Reviewerly/exHarmony huggingface dataset:
HaskellCompass: Extending the CodeCompass comprehension framework for Haskell

While functional programming is an emerging and efficient way to express complex software, the tool support for functional programming languages, like Haskell, is not always satisfactory. There are no tools that can provide code insight for large Haskell projects that limits the potential of the language’s usability.CodeCompass is an open-source LLVM/Clang-based tool developed by Ericsson Ltd. and Eötvös Loránd University, Budapest to help the understanding of large legacy software systems. Based on the LLVM/Clang compiler infrastructure, CodeCompass gives exact information on complex C/C++ language elements and is regularly used by hundreds of designers and developers.In this paper we present a Haskell plugin for the CodeCompass tool that is able to analyse large Haskell projects and provide useful information for the developers. We suggest useful comprehension queries and actions that enhance developer efficiency. Additionally we show the implementation of such a solution and measure the performance cost of using it.
A Survey: Implemented Architectures of 3D Convolutional Neural Networks

Convolutional Neural Networks are those types of deep neural networks that have shown great performance on a varied set of benchmarks. This powerful learning ability of a convolutional neural network is mainly due to the use of several hidden layers that can automatically extract all the important features from the data. Due to the availability of datasets in large amount and discovery for better hardware units has extended the research in CNNs to a great extent, and due to such extensive research work, a large number of architectures have been reported and implemented on real-world data. The introduction of 3D sensors encouraged pursuing research in visionary aspect of computers for many real-world application areas including virtual-reality, augmented-reality and medical imaging. Recently, many 2D and 3D classification methods depend on CNNs, which have a reliable resource in extracting features. But, they cannot find out all the dimensional relationships between features due to the max-pooling layers, and they require a vast amount of data for training. In this paper, we survey on different implementations of 3D Convolutional neural networks and their respective accuracies for different datasets. We then compare all the architectures to find which one is most suitable to perform flexibly on CBCT scanned images.
Feature-Based Discrimination of Thyroid Cancer on Ultrasound Images

In this study, to improve the diagnostic accuracy of thyroid cancer, we evaluate the performance of feature-based models on ultrasound image for discrimination. A automated segmentation method was used to characterize microcalcifications, masses and nodules. The model performed discrimination of thyroid cancer based on texture and morphological features extracted from ultrasound images. Performances were compared to benchmark models. Our feature-based model achieved a discriminatory accuracy compared to other methods. Experimental results on three scenarios demonstrate the effectiveness of the proposed model. Especially for samples that are very difficult for doctors to judge, the model showed good performance. This may have clinical value for early detection and treatment of thyroid cancer.
Upright and Stabilized Omnidirectional Depth Estimation for Wide-baseline Multi-camera Inertial Systems

This paper presents an upright and stabilized omnidirectional depth estimation for an arbitrarily rotated wide- baseline multi-camera inertial system. By aligning the reference rig coordinate system with the gravity direction acquired from an inertial measurement unit, we sample depth hypotheses for omnidirectional stereo matching by sweeping global spheres whose equators are parallel to the ground plane. Then, unary features extracted from each input image by 2D convolutional neural networks (CNN) are warped onto the swept spheres, and the final omnidirectional depth map is output through cost computation by a 3D CNN-based hourglass module and a softargmax operation. This can eliminate wavy or unrecognizable visual artifacts in equirectangular depth maps which can cause failures in scene understanding. We show the capability of our upright and stabilized omnidirectional depth estimation through experiments on real data.
Implementation of the Real-Time Intelligent System Based on the Integration Approach

The paper describes the possibilities and basic means of constructing intelligent systems of real-time based on an integrated approach. A multi-agent approach, flexible decision search algorithms, forecasting algorithms based on reinforced learning are used. The architecture of forecasting module, module of deep reinforced learning and the architecture of the forecasting subsystem are given. The results of computer simulation of reinforcement learning algorithms based on temporal differences are presented and the corresponding recommendations for their use in multi-agent systems are given.
Pattern-based Interactive Configuration Derivation for Cyber-physical System Product Lines

Deriving a Cyber-Physical System (CPS) product from a product line requires configuring hundreds to thousands of configurable parameters of components and devices from multiple domains, e.g., computing, control, and communication. A fully automated configuration process for a CPS product line is seldom possible in practice, and a dynamic and interactive process is expected. Therefore, some configurable parameters are to be configured manually, and the rest can be configured either automatically or manually, depending on pre-defined constraints, the order of configuration steps, and previous configuration data in such a dynamic and interactive configuration process. In this article, we propose a pattern-based, interactive configuration derivation methodology (named as Pi-CD) to maximize opportunities of automatically deriving correct configurations of CPSs by benefiting from pre-defined constraints and configuration data of previous configuration steps. Pi-CD requires architectures of CPS product lines modeled with Unified Modeling Language extended with four types of variabilities, along with constraints specified in Object Constraint Language (OCL). Pi-CD is equipped with 324 configuration derivation patterns that we defined by systematically analyzing the OCL constructs and semantics. We evaluated Pi-CD by configuring 20 CPS products of varying complexity from two real-world CPS product lines. Results show that Pi-CD can achieve up to 72% automation degree with a negligible time cost. Moreover, its time performance remains stable with the increase in the number of configuration parameters as well as constraints.
OSCAR: An Optical Stochastic Computing AcceleRator for Polynomial Functions

Approximate computing allows improving design energy efficiency at the cost of computing accuracy. Stochastic computing is an approximate computing technique, where numbers are represented as probabilities using stochastic bit streams. The serial processing of the bit streams leads to reduced hardware complexity but induces high processing latency. Silicon photonics has the potential to overcome this limitation thanks to high propagation speed of signals and high bandwidth. However, the technology remains costly, which calls for optical accelerators capable to adapt to application-specific requirements. In this paper, we propose a reconfigurable optical accelerator capable to adapt to computing accuracy, energy efficiency, and throughput objectives. The architecture can be configured to execute i) 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> order function for high accuracy processing or ii) 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> order function for high-energy efficiency or high throughput purposes. Evaluations are carried out using image processing Gamma correction application. Compared to a static architecture for which accuracy is defined at design time, the proposed architecture leads to 36.8% energy overhead but increases the range of reachable accuracy by 65%.
Markov Encrypted Data Prefetching Model Based On Attribute Classification

In order to improve the buffering performance of the data encrypted by CP-ABE (ciphertext policy attribute based encryption), this paper proposed a Markov prefetching model based on attribute classification. The prefetching model combines the access strategy of CP-ABE encrypted file, establishes the user relationship network according to the attribute value of the user, classifies the user by the modularity-based community partitioning algorithm, and establishes a Markov prefetching model based on attribute classification. In comparison with the traditional Markov prefetching model and the classification-based Markov prefetching model, the attribute-based Markov prefetching model is proposed in this paper has higher prefetch accuracy and coverage.
Face pose estimation of SDM based on multi-template matching

Abstract This paper presents the face pose research of SDM algorithm and POSIT algorithm with multiple template matching. Firstly we detect the input face image, locate the feature points in the detected facial area, then compare the traditional SDM algorithm with the improved multi-template SDM algorithm, and then select the multi-template SDM algorithm. Feature point location is finally applied to the POSIT algorithm. Experimental results prove the effectiveness of the method and make it competitive.
Extreme learning machine adapted to noise based on optimization algorithms

Abstract The extreme learning machine for neural networks of feedforward of a single hidden layer randomly assigns the weights of entry and analytically determines the weights the output by means the Moore-Penrose inverse, this algorithm tends to provide an extremely fast learning speed preserving the adjustment levels achieved by classifiers such as multilayer perception and support vector machine. However, the Moore-Penrose inverse loses precision when using data with additive noise in training. That is why in this paper a method to robustness of extreme learning machine to additive noise proposed. The method consists in computing the weights of the output layer using non-linear optimization algorithms without restrictions. Tests are performed with the gradient descent optimization algorithm and with the Levenberg-Marquardt algorithm. From the implementation it is observed that through the use of these algorithms, smaller errors are achieved than those obtained with the Moore-Penrose inverse.
Exploring Online Ad Images using Deep-Learning Approach

Web-based advertising is an immense, quickly developing promotion marketing strategy. Image advertisement is on the basic type used for internet advertisement. The sponsor decide to show the best advertisement to the client right now and made numerous calculations.These calculations center around varieties of the promotion, enhancing among various properties, for example, foundation shading, image size, or set of images and however none of them characterize the property of articles.. In this research, a lot of calculations is presented that uses Artificial Intelligence to examine web-based ad and build object recognition models which can predict objects that are probably going to be in progressive advertisement image. The important point of results is to get a high achievement rate in advertisement images with objects to show up in it. Two methodologies, sinking trainer and R-CNN, are examined and analyzed using HOG and CNN . R-CNN gives a preferable outcome, however requires more opportunity to prepare.
Active Defense Technology Based on Natural Software Diversity in Java Web Services

Abstract With the popular use of network application services and the growing number of users, web services have become the main target of hackers. Traditional single software has insufficient security protection against unknown threats and is prone to various vulnerabilities. In this paper, we adopt a new type of active defense strategy in java web services. At different application layers, we use existing components based on the natural software diversity to form different software stacks, and use dynamic scheduling mechanisms to change the attack surface at all times to obtain security protection. Our analysis shows that the flexibility of java web services using this defense strategy is enhanced, and the exploitability of its vulnerabilities is effectively reduced.
Protected Health Information De-Identification on Visual and Textual Features using Transfer Learning

Cyber data breaching has tremendously increased over the years, especially in countries like India. Medical documents have information more valuable than any other document shared online. Various laws came into existence which restricts the flow of protected health information in any sort of communication. Considering the possible threats involved in medical data transfer, there is a need to de-identify protected health information in any type of medical document effectively and also minimize the possibility of reverse engineering or re-identification. Existing works completely focus on electronic health records with only textual data. Our aim is to present the importance of de-identification in visual data along with textual features and also show a method for achieving it using a transfer learning based approach. An end-to-end artificial intelligence based application is presented which de-identifies all types of protected health information from either an image or a pdf with both textual and image data or DICOMs.
Development of Teaching Learning Materials Javanese for Class 1 Madrasah Ibtidaiyah Muhammadiyah

Abstract .This study aims to develop teaching materials in Javanese language for classes 1 Madrasah IbtidaiyahMuhammadiyahin the gombong district. This is Research and Development . With stages (1) potential and problems, (2) data collection, (3) product design (4) design validation, (5) design revision, (6) product testing, (7) product revision I, (8) trial use, (9) product revisions, (10) final products IIData analysis techniques using qualitative descriptives.The results showed that the teaching materials made can improve learning outcomes namely increasing the class average from 68.7 to 78. Keywords : teaching materials, Javanese language
Analysis and Solution of CNN Accuracy Reduction over Channel Loop Tiling

Owing to the growth of the size of convolutional neural networks (CNNs), quantization and loop tiling (also called loop breaking) are mandatory to implement CNN on an embedded system. However, channel loop tiling of quantized CNNs induces unexpected errors. We explain why channel loop tiling of quantized CNNs induces the unexpected errors, and how the errors affect the accuracy of state-of-the-art CNNs. We also propose a method to recover accuracy under channel tiling by compressing and decompressing the most-significant bits of partial sums. Using the proposed method, we can recover accuracy by 12.3% with only 1% circuit area overhead and an additional 2% of power consumption.
Representation, abstraction, and simple-minded sophisticates

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.
Implementation of Graphic Plugin Loading Platform Based on Python

Abstract With the scripting language getting more and more mature, individual developers can also develop script programs according to their own needs based on the existing software library. In order to help individual developers to quickly and simply complete the software graphical problems, a graphical plugin loading platform is developed based on Python language. Through the specified plugin class standard, developers can quickly convert existing scripts or develop scripts according to plugin class, and easily realize the software graphical.
AMSA: Adaptive Merkle Signature Architecture

Hash-based signatures (HBS) are promising candidates for quantum-secure signatures on embedded IoT devices because they only use fast integer math, are well understood, produce small public keys, and offer many design parameters. However, HBS can only sign a limited amount of messages and produce - similar to most post-quantum schemes - large signatures of several kilo bytes.In this paper, we explore possibilities to reduce the size of the signatures by 1. improving the Winternitz One-Time Signature with a more efficient encoding and 2. offloading auxiliary data to a gateway.We show that for similar security and performance, our approach produces 2.6 % smaller signatures in general and up to 17.3 % smaller signatures for the sender compared to the related approaches LMS and XMSS. Furthermore, our open-source implementation allows a wider set of parameters that allows to tailor the scheme to the available resources of an embedded device, which is an important factor to overcome the security challenges in IoT.
A Novel Evaluation Approach for the Elevation Datum Product

The traditional evaluation of elevation datum products usually focus only the aspect of accuracy and spatial resolution. Which cannot comprehensively show the applicability in the special fields. Therefore, a novel evaluation programme from aspects both of the quality and production benefit of the elevation datum products, is presented in this paper. The experiments results based on the DOM process in surveying and mapping fields show that, the approach works well for the evaluation for the regional or global Geoid models such as HNGG2017, EGM96 and EGM2008.
The (mis)use of statistics in referendum votes

For the well-functioning of a direct democracy it is essential that citizens have access to and know about the relevant facts when they are called to vote on policy proposals. In direct-democratic campaigns, however, both proponents and opponents of a policy proposal have incentives to present only selected facts that serve their own position and help sway public opinion in the desired direction. In this chapter, the authors propose a novel typology of how statistics – one of the most often used tools to substantiate arguments with seemingly objective facts – can be (mis)used in direct-democratic campaigns. The typology is based on the distinction between misinformation and disinformation. Whereas the former represents unintentionally false information, the latter refers to the deliberate distribution of inaccurate information. The authors identify four major types of misuse of statistical information: (1) flawed statistics, (2) false predictions, (3) misleading examples, and (4) manipulated numbers. They illustrate each type with an example drawn from recent direct-democratic votes in Switzerland. The examples show that mis- and disinformation in vote campaigns can have far-reaching political and legal consequences.
Inferring Occupants Count from Low Power IP based Devices in Building through Data Fusion

Building automation and building climate control efficiency can be improved by applying accurate occupancy information into the building management system. Extracting the occupancy status from the systems already existing in the building has a great potential to provide precise information with lower cost. The present work proposes an occupants count inference system using low power IP based devices carried by the occupants. Various infrastructure-based sensing elements are used to detect the IP based devices carried by occupants in the building environment. Here, we propose a mass assignment method which is based on the probabilistic approach and relative trust in each sensing element to infer the occupants' count in the zone. This paper presents some preliminary results, with experimental verification, in this direction.
Analysis of Noise Estimators of Rician Distributed MR Images using Local Statistics

Estimation of noise in signal dependant data can be done using estimators based on local statistics. In this research paper, a comparative analysis of estimators based on second order moment and local mean is done. An empirical correction factor constant is introduced in the local mean estimator which makes the estimated standard deviation more converged to the input noise. The study was performed on T1 and T2 weighted synthetic MR Images from BrainWeb and comparative results were drawn. The algorithm was implemented using MATLAB 2015a.
Non-Linear Data Structure for Data Coding for Size Compression

Abstract Data compression is a special case of communication system which is a basic problem in engineering and computer science. In this work, the new scheme has been introduced for data coding to compress the size. Compression by using one type of Non-linear Data-Structure which is Prefix Binary Tree data compression is essential and beneficial in the diversity of application which is embraced in general into two branches: the storage and transmission.the storage of a file in a compressed form takes fewer bit than in uncompressed form, therefore make the most of storage resources (containing main memory itself), and by transmission means sending the files over a channel with reduced number of bits by compression process. This leads to more efficacious transmission process. The compression process of stored data or that to be transmitted leads to significantly reducing both of the storage and transmission. We use text type as a data in the experiments. The results show the compression rate is reduce the actual size above 50%.
Portable robotic telescope for the teaching of solar astronomy

Abstract This work describes a prototype of a miniature robotic telescope, with the didactic purpose of the teaching of solar astronomy, Arduino programming and basic control theory. The telescope is made from low cost components: a 3D printed base that supports a commercial lens (typically used for bird watching and covered with a white-light solar filter). The system is guided by a program that uses the amount of light collected by a set of four light detecting resistors as an input signal to rotate a pair of small stepper motors, pointing the lens towards the Sun. A simple control scheme is implemented for ensuring that the image of the Sun is centered within the frame. The images and video captures of the Sun is collected by a small photographic camera and uploaded to a website.
The Application Status and Reflection of Learning Tools Centered on Learning Activities—Based on the Ranking Analysis of Top100 Learning Tools in 2018

The advent of the Internet era has promoted the continuous reform and development of college education. In order to adapt to the demand of information technology for teaching, the application of digital learning tools is increasing day by day, but it is still in the exploration stage and faces many opportunities and challenges. Through the analysis of Top Tools for learning ranking in 2018, this paper sorts out the development status and evolution trend of learning tool application, and summarizes, classifies and reflects on the application methods of learning tools in literature research, and finally builds learning tools in learning activities. Application model.