Reviewerly/exHarmony huggingface dataset:
Probabilistic Learning Coherent Point Drift for 3D Ultrasound Fetal Head Registration

Quantification of brain growth is crucial for the assessment of fetal well being, for which ultrasound (US) images are the chosen clinical modality. However, they present artefacts, such as acoustic occlusion, especially after the 18 th gestational week, when cranial calcification appears. Fetal US volume registration is useful in one or all of the following cases: to monitor the evolution of fetometry indicators, to segment different structures using a fetal brain atlas, and to align and combine multiple fetal brain acquisitions. This paper presents a new approach for automatic registration of real 3D US fetal brain volumes, volumes that contain a considerable degree of occlusion artefacts, noise, and missing data. To achieve this, a novel variant of the coherent point drift method is proposed. This work employs supervised learning to segment and conform a point cloud automatically and to estimate their subsequent weight factors. These factors are obtained by a random forest-based classification and are used to appropriately assign nonuniform membership probability values of a Gaussian mixture model. These characteristics allow for the automatic registration of 3D US fetal brain volumes with occlusions and multiplicative noise, without needing an initial point cloud. Compared to other intensity and geometry-based algorithms, the proposed method achieves an error reduction of 7.4% to 60.7%, with a target registration error of only 6.38 ± 3.24 mm. This makes the herein proposed approach highly suitable for 3D automatic registration of fetal head US volumes, an approach which can be useful to monitor fetal growth, segment several brain structures, or even compound multiple acquisitions taken from different projections.
Writing Motivation and The Ability in Writing a Research Proposal of Generation Z Students Based on Cognitive Style

This article presents a comprehensive analysis of writing motivation and the ability in writing a research proposal of Generation Z students based on differences in their cognitive styles. The research involved 70 Generation Z students in Faculty of Teacher Training and Education, Universitas Maritim Raja Ali Haji, Indonesia. After going through the Group Embedded Figures Test (GEFT) stages, these students were divided into 2 groups based on their cognitive style, field independent (FI) and field dependent (FD). Writing motivation data was collected through a questionnaire and the writing ability of writing a research proposal was collected through an assessment rubric. Analysis of research data using t-test and linear regression with SPSS 23. The results of this study stated that the writing motivation of Generation Z students between the FI group and the FD group did not differ significantly. Meanwhile, the ability in writing research proposals for Generation Z students in the FI group was higher than for Generation Z students in the FD group. Writing motivation has a significant effect on the ability in writing research proposals in both groups. The results of this study recommend for analysis other perspectives and the exploration of thinking characteristics of Generation Z students in written language.
Improving Text Image Resolution using a Deep Generative Adversarial Network for Optical Character Recognition

Optical character recognition (OCR) has been widely studied in previous work. Except for the models used, the recognition accuracy depends most on the resolution of the image to be recognized. To enhance OCR performance, this paper proposes an approach based on a generative adversarial network to improve text image resolution. Our approach uses a perceptual loss function that consists of an adversarial loss, a content loss and an L1 loss. The adversarial loss and the L1 loss are used to ensure the generated super-resolved images are closer to the ground truth high-resolution images. Meanwhile, the content loss is used to ensure the generated super-resolved images and the input low-resolution images have similar features on the basis of perceptual instead of pixel similarity. To evaluate the proposed approach, we compare the recognition accuracies before and after improving the resolution of both English and Chinese text images. The results show that the recognition accuracies on the super-resolved text images obtained with our approach are significantly higher than those on the low-resolution images without processing.
OBFS: OpenCL Based BFS Optimizations on Software Programmable FPGAs

Breadth First Search (BFS) is a key building block of graph processing and there have been considerable efforts devoted to accelerating BFS on FPGAs for both performance and energy efficiency. Prior work typically built the BFS accelerator through handcrafted circuit design using hardware description language (HDL). Despite the relatively good performance, the HDL based design leads to extremely low design productivity, and incurs high portability and maintenance cost. While high level synthesis (HLS) tools make it convenient to create a functionally correct BFS accelerator, the performance can be much lower the handcrafted design with HDL. To obtain both the near handcrafted design performance and better software-like features such as portability and maintenance, we propose OBFS, an OpenCL based BFS accelerator on software programmable FPGAs. With the observation that OpenCL based FPGA design is rather inefficient on irregular memory accesses, we propose approaches including data alignment, graph reordering and batching to ensure coalesced memory accesses. In addition, we take advantage of the on-chip buffer to mitigate the inefficient random DDR accesses. Finally, we shift the random level update in BFS out from the main processing pipeline and have it overlapped with the following BFS processing task. According to the experiments, OBFS achieves 9.5X and 5.5X performance speedup on average compared to a vertex-centric implementation and an edge-centric implementation respectively on Intel Harp-v2. When compared to prior handcrafted designs, it achieves comparable or even better performance.
Secure V2V Communication in IOV using IBE and PKI based Hybrid Approach

We live in the world of "Internet of Everything", which lead to advent of different applications and Internet of vehicles (IOV) of one among them, which is a major step forward for the future of transportation system.Vehicle to vehicle (V2V) communication plays a major role in which a vehicle may send sensitive, non-sensitive messages and these messages are encrypted with public keys, which makes distribution of public keys is a major problem due to the vehicle need to be anonymous having pseudonyms which changes more frequently and makes it more complicated.Here we proposed a hybrid approach, which uses existing Public key certificate for authorization of the vehicle and Identity Based Encryption to generate public keys from the pseudonyms and use it in secure V2V communication without compromising anonymity of the vehicle.
Mobile augmented reality techniques for emergency response

In an emergency situation, each response agent must act quickly and accurately. The support of a mobile device that can provide an appropriate insight of the surroundings and that allows users to exchange information with the other members of the emergency teams, can prevent harm and even save many lives. This paper presents a mobile application that integrates a georeferenced system with augmented reality techniques, in order to serve the needs of the operatives in emergency situations. The work intends to introduce solutions which optimize the performance with which the user takes advantage of the mobile application, such as the organization of the data flow that is displayed and the augmentation of the surrounding area. User studies were conducted with members of the National Navy. The results were positive although there are still some aspects that should be improved.
Hand gesture recognition using discrete wavelet transform and convolutional neural network

Public services are available to all communities including people with disabilities. One obstacle that impedes persons with disabilities from participating in various community activities and enjoying the various public services available to the community is information and communication barriers. One way to communicate with people with disabilities is with hand gestures. Therefore, the hand gesture technology is needed, in order to facilitate the public to interact with the disability. This study proposes a reliable hand gesture recognition system using the convolutional neural network method. The first step, carried out pre-processing, to separate the foreground and background. Then the foreground is transformed using the discrete wavelet transform (DWT) to take the most significant subband. The last step is image classification with convolutional neural network. The amount of training and test data used are 400 and 100 images repectively, containing five classes namely class A, B, C, # 5, and pointing. This study engendered a hand gesture recognition system that had an accuracy of 100% for dataset A and 90% for dataset B.
IMAGE MOSAIC ALGORITHM BASED ON PCA-ORB FEATURE MATCHING

Abstract. In the process of image stitching, the ORB (Oriented FAST and Rotated BRIEF) algorithm lacks the characteristics of scale invariance and high mismatch rate. A principal component invariant feature transform (PCA-ORB, Principal Component Analysis- Oriented) is proposed. FAST and Rotated BRIEF) image stitching method. Firstly, the ORB algorithm is used to optimize the feature points to obtain the feature points with uniform distribution. Secondly, the principal component analysis (PCA) method can reduce the dimension of the traditional ORB feature descriptor and reduce the complexity of the feature point descriptor data. Thirdly, KNN (K-Nearest Neighbor) is used, and the k-nearest neighbor algorithm performs roughly matching on the feature points after dimensionality reduction. Then the random matching consistency algorithm (RANSAC, Random Sample Consensus) is used to remove the mismatched points. Finally, the fading and fading fusion algorithm is used to fuse the images. In 8 sets of simulation experiments, the image stitching speed is improved relative to the PCA-SIFT algorithm. The experimental results show that the proposed algorithm improves the image stitching speed under the premise of ensuring the quality of stitching, and can play a role in fast, real-time and large-scale applications, which are conducive to image fusion.
Triple layer image security using bit-shift, chaos, and stream encryption

One popular image security technique is image encryption. This research proposes an image encryption technique that consists of three encryption layers, i.e. bit-shift encryption, chaos-based encryption, and stream encryption. The chaos algorithm used is Arnold's chaotic map, while the stream cipher algorithm used is RC4. Each layer has different cryptology characteristics in order to obtain safer image encryption. The characteristics of cryptology are permutation, confusion, diffusion, and substitution. The combination of the proposed encryption method aims to secure images against various attacks, especially attacks on statistics and differentials. The encryption method testing is done by various measuring instruments such as statistical analysis, i.e. entropy information, avalanche effect, and histogram, differential analysis, i.e. UACI and NPCR, visual analysis using PSNR and SSIM, and bit error ratio. Based on the results of experiments that the encryption method that we propose can work excellently based on various measurement instruments. The decryption process can also work perfectly this is evidenced by the ∞ value based on PSNR, and zero value based on SSIM and BER.
Coherence concurrence for X states

We study the properties of coherence concurrence and present a physical explanation analogous to the coherence of assistance. We give an optimal pure state decomposition which attains the coherence concurrence for qubit states. We prove the additivity of coherence concurrence under direct sum operations in another way. Using these results, we calculate analytically the coherence concurrence for X states and show its optimal pure state decompositions. Moreover, we show that the coherence concurrence is exactly twice the convex roof-extended negativity of the Schmidt correlated states, thus establishing a direct relation between coherence concurrence and quantum entanglement.
HMTNet: 3D Hand Pose Estimation From Single Depth Image Based on Hand Morphological Topology

Thanks to the rapid development of CNNs and depth sensors, great progress has been made in 3D hand pose estimation. Nevertheless, it is still far from being solved for its cluttered circumstance and severe self-occlusion of hand. In this paper, we propose a method that takes advantage of human hand morphological topology (HMT) structure to improve the pose estimation performance. The main contributions of our work can be listed as below. Firstly, in order to extract more powerful features, we concatenate original and last layer of initial feature extraction module to preserve hand information better. Next, regression module inspired from hand morphological topology is proposed. In this submodule, we design a tree-like network structure according to hand joints distribution to make use of high order dependency of hand joints. Lastly, we conducted sufficient ablation experiments to verify our proposed method on each dataset. Experimental results on three popular hand pose dataset show superior performance of our method compared with the state-of-the-art methods. On ICVL and NYU dataset, our method outperforms great improvement over 2D state-of-the-art methods. On MSRA dataset, our method achieves comparable accuracy with the state-of-the-art methods. To summarize, our method is the most efficient method which can run at 220.7 fps on a single GPU compared with approximate accurate methods at present.
Finite-dimensional quantum states generated by conditional measurements on beam splitters

In this work, the basic idea of the quantum scissors (QS) device is slightly modified to generate finite-dimensional quantum states by means of conditional measurements on beam splitters (BSs). It turns out that a QS device with two single-photon inputs and two single-photon detections is just a projection operator composed of the vacuum state, one-photon state, and two-photon state, depending upon the transmission coefficients of BSs. As the most general example, we consider the squeezed coherent state as the input state and derive the analytical expression of the output state. Its nonclassical characteristics are analyzed in detail by means of the average photon number, intensity gain, and Wigner function. In addition, we extend this technique to the two-mode squeezed vacuum state (TMSVS). The resulting state is just the generalized Bell state, containing only the twin vacuum, twin one-photon, and twin two-photon components, whose entanglement properties are quantified by the von Neumann entropy and Einstein–Podolsky–Rosen correlation. The results show that the entanglement of the truncated TMSVS is stronger than that of TMSVS within a certain range of squeezing parameter and transmissivity.
Development of a Low Cost and Path-free Autonomous Patrol System Based on Stereo Vision System and Checking Flags

Nowadays, security guard patrol services are becoming roboticized. However, high construction prices and complex systems make patrol robots difficult to be popularized. In this research, a simplified autonomous patrolling robot is proposed, which is fabricated by upgrading a wheeling household robot with stereo vision system (SVS), radio frequency identification (RFID) module, and laptop. The robot has four functions: independent patrolling without path planning, checking, intruder detection, and wireless backup. At first, depth information of the environment is analyzed through SVS to find a passable path for independent patrolling. Moreover, the checkpoints made with RFID tag and color pattern are placed in appropriate positions within a guard area. While a color pattern is detected by the SVS, the patrolling robot is guided to approach the pattern and check its RFID tag. For more, the human identification function of SVS is used to detect an intruder. While a skeleton information of the human is analyzed by SVS, the intruder detection function is triggered, then the robot follows the intruder and record the images of the intruder. The recorded images are transmitted to a server through Wi-Fi to realize the remote backup, and users can query the recorded images from the network. Finally, an experiment is made to test the functions of the autonomous patrolling robot successfully.
Evaluating AI in breast cancer screening: a complex task

Today, artificial intelligence (AI) seems to be making an impact in almost every field of research. Breast cancer screening is one of them. Digital mammography is the current standard modality for early breast cancer detection, but as with every screening tool, it has its limitations. Sensitivity in a given screening round is estimated at 75–85%,1 meaning that a substantial fraction of cancers go undetected. In recent screening studies, digital breast tomosynthesis has found up to 40% more cancers than digital mammography,2–4 suggesting that previously reported sensitivities for digital mammography are too high.
Estimation approaches in cognitive diagnosis modeling when attributes are hierarchically structured.

Although research in cognitive psychology suggests refraining from investigating cognitive skills inisolation, many cognitive diagnosis model (CDM) examples do not take hierarchical attribute structures into account. When hierarchical relationships among the attributes are not considered, CDM estimates may be biased.The current study, through simulation and real data analyses, examines the impact of different MMLE-EM approaches on the item and person parameter estimates of the G-DINA, DINA and DINO models when attributes have a hierarchical structure. A number of estimation approaches that can result from modifying either the Q-matrix or prior distribution are proposed. Impact of the proposed approaches on item parameter estimation accuracy and attribute classification are investigated.For the G-DINA model estimation, the Q-matrix type (i.e, explicit vs. implicit) has greater impact than structuring the prior distribution. Specifically, explicit Q-matrices result in better item parameter recovery and higher correct classification rates. In contrast, structuring the prior distribution is more influential on item and person parameter estimates for the reduced models. When prior distribution is structured, the Q-matrix type has almost no influence on item and person parameter estimates of the DINA and DINO models.We can conclude that the Q-matrix type has a significant impact on CDM estimation, especially when the estimating model is G-DINA.
A hybrid safe semi-supervised learning method

Within the past few years, Safe Semi-Supervised Learning (S3L) has become a hot topic in the machine learning field and many related S3L methods have been proposed to safely exploit the unlabeled information. However, these methods only considered the risk from a single level, such as the instance or model level. They can not reduce the adverse effects of both the risky unlabeled instances and inappropriate learning models. Therefore, it is important to investigate a novel effective S3L method. In this paper, we present a hybrid S3L method which can inherit the merits of both the instance-level and model-level approaches. In our algorithm, multiple Graph-based SSL (GSSL) classifiers are firstly trained and used to predict the unlabeled instances. The risk degrees of the unlabeled instances and the qualities of the constructed graphs are then estimated through the predictions of multiple GSSL classifiers. Finally, we build two regularization terms to constrain the predictions of the unlabeled instances and adaptively select the graphs with high qualities. These regularization terms aim at reducing the negative effect of both the risky unlabeled instances and inappropriate learning models with low-quality graphs. Experimental results on different real-world datasets verify the effectiveness of our algorithm by comparisons to the state-of-the-art Supervised Learning (SL), SSL and S3L methods. In conclusion, our algorithm can not only enrich the research of S3L, but enlarge the practical scope of SSL in the expert and intelligent systems to a certain extent.
“We Use Scrum, but …”: Agile Modifications and Project Success

While the Agile-Scrum (scrum) framework has specific guidelines, these guidelines are often adapted by practitioners.This research aims to understand how scrum changes in practice and how these changes impact various aspects of project success.Through interviews with representatives from 11 organizations who use scrum for software development, we found variability in the application of the guidelines, namely, that only a small number of guidelines are systematically followed, and that some guidelines are rarely followed consistently.Examining these method deviations and mapping them to specific dimensions of project success, four patterns emerged.Further, we uncovered practices that are often followed but were not part of the original Scrum guidelines, including how organizations scale scrum projects.These insights into how scrum is used in practice can help industry professionals determine how to best adapt scrum.They also serve as a promising agenda for research on the application of the scrum framework in industry.
Investigation of white blood cell biomaker model for acute lymphoblastic leukemia detection based on convolutional neural network

Acute Lymphoblastic Leukemia (ALL) is a disease that is defined by uncontrollable growth of malignant and immature White Blood Cells (WBCs) which is called lymphoblast. Traditionally, lymphoblast analysis is done manually and highly dependent on the pathologist’s skill and experience which sometimes yields inaccurate result. For that reason, in this project an algorithm to automatically detect WBC and subsequently examine ALL disease using Convolutional Neural Network (CNN) is proposed. Several pretrained CNN models which are VGG, GoogleNet and Alexnet were analaysed to compare its performance for differentiating lymphoblast and non-lymphoblast cells from IDB database. The tuning is done by experimenting the convolution layer, pooling layer and fully connected layer. Technically, 70% of the images are used for training and another 30% for testing. From the experiments, it is found that the best pretrained models are VGG and GoogleNet compared to AlexNet by achieving 100% accuracy for training. As for testing, VGG obtained the highest performance which is 99.13% accuracy. Apart from that, VGG also proven to have better result based on the training graph which is more stable and contains less error compared to the other two models.
A Geometric Construction of a (56,2)-Blocking Set in PG(2,19) and on Three Dimensional Linear [325,3,307]_19Griesmer Code

In this paper we give a geometrical construction of a ( 56, 2)-blocking set in PG( 2, 19) and We obtain a new (325,18)-arc and a new linear code [325,3,307] 19 and apply the Grismer rule so that we prove it an optimal or nonoptimal code, giving some examples of field 19 arcs Theorem (2.1)