deviation of the formed transient characteristics of the regulation channels from the standard values. The proposed scheme of dynamic correction may be of interest not only for the regulation problems. In particular, it is applicable to the problems of software and remote control. Let us note the methodological aspect of the received results. They point to the principle possibility of convergence of the classical concept of the dynamic quality of the regulation processes and the AFOR apparatus.
Hybrid motion control for multiple mobile robot systems

Hybrid motion control for multiple mobile robot systemsELZBIETA ROSZKOWSKAThis article presents a hybrid control system for a group of mobile robots.The components of this system are the supervisory controller(s), employing a discrete, event-driven model of concurrent robot processes, and robot motion controllers, employing a continuous time model with event-switched modes.The missions of the robots are specified by a sequence of to-be visited points, and the developed methodology ensures in a formal way their correct accomplishment.
The Development and Challenges of Face Alignment Algorithms

Abstract A comprehensive survey of face alignment using different methods is presented in this paper. Face alignment is the fundamental task of facial applications, e.g., face recognition, 3D face modelling and face expression analysis, etc. State-of-the-art methods can be mainly categorized into the three groups: gradient descent-based, deep learning-based, and 3D model-based. In gradient descent-based methods, landmarks are localized and adjusted by solving a nonlinear regression function. Deep learning-based methods construct one or several cascaded neural networks to improve landmark localization accuracy. Beside the above two categories of methods solving problems on a 2D plane, there is also other category of methods like 3D model-based methods. Despite significant progress that has been made, face alignment faces challenges from real-world conditions: variation across poses, genders and ages, facial expressions, and facial attributes. This paper offers a brief illustration and analysis of several typical methods of face alignment, provides an overall understanding and insight into the field, which will motivate us to explore promising future directions.
Hierarchical Attention-based Fuzzy Neural Network for Subject Classification of Power Customer Service Work Orders

Subject classification of power customer service work orders is an important part of power system customer service. However, at present, the power industry is still at the stage of relying on manual classification. Therefore, it is particularly important to propose a method for fast and accurate automatic classification of power customer service orders. In this paper, a Hierarchical Attention-based Fuzzy Neural Network (HABFNN) model based on is proposed for the subject classification of power customer service work orders. The model consists of two parts: the word-sentence layer, which uses the Convolutional Neural Network (CNN) trained by Dense-Fuzzy-Dense (DFD) to model sentences on the basis of word vector representation; the sentence-document layer, which uses CNN and Bidirectional Long Short-Term Memory (BiLSTM) network with attention mechanism to model documents on the basis of the sentence representations. The model can extract important information from the text of power customer service work orders, mine the semantics of the text and classify it. The model is compared with existing text classification methods through experiments. The experimental results show that this model has higher classification accuracy and F1 score, can better classify power customer service work orders and can effectively improve the efficiency and quality of power industry customer service.
Mono-Stereoscopic Camera in a Virtual Reality Environment: Case Study in Cybersickness

Cybersickness in Virtual Reality (VR) is a serious issue affecting the overall experience. Many research papers have investigated the causes of cybersickness and offered potential solutions for reducing cybersickness. In this paper, we demonstrate a method to reduce cybersickness by using a novel rendering technique in the virtual environment (VE)- Dynamic Mono-Stereoscopic Rendering System (DMSRS). The DMSRS system uses two different cameras to create a hybrid rendering that includes monoscopic and stereoscopic systems. By default, VEs are rendered using stereoscopic or monoscopic rendering exclusively. The results indicate that cybersickness decreased amongst users with little to no VR experience hindered when using the DMSRS.
A Logic of Knowing How with Skippable Plans

The paper expands upon the work by Wang [16], who proposes a single-agent modal logic framework for reasoning about “knowing how”. This paper proposes a more flexible semantics to the knowing-how operator. According to this semantics, an agent knows how to achieve $$\varphi $$ given $$\psi $$ if there exists a finite linear plan such that it will end up with some $$\varphi $$ -state from any $$\psi $$ -state by executing the plan, either fully or skipping some non-executable steps. We give a sound and complete axiomatization of this logic. Finally we introduce a suitable notion of bisimulation for this logic.
VIA VR: A Technology Platform for Virtual Adventures for Healthcare and Well-Being

To harness the potential of virtual reality (VR) in the healthcare sector, the expenditures for users such as clinics, doctors or health insurances, have to be reduced - a requirement the technology platform VIA VR (an acronym from “virtual reality adventures” and VR) promises to fulfill by combining several key technologies to allow specialists from the healthcare sector to create high-impact VR adventures without the need for a background in programming or the design of virtual worlds. This paper fleshes out the concept of VIA VR, its technological pillars and the planned R&D agenda.
Senarai: A Sustainable Public Blockchain-Based Permanent Storage Protocol

Storing data in the blockchain brings many benefits. The block-and-chain structure protects the data stored in the blockchain from unauthorised changes. The data will also be duplicated to all nodes which maintain the blockchain. These features guarantee the data immutability and availability. However, such operation is expensive when large size data needs to be stored. We propose Senarai, a protocol which provides an affordable blockchain data storage. Our protocol utilises the fee-less transaction feature provided by Tron. By using this feature, storing an arbitrary size data can be done cheaply. We show that our protocol is sustainable such that after initialisation phase, the permanent data storage capacity is provided for free on daily basis.
Sampling Using Fuzzy and Crisp Clustering to Improve Recall of Building Comfort Feedback

The primary objective of a building energy management system is to ensure the comfort of its occupants. This paper reports experiments with a user feedback system for a central temperature controlled building. Individual room temperatures are further adjusted based on occupant feedback. The number of feedback points are limited creating a data imbalance. The paper describes how crisp and fuzzy clustering can be used to sample the data points when there is no feedback from the users. The results of the proposed sampling is compared with other sampling strategies. The goal of the research is to maximize the recall of feedback through machine learning.
A public dataset for ship classification in remote sensing images

Ship classification in remote sensing images has been rarely studied because of relative scarcity of publicly available datasets. It is well known that datasets have played an important role in object classification research, especially for CNN-based algorithms which have been proved to perform well. In this paper, we introduce a public Dataset for Ship Classification in Remote sensing images (DSCR). We collect 1,951 remote sensing images from DOTA, HRSC2016, NWPU VHR-10 and Google Earth, containing warships and civilian ships of various scales. For object classification, we cut out ships of different categories from the collected images. The whole dataset contains about 20,675 instances which are divided into seven categories, i.e. aircraft carrier, destroyer, assault ship, combat ship, cruiser, other military ship and civilian ship. Each image contains ships of the same category, which is labeled by the category name. Since our dataset contains most models of major warships, it is relatively comprehensive for ship classification. To build a benchmark for ship classification, we evaluated six popular CNN-based object classification algorithms on our dataset, including ResNet, ResNext, VGG, GoogLeNet, DenseNet, and AlexNet. Experiments demonstrates that our dataset can be used for verifying ship classification algorithms and may advance the development of ship classification in remote sensing images.
Semi-Automatic Word-Aligned Tool for Thai-Vietnamese Parallel Corpus Construction

A corpus, especially a parallel corpus, which contains both source and target language, is an important resource in Natural Language Processing (NLP) research, particularly in machine translation. A quality corpus can improve the accuracy of the translation results significantly; however, corpus construction is very time consuming, and requires the expertise of linguistic experts. In this paper, we present Thai-Vietnamese parallel corpus construction and the process of building a Thai-Vietnamese parallel corpus. This work focuses on the construction of a semi-automatic word-alignment tool, capable of assisting researchers in the construction of a parallel corpus. The collection and validation within this study was achieved through the use of our development tool. In the first stage, the Vietnamese -Thai parallel corpus, containing 14,771 sentence pairs; was collected, aligned at word level, and validated by linguistic experts. This parallel corpus can be used as a reliable resource for statistical machine translation and other applications.
A New Precise Contactless Medical Image Multimodal Interaction System for Surgical Practice

Blind spot confirmation as a major challenge in surgical practice has led to booming of 3D modeling and printing technology and applications, but existing technologies could not offer readily available service of medical images in the operation room (OR) for surgeons.Contacting with keyboards and mice or asking assistants to help in viewing medical images during the operation could increase operation risk, infection and prolong of the operation time.This paper presents a new precise contactless medical image multimodal interaction system for surgeons in the OR.By supporting seamless multimodal interaction via 2D laser positioning, 3D gesture and voice recognition, it overcomes the design flaws in other similar solutions such as big motion for gesture recognition, round trip towards the screen of surgons, difficulty in selection and comparison of images, and more.Following the system engineering and evidence-based approaches, the design and development of this new precise contactless multimodal medical image interaction system meet surgeons' real world requirements for efficient and effective 2D medical images and 3D model operation under both preoperative and intraoperative scenarios.The system evaluation study is carried out in conjunction with a group of 10 neurosurgeons with 3 to 10 years' of surgical experience from different hospitals.Evaluation results successfully demonstrate the system's feasibility, efficiency and usability of in surgical contexts.
Unsupervised hybrid anomaly detection model for logistics fleet management systems

Unsupervised anomaly detection in high-dimensional data is crucial for both machine learning research and industrial applications. Over the past few years, the logistics agencies’ operation efficiency decreased due to the lack of understanding how best to handle potential client requests, while current anomaly detection approaches might be inefficient in distinguishing normal and abnormal behaviours from the high-dimensional data. Although previous studies continue to improve detection models, they suffer from the inability to preserve vital information while performing a dimensional reduction process. In this study, the authors aim to improve anomaly detection by proposing an ensemble method that is built and trained on two hybrid models. Eventually, after two trained hybrid models were introduced, an ensemble probability rule was applied to combine their prediction results for performing final decision-making of anomaly detection. To demonstrate the practical use of our proposed model, we have set up a case study with a logistics agency and the experiment shows that the proposed model improved accuracy by 0.88 over current models.
Looking Inside the Black Box: Core Semantics Towards Accountability of Artificial Intelligence

Recent advances in artificial intelligence raise a number of concerns. Among the challenges to be addressed by researchers, accountability of artificial intelligence solutions is one of the most critical. This paper focuses on artificial intelligence applications using natural language to investigate if the core semantics defined for a large-scale natural language processing system could assist in addressing accountability issues. Core semantics aims to obtain a full interpretation of the content of natural language texts, representing both implicit and explicit knowledge, using only ‘subj-action-(obj)’ structures and causal, temporal, spatial and personal-world links. The first part of the paper offers a summary of the difficulties to be addressed and of the reasons why representing the meaning of a natural language text is relevant for artificial intelligence accountability. In the second part, a-proof-of-concept for the application of such a knowledge representation to support accountability, and a detailed example of the analysis obtained with a prototype system named CoreSystem is illustrated. While only preliminary, these results give some new insights and indicate that the provided knowledge representation can be used to support accountability, looking inside the box.
Computer Vision for Image Understanding: A Comprehensive Review

Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user's satisfaction.
Forecasting Suspicious Account Activity at Large-Scale Online Service Providers

In the face of large-scale automated social engineering attacks to large online services, fast detection and remediation of compromised accounts are crucial to limit the spread of the attack and to mitigate the overall damage to users, companies, and the public at large. We advocate a fully automated approach based on machine learning: we develop an early warning system that harnesses account activity traces to predict which accounts are likely to be compromised in the future. We demonstrate the feasibility and applicability of the system through an experiment at a large-scale online service provider using four months of real-world production data encompassing hundreds of millions of users. We show that—even limiting ourselves to login data only in order to derive features with low computational cost, and a basic model selection approach—our classifier can be tuned to achieve good classification precision when used for forecasting. Our system correctly identifies up to one month in advance the accounts later flagged as suspicious with precision, recall, and false positive rates that indicate the mechanism is likely to prove valuable in operational settings to support additional layers of defense.