Reviewerly/exHarmony huggingface dataset:
Probe Sector Matching for Freehand 3D Ultrasound Reconstruction

A 3D ultrasound image reconstruction technique, named probe sector matching (PSM), is proposed in this paper for a freehand linear array ultrasound probe equipped with multiple sensors, providing the position and attitude of the transducer and the pressure between the transducer and the target surface. The proposed PSM method includes three main steps. First, the imaging target and the working range of the probe are set to be the center and the radius of the imaging field of view, respectively. To reconstruct a 3D volume, the positions of all necessary probe sectors are pre-calculated inversely to form a sector database. Second, 2D cross-section probe sectors with the corresponding optical positioning, attitude and pressure information are collected when the ultrasound probe is moving around the imaging target. Last, an improved 3D Hough transform is used to match the plane of the current probe sector to the existing sector images in the sector database. After all pre-calculated probe sectors are acquired and matched into the 3D space defined by the sector database, a 3D ultrasound reconstruction is completed. The PSM is validated through two experiments: a virtual simulation using a numerical model and a lab experiment using a real physical model. The experimental results show that the PSM effectively reduces the errors caused by changes in the target position due to the uneven surface pressure or the inhomogeneity of the transmission media. We conclude that the PSM proposed in this study may help to design a lightweight, inexpensive and flexible ultrasound device with accurate 3D imaging capacity.
A function fitting method

Abstract In this article, we describe a function fitting method that has potential applications in machine learning and also prove relevant theorems. The described function fitting method is a convex minimization problem which can be solved using a gradient descent algorithm. We also provide qualitative analysis on fitness to data of this function fitting method. The function fitting problem is also shown to be a solution of a linear, weak partial differential equation (PDE). We describe a simple numerical solution using a gradient descent algorithm that converges uniformly to the actual solution. As the functional of the minimization problem is a quadratic form, there also exists a numerical method using linear algebra.
Identifying Cognitive Attributes Using Deep Learning Classification Techniques

Cognitive diagnosis is very useful to teachers and students, but its application is limited at present. This is largely because identifying the cognitive attributes of items currently is labor intensive and time-consuming. In this study, we used text classification techniques to automatically identify cognitive attributes. Specifically, two popular deep learning classification models, long-short term memory and bi-directional long-short term memory, were employed in conjunction with word embeddings. As the baseline, support vector machine with feature selection using information gain was also adopted. Experiments based on a sample of 805 third grade math items showed that both the deep learning models performed better than support vector machine, and bi-directional long-short term memory achieved the best performance, yielding the accuracy of 82% and the F1 measure of 80%. Our result indicated that text classification methods, especially deep learning models, have great potential in identifying cognitive attributes efficiently, and in turn, make cognitive diagnostic more feasible to practitioners.
Completed Extremely Nonnegative DMD for Color Texture Classification

Dense micro-block difference (DMD) has achieved good performance in gray texture representation and classification. However, its performance is not satisfactory when representing color texture. To alleviate this problem, we propose a novel color texture representation method based on Completed Extremely Nonnegative DMD (CEN-DMD) in this paper. In particular, we first use DMD to model interchannel features and interchannel features of color texture images. Considering that negative value is meaningless in a digital image, we perform a nonnegative operation during the difference process. Due to that the maximum value in a nonnegative difference patch represents a significant difference, we construct the Extremely Nonnegative DMD (EN-DMD) by fusing the maximum values of the intrachannel features and the maximum of interchannel features, and further build Completed Extremely Nonnegative DMD (CEN-DMD) by fusing EN-DMDs at five scales and the global feature of the color texture images. Finally, the Fisher Vector is used to encode the CEN-DMD to obtain a color texture descriptor. Experimental results on five published standard color texture datasets (CUReT, Colored Brodatz, VisTex, USPTex and KTH-TIPS) reveal that CEN-DMD is effective when compared to the thirteen representative color texture classification methods.
Using Fuzzy Cognitive Map Approach for Assessing Cybersecurity for Telehealth Scenario

Health organizations are investing in the development of telehealth systems to expand advances in health care to the homes of the Brazilian population. The adoption of telehealth aims to broaden basic monitoring and promote access to health services. Telehealth systems present a confidential data set containing patient health history, medication prescriptions, and medical diagnostics. However, in Brazil, there are no cybersecurity studies to address factors that impact patient manipulation and data transfer. Understanding cybersecurity impacts is critical for telehealth development strategies. The research reported here used several factors related to cyberattacks and cybersecurity vulnerabilities, combined with the approach to Fuzzy Cognitive Maps (FCMs), to identify the links between these elements. An evaluation using FCMs has proven to be able to describe the complexity of the system by providing an appropriate visual tool for staff to develop planning. The experimental results of the study contributed to supporting cybersecurity improvements in telehealth.
Electronic Payment Fraud Detection Using Supervised and Unsupervised Learning

With the digital transformation over the years and the recent expansion made possible by the use of different applications, it can been seen the diversification of payments by electronic means that contributed to companies suffering even more from fraud, as well as the increase in payments made with the credit card that contributed to this growth. Fraud can been defined as an illegal activity applied with the intention of obtaining financial benefits without taking into consideration the consequences of this act. Thus, it was be proposed the construction of artifact capable of minimizing the problem using unsupervised and supervised algorithms based on machine learning techniques. The solution also considered mining techniques for data processing. The experiment used data from a company that operates private transport by application and relied on the knowledge of the company’s fraud specialist to identify existing frauds. K-means was be applied for grouping races with similar characteristics and after labeling in legitimate and fraudulent races, the performance of the SVM - support vector machine and ANN - artificial neural network algorithms for performance was compared.
Capturing and Exploiting Citation Knowledge for Recommending Recently Published Papers

With the continuous growth of scientific literature, discovering relevant academic papers for a researcher has become a challenging task, especially when looking for the latest, most recent papers.In this case, traditional collaborative filtering systems are ineffective, since they are unable to recommend items not previously seen, rated or cited.This is known as the item cold-start problem.In this paper, we explore the potential of exploiting citation knowledge to provide a given user with relevant suggestions about recent scientific publications.A novel hybrid recommendation method that encapsulates such citation knowledge is proposed.Experimental results show improvements over baseline methods, evidencing benefits of using citation knowledge to recommend recently published papers in a personalised way.Moreover, as a result of our work, we also provide a unique dataset that, differently to previous corpora, contains detailed paper citation information.
Biomedical Lung Image Segmentation Using CNN

A variety of DNN is applied in segmentation of medical image and has achieved acceptable performance. Unlike natural pictures, medical pictures of a similar imaging modality are characterized by a similar pattern that indicates same traditional organs or tissues found at similar positions within the pictures. Thus, we tend to attempt to incorporate the previous information of medical pictures for correct segmentation into the structure of neural networks. Supporting this idea, we would like to propose a project paper on deep network known as Segmentation and analysis of Lung organ using CNN. The segmentation operate is analyzed. Experiments validate our information assumption concerning the incorporation of previous information into the convolution kernels and to show that we have attained an affordable segmentation and a satisfactory accuracy.
Voluntary Pupil Control in Noisy Environments

Eyes-only interaction in HCI usually requires visual focusing to carry out input arguments. For complex items, however, input requests are mixed with visual inspection, increasing the likelihood of false positive entries. Recent research applied cognitive control of pupil dilations as an input mechanism that works independently from any fixation times. However, experiments were exclusively conducted under laboratory conditions. The present study investigates the potential of exerting control over pupil diameter in noisy environments. Participants explore various techniques in a controlled sequence of (real-time) feedback sessions. Thereafter, they follow a predefined course across indoor and outdoor stations to either induce dilations or perform a control task. Results indicate strong interindividual differences in performance. Outdoor pupil dynamics exhibit a high degree of variation including a considerable number of unintended dilations. Accordingly, environmental control seems to constitute a necessary condition to exert cognitive control on pupil diameter and enable pupil-based interaction in HCI.
Coding and Music Creation in a Multi-Agent Environment

This paper reports on an extension of NetLogo designed to enable immediate, visual-based creation of multi-voice music at the level of notes, motifs, and phrases. The extension, NetMusic, uses the NetLogo interface as a visual sketchpad and configurable application programming interface for multi-voice music making. Student coders compose music by learning to use, modify, and create NetLogo procedures. A curricular module, taught during several weeks of a small introductory computer science course for students with no background in programming and centered on multi-agent simulation, used music composition to engage students in music creation via coding. In this report we focus on the the tool and curriculum, concluding with preliminary response from students in the course.
Common Passwords and Common Words in Passwords

Passwords often include dictionary words or meaningful strings. Figuring out these words or strings may significantly reduce the number of password guessing. The wordlists used by password cracking software, such as Hashcat, typically include the words from various dictionaries and leaked plain passwords. Is it really necessary to put all dictionary words and leaked passwords into the wordlist? In this work, we use Mac system dictionary and rockyou.com leak as two sample wordlists to check the substrings of over 600 million leaked passwords from different websites. We find only a small portion of words from these two wordlists are used by the leaked passwords. More specifically, about 90,000 out of 235,886 Mac dictionary words and about six millions out of 13 millions rockyou.com unique passwords are used by the leaked passwords. In addition to that, we find that a small portion of unique passwords are shared by a large portion of accounts.
Innovative Education: Facilitation as an Effective Method of Educational Process Organization

The article treats the phenomenon of facilitation in teaching Russian language and Literature. Having defined social and pedagogical facilitation, the authors describe their use in educational process, provide the characteristics of facilitating atmosphere, determine the teacher’s role and his main tasks. It is found that the use of facilitation can significantly intensify students’ training activities when learning Russian language and Literature.
The Emerging AI Policy for e-commerce Industry

For the fast-growing e-Commerce industry, the AI is the game-changer but is it really utilised in a most effective way or is it just a risky 'artificial balloon'? Is it gambling to rely on AI technologies without forming a dedicated policy? To answer these questions and propose a cautious approach towards the emerging technologies, in this explanatory research, the risks related to the fast adaptation of AI technologies addressed with recommended actions. Within the scope of the research, eCommerce industry is analysed to reveal the issues related to AI implementations. Accordingly, the challenges of AI technologies have been questioned so that necessary precaution, preparation, and considerations can be pointed out. Accordingly, an AI policy for e-Commerce industry is formed for the businesses to benefit from the most recent technologies without risking the possible issues. Three main policy subjects have been determined as transparency of the technologies, accountability for the purpose, process and performance of them, and lastly, emerging user privacy and security related issues. For each policy subject, a review of the AI implementations, recent critics and forecasted expectations are investigated to list the recommendations for the candidate AI implementer. The research aims to provide an AI policy guideline for e-Commerce industry with a detailed overview of the outstanding issues, best practices and recommendations from scholars.
Evolution of HTTPS Usage by Portuguese Municipalities

This paper presents a study on the evolution of the use of HTTPS by the official websites of all (308) Portuguese municipalities. One year ago, we found a bad situation regarding HTTPS usage: only a small percentage of websites adopted HTTPS correctly. The results were communicated to the relevant entities so actions could be taken. After one year, we performed a new assessment to check for evolution. This paper presents the results of this second assessment. We found a significantly better situation, although still with plenty of room for improvement: 31 municipal websites were classified as Good (20 more), while 42 less were classified as Bad (100 in total). We concluded that two determinants that were identified as contributing to explain the results of the first study - municipal taxes and total population - do not contribute to explain the improvements observed in this assessment. We believe that we contributed to those improvements by raising awareness to the high number of municipalities not using or badly using HTTPS.
Modeling the Risk of Data Breach Incidents at the Firm Level

Many firms and organizations are at risk of cyberattack nowadays. For example, in 2018 alone, 443 data breaches in Japan compromised some 5.61 million records of personal information. To respond to this threat, firms asset a risk of cybersecurity and introduce IT security management practices. However, it is unclear whether firms are able to identifying the tradeoff between effect of development of IT security practices and the risk of data breach. To address this, we propose a probabilistic model that estimates the risk of a data breach for a given firm using the Japan Network Security Association incident dataset, being a historical collection of cyber incidents from 2005 to 2018. This model yields the conditional probabilities of a data breach given conditions, which follows a negative binomial distribution. We highlight the difference in inter-arrival time between firms with security management and one without it. Based on the experimental results, we evaluate effects of security management and discuss some reasons for these differences.
A Peer-to-Peer Market Algorithm for a Blockchain Platform

In an era of technological revolution in which everything becomes smarter and connected, the blockchain can introduce a new model for energy transactions able to grant more simplicity, security and transparency for end-users. The blockchain technology is characterized by a distributed architecture without a trusted and centralized authority, and, therefore, it appears as the perfect solutions for managing exchanges between peers. In this paper, a market algorithm that can be easily transferred to a smart contract for maximizing the match between produced and consumed energy in a micro-grid is presented. The algorithm supports energy transactions between peers (both producers and consumers) and could be one of the main executables implemented using a blockchain platform. The case study presented in this paper shows how the end-users through the blockchain could select among the possible energy transactions those more suitable to offer specific ancillary services to the grid operator without involving the grid operator itself or a third-party aggregator.
Sift And Gabor Features For Very High Resolution Image Classification

this paper presents a new approach to extract features from high resolution images inspired by the sift descriptor and gabor features. both of these two methods are powerful when used separately or together in region-based or pixel-based classification, they brought a high accuracy. our approach was applied to classify two very high resolution images of boumerdes (algeria) and djeddah $(ksa)$ using knn and svm. the obtained results achieved promising performance compared to using spectral information alone.
Enterprise Applications Reimagined-Cloud abridges mystery of Mobile Offline Working

Software as a Service (SaaS) has the potential to transform the way information-technology departments relate to and even think about their role as providers of computing services to the rest of the enterprise. In this paper we present an empirical case study on how cloud computing eases the development of mobile applications that work efficiently even with intermittent network access. We refer to a situation as offline where there is limited or no internet connectivity. We draw comparisons between the rudimentary way of creating offline mobile applications (via On-premise mobile platforms) to the new cloud development model. With the help of measurable KPIs (Key Performance Indicators), we conclude the effectiveness of development on Cloud. Additionally, we have taken a case study instance of enterprise mobile application that is used for data capture at remote locations in Oil fields. Then we describe design implementation details as well as provide recommendations for seamless integration to backend On-premise (OP) systems.
Relation between the entropy and the linear entropy in the ion–laser interaction

It is shown that, in the interaction of a field and a qubit, there exists a relation between the linear entropy and the von Neumann entropy. The Cayley–Hamilton theorem is used to obtain such relation. In the case we study, the qubit is given by the internal degrees of freedom of an ion trapped in a Paul trap and the field given by its external (vibrational) degrees of freedom.
swGBDT: Efficient Gradient Boosted Decision Tree on Sunway Many-Core Processor

Gradient Boosted Decision Trees (GBDT) is a practical machine learning method, which has been widely used in various application fields such as recommendation system. Optimizing the performance of GBDT on heterogeneous many-core processors exposes several challenges such as designing efficient parallelization scheme and mitigating the latency of irregular memory access. In this paper, we propose swGBDT, an efficient GBDT implementation on Sunway processor. In swGBDT, we divide the 64 CPEs in a core group into multiple roles such as loader, saver and worker in order to hide the latency of irregular global memory access. In addition, we partition the data into two granularities such as block and tile to better utilize the LDM on each CPE for data caching. Moreover, we utilize register communication for collaboration among CPEs. Our evaluation with representative datasets shows that swGBDT achieves 4.6$$\times $$ and 2$$\times $$ performance speedup on average compared to the serial implementation on MPE and parallel XGBoost on CPEs respectively.
A Virtual Resource Pricing Mechanism Based on Three-Side Gaming Model in Large-Scale Cloud Environments

Recently, network virtualization technology has become a promising approach to efficiently share physical substrate networks in a cloud. However, finding an appropriate mapping between nodes and links in virtual networks is still a challenging problem. To overcome the demerits of existing price mechanisms, the author presents a game-based pricing model, in which resource configuration and provision among virtual networks is defined as a two-phrase gaming model. In this gaming model, a cooperative gaming model is applied to optimize resource benefits, while a non-cooperative gaming model is used to balance user costs and provider benefits. Extensive experiments are conducted in a real-world cloud, and the results show that this pricing mechanism can effectively improve the resource allocation efficiency as well as the resource profits of cloud providers. In addition, it also exhibits better robustness than many existing methods when a cloud system is facing intensive workloads.
Complexity of Unordered CNF Games

The classic TQBF problem is to determine who has a winning strategy in a game played on a given conjunctive normal form formula (CNF), where the two players alternate turns picking truth values for the variables in a given order, and the winner is determined by whether the CNF gets satisfied. We study variants of this game in which the variables may be played in any order, and each turn consists of picking a remaining variable and a truth value for it. For the version where the set of variables is partitioned into two halves and each player may only pick variables from his or her half, we prove that the problem is PSPACE-complete for 5-CNFs and in P for 2-CNFs. Previously, it was known to be PSPACE-complete for unbounded-width CNFs (Schaefer, STOC 1976). For the general unordered version (where each variable can be picked by either player), we also prove that the problem is PSPACE-complete for 5-CNFs and in P for 2-CNFs. Previously, it was known to be PSPACE-complete for 6-CNFs (Ahlroth and Orponen, MFCS 2012) and PSPACE-complete for positive 11-CNFs (Schaefer, STOC 1976).
TACAI: an intermediate representation based on abstract interpretation

Most Java static analysis frameworks provide an intermediate presentation (IR) of Java Bytecode to facilitate the development of static analyses. While such IRs are often based on three-address code, the transformation itself is a great opportunity to apply optimizations to the transformed code, such as constant propagation.