Reviewerly/exHarmony huggingface dataset:
Source Code Authorship Identification Using Tokenization and Boosting Algorithms

Each programmer has his unique coding style. Identification source code authorship solves the problem of determining the most likely creator of the source code, in particular, for plagiarism and disputes about intellectual property violations, as well as to help in finding the creators of malware. Extraction a unique style helps to maintain the uniformity of code in repositories, considering the different influence of programmers. Currently, methods based on random forests and abstract syntax trees, short n-grams for structure preservation and Bayes classifier and others are proposed. We present a new model, called StyleIndex, based on tokenization and tools for analyzing the semantics of programming languages and context of tokens in the program text, and extraction unique author’s style Index. The algorithm applies to various programming languages and shows very high classification accuracy. Moreover, our algorithm is able not only to correlate the source code and its creator, examples of programs which are available for training, but also to divide the program into categories by the alleged authors and have trained on other authors, thereby extraction the components define the style as a global concept, independent from specific authors. The main factors that determine the style are also identified.
An updated and more efficient search strategy to identify primary care–relevant clinical prediction rules

Objectives The aim of the study was to develop an improved search strategy for clinical prediction rules. Study Design and Setting We first refined a list of 30 primary care–relevant journals and improved the efficiency of the Haynes Narrow Filter/Teljour/Murphy Inclusion Filter with 26 items by removing one term (Modified Haynes 26 filter). We then developed the “Royal College of Surgeons in Ireland (RCSI) filter” and compared it with the modified HNF/TMIF26 for its ability to detect prediction rules in the primary care literature. All abstracts and, if necessary, full text were reviewed independently in parallel by primary care physicians. The key outcomes were the percentage of prediction rules identified out of the total identified by both search strategies (sensitivity) and the number of articles that had to be reviewed to identify them (efficiency). Results The Modified Haynes 26 filter returned 1,701 abstracts vs. 1,062 for the RCSI filter. The RCSI filter identified 105 of 111 of all prediction rules identified by either filter, compared with 107 of 111 by the Modified Haynes 26 filter (94.6% vs. 96.4%; P = 0.52). In addition, 9.9% of abstracts found using the RCSI filter were prediction rules, compared with only 6.3% using the Modified Haynes 25 filter (P = 0.001). Conclusion We have developed a novel “RCSI filter” that more efficiently identifies prediction rules in the medical literature.
Text Region Extraction From Scene Images Using AGF and MSER

The natural scene images contain text as an integral part of that image that supplies paramount knowledge about it. This information and knowledge can be used in the variety of purposes like image-based searching, automatic number plate recognition, robot navigation, etc. but text region extraction and detection in scenery images could be quite a challenging job due to image blur, distortion, noise, etc. In this paper, we discuss a method for extraction of text regions by generating prospective components by applying maximally stable extremal regions (MSER) and boundary smoothing by Alternating guided image filter, which is one of the newest filters to deal with noise and halo effect elimination. The separation of non-text &amp; text components is achieved by AdaBoost classifier that separates text and non-text on the basis of the three text specific features namely maximum stroke width ratio, compactness, color divergence. The proposed method assist in extracting text regions from the blurred and low contrast natural scene images effectively. The ICDAR 2013 training and testing dataset is applied for the experiments and evaluation of the method. The evaluation is carried out using deteval software for calculating precision, f-measure, recall for the detected, and extracted text regions.
Sampling Without Compromising Accuracy in Adaptive Data Analysis

In this work, we study how to use sampling to speed up mechanisms for answering adaptive queries into datasets without reducing the accuracy of those mechanisms. This is important to do when both the datasets and the number of queries asked are very large. In particular, we describe a mechanism that provides a polynomial speed-up per query over previous mechanisms, without needing to increase the total amount of data required to maintain the same generalization error as before. We prove that this speed-up holds for arbitrary statistical queries. We also provide an even faster method for achieving statistically-meaningful responses wherein the mechanism is only allowed to see a constant number of samples from the data per query. Finally, we show that our general results yield a simple, fast, and unified approach for adaptively optimizing convex and strongly convex functions over a dataset.
Discriminative block-diagonal covariance descriptors for image set classification

Image set classification has recently received much attention due to its various applications in pattern recognition and computer vision. To compare and match image sets, the major challenges are to devise an effective and efficient representation and to define a measure of similarity between image sets. In this paper, we propose a method for representing image sets based on block-diagonal Covariance Descriptors (CovDs). In particular, the proposed image set representation is in the form of non-singular covariance matrices, also known as Symmetric Positive Definite (SPD) matrices, that lie on Riemannian manifold. By dividing each image of an image set into square blocks of the same size, we compute the corresponding block CovDs instead of the global one. Taking the relative discriminative power of these block CovDs into account, a block-diagonal SPD matrix can be constructed to achieve a better discriminative capability. We extend the proposed approach to work with bidirectional CovDs and achieve a further boost in performance. The resulting block-diagonal SPD matrices combined with Riemannian metrics are shown to provide a powerful basis for image set classification. We perform an extensive evaluation on four datasets for several image set classification tasks. The experimental results demonstrate the effectiveness and efficiency of the proposed method.
Improving Vulnerability Scanner Performance in Detecting AJAX Application Vulnerabilities

The development of internet and web application gave birth to a new technology called AJAX. Web application utilizes this technology and create a new trend in web application - AJAX application. As a web application, there are some security issues related to AJAX application, one of which is the existence of web application vulnerabilities. These vulnerabilities can be detected using a tool called vulnerability scanner. A vulnerability scanner has three processes - crawling, attack and analysis. However, AJAX's characteristics makes it hard for vulnerability scanner to do its job properly. One of the reason is the ability to change the content of DOM without reloading the page, called dynamic DOM, in AJAX application. A dynamic DOM makes it impossible for conventional crawling method to crawl an AJAX application properly and thus, reducing the overall performance of vulnerability scanner. A method to crawl an AJAX application is called AJAX crawling. In this paper, we propose a method to improve the performance of vulnerability scanner by implementing AJAX crawling as its crawling process. This method has been tested by scanning a news aggregator web application called Gregarius version 0.5.2. From the result, this method can be used to detect vulnerabilities in AJAX application.
A DASH 360<sup>°</sup> immersive video streaming control system

This letter proposes a DASH‐compliant video streaming control system for 360 ° immersive videos. The overall system is composed of two control algorithms which cooperate both to adapt the video bitrate to match the time‐varying end‐to‐end network bandwidth and to select the most appropriate view of the panoramic scene in response to the varying point of view of the user. The proposed system has been implemented and an extensive experimental evaluation has been carried out in a realistic emulated network scenario to assess the obtainable performances in terms of visual quality and bitrate reduction.
Monitoring System of Multi-Tenant Software as a Service (SaaS)

This research proposes a monitoring system for multi-tenant Software as a Service (SaaS) to support the need of the services provider and tenants. SaaS is widely used to deliver a software which can be accessed remotely by users. Multi-tenant model is commonly used to develop SaaS. Using this model, a sharing application instance serves multiple tenants. To maintain service quality in multi-tenant model, the service provider needs to manage their tenants and their services condition. A monitoring system is needed to ease the process. It would be better if there is a generic monitoring system that can be configured to monitor specific SaaS application. This research proposed a centered monitoring system that provides resource usage, service usage, SLA (service level agreement), billing, and user activity metrics. The monitoring system use metrics which are defined by analyzing the tenant and service management in SaaS from previous studies. The source of data input for these metrics include the SaaS application, the server used, the SLA database, and the user database. The system architecture consists of four components: StatsD middleware, Telegraf, Monsant, and Dashboard. Monsant is a web service built to serve monitoring result by processing time series data stored in database. Dashboard was built as a web application with three main views to monitor tenants, services or resources. This monitoring system is ready to be integrated with SaaS applications. With the five metrics provided, this system can be used to support the service provider needs to manage tenants and services of a multi-tenant SaaS application.
ICT for Rural and Remote Areas in India

Information is critical to the social and economic activities that comprise the development process. Telecommunication, as a means of sharing information is not simply a connection between people, but a link in the chain of the development process itself (Hudson 1995)”. This article focuses mainly on how ICT serves as an engine of growth that pulls along the rural economy of the country. To speed up sustainable development, it is essential that the rural populace gets benefited from the global information revolution, thereby diminishing the gap between the educationally & technologically backward and forward sections of the society. An attempt has been made to identify the ways and means to help service providers to build a world with networks everywhere allowing people to access the Internet anywhere, anytime and through any terminal.
Sentiment Classification of Online Reviews Based on LDA and Semantic Analysis of Sentimental Words

Online reviews can reflect the sentimental tendency of the reviewers, and sentiment classification is one of the most important methods to realize sentimental tendency analysis. It is very important that how to select the features that reflect the sentimental information of the document and have good classification ability. In this paper, an online review sentiment classification method based on topic analysis of text document and semantic analysis of sentiment words is proposed. First of all, the topic of text documents and topic of words are got based on LDA method. Then, in order to get the vector representation of words in text documents and the words in sentiment dictionary, Word2Vec is used for word vector training. The semantic similarity between words and documents and semantic similarity between words in text document and words in sentiment dictionary are calculated. Finally, the words with maximum similarity are selected as key classification features of text document by using the two kinds of semantic similarity. Experiments indicate that the algorithm has a good performance and can improve the sentiment classification effect of online reviews.
A Hybrid Hypercube and Spherical Evolution for Optimization

In recent two decades, nature-inspired metaheuristic algorithms have been paid more and more attention. Although many new algorithms have been proposed, there is no quintessential difference among classic metaheuristic algorithms. Therefore, some researchers have focused on the essence of search operators in these optimizers. Spherical Evolution (SE) is one of the recent studies on the search style in metaheuristic algorithms. Contrary to other algorithms, SE adopts a spherical search instead of a hypercube search. In this paper, we focus on the advantages of the two search styles, We propose a hybrid optimizer based on the two complementary search styles, and design a rule to control their utilization. Experimental results based on 30 benchmark functions of CEC2017 show that the proposed optimizer outperforms other state-of-the-art algorithms in terms of effectiveness and robustness.
On quaternary Goppa codes

In Goppa (1970), V. D. Goppa presents a new family of linear codes over finite fields. This family, now known as Goppa codes, has been studied for nearly 50 years, including efficient decoding algorithms (Patterson, 1975) and its applications in cryptography (McEliece, 1978). Moreover, in Andrade et al. (2005), de Andrade and Palazzo present a generalization of Goppa codes to finite rings. Motivated by cryptographic applications, in this article we focus on Goppa codes over Z∕4Z and study some of their properties.
A Robust Graph Convolutional Network for Relation Extraction by Combining Edge Information

Graph convolutional network was widely used in natural language processing this year and achieved great success. However, existed Graph-based methods do not account for the edge information for node representation. In this paper, we propose a new graph convolutional network for relation extraction, which can embed the edge information for node representation. Specifically, we apply an edge embedding model to embed the edge label into vectors. Then we construct a new adjacent matrix which has multi-channel and can represent the edge information. During the computation of the hidden representation of the sentence, we split the adjacent matrix into several different 2D matrices where each one represents one channel matrix. Then the 2D matrices and tokens' embedding of the sentence are fed into the Attention Guided Graph Convolutional Networks (AGGCN) model to get the hidden representation of each one channel. Finally, the representations of different channels are concatenated together and then make a relation prediction through a feed-forward neural network (FFNN) and logistic regression classifier. The experiment results show that edge information contributes to the model's performance for relation extraction.
An Image Encryption Schema Based on Hybrid Optimized Chaotic System

The purpose of this paper is to improve the safety of chaotic image encryption algorithm. Firstly, to achieve this goal, it put forward two improved chaotic system logistic and henon, which covered an promoted henon chaotic system with better probability density, and an 2-dimension logistic chaotic system with high Lyapunov exponents. Secondly, the chaotic key stream was generated by the new 2D logistic chaotic system and optimized henon mapping, which mixed in dynamic proportions. The conducted sequence has better randomness and higher safety for image cryptosystem. Thirdly, we proposed algorithm takes advantage of the compounded chaotic system Simulation experiment results and security analysis showed that the proposed scheme was more effective and secure. It can resist various typical attacks, has high security, satisfies the requirements of image encryption theoretical.
Improving Local Motion Planning with a Reinforcement Learning Approach

This paper introduces a new Reinforcement Learning (RL) based local motion planning (RL-LMP) approach for mobile robots. This method enables virtual or real mobile platforms, to follow a path and navigate from location A to B. The method consists of two stages: a training stage and an online stage. The training stage consists in the robot iteratively learning to follow previously defined paths in a simulation environment. New RL state representations are proposed as well as a strategy to deal with the delayed reward problem and an approach to guide the exploration-exploitation of the RL model considering prior knowledge. Through training in a virtual environment and assimilation of human driving behaviors (using a gamepad), an RL model is obtained and used in the online stage, enabling a mobile platform, in a simulation or real environment, to move along a path avoiding obstacles. A set of tests and experiments were performed in different scenarios in virtual and real environments in order to assess the performance of the local motion planning approach. Validation of the RL-LMP approach, in a real environment, was carried out in the ISR-InterBot platform with the algorithm developed in ROS. The obtained results show that the RL-LMP approach provides a valid solution to the local motion planning problem (small lateral error while following paths) and suggests promising perspectives for improvement in the future.
eRSA: An energy-efficient Reconfigurable Sorting Accelerator

Sorting algorithm is an important branch in the image recognition field. In this field, the bitonic sort is one of the typical merge-based parallel sorting algorithms. To meet requirements of performance and power consumption, kinds of accelerator for sorting algorithm were proposed. In this work, an energy-efficient reconfigurable sorting accelerator is designed and integrated into one RISC-V SoC to prove the efficiency. Compared with sorting results on CPU, our accelerator can help achieve 462x times.
Scheduling of Distributed Applications in HHPCaaS Clouds for Internet of Things

The High Performance Computing as a Service provides the computing power to client applications on a pay-per-use basis. Contemporary high-performance servers gain high computing power by applying multi-core processors, graphics processors and FPGA accelerators. But efficient utilization of computing resources requires adequate management methods. In this paper, we propose a new method of scheduling IoT distributed applications in the heterogeneous cloud environment. We assume that applications, which have been requested for execution, are specified as directed acyclic graphs. Our method dynamically schedules all tasks using resource sharing by the applications. The goal of scheduling is to minimize the cost of resource hiring and to minimize the latency of all activated applications. Experimental results showed that our method gives significantly better utilization of computational resources than existing management methods for clouds. Thus, the cost of using the cloud can be reduced.
A Hybrid Framework for Uncertainty-Aware Depth Prediction in the Underwater Environment

Depth estimation is an important task that can be used in many applications such as scene reconstruction, virtual and augmented-reality, scene understanding and autonomous robots. Although several methods exist and perform well above water, for the underwater environment this task is undermined by challenging water properties. In this paper, we propose a framework for obtaining partial depth map supervision using a Structure From Motion (SfM) framework, and then train a deep neural network for real-time depth map estimation. Additionally, we estimate the model's uncertainty in the predictions, allowing us to filter out uncertain predictions and improve performance in all evaluated metrics. By estimating the model's uncertainty, the accuracy score A1 improved from 92.0% to 94.5%, and the Root Mean Squared Error (RMSE) reduced from 0.120 to 0.107.
When Worlds Collide: Protecting Physical World Interests Against Virtual World Malfeasance

If a virtual-world-game character is cast upon real-world property without the consent of the landowner, inducing or encouraging players to trespass, is the virtual-world creator liable for damages? The United States Supreme Court has recognized that digital technology presents novel issues, the resolution of which must anticipate its further rapid development. It is beyond dispute that protective legislation will be unable to keep up with rapidly evolving technology. The burden of anticipating and addressing issues presented by emerging technologies will ultimately fall upon the businesses responsible for generating them. This duty was most notably adopted by the creators of Pokémon Go in settlement of nuisance and trespass claims brought by a nationwide class seeking injunctive relief from the placement of virtual Pokéstops and Pokémon Gyms (“Gyms”) on real property. This article is the first to address this landmark settlement and proposes that future developers and creators seeking to avoid similar liability exposure implement self-regulatory practices, such as Value Sensitive Design, to create human values-based frameworks within which they can create and advance technologies. The societal need and social impact of such self-regulation is clearly illustrated by emerging litigation seeking to hold virtual-world actors responsible for real-world consequences utilizing common law tort theories. In the absence of legislation, as case law develops, self-regulatory frameworks like Value Sensitive Design are essential to create constructs within which creators can develop technologies that consider human values, address civic concerns, and avoid lawsuits, while still achieving commercial and technological objectives.
Prediction of Language Impairments in Children Using Deep Relational Reasoning with DWI Data

This paper proposes a new deep learning model using relational reasoning with diffusion-weighted imaging (DWI) data. We investigate how effectively and comprehensively DWI tractography-based connectome predicts the impairment of expressive and receptive language ability in individual children with focal epilepsy (FE). The proposed model constitutes a combination of a dilated convolutional neural network (CNN) and a relation network (RN), with the latter being applied to the dependencies of axonal connections across cortical regions in the whole brain. The presented results from 51 FE children demonstrate that the proposed model outperforms other existing state-of-the-art algorithms to predict language abilities without depending on connec-tome densities, with average improvement of up to 96.2% and 83.8% in expressive and receptive language prediction, respectively.
Multi-Agent Temporal Nontransitive Linear Logics and the Admissibility Problem

We study an extension of temporal logic, a multi-agent logic on models with nontransitive linear time (which is, in a sense, also an extension of interval logic). The proposed relational models admit lacunas in admissibility relations among agents: information accessible for one agent may be inaccessible for others. A logical language uses temporary operators ‘until’ and ‘next’ (for each of the agents), via which we can introduce modal operations ‘possible’ and ‘necessary.’ The main problem under study for the logic introduced is the recognition problem for admissibility of inference rules. Previously, this problem was dealt with for a logic in which transitivity intervals have a fixed uniform length. Here the uniformity of length is not assumed, and the logic is extended by individual temporal operators for different agents. An algorithm is found which decides the admissibility problem in a given logic, i.e., it recognizes admissible inference rules.