Reviewerly/exHarmony huggingface dataset:
Evaluating the Performance of Augmented Reality in Displaying Magnetic Resonance Imaging–Derived Three-Dimensional Holographic Models

Introduction/Background Establishing accuracy and precision of magnetic resonance (MR)–derived augmented reality (AR) models is critical before clinical utilization, particularly in preoperative planning. We investigate the performance of an AR application in representing and displaying MR-derived three-dimensional holographic models. Methods Thirty gold standard (GS) measurements were obtained on a magnetic resonance imaging (MRI) phantom (six interfiducial distances and five configurations). Four MRI pulse sequences were obtained for each of the five configurations, and distances measured in Picture Archiving and Communication System (PACS). Digital imaging and communications in medicine files were translated into three-dimensional models and then loaded onto a novel AR platform. Measurements were also obtained with the software's AR caliper tool. Significant differences among the three groups (GS, PACS, and AR) were assessed with the Kruskal–Wallis test and nonsample median test. Accuracy analysis of GS vs. AR was performed. Precision (percent deviation) of the AR-based caliper tool was also assessed. Results No statistically significant difference existed between AR and GS measurements (P = .6208). PACS demonstrated mean squared error (MSE) of 0.29%. AR digital caliper demonstrated an MSE of 0.3%. Three-dimensional T2 CUBE AR measurements using the platform's AR caliper tool demonstrated an MSE of 8.6%. Percent deviation of AR software caliper tool ranged between 1.9% and 3.9%. Discussion AR demonstrated a high degree of accuracy in comparison to GS, comparable to PACS-based measurements. AR caliper tool demonstrated overall lower accuracy than with physical calipers, although with MSE <10% and greatest measured difference from GS measuring <5 mm. AR-based caliper demonstrated a high degree of precision. Conclusion There was no statistically significant difference between GS measurements and three-dimensional AR measurements in MRI phantom models.
An adaptive image encryption scheme based on bit-level permutation

The paper describes an adaptive image encryption scheme. In the proposed encryption scheme, the chaotic map is used to design the plaintext dependent bit-level permutation. The plain image is firstly divided into 8 bit panels and then arranged into two groups. The permutation is performed on each group based on the feature of the other group. After that, the keystream is employed to mask the permutated image to get the cipher-image. The different types of experiments have been conducted. The experimental results and security analysis demonstrate that the proposed encryption scheme is efficient and highly secure.
Critical initialisation in continuous approximations of binary neural networks

The training of stochastic neural network models with binary ($\pm1$) weights and activations via continuous surrogate networks is investigated. We derive, using mean field theory, a set of scalar equations describing how input signals propagate through surrogate networks. The equations reveal that depending on the choice of surrogate model, the networks may or may not exhibit an order to chaos transition, and the presence of depth scales that limit the maximum trainable depth. Specifically, in solving the equations for edge of chaos conditions, we show that surrogates derived using the Gaussian local reparameterisation trick have no critical initialisation, whereas a deterministic surrogates based on analytic Gaussian integration do. The theory is applied to a range of binary neuron and weight design choices, such as different neuron noise models, allowing the categorisation of algorithms in terms of their behaviour at initialisation. Moreover, we predict theoretically and confirm numerically, that common weight initialization schemes used in standard continuous networks, when applied to the mean values of the stochastic binary weights, yield poor training performance. This study shows that, contrary to common intuition, the means of the stochastic binary weights should be initialised close to close to $\pm 1$ for deeper networks to be trainable.
Minimize transportation cost with clark and wright algorithm saving heuristic method with considering traffic congestion factor

Abstract Bandung has more than 500 city parks that spread in 5 sectors which need to be maintained. As the department in charge of the city park watering, Diskamtam need to design their daily watering route and schedules to minimize operational costs. The watering route will be designed by using Clark and Wright Saving Heuristic method. By taking into account congestion factors, transportation cost can be minimized, and watering schedule can be developed. Trucks with the capacity of 5.000 litres will be assigned to water every 25 parks in the sector 2 route and the sector 4 route. This study find sector 2 need to be divided into five watering routes and sector 4 need to be divided into 4 watering routes. as a result, there is a 0.83% increase in operational cost. this is due to the congestion factor.
An Object Detection Algorithm for UAV Reconnaissance Image Based on Deep Convolution Network

In recent years, the UAV technology has developed rapidly and played an important role in many fields, especially in intelligence, reconnaissance, and monitoring. Object detection can provide accurate target location and target category for reconnaissance missions, providing detailed command information for commanders. However, the current object detection algorithm based on deep convolution network does not work well on detection for small objects and so cannot be applied to small objects in the reconnaissance image of UAV. In this paper, an object detection algorithm for UAV reconnaissance image based on deep convolution network is proposed. The image is adaptively divided according to the UAV flight parameters and the payload parameters before sent into the network. Through this way, small objects can be located and classified in a high accuracy of location and classification. This method can detect objects with small size, multiple quantities, and multiple categories on UAV.
On Limitations of Modern Static Analysis Tools

Static analysis is one of the most important tools for developers in the modern software industry. However, due to limitations by current tools, many developers opt out of using static analysis in their development process. Some of these limitations include the lack of a concise, coherent overview, missing support for multiple repository applications and multiple languages and lastly a lack of standardized integration mechanisms for third-party frameworks. We propose an evaluation metric for static analysis tools and offer a comparison of many common static analysis tools. To demonstrate the goal of our metric we introduce the Fabric8-Analytics Quality Assurance Tool as a benchmark of a tool which successfully passes our evaluation metric. We demonstrate usage of this tool via a case study on the Fabric8-Analytics Framework, a framework for finding vulnerabilities in application dependencies. We issue a challenge to developers of modern static analysis tools to make their tools more usable and appealing to developers.
Real Time Non-Linear Cloud Workload Forecasting using the Holt-Winter Model

Cloud computing facilitates on-demand services to its users. This elastic nature of the cloud environment-attracts more users ranging from individual ones to big enterprises. The increasing use of cloud resource necessitates the need for better and efficient resource management techniques for the cloud. In this paper, we focus on one such key resource management technique which is demand forecasting. It is important to forecast the demand because, under provisioning of resources results in violation of the service level agreement with the cloud users. Contrarily, over provisioning of resources results in resource wastage such as network and power usage. Therefore, we propose a seasonality based demand forecasting system. This system uses Holt-Winter's time series approach for demand forecasting in the real time non-linear cloud environment. Then we analyze the performance of our model for short term and long term forecasting. We also measure the accuracy of our proposed system using MAPE values.
Building a Framework for Mapping RDBMS to RDF With Semantic Query Capabilities

The usage of semantic web is growing, and all the signs are indicating that the growth will not decrease anytime soon. While web applications usually keep their data in relational database management systems, they cannot be directly queried by semantic query languages as SPARQL or by user queries. An example for such web an application is a supply chain management system or a social network application. In this paper we will introduce a framework which is capable for mapping the data located inside the relational database management systems into RDF database with the facility to be queried by two types of semantic queries the SPARQL query as well as user query (objective questions). The results will show an example for converting the user query into SPARQL to answer objective questions which usually have one potential answer, whereas there could be also other answers which are close.
Performance Analysis of Schema Design Approaches for Migration from RDBMS to NoSQL Databases

State-of-the-art database paradigm allows data to generate from different types of devices but these data have no fixed format according to RDBMS structure. Due to industrial need for business expansion, these data need to be restructured into effective database. Therefore, there is a need for migrating existing data into new database technology such as NoSQL which can efficiently handle them. NoSQL is a group of technologies, which does not follow the concept of relational database. NoSQL stores information in different types of data model such as column oriented, document oriented, or graph based. These models have their own way of storing information and schema design. Various research works have been performed in finding appropriate schema design for migration of data from relational model to NoSQL. In this paper, we have performed performance analysis of different database schema design for migration from RDBMS to NoSQL databases. The selected replication schema design provides better performance in execution time.
Survey and Planning of High-Payload Human-Robot Collaboration: Multi-modal Communication Based on Sensor Fusion

Human-Robot Collaboration (HRC) has gained increased attention with the widespread commissioning and usage of collaborative robots. However, recent studies show that the fenceless collaborative robots are not as harmless as they look like. In addition, collaborative robots usually have a very limited payload (up to 12 kg), which is not satisfactory for most of the industrial applications. To use high-payload industrial robots in HRC, today’s safety systems has only one option, limiting speeds of robot motion execution and redundant systems for supervision of forces. The reduction of execution speed, reduces efficiency, which limits more widespread of automation. To overcome this limitation, in this paper, we propose novel sensor fusion of different safety related sensors and combine these in a way that they ensure safety, while the human operator can focus on task execution and communicate with the system in a natural way. Different communication channels are explored (multi-modal) and demonstration scenarios are presented.
AMS Solar Energy Prediction: A Comparative Study of Regression Models

The prediction of renewable energies production such as solar energy could improve their integration in the energy mix which is heavily penalised by their intermittent nature. Accurately predicting the total renewable power available helps to control the electricity flows in the grid and therefore guarantee the balance between production and consumption and avoid power overloads or outages. It is in this light that the American Meteorological Society organised a solar energy prediction competition aiming at predicting the total daily solar energy received at 98 solar farms based on the outputs of various weather forecast models. In this paper, a methodology to reach this goal has been explained and the performance of the most used regression models in the field of solar energy prediction has been compared under the same experimental setup. The results have shown that the best performance is obtained using the Ridge regression with a Mean Absolute Error of 2.215E+6.
Lessons Learned from Integrating Batch and Stream Processing using IoT Data

The unbounded data streams generated by IoT sensors/devices are posing many technical challenges and requires a one-size-fits-all solution to cope with the massive amount and the high speed of the incoming IoT data arriving simultaneously. In this study, we try to integrate batch and stream processing in a unique system as a premise to handle Volume and Velocity aspects of IoT data simultaneously. In order to handle current, outdated, and historical IoT data streams, we built a cloud architecture to execute the analytical workflows using both batch and stream processing in a synergetic manner. A smart parking case study is used to evaluate the architecture and two experiments are implemented to demonstrate a web application for predicting parking spot availability. Herein, we learned our lessons that there are several hindrances to finding a middle ground where current, outdated and historical IoT data streams can be used in a strategic way.
Yet Another Network Steganography Technique Basedon TCP Retransmissions

This paper introduces a new technique of network steganography using the TCP protocol retransmission mechanism. The method can use any user-generated network traffic as hidden transmission channel. It utilizes overwriting TCP segments payload without recalculating checksums. Therefore, a segment after reaching its target is considered incorrect and it can be taken over to extract hidden data. Despite that, the segment is normally retransmitted according to the protocol specification; thanks to this, normal user communication is not affected. Following this idea, the basic mechanisms of this technique were designed and implemented. The testbed environment was prepared to conduct the initial proof-of-concept experiments and to measure basic operational metrics. The average steganographic bandwidth at the level of 2 kB/s was achieved for the conditions ensuring the proper level of undetectability. Discussion of the method detection possibilities was carried out and the directions of research development in this area were presented.
A Scalable ($\alpha, k$)-Anonymization Approach using MapReduce for Privacy Preserving Big Data Publishing

Different tools and sources are used to collect big data, which may create privacy issues. k-anonymity, l-diversity, t-closeness etc. privacy preserving data publishing approaches are used data de-identification, but as multiple sources is used to collect the data, chance of re-identification is very high. Anonymization large data is not a trivial task, hence, privacy preserving approaches scalability has become a challenging research area. Researchers explore it by proposing algorithms for scalable anonymization. We further found that in some scenarios efficient anonymization is not enough, timely anonymization is also required. Hence, to incorporate the velocity of data with Scalable k-Anonymization (SKA) approach, we propose a novel approach, Scalable ( α, k)-Anonymization (SAKA). Our proposed approach outperforms in terms of information loss and running time as compared to existing approaches. With best of our knowledge, this is the first proposed scalable anonymization approach for the velocity of data.
Camera self-calibration using linear features from targets

Objective of camera calibration is to provide a transformation between object space and image space in practical computer vision tasks. Thus, in order to acquire the accurate and reliable information on visual image, intrinsic and extrinsic parameters of camera should be precisely calibrated. Compared with traditional methods and active vision based methods, self-calibration methods are not rely on prior information of visual scenes, which have more widely application prospects. However, in spite of self-calibration has been studied for years, and multitudes of ideas have been proposed, current research difficulties on self-calibration methods still lies in calibration precision. This paper put forward a camera self-calibration method only using linear features from targets, which could calibrate camera parameters in a highly accuracy. Moreover, an experiment is implemented to verify the effectiveness of our method, simultaneously, the experimental result illustrate that the proposed method has a highly calibration precision.
Tamper Detection and Recovery on RGB Images

Special devices are required to perform tampering on an analogue image. This requires a serious cost and skill. But it is easy to tamper on a digital image. Digital images can be tampered even with easy accessible free digital image editing software. Moreover, this tampering can occur even with a mobile phone. In particular, tampering on important images such as images containing financial data, images of official documents, etc., can lead to money and time losses. In this study, a method of tamper detection in order to protect the RGB images and performing a recovery of the tampered region has been proposed. The proposed method can recover a tampered image even without any information of the original. Method backups the value of each pixel of the image to the farthest pixel possible. The image is first separated into layers R, G and B. Each layer is then divided into blocks of equal size 2 × 2. The pixel of each layer is embedded in different layers of the corresponding blocks by the LSB method before sending it to receiver side. The image obtained on the receiver side is scanned by the inverse algorithm. The value of the detected pixels is withdrawn from the corresponding block pixels and the image is repaired. Method seems to work quickly and effectively and it is tested on RGB credit card images. The results are shown in the study.
Group-level emotion recognition based on faces, scenes, skeletons features

In this paper, we propose a deep neural network based approach for the group-level emotion recognition in 6th Emotion Recognition in the Wild Challenge (EmotiW 2018). The task of this challenge is to classify a group’s perceived emotion as Positive, Neutral or Negative. Like the most of current researchers on visual emotion recognition, we mainly focus on facial, scene and body clues in images. We treat each clue as mono-model feature and apply early fusion method to combine them together. Experimental results show that our proposed method has outperformed the baseline techniques with the overall test accuracy of 62.90%.
The Russian school's promptitude for the digital transformation

The article covers the results of research related to readiness of Russian schools for digital transformation, intrusion of digital educational environment which was held in 2019. The conclusions are based on processing of 7190 questionnaires filled-in by school heads as well as on analysis of local and foreign experience in implementation of digital technologies. The results of the survey indicate the following risks of digital transformation for schools: insufficient and uneven development of infrastructure, insufficient digital competencies among teachers and administrative personnel, over-regulation of issues related to usage of digital technologies (up to ban), and most importantly, ignoring of need for a fundamental review of the basic and supporting processes in the educational organization, of educational and managerial practices and approaches, which form the basis of transformation. The compassing of local and foreign experiences enables to conclude about similarity of discussed problems and risks. Same time, foreign experience can’t be just copied in Russia and should be reviewed and examined in order to be successfully implemented subject to Russian conditions. The above relates to both digital transformation or strategic directions and tactic approaches — sources of financing and educational management problems.
Performance Analysis of Hybrid Automatic Continuous Speech Recognition Framework for Kannada Dialect

This paper demonstrates the execution investigation of the automatic continuous speech recognition system for Kannada language using hybrid modelling techniques. The well-known modelling techniques, in particular, deep neural system (DNN), hidden Markov display (HMM), subspace Gaussian mixture model (SGMM) and maximum mutual information (MMI) have been combined to form the hybrid modelling for speech recognition. The persistent Kannada speech information is gathered from 1600 speakers (960 males and 640 females) of the age bunch in the scope of 8 years-80 years. The speech information is acquired from different geographical regions of the Karnataka state under certifiable condition. It comprises of 20,000 words that spread 30 locale. The point of this paper is to examine the execution of hybrid modelling techniques with regards to Kannada speech recognition. Kaldi toolbox is utilized for the implementation of this system, in which Mel frequency cepstral coefficient (MFCC) is used as a feature extraction procedure. The word error rate (WRR) is the error metric used to determine the efficiency of the automatic speech recognition (ASR) system. The experimental results demonstrate that the recognition rate got through the combination of DNN and HMM is better over other hybrid ASR modelling strategies.
Programs Features Clustering to Find Optimization Sequence Using Genetic Algorithm

Finding the best optimization sequence order that can improve the performance for even a simple program is not an easy task. However, the modern compilers provide dozens of optimizations, making it not a practical solution to try all the optimization sequences manually to find the optimal one. In this paper, genetic algorithm is proposed to select the best optimization sequence for a cluster of similar programs. However, wide set of programs are elected to cover as much as possible all the features. The set of the programs are classified into three clusters depending on them features. Thus, the genetic algorithm in this work is learning method. This means any new program, unseen program, can take the optimization sequence of the cluster that has similar features to it. Moreover, two scenarios are proposed using genetic algorithm to find the best optimization sequence for each cluster. In the first scenario, programs are classified into three clusters according to program dynamic features. The genetic algorithm with Tournament selection method is applied on each cluster independently to obtain a good optimization sequence for a cluster. Moreover, the proposed method improved the execution time on average by 77% compared with the O2. The second scenario was exactly similar to the first one. While, different selection methods are used for each cluster. The improved average execution time for this scenario was 78% compared with the O2. LLVM framework is used to validate and execute the proposed method. In addition, Bolybench, Standerford, Shootout benchmarks are used to verify the effectiveness of the proposed method.