Reviewerly/exHarmony huggingface dataset:
Developing augmented reality based application for character education using unity with Vuforia SDK

Abstract Character education is an education-focused in implementing moral values, ethics and citizenship education. This is an approach to developing good character and developing the school as a caring community. The youngest generation nowadays, which primarily targeted by character education was born facing already-advanced ICT (Information and Communication Technology), so it recommended using an ICT's benefit like AR. An AR (Augmented Reality is a system that supplements the real world with virtual objects that appear to coexist in the same space as the real world. Augmented Reality offers an immersive virtual learning experience; as a result, an education that was giving more than standard audio-visual material. In this research, the developing an ICT based application for character education using Augmented Reality as the main feature, and will be discussed explicitly about an augmented reality that running on the mobile device that will be using specifically for the character education. The program will be developed throughout the following methodology: Literature Review, to provide information and knowledge how to build and to apply an AR-based application and finding the right sampling method, Building the Apps, using Unity integrated with Vuforia Engine. Unity is a game engine capable of developing computer, console or mobile game. Vuforia is a platform for developing Augmented Reality and Mixed Reality, Application Testing, test the AR system in the real world environment. The Result is an android app that could capture the "marker" and make the AR appearing in the smartphone device. This result used to help teachers to educate the young students that still need a character education to build their good character stronger.
Edge-Informed Single Image Super-Resolution

The recent increase in the extensive use of digital imaging technologies has brought with it a simultaneous demand for higher-resolution images. We develop a novel "edge-informed" approach to single image super-resolution (SISR). The SISR problem is reformulated as an image inpainting task. We use a two-stage inpainting model as a baseline for super-resolution and show its effectiveness for different scale factors (×2, ×4, ×8) compared to basic interpolation schemes. This model is trained using a joint optimization of image contents (texture and color) and structures (edges). Quantitative and qualitative comparisons are included and the proposed model is compared with current state-of-the-art techniques. We show that our method of decoupling structure and texture reconstruction improves the quality of the final reconstructed high-resolution image.
LIDAR and Monocular Camera Fusion: On-road Depth Completion for Autonomous Driving

LIDAR and RGB cameras are commonly used sensors in autonomous vehicles. However, both of them have limitations: LIDAR provides accurate depth but is sparse in vertical and horizontal resolution; RGB images provide dense texture but lack depth information. In this paper, we fuse LIDAR and RGB images by a deep neural network, which completes a denser pixel-wise depth map. The proposed architecture reconstructs the pixel-wise depth map, taking advantage of both the dense color features and sparse 3D spatial features. We applied the early fusion technique and fine-tuned the ResNet model as the encoder. The designed Residual Up-Projection block recovers the spatial resolution of the feature map and captures context within the depth map. We introduced a depth feature tensor which propagates context information from encoder blocks to decoder blocks. Our proposed method is evaluated on the large-scale indoor NYUdepthV2 and KITTI odometry datasets and outperforms the state-of-the-art single RGB image and depth fusion method. The proposed method is also evaluated on a reduced-resolution KITTI dataset which synthesizes the planar LIDAR and RGB image fusion.
Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony

Association Rule Hiding (ARH) is the process of protecting sensitive knowledge using data transformation. Although there are some evolutionary-based ARH algorithms, they mostly focus on the itemset hiding instead of the rule hiding. Besides, unstable convergence to the global optimum solution and designing long solutions make them inappropriate in reducing side effects. They use the basic versions of evolutionary approaches, resulting in inappropriate performance in ARH domain where the search space is large and the algorithms easily get trapped in local optima. To deal with these problems, we propose a new rule hiding algorithm based on a binary Artificial Bee Colony (ABC) approach which has good exploration. However, we improve the binary ABC algorithm to enhance its poor exploitation by designing a new neighborhood generation mechanism to balance between exploration and exploitation. We called this algorithm Improved Binary ABC (IBABC). IBABC approach is coupled with our proposed rule hiding algorithm, called ABC4ARH, to select sensitive transactions for modification. To choose victim items, ABC4ARH formulates a heuristic. The performance of ABC4ARH algorithm on the side effects is demonstrated using extensive experiments conducted on five real datasets. Furthermore, the effectiveness of IBABC is verified using the uncapacitated facility location problem and 0–1 knapsack problem.
Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images

Peripheral blood smear analysis plays a vital role in diagnosing many diseases including cancer. Leukemia is a type of cancer which begins in bone marrow and results in increased number of white blood cells in peripheral blood. Unusual variations in appearance of white blood cells indicate leukemia. In this paper, an automated method for detection of leukemia using image processing approach is proposed. In the present study, 1159 images of different brightness levels and color shades were acquired from Leishman stained peripheral blood smears. SVM classifier was used for classification of white blood cells into normal and abnormal, and also for detection of leukemic WBCs from the abnormal class. Classification of the normal white blood cells into five sub-types was performed using NN classifier. Overall classification accuracy of 98.8% was obtained using the combination of NN and SVM.
An Analysis of Segmentation Approaches and Window Sizes in Wearable-Based Critical Fall Detection Systems With Machine Learning Models

Falls are the major risks and threats among elderly population. Various studies have developed automatic critical fall detection systems for emergency alarms and medical services. Window sizes and segmentation approaches would affect the system performance in terms of power consumption, computational speed and reliability that are essential considerations in the real-world implementation. Intuitively, employing shorter window sizes and simpler segmentation approaches leads to a faster detection speed and better power efficiency. However, few works explore the effects of windowing methods on detection performance, especially for machine learning models. This paper investigates the impact of segmentation approaches and window sizes in wearable-based critical fall detection systems with machine learning models. Two generally used segmentation approaches, including sliding windows and impact-defined windows, are explored with a range of window sizes from 0.5 s to 5.0 s and four machine learning models: Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Classification and Regression Tree (CART) and Naïve Bayes (NB). The results show that sliding windows are more sensitive to the changes of window sizes compared to impact-defined windows. However, the differences between the best performance of both segmentation approaches are within 1% (99.66% in sliding windows and 99.78% in impact-defined windows). For systems with SVM and k-NN models, using 0.5 s window sizes in sliding windows and combinations of 1.5 s backward and forward windows in impact-defined windows are sufficient to achieve at least 94% accuracy and 98% accuracy.
Object Detection Learning Techniques for Autonomous Vehicle Applications

Autonomous vehicles have been considered as one of the most important trending topics in the domain of intelligent transportation systems. It is expected that most of the leading companies will launch their fully self-driving vehicles by the end of the next decade. Such a technology must assure a high safety level on road networks to reduce the number of accidents caused by human errors. All the built-in technologies must be integrated and complemented to achieve these goals. Automated object and obstacle detection is one of the main research tasks that must be undertaken. A number of vision-based learning techniques have been designed to improve the vehicle detection capabilities and reduce the shortcomings of other sensors such as LIDAR system that have shown poor results during severe weather conditions. In this paper, we overview and investigate the main learning models for video-based object detection specifically that can be applied with autonomous vehicle. We focus on a machine learning solution, namely, the Support Vector Machine (SVM) algorithm and two deep learning solutions: the "You Only Look Once" (YOLO) and the Single Shot Multibox Detector (SSD) methods. Afterwards, we provide an exhaustive comparison of the different approaches when applied to autonomous vehicles. Simulations results indicate that the SVM poorly performs and its speed can not assure real-time response, while the YOLO model and SSD can reach higher accuracy with a notable ability to detect objects in real-time when rapid driving decisions need to be made.
MILP-aided Method of Searching Division Property Using Three Subsets and Applications

Division property is a generalized integral property proposed by Todo at EUROCRYPT 2015, and then conventional bit-based division property (CBDP) and bit-based division property using three subsets (BDPT) were proposed by Todo and Morii at FSE 2016. At the very beginning, the two kinds of bit-based division properties once couldn't be applied to ciphers with large block size just because of the huge time and memory complexity. At ASIACRYPT 2016, Xiang et al. extended Mixed Integer Linear Programming (MILP) method to search integral distinguishers based on CBDP. BDPT can find more accurate integral distinguishers than CBDP, but it couldn't be modeled efficiently. This paper focuses on the feasibility of searching integral distinguishers based on BDPT. We propose the pruning techniques and fast propagation of BDPT for the first time. Based on these, an MILP-aided method for the propagation of BDPT is proposed. Then, we apply this method to some block ciphers. For SIMON64, PRESENT, and RECTANGLE, we find more balanced bits than the previous longest distinguishers. For LBlock, we find a better 16-round integral distinguisher with less active bits. For other block ciphers, our results are in accordance with the previous longest distinguishers. Cube attack is an important cryptanalytic technique against symmetric cryptosystems, especially for stream ciphers. And the most important step in cube attack is superpoly recovery. Inspired by the CBDP based cube attack proposed by Todo at CRYPTO 2017, we propose a method which uses BDPT to recover the superpoly in cube attack. We apply this new method to round-reduced Trivium. To be specific, the time complexity of recovering the superpoly of 832-round Trivium at CRYPTO 2017 is reduced from $$2^{77}$$ to practical, and the time complexity of recovering the superpoly of 839-round Trivium at CRYPTO 2018 is reduced from $$2^{79}$$ to practical. Then, we propose a theoretical attack which can recover the superpoly of Trivium up to 841 round.
Hand Gesture Recognition Using 3D-CNN Model

Automatic hand gesture recognition is the most important part of sign language translation. Its importance increases with the growth of deaf and hard of hearing population and cognitive computing. In this article, we propose an efficient system for automatic hand gesture recognition based on deep learning. The proposed system is based on a convolutional neural network (CNN). It employs a transfer learning of 3D CNN for hand gesture recognition. Three different datasets are used to evaluate the proposed system in signer dependent and signer independent modes.
Multiclass Breast Cancer Classification Using Convolutional Neural Network

Nowadays, the quality of classification systems depends on the presentation of the dataset, a process that takes time to use in-depth knowledge to produce specific characteristics. Meanwhile, deep learning can extract features from a dataset, without having to design feature extractors. Convolutional Neural Network (CNN) is a special type of deep learning that achieves many accomplishments in speech recognition, image recognition and classification. In this paper, we use CNN to classify and recognize breast cancer images from public BreakHis dataset. This dataset includes 7,909 breast cancer (BC) histopathology images with four benign subclasses and four malignant subclasses. Our new task with this dataset is the automated multi-classification of these breast cancer images in eight classes, which can help reducing death rates and saving people's lives in the world. Our method based on the resizing original images for building CNN model and classifying breast cancer classes.
Improving reference prioritisation with PICO recognition

Abstract Background Machine learning can assist with multiple tasks during systematic reviews to facilitate the rapid retrieval of relevant references during screening and to identify and extract information relevant to the study characteristics, which include the PICO elements of patient/population, intervention, comparator, and outcomes. The latter requires techniques for identifying and categorising fragments of text, known as named entity recognition. Methods A publicly available corpus of PICO annotations on biomedical abstracts is used to train a named entity recognition model, which is implemented as a recurrent neural network. This model is then applied to a separate collection of abstracts for references from systematic reviews within biomedical and health domains. The occurrences of words tagged in the context of specific PICO contexts are used as additional features for a relevancy classification model. Simulations of the machine learning-assisted screening are used to evaluate the work saved by the relevancy model with and without the PICO features. Chi-squared and statistical significance of positive predicted values are used to identify words that are more indicative of relevancy within PICO contexts. Results Inclusion of PICO features improves the performance metric on 15 of the 20 collections, with substantial gains on certain systematic reviews. Examples of words whose PICO context are more precise can explain this increase. Conclusions Words within PICO tagged segments in abstracts are predictive features for determining inclusion. Combining PICO annotation model into the relevancy classification pipeline is a promising approach. The annotations may be useful on their own to aid users in pinpointing necessary information for data extraction, or to facilitate semantic search.
Big data challenges and achievements

In this paper, the Big Data challenges and the processing is analyzed, recently great attention has been paid to the challenges for great data, largely due to the wide spread of applications and systems used in real life, such as presentation, modeling, processing and large (often unlimited) data storage. Mass Data Survey, OLAP Mass Data, Mass Data Dissemination and Mass Data Protection. Consequently, we focus on further research trends and, as a default, we will explore a future research challenge research project in this area of research.
LPV-MP planning for autonomous racing vehicles considering obstacles

Abstract In this paper, we present an effective online planning solution for autonomous vehicles that aims at improving the computational load while preserving high levels of performance in racing scenarios. The method follows the structure of the model predictive (MP) optimal strategy where the main objective is to maximize the velocity while smoothing the dynamic behavior and fulfilling varying constraints. We focus on reformulating the non-linear original problem into a pseudo-linear problem by convexifying the objective function and reformulating the non-linear vehicle equations to be expressed in a Linear Parameter Varying (LPV) form. In addition, the ability of avoiding obstacles is introduced in a simple way and with reduced computational cost. We test and compare the performance of the proposed strategy against its non-linear approach through simulations. We focus on testing the performance of the trajectory planning approach in a racing scenario. First, the case of free obstacles track and afterwards a scenario including static obstacles. Simulation results show the effectiveness of the proposed strategy by reducing the algorithm elapsed time while finding appropriate trajectories under several input/state constraints.
HDRANet: Hybrid Dilated Residual Attention Network for SAR Image Despeckling

In order to remove speckle noise from original synthetic aperture radar (SAR) images effectively and efficiently, this paper proposes a hybrid dilated residual attention network (HDRANet) with residual learning for SAR despeckling. Firstly, HDRANet employs the hybrid dilated convolution (HDC) in lightweight network architecture to enlarge the receptive field and aggregate global information. Then, a simple yet effective attention module, convolutional block attention module (CBAM), is integrated into the proposed model to constitute a residual HDC attention block through skip connection, which further enhances representation power and performance of the model. Extensive experimental results on the synthetic and real SAR images demonstrate the superior performance of HDRANet over the state-of-the-art methods in terms of quantitative metrics and visual quality.
Sentiment Analysis of Bengali Texts on Online Restaurant Reviews Using Multinomial Naïve Bayes

Recently, determining the customer impression is considered one of the prominent factors on the success of the restaurant businesses. Due to the rapid growth of digital contents related to restaurant or foods in the web, people are more inclined on reviews before going to any restaurant so the significance of customer review is inevitable. In order to selects a restaurant customer needs to check thousands of feedback's to understand the restaurant quality or services. Therefore, classification of a significant amount of reviews into a sentimental category is required to attain meaningful insights so that the customer can choose restaurants based on their preferences. This classification can be done by sentiment analysis. This paper proposes a system that can classify customer reviews into positive and negative classes based on their sentimental feedback. We have tested the proposed system with 1000 restaurant reviews text written in Bengali. The experimental result shows that the proposed the system can classify restaurant reviews with 80.48% accuracy using multinomial Naïve Bayes.
Finance, technology and disruption

In this paper, we assess how recent technology advances have changed the way we coordinate.After a brief discussion of the common challenges to effective coordination, we highlight some important implications of technology on addressing informational and behavioral frictions.We focus on discussing the effects of three specific technology developments including artificial intelligence (AI), automation, and blockchain, on the choice of coordination modes.We argue that technology is shifting the boundaries between firms and markets and is opening the door to new research directions.
A Theoretical Implementation: Agriculture-Food Supply Chain Management using Blockchain Technology

The traceability of Agriculture food supply chain management is important to ensure the food safety. It also increases the customer satisfaction and peer-to-peer productivity. The centralized data storage makes it more difficult to assure quality, rate and origin of the products. So we are in need of a decentralized system where transparency is available which makes people from the producers to consumers satisfaction. Blockchain technology, which is a digital technology that allows us to acquire traceability and transparency in the supply chain. Making use of this technology actually improves the community between different stakeholders and farmers. The properties of blockchain essentially provides increased capacity, better security, immutability, minting, faster settlement and full traceability of stored transactions records. This paper presents a fully decentralized blockchain based traceability that enables to build blocks for agriculture that continuously integrate with IoT devices from provider to consumer. To implement, we introduced “Provider-Consumer Network” -a theoretical end to end food traceability application. The objective is to create distributed ledger that is accessible by all users in the network that in turn brings transparency.
3D acquisition and stereo-camera calibration by active devices: A unique structured light encoding framework

The use of light encoding techniques is widespread in the field of 3D surface reconstruction. This paper presents a stereo-camera calibration methodology, which integrates structured light encoding with an active digital device. The structured light encoding approach is proposed to unambiguously solve the stereo matching issue for stereo-camera setups. A sequence of vertical and horizontal binary striped patterns, combined with a checkerboard pattern, is displayed by a high-resolution LCD screen, which is used as calibration board. A bundle adjustment technique is adopted to simultaneously adjust both camera parameters and screen geometry, as part of the stereo-camera calibration process, thus taking into account the possible inaccuracies of the digital display. The same structured light approach, with small variants, is projected by a multimedia digital projector to carry out 3D surface reconstruction. The proposed methodology defines a comprehensive framework for the development of a 3D optical scanner, from calibration to 3D acquisition, which has been validated by measuring primitive surfaces and reconstructing free-form shapes with different stereo-camera setups.
Lügenpresse: The lying press and German journalists’ responses to a stigma

The term Lügenpresse, ‘lying press’, was used by the German National Socialist Party before and during the Third Reich to discredit the news media and to undermine public trust. By 2014, reports of verbal and physical attacks on journalists and news organizations by individuals calling them Lügenpresse, had again become a frequent feature of the public discourse in Germany. While the term ‘fake news’ is used to similar effect and intent in the United States, Lügenpresse is a historically and politically charged expression of distrust in news media on an institutional level. This research examines the responses and institutional strategies of 27 news editors and executive editors in Germany’s leading broadcast, print and online news organizations to the accusations that they are lying to their audiences. Findings indicate that the reemergence of the term Lügenpresse, has led to considerable self-reflection within institutions, in an effort to counter the lack of trust and to demonstratively better serve the public. The main focus across newsrooms is on improving established processes and on making professional standards and practices more visible to the audience.