Reviewerly/exHarmony huggingface dataset:
A New Triplet Convolutional Neural Network for Classification of Lesions on Mammograms

Mammography provides a useful tool for breast cancer detection.However, many doctors have difficulty in making the right decision based on mammograms.This paper aims to set up a deep learning (DL) architecture that can effectively differentiate between benign and malignant tumors.Specifically, a new triplet convolutional neural network (CNN) was established with three subnetworks, each of which contains a succession of layer blocks.Each block consists of two convolutional layers, a dropout layer and a max-pooling layer.During operation, the region of interest (ROI) extracted from the mammogram is imputed to the first subnetwork, and processed by the Canny filter.The filtered results become the input of the second subnetwork, while the third subnetwork takes the whole image as input.To verify the effectiveness of our architecture, a set of 500 images from 301 patients was extracted from the DDSM database and augmented to 4,000 images, and divided into a training set (80 %) and a testing set (20 %).The results show that our architecture achieved an accuracy of 93.13 %, a sensitivity of 96 % and a specificity of 90.25 %.This research provides a desirable way to identify breast cancer based on mammography.
Effective competency based differential evolution algorithm

The DE algorithm is considered as an efficient algorithm in the field of evolutionary algorithms. In case of DE the solution search process is a combination of evolutionary (evolution of population) and swarm intelligence (position update of individual) search process. In DE, the diversification and convergence of the population is controlled by the crossover rate (CR), the scale factor (F) and the vector differences of three randomly selected individuals. In DE algorithm, every solution is given equal chance to take part in the solution search and in case of stagnation; it is difficult to get out from this situation. Therefore, a competency based position update process is integrated in DE to boost the speed of convergence in addition to the diversification ability of the algorithm. The accuracy, efficiency, robustness, and reliability of the proposed algorithm, namely efficient competency based DE (ECDE) are analyzed over a set of 20 benchmark problems with diverse characteristics. Results are compared with DE and its two recent variants, namely FBDE, FSADE, and other algorithms, namely ABC and PSO.
A Simulation Data-Driven Design Approach for Rapid Product Optimization

Abstract Traditional design optimization is an iterative process of design, simulation, and redesign, which requires extensive calculations and analysis. The designer needs to adjust and evaluate the design parameters manually and continually based on the simulation results until a satisfactory design is obtained. However, the expensive computational costs and large resource consumption of complex products hinder the wide application of simulation in industry. It is not an easy task to search the optimal design solution intelligently and efficiently. Therefore, a simulation data-driven design approach which combines dynamic simulation data mining and design optimization is proposed to achieve this purpose in this study. The dynamic simulation data mining algorithm—on-line sequential extreme learning machine with adaptive weights (WadaptiveOS-ELM)—is adopted to train the dynamic prediction model to effectively evaluate the merits of new design solutions in the optimization process. Meanwhile, the prediction model is updated incrementally by combining new “good” data set to reduce the modeling cost and improve the prediction accuracy. Furthermore, the improved heuristic optimization algorithm—adaptive and weighted center particle swarm optimization (AWCPSO)—is introduced to guide the design change direction intelligently to improve the search efficiency. In this way, the optimal design solution can be searched automatically with less actual simulation iterations and higher optimization efficiency, and thus supporting the rapid product optimization effectively. The experimental results demonstrate the feasibility and effectiveness of the proposed approach.
Adaptive iir filter design using self-adaptive search equation based artificial beecolony algorithm

Infinite impulse response (IIR) system identification problem is defined as an IIR filter modeling to represent an unknown system. During a modeling task, unknown system parameters are estimated by metaheuristic algorithms through the IIR filter. This work deals with the self-adaptive search-equation-based artificial bee colony (SSEABC) algorithm that is adapted to optimal IIR filter design. SSEABC algorithm is a recent and improved variant of artificial bee colony (ABC) algorithm in which appropriate search equation is determined with a self-adaptive strategy. Moreover, the success of the SSEABC algorithm enhanced with a competitive local search selection strategy was proved on benchmark functions in our previous studies. The SSEABC algorithm is utilized in filter modelings which have different cases. In order to demonstrate the performance of the SSEABC algorithm on IIR filter design, we have also used canonical ABC, modified ABC (MABC), best neighbor-guided ABC, and an ABC with an adaptive population size (APABC) algorithms as well as other algorithms in the literature for comparison. The obtained results and the analysis on performance evolution of compared algorithms on several filter design cases indicate that SSEABC outperforms all considered ABC variants and other algorithms in the literature.
SEAMLESS CO-REGISTRATION OF IMAGES FROM MULTI-SENSOR MULTISPECTRAL CAMERAS

Abstract. Small-format, consumer-grade multi-camera multispectral systems have gained popularity in recent years. This is specifically due to the simplicity of their integration onboard platforms with limited payload capacity, such as Unmanned Aerial Vehicles (UAVs). Commercially available photogrammetric software can process the image data collected by these cameras to create multispectral ortho-rectified mosaics. However, misalignments of several pixels between spectral bands have been observed to be a common issue when employing these solutions, which can undermine the spectral and geometric integrity of the data. Besides, in advanced processing workflows such as object detection and classification with deep learning algorithms, band-to-band co-registered images are needed rather than one mosaic. We propose a two-fold solution for seamless band-to-band registration of images captured by five cameras integrated into a miniature multispectral camera system. This approach consists of 1) a robust self-calibration of the multispectral camera system to accurately estimate the intrinsic calibration parameters and relative orientation parameters of all cameras; 2) a single capture, band-to-band co-registration method based on trifocal constraints. This approach differs from existing literature since it is fully automatic, does not make any assumptions about the scene, does not use any best-fit projective or similarity transformations, and does not attempt cross-spectral feature-point matching. Our experiments confirm that the proposed co-registration method can accurately fuse multispectral images from a miniature multi-camera system and is invariant to large depth-variations in the captured scene.
Accurate hyperspectral and infrared satellite image registration method using structured topological constraints

As the hyperspectral and infrared characteristics of satellite images are different, the accurate registration between these two kinds of images is a challenge. Considering the robustness of scene structure, this paper proposes an accurate hyperspectral and infrared satellite image registration method using structured topological constraints. First, Delaunay triangulation is used to correlate the scattered feature points, and these local points are combined in a graph arrangement to form triangular mesh. And then, based on the correspondences between local points, triangular edges and triangular surfaces, the multi-level structured topological constraints are established to dynamically remove the outliers. Further, based on the established spatial topology, an image fitting method using multiple transformation models is proposed, which can solve the problem of local distortion of wide-band satellite images rapidly through region merging. Compared with the existing state-of-the-arts, the experimental results show that the proposed method has high registration accuracy, good robustness and fast computational speed for satellite images.
Word Sense Disambiguation Based on Constrained RandomWalks in Linked Semantic Networks

Word Sense Disambiguation remains a challenging NLP task.Due to the lack of annotated training data, especially for rare senses, the supervised approaches are usually designed for specific subdomains limited to a narrow subset of identified senses.Recent advances in this area have shown that knowledge-based approaches are more scalable and obtain more promising results in all-words WSD scenarios.In this work we present a faster WSD algorithm based on the Monte Carlo approximation of sense probabilities given a context using constrained random walks over linked semantic networks.We show that the local semantic relatedness is mostly sufficient to successfully identify correct senses when an extensive knowledge base and a proper weighting scheme are used.The proposed methods are evaluated on English (SenseEval, SemEval) and Polish (Składnica, KPWr) datasets.
Implementation of Blended Learning through Youtube Media to Improve Students’ Speaking Skill

With new technology and the way students learn, a vlog is one way to improve students' speaking skills. This research focuses on speaking skill via vlog to improve students' speaking and creativity skills and analyze the interrelationship between the implementation of blended learning and an increase in student self-confidence. This research used mixed methods. The subjects were 13 vlogs made by students on the topic of tourism. The 13 Vlogs are assessed using the rubric developed by Dan Rooney and Elijah Punzalan. The results showed that the highest vlog value was 84 and the lowest was 74, which generally increased students' creativity. Common mistakes on average are ways of delivering or introducing introduction and introduction that are still not good, how to give conclusions or provide summaries and finally the accuracy of the grammatical structure used. There is an increase in speaking ability including vocabulary, fluency, pronunciation and intonation sections. It can be seen that there is a ratio of 2.8 points for vocabulary, 7.9 points for fluency, 10.9 points for pronunciation and 2.1 points for intonation. The findings also infer that the application of speaking through blended learning vlogging media YouTube was declared successful and improved students' speaking abilities.
Determination of Diffused Irradiation from Horizontal Global Irradiation - Study for the City of Curitiba

Understanding the solar radiation values in the surface is important for the development of solar energy projects, obtaining through radiometers installed in certain places or publications as in solar energy atlases.Typically, solar or weather stations do not have sensors to measure diffuse irradiation due to high investment and the need for constant maintenance.This paper presents methods for the determination of diffuse irradiation from surface measured data of global horizontal irradiation in the city of Curitiba, obtained through pyranometers installed at the Federal University of Technology -Paraná (UTFPR) headquarters and from Curitiba-A807 National Institute of Meteorology (INMET) station.Two models are presented for the estimation of the monthly mean daily diffuse irradiation, based on the studies of Liu and Jordan (1960) and Page (1961).Finally, these data were compared with those presented by the Solar Energy Atlas -Paraná, verifying the percentage differences presented.It was observed that the model elaborated by Page presented better results in the diffuse radiation estimates when compared to the values HIGHLIGHTS• Review of solar radiation components.• Acquisition of global irradiation data from INMET and UTFPR.• Use of Liu and Jordan's and Page's method to calculate diffuse irradiation.• Comparison with the results obtained in the Solar Energy Atlas -Paraná.Brazilian Archives of Biology and Technology.Vol.62 no.spe: e19190014, 2019www.scielo.br/babtpresented by the Paraná Atlas, with an average variation of -1.39% and -1.55%, for data from INMET and UTFPR respectively.
AI-Driven Cyber Security Analytics and Privacy Protection

has gone through a rapid development in today's internet connected world.With the wide application of the booming technologies such as the Internet of ings (IoT) and the cloud computing, huge amount of data are generated and collected.While the data can be used to better serve the corresponding business needs, they also pose big challenges for the cyber security and privacy protection.It becomes very di cult if not impossible to discover the malicious behavior among the big data in real time.us, this gives rise to the cyber security solutions which are driven by AI-based technologies, such as machine learning, statistical inference, big data analysis, deep learning, and so on.AIdriven cyber security analytics has already found its applications in the next generation rewall which includes the automatic intrusion detection system, encrypted tra c classi cation, malicious software detection, and so on.In the area of cryptography, AI-driven solution starts to help the researchers optimize the algorithm design and can largely reduce the cryptanalysis e ort such as searching the di erential trails which is crucial in di erential cryptanalysis.
Word Embedding With Zipf’s Context

Word embeddings generated by neural language models have achieved great success in many NLP tasks. However, neural language models may be difficult to train and time consuming. In this paper, we introduce a simpler but efficient word embedding method based on cooccurrence matrix factorization. Our method drastically reduces the dimensions of the cooccurrence matrix according to the famous Zipf’s word frequency law. We observe that if the sampling times of a target word increase to a certain extent, the context of the target word will follow the Zipf’s distribution. Enlightened by this, we propose a novel transformation for the cooccurrence matrix. The built cooccurrence matrix is then factorized by PCA. As PCA simply factorizes the cooccurrence matrix linearly and cannot capture the nonlinear relations of features, we construct an autoencoder to further transform the vectors. We compare our method with some well-known neural language models. Our method shows a comparable performance though it is much simpler than the neural language models.
Challenges and Solutions of Applying Large-Scale Agile at Organization-al Level

Large-scale agile development receives widespread interest today in research and practice.Two different dimensions could be considered in the context of applying agile at large-scale: individual level, like project or team, and organization level.The majority of literature treats the subject of large-scale agile implementation at the individual level or not very specific.In our paper, we placed at organization level with the aim to find out the challenges and solutions that are relevant for enterprise-wide agile implementation in large organizations in order to create agility beyond individual team or project.Based on the literature review, we have found twelve challenges, grouped in three categories and three general solutions.
READING LITERACY AND METACOGNITIVE STRATEGY FOR PREDICTING ACADEMIC ACHIEVEMENT

The importance of reading literacy is shown in the results of achievement among international countries. Reading literacy has not become a culture for most students. Some students have not used reading strategies to improve their understanding. This will affect the academic achievement. This expost facto research design aims to examine: (1) wether reading literacy can predict academic achievement in knowledge and skill, (2) wether metacognitive strategy can predict academic achievement in knowledge and skill, and (3) wether reading literacy and metacognitive strategy together can predict academic achievement on knowledge and skill. The population is 543 students of three pilot senior high schools of Curriculum 2013 in Kulon Progo. The sample is 359 students established using the Table of Isaac and Michael. The instrument used is a questionnaire. The result is as follows. (1) Reading literacy can predict academic achievement in knowledge, but it cannot predict academic achievement in skill. (2) Metacognitive strategy can predict academic achievement in knowledge and skill. (3) Reading literacy and metacognitive strategy simultaneously can predict academic achievement on knowledge and skill. Therefore literacy habituation and the use of metacognitive strategies are strongly recommended to improve academic achievement of knowledge and skills.
Single Image Reflection Removal Using Non-Linearly Synthesized Glass Images and Semantic Context

An image captured through a glass plane usually contains both of a target transmitted scene behind the glass plane and a reflected scene in front of the glass plane. We propose a semantic context based network to remove reflection artifacts from a single glass image. We first investigate a non-linear intensity mapping relationship for glass images to synthesize more realistic training sets. Then we devise an efficient reflection removal network using multi-scale generators and an interpreter, where the semantic context of the transmission image is adopted as a high level cue for the interpreter to guide the generators. We also provide a new test data set of real glass images including the ground truth transmission and reflection images. Experiments are performed on four test data sets and we show that the proposed algorithm decomposes an input glass image into a transmission image and a reflection image more faithfully compared with the four existing state-of-the-art methods.
Middle-Product Learning with Rounding Problem and Its Applications

At CRYPTO 2017, Roşca et al. introduce a new variant of the Learning With Errors (LWE) problem, called the Middle-Product LWE ( $${\mathrm {MP}\text {-}\mathrm{LWE}}$$ ). The hardness of this new assumption is based on the hardness of the Polynomial LWE (P-LWE) problem parameterized by a set of polynomials, making it more secure against the possible weakness of a single defining polynomial. As a cryptographic application, they also provide an encryption scheme based on the $${\mathrm {MP}\text {-}\mathrm{LWE}}$$ problem. In this paper, we propose a deterministic variant of their encryption scheme, which does not need Gaussian sampling and is thus simpler than the original one. Still, it has the same quasi-optimal asymptotic key and ciphertext sizes. The main ingredient for this purpose is the Learning With Rounding (LWR) problem which has already been used to derandomize LWE type encryption. The hardness of our scheme is based on a new assumption called Middle-Product Computational Learning With Rounding, an adaption of the computational LWR problem over rings, introduced by Chen et al. at ASIACRYPT 2018. We prove that this new assumption is as hard as the decisional version of MP-LWE and thus benefits from worst-case to average-case hardness guarantees.
Second-Order Sliding Mode Formation Control of Multiple Robots by Extreme Learning Machine

This paper addresses a second-order sliding mode control method for the formation problem of multirobot systems. The formation patterns are usually symmetrical. This sliding mode control is based on the super-twisting law. In many real-world applications, the robots suffer from a great diversity of uncertainties and disturbances that greatly challenge super-twisting sliding mode formation maneuvers. In particular, such a challenge has adverse effects on the formation performance when the uncertainties and disturbances have an unknown bound. This paper focuses on this issue and utilizes the technique of an extreme learning machine to meet this challenge. Within the leader–follower framework, this paper investigates the integration of the super-twisting sliding mode control method and the extreme learning machine. The output weights of this extreme learning machine are adaptively adjusted so that this integrated formation design has guaranteed closed-loop stability in the sense of Lyaponov. In the end, some simulations are implemented via a multirobot platform, illustrating the superiority and effectiveness of the integrated formation design in spite of uncertainties and disturbances.
Multiple Objects Tracking Based Vehicle Speed Analysis with Gaussian Filter from Drone Video

Vehicle speed analysis based on the video is a challenging task in the field of traffic safety, which has high requirements for accuracy and computational burden. The drone's video is taken from a top-down perspective, providing more complete view comparing to the common surveillance cameras in poles. In this paper, we introduce a Gaussian Filter to deal with the estimated speed data which are extracted by a multiple objects tracking method composed of You Only Look Once (YOLOv3) and Kalman Filter. We exploit the capability of Gaussian Filter to suppress data noise appearing in the process of tracking vehicles from drone videos, and thus use the filter to solve the case where the estimated vehicle speed is fluctuated along the ongoing direction. On the other hand, we built a vehicle dataset from the drone's videos we mentioned above which additionally contains vehicle's real speed information. Experimental results showed that our method is effective to improve the accuracy of vehicle speed estimated by our tracking module. It can improve Mean Squared error (MSE) accuracy 80.5% on experimental data.
Blockchain and Accounting Governance: Emerging Issues and Considerations for Accounting and Assurance Professionals

Blockchain technology has been a disruptive force in currency, supply chain, and information sharing practices across a variety of industries. Its usage has only recently expanded into assurance and financial reporting. This paper explores blockchain's impact in these areas and provides guidance for organizations and auditors utilizing blockchain by addressing financial data integrity issues, financial reporting risks, and implications for external auditors and firms' corporate governance practices. Organizations utilizing blockchain must adapt their policies and procedures over internal controls and counterparty risk assessment to address increasing regulation over the distribution of financial data while their audit committees must be prepared to address these challenges leading up to financial statement preparation. External auditors need to assess blockchain implementation as a financial reporting risk and balance the potentially more reliable and timelier audit evidence obtained from blockchain-based reporting systems against the related increase in internal control testing.