Reviewerly/exHarmony huggingface dataset:
Automatic Multiclass Document Classification of Hindi Poems using Machine Learning Techniques

Text Classification of Indic language face fundamental challenges in terms of achieving good accuracy, as the languages are morphologically rich and too much information is fused in words. In this paper an actual experiment implemented is demonstrated for Classification of Hindi Poem documents to classify poems into 3 classes namely Shringar, Karuna and Veera. Poem content represents mood and have sentiments associated, the classification of emotions become more challenging when the language is morphologically rich. In current experiment 122 documents manually collected from web were processed and after preprocessing 122 documents were generated containing only meaningful data, than processed documents were used to extract features using Bag of Words Model and those features are converted into numeric representation for passing them into Training model. For classification 5 machine-learning classification algorithms namely Random Forest, Support Vector Machine, Decision Tree Algorithm, K nearest Neighbors and Naive Bayes each with it's two versions are used. The model is tested with 20% of test data and the results are compared with stored label of this data to calculate accuracy. Experiments shows that Naïve Bayes with 64% accuracy and Random Forest with 56% are performing better as compared to other algorithms for Hindi Poem Classification.
Artificial intelligence and moral rights

Abstract Whether copyrights should exist in content generated by an artificial intelligence is a frequently discussed issue in the legal literature. Most of the discussion focuses on economic rights, whereas the relationship of artificial intelligence and moral rights remains relatively obscure. However, as moral rights traditionally aim at protecting the author’s “personal sphere”, the question whether the law should recognize such protection in the content produced by machines is pressing; this is especially true considering that artificial intelligence is continuously further developed and increasingly hard to comprehend for human beings. This paper first provides the background on the protection of moral rights under existing international, U.S. and European copyright laws. On this basis, the paper then proceeds to highlight special issues in connection with moral rights and content produced by artificial intelligence, in particular whether an artificial intelligence itself, the creator or users of an artificial intelligence should be considered as owners of moral rights. Finally, the present research discusses possible future solutions, in particular alternative forms of attribution rights or the introduction of related rights.
IncGraph: An Improved Distributed Incremental Graph Computing Model and Framework based on Spark GraphX

The excavated information will become obsolete when the data changes in dynamic graphs. To compute the up-to-date results, the graph algorithm has to re-compute the entire data from scratch, which will consume huge computation time and resources. To reduce the cost of such calculations, this paper proposes a model called IncGraph to support incremental iterative computation over dynamic graphs. Different from the way of traditional iteration, IncGraph executes the graph algorithm through reusing the results of the previous graph and performs computation on the part of the graph that has changed. IncGraph has two critical components: (1) an incremental iterative computation model that consists of two steps: an incremental step to calculate the results on the changed vertices of the graph, and a merge step to calculate the results on the entire graph by using the results of the previous graph and the incremental step; and (2) an incremental update method to accelerate the iterative process within the iterative graph algorithm. We implement IncGraph model on GraphX and evaluate its performance by using several representative iterative graph algorithms: PageRank, Connected components, and Single Source Shortest Path. The results show that compared with the traditional iteration, when adding the 100k of vertices in different size data sets, the performance optimization ratio of IncGraph is 31.79 percent averagely, and 50.2 percent maximum; and when the percentage of added vertices varied from 0.01 to 10 percent in different data sets, the performance optimization ratio of IncGraph varied from 19.9 to 66.1 percent. Moreover, the result errors of IncGraph is small and can be neglected.
Money Cannot Buy Everything: Trading Mobile Data with Controllable Privacy Loss

As personal data has been the new oil of the digital era, there is a growing trend perceiving personal data as a commodity. Existing studies have built theories on how to map the privacy loss to an arbitrage-free price. They assumed that a data buyer could purchase arbitrarily accurate results as long as she could compensate data owners for their privacy loss. However, it may not be a viable business model under strict privacy regulations, such as GDPR and CCPA, and data owners' emerging privacy concerns. In this paper, we study how to empower data owners with the control of privacy loss when continuously trading their personal mobile data. Concretely, we propose a framework for trading infinite streaming mobile data which enables each data owner to bound her privacy loss in a w-length sliding window. Introducing such upper bounds of privacy loss makes the existing trading frameworks invalid and raises new technical challenges in terms of budget allocation and arbitrage-free pricing. To address these problems, we propose a modularized trading framework with instances that allows data owners to personalize their privacy loss while the price is still arbitrage-free. Finally, we conduct experiments to verify the effectiveness of the proposed trading protocols.
Artificial Intelligence in Pathology: A Simple and Practical Guide

Artificial intelligence (AI) is having an increasing impact on the field of pathology, as computation techniques allow computers to perform tasks previously performed by people. Here, we offer a simple and practical guide to AI methods used in pathology, such as digital image analysis, next-generation sequencing, and natural language processing. We not only provide a comprehensive review, but also discuss relevant history and future directions of AI in pathology. We additionally provide a short tabular dictionary of AI terminology which will help practicing pathologists and researchers to understand this field.
Effects of a virtual reality teaching application on engineering design creativity of boys and girls

In the context of the importance of promoting women’s participation in engineering design and the trend toward applying virtual reality (VR) to teaching, this study investigated the influences of gender and a VR teaching application on engineering design creativity. This study adopted a quasi-experimental design with a pre-test and post-test. A teaching experiment was conducted with 169 seventh-grade students in a junior high school in Taipei City, Taiwan.The primary conclusions reached were as follows: 1.Application of VR to teaching has a moderately positive influence on the Empathize, Define, and Ideate stages of the design process as well as on the total scores for engineering design thinking. This effect is more pronounced in girls than in boys. 2.The application of VR to teaching has a moderately positive influence on the engineering design thought processes of students, with a greater effect observed in girls than in boys.
UAV Trajectory Planning in a Port Environment

In many situations, the trajectory of an unmanned aerial vehicle (UAV) is very likely to deviate from the initial path generated by a path planning algorithm. This is in fact due to the existence of dynamic constraints of the UAV. In order to reduce the degree of such a deviation, this research introduces a trajectory planning algorithm, the objective of which is to minimize distance while maintaining security. The algorithm first develops preprocess trajectory points by constructing isosceles triangles then, on the basis of a minimum snap trajectory method, it applies a corridor constraint to an optimization objective function, while the deviation evaluation function is established to quantitatively evaluate the deviation distance. A series of experiments were carried out in a simulation environment with a simplified quay crane model. The results show that the proposed method not only optimizes the time and length of the generated trajectory, but also reduces the average deviation distance by 88.7%. Moreover, the generated trajectory can be well tracked by the UAV through qualitative and quantitative analysis. Overall, the experiments show that the proposed method can generate a higher UAV trajectory quality.
A generalized framework for ANFIS synthesis procedures by clustering techniques

The application of machine learning and soft computing techniques for function approximation is a widely explored topic in literature. Neural networks, evolutionary algorithms and support vector machines proved to be very effective, although these models suffer from very low level of interpretability by human operators. Conversely, Adaptive Neuro Fuzzy Inference Systems (ANFISs) demonstrated to be very accurate models featured by a considerable degree of interpretability. In this paper, a general framework for ANFIS training by clustering is proposed and investigated. In particular, different derivative-free ANFIS synthesis procedures are considered for performance evaluation, by taking into account different clustering algorithms, dissimilarity measures and by including an additional neuro-fuzzy classifier downstream the clustering phase targeted to rule base refinement. The resulting ANFISs have been compared, in terms of effectiveness and efficiency, on several benchmark datasets against three suitable competitors, namely a Support Vector Regression, MultiLayer Perceptron and a K-Nearest Neighbour decision rule. Computational results show that the proposed techniques tend to outperform competing strategies while, at same time, featuring models with lower structural complexity. A complete software suite implementing the proposed framework is freely available under an open-source licence.
URSA: Precise Capacity Planning and Fair Scheduling based on Low-level Statistics for Public Clouds

Database platform-as-a-service (dbPaaS) is developing rapidly and a large number of databases have been migrated to run on the Clouds for the low cost and flexibility. Emerging Clouds rely on the tenants to provide the resource specification for their database workloads. However, they tend to over-estimate the resource requirement of their databases, resulting in the unnecessarily high cost and low Cloud utilization. A methodology that automatically suggests the “just-enough” resource specification that fulfills the performance requirement of every database workload is profitable.
A Machine Learning-based Approach for Automated Vulnerability Remediation Analysis

Security vulnerabilities in firmware/software pose an important threat ton power grid security, and thus electric utility companies should quickly decide how to remediate vulnerabilities after they are discovered. Making remediation decisions is a challenging task in the electric industry due to the many factors to consider, the balance to maintain between patching and service reliability, and the large amount of vulnerabilities to deal with. Unfortunately, remediation decisions are current manually made which take a long time. This increases security risks and incurs high cost of vulnerability management. In this paper, we propose a machine learning-based automation framework to automate remediation decision analysis for electric utilities. We apply it to an electric utility and conduct extensive experiments over two real operation datasets obtained from the utility. Results show the high effectiveness of the solution.
Search-Based Wrapper Feature Selection Methods in Software Defect Prediction: An Empirical Analysis

High dimensionality is a data quality problem that negatively influences the predictive capabilities of prediction models in software defect prediction (SDP). As a viable solution, feature selection (FS) has been used to address the high dimensionality problem in SDP. From existing studies, Filter-based feature selection (FFS) and Wrapper Feature Selection (WFS) are the two basic types of FS methods. WFS methods have been regarded to have superior performance between the two. However, WFS methods have been known to have high computational cost as the number of executions required for feature subset search, evaluation and selection is not known prior. This often leads to overfitting of prediction models due to easy trapping in local maxima. Applying appropriate search method in WFS subset evaluator phase can resolve its trapping in local maxima. Best First Search (BFS) and Greedy Step-wise Search (GSS) methods have been extensively and conventionally used as viable search methods in WFS with positive impacts. However, metaheuristic search methods can also be as effective as BFS and GSS. Consequently, this study conducts an empirical comparative analysis of 13 search methods (11 state-of-the-art metaheuristic search and 2 conventional search methods) in WFS methods for SDP. The experimental results showed that metaheuristic (AS, BS, BAT, CS, ES, FS, FLS, GS, NSGA-II, PSOS, RS) as search methods in WFS proved to be better than conventional search methods (BFS and GSS). Although the average computational time of metaheuristic-based WFS methods is relatively high. We recommend that metaheuristic search can be used as alternate search methods for WFS methods in SDP.
What predicts the legal status of cryptocurrencies?

The paper aims to identify pivotal predictors of cryptocurrency legal status in a sample of 134 countries, which includes jurisdictions where cryptocurrencies are legal as well as implicitly or explicitly banned. By applying the Bayesian model averaging (BMA) for logit models and sparse group least absolute shrinkage and selection operator (LASSO) to 26 candidate predictors, we find that higher values of voice and accountability index, capturing governance quality, increase the likelihood of free cryptocurrency circulation, whereas an enhanced access to electricity, conducive to intense cryptocurrency mining, produces the opposite effect. The latter may arise from the fact that regulators perceive an increased access to electricity as an early warning signal of excessive and, thus, speculative cryptocurrency activities in the future, thereby imposing preemptive regulatory restrictions. A battery of robustness checks confirm the relevance of voice and accountability index as well as access to electricity, also revealing digital adoption index, which promotes cryptocurrency legality, as their closest contender in terms of variable importance. Governments need to take into account the identified factors when shaping their regulatory stance on cryptocurrencies.
Fast and Secure kNN Query Processing in Cloud Computing

Advances in sensing and tracking technology lead to the proliferation of location-based services. Location service providers (LSPs) often resort to commercial public clouds to store the tremendous geospatial data and process location-based queries from data users. To protect the privacy of LSP's geospatial data and data user's query location against the untrusted cloud, they are required to be encrypted before sending to the cloud. Nevertheless, it is not easy to design a fast and secure location-based query processing scheme over the encrypted data. In this paper, we propose a Fast and Secure kNN (FSkNN) scheme to support secure k nearest neighbor (k NN) search in cloud computing. We reveal the inherent connection between an Sk NN protocol and a secure range query protocol and further describe how to construct FSkNN based on a secure range query protocol. FSkNN leverages a customized accuracy-assured strategy to ensure the result accuracy and adopts a data structure named random Bloom filter (RBF) to build a secure index for efficiently searching. We formally prove the security of FSkNN under the random oracle model. Our evaluation results show that FSkNN is highly practical.
Evaluation of text summaries without human references based on the linear optimization of content metrics using a genetic algorithm

The Evaluation of Text Summaries (ETS) has been a task of constant challenges to the development of Automatic Text Summarization (ATS). Within the ATS task, the ETS is crucial to determine the performance of text summaries. Over the last two decades, the scientific community has used the ROUGE system as a standard package to assess the content of automatic summaries. However, if there are not human-made summaries (called human references), then the evaluation cannot be carried out. For this reason, the different state-of-the-art evaluation methods have been proposed that analyze the summary content using the source documents. Nonetheless, these methods do not highly correlate with human assessment. In this paper, a linear optimization of content-based metrics is proposed using a Genetic Algorithm (GA) to improve the correlation between automatic and manual evaluation. The proposed method combines 31 content metrics based on the evaluation without human references. The results of the linear optimization show correlation improvements concerning other evaluation metrics on DUC01 and DUC02 datasets.
Metamorphic Security Testing for Web Systems

Security testing verifies that the data and the resources of software systems are protected from attackers. Unfortunately, it suffers from the oracle problem, which refers to the challenge, given an input for a system, of distinguishing correct from incorrect behavior. In many situations where potential vulnerabilities are tested, a test oracle may not exist, or it might be impractical due to the many inputs for which specific oracles have to be defined. In this paper, we propose a metamorphic testing approach that alleviates the oracle problem in security testing. It enables engineers to specify metamorphic relations (MRs) that capture security properties of the system. Such MRs are then used to automate testing and detect vulnerabilities. We provide a catalog of 22 system-agnostic MRs to automate security testing in Web systems. Our approach targets 39% of the OWASP security testing activities not automated by state-of-the-art techniques. It automatically detected 10 out of 12 vulnerabilities affecting two widely used systems, one commercial and the other open source (Jenkins).
Software Demodulation of Weak Radio Signals using Convolutional Neural Network

In this paper we proposed the use of JT65A radio communication protocol for data exchange in wide-area monitoring systems in electric power systems. We investigated the software demodulation of the multiple frequency shift keying weak signals transmitted with JT65A communication protocol using deep convolutional neural network. We presented the demodulation performance in form of symbol and bit error rates. We focused on the interference immunity of the protocol over an additive white Gaussian noise with average signal-to-noise ratios in the range from -30 dB to 0 dB, which was obtained for the first time. We proved that the interference immunity is about 1.5 dB less than the theoretical limit of non-coherent demodulation of orthogonal MFSK signals.
Extremely Low-bit Convolution Optimization for Quantized Neural Network on Modern Computer Architectures

With the continuous demand for higher accuracy of deep neural networks, the model size has increased significantly. Quantization is one of the most widely used model compression methods, which can effectively reduce the model size without severe accuracy loss. Modern processors such as ARM CPU and NVIDIA GPU have already provided the support of low-bit arithmetic instructions. However, there lack efficient and practical optimizations for convolution computation towards extremely low-bit on ARM CPU (e.g., 2 ∼ 8-bit) and NVIDIA GPU (e.g., 4-bit and 8-bit). This paper explores the performance optimization methods of extremely low-bit convolution on diverse architectures. On ARM CPU, we propose two instruction schemes for 2 ∼ 3-bit and 4 ∼ 8-bit convolution with corresponding register allocation methods. In addition, we re-design the GEMM computation with data padding and packing optimizations. We also implement winograd algorithm for convolution with some specific bit width (e.g., 4 ∼ 6-bit) to achieve higher performance. On NVIDIA GPU, we propose a data partition mechanism and multi-level memory access optimizations, to better adapt the computation to GPU thread and memory hierarchy. We also propose quantization fusion to eliminate unnecessary data access. The experiment results demonstrate our implementations achieve better performance of extremely low-bit convolution compared to the state-of-the-art frameworks and libraries such as ncnn and cuDNN. To the best of our knowledge, this is the first work that provides efficient implementations of extremely low-bit convolutions covering 2 ∼ 8-bit on ARM CPU and 4-bit/8-bit on NVIDIA GPU.
Impact of mobile cross-platform development on CPU, memory and battery of mobile devices when using common mobile app features

More and more apps are built with mobile cross-platform development (MCPD) approaches. MCPD approaches offer developers several advantages for porting a mobile app onto several mobile platforms. However, these approaches come at the cost of a higher resource usage on the mobile device. In this study the resource and energy efficiency of the widely-used MCPD approach React Native is compared to native Android. The study is based on a mobile app, which uses functionality commonly found in mobile apps. Within a controlled usage scenario the study shows that the investigated MCPD approach consumed between 6% to 8% more energy than the Android native counterpart.
Face Recognition Based Smart Attendance System

Education institutes today are concerned about the consistency of students ' performance. One cause of this decrease in student performance is the inadequate attendance. There are several ways to mark your attendance, the most common ways to sign or call the students. It took longer and was problematic. From now on, a computer-based student attendance checking system is required that supports the faculty to keep records of attendance. We have used an intelligent attendance system based on face recognition in this project. We have proposed to implement a "Smart Attendance System for Face Recognition" through this large applications are incorporated. The present implementation includes facial identification that is time saving and eradicates the possibilities of proxy attendance due to the facial authorization. This system can now be used in an area in which participation plays an important role. Raspberry Pi, Open CV and Dlib using python are the basic requirements for this system. The system implemented uses LBPH face recognizer to identify the face of the person in real time. Eigen faces and Fisher faces are affected both by light and we cannot ensure perfect light conditions in real life. An improvement in the LBPH faces recognizer to overcome this problem. This system compares the image of the test and the training image and determines who is and is not present. The attendance data is stored in an excel sheet that is automatically updated in the system. If a student is absent a message will be automatically sent to their parent's phone number using GSM. Student's can check their attendance using an Android application that we have developed using MIT app Inventor.