Reviewerly/exHarmony huggingface dataset:
Emotional tendency of online legal course review texts based on SVM algorithm and network data acquisition

The lack of effective evaluation of online education is a worldwide malpractice, and it is impossible to help students improve the correctness of online learning choices through existing reviews. Based on the current mainstream sentiment lexicon and text sentiment analysis, the authors use machine learning method to analyze the sentiment orientation of the legal course review text, through method that combines PMI and SVM. At the same time, this paper uses LibSVM tool to train and predict data, collect and pre-process data through network data collection, and, based on traditional algorithms, propose improved experimental scheme based on their respective advantages and disadvantages. In addition, the model proposed in this study is used to classify and process the emotional text, and the two methods are combined to obtain the final result. Finally, this paper combines experiments to analyze the performance of the comprehensive model proposed in this study. The research shows that the classification effect of the text sentiment analysis of model is good, it can be applied to practice, and it can provide theoretical reference for subsequent related research.
Threats to Reliability and Validity of Mixed Methods Research in User eXperience

The purpose of this paper is to shed some light on threats to reliability and validity of mixed methods (MM) research in user experience (UX) studies wherein quantitative and qualitative approaches are combined in data collection, analysis, and interpretation stages. The paper presents an empirical study of user experience where an integrated data analysis indicates a wide range of threats to the validity and reliability of mixed methods research. The authors also introduce and clarify how the data reduction technique and the typology of answers described in this paper could help to identify and address the quality shortcomings of mixed methods research in user experience and how the collected qualitative and quantitative data can supplement each other in the process. Given the early stage, exploratory nature and limited scope of the empirical study, the authors stop short from offering any prescriptive measures to those conducting or evaluating empirical mixed methods research in user experience, let alone other domains.
Framework for Anticipatory Self-Protective 5G Environments

The forthcoming 5G operational environment entails heterogeneous and multi-dimensional ecosystems where cyber assets, digital actors and cyber-physical risks coexist. In this context, the prediction and anticipation of the attacks propagation thorough the targeted systems promises to be some of the major workhorses of the emerging self-protection capabilities. In the grounds of the Self-Organizing Network (SON) paradigm, it is expected that by taking into account proactive actuations, the decision and enforcement of the best courses of action will be enhanced. With the aim on contributing to their planning and execution, this paper introduces a novel framework for proactive self-protection on 5G environments, the description of an architectural framework able to sustain the rest of the anticipation enablers, the formalization of a knowledge representation and reasoning strategy for active cyber threat mitigation, and a prediction strategy adapted to the difficulties inherent in analyzing events on 5G scenarios. The effectiveness of the proposal has been demonstrated by proof-of-concept instantiation for anticipating the impact of Denial of Service (DoS) attacks on a real communication environment.
Design of a Lightweight Autonomous Learning System for the Course of Software Testing Based on Android

A lightweight autonomous learning system for the course of software testing based on Android is developed according to the local teaching environment, and the system is fully applied on autonomous learning process outside class on campus. The system is designed on the architecture of SSH (Struts-Spring-Hibernate) which is based on J2EE for web server, and the functions of Course Information, Resource Management, Exercise, User Management and Online Discussion can be provided to client users by Android platform. Finally, further work such as how to add the function of online exam for subjective exam items in the system, how to add powerful function of micro video content editing for the management of course resource, and how to integrate this system into the local informational learning platform to complete the blended learning mode on SPOC (Small Private Online Course) are expected in the paper.
String‐Based Synthesis of Structured Shapes

Abstract We propose a novel method to synthesize geometric models from a given class of context‐aware structured shapes such as buildings and other man‐made objects. The central idea is to leverage powerful machine learning methods from the area of natural language processing for this task. To this end, we propose a technique that maps shapes to strings and vice versa, through an intermediate shape graph representation. We then convert procedurally generated shape repositories into text databases that, in turn, can be used to train a variational autoencoder. The autoencoder enables higher level shape manipulation and synthesis like, for example, interpolation and sampling via its continuous latent space. We provide project code and pre‐trained models.
The Applicability of a SIEM Solution: Requirements and Evaluation

The need for SIEM systems increased in the last few years, especially as cyber-attacks are evolving and targeting enterprises, which may cause discontinuity of their services, leakage of their data, and affect their reputation. Cybersecurity breaches can range from no or limited impact to stealing or manipulation of data, or even taking control of systems. Many companies seek to reinforce their security capabilities to better safeguard against cybersecurity threats, so they adopt multi-layered security strategies that include using a SIEM solution. A significant factor for the increasing adoption of SIEMs is the capabilities that such systems offer, being able to provide near-real time analysis of security alerts and logs generated from various set of sources within an organization IT infrastructure. However, implementing a SIEM solution is not just an installation phase that fits any scenario within any organization; the best SIEM system for an organization may not be suitable at all for another one. An organization should consider other factors along with the technical side when evaluating a SIEM solution. This paper proposes an approach to aid enterprises, in selecting the most suitable SIEM solution; it suggests technical and organizational requirements that should be addressed and examines the SIEM applicability using quantitative and qualitative evaluation criteria.
Fault Sensitivity Analysis of Lattice-Based Post-Quantum Cryptographic Components

Post-Quantum Cryptography (PQC) is currently receiving significant interest, as the construction of a practical quantum computer capable of executing Shor’s algorithm is expected in the near-to-medium future. Lattice-based PQC algorithms are among the most promising candidates discussed today, due to their performance and versatility. In this paper, we demonstrate fault sensitivity analysis (FSA) of circuit blocks used in lattice-based cryptographic implementations and a representative complete post-quantum algorithm. FSA correlates the sensitivity of the algorithm’s circuit implementation to faults with the processed data and recovers parts of the used secret key. In contrast to other types of fault attacks, FSA makes limited assumptions about the precision of fault injections and is therefore accessible even to poorly-equipped adversaries. We investigate traditional FSA based on simple models as well as its more advanced variants using templates with different construction procedures and aggregation functions, and systematically explore the conditions under which the analysis is successful. To the best of our knowledge, this is the most complex cryptographic implementation so far broken by FSA, and the first such PQC implementation.
Analysis of Entropy Estimator of True Random Number Generation Using Beta Source

In this paper, we analyze the characteristics of intrinsic random numbers based on beta source. SP 800-22 and SP800-90B of NIST in the United States are the standards used to evaluate the stability of intrinsic random numbers. Recently, SP 800-90B was revised to Second Draft, and it was detected and evaluated the characteristic which is the period of the minimum entropy estimation of non-IID track. The detected data was analyzed using SP800-22 standard of statistical analysis Respectively. The data of the beta source is obtained by using a beta source, amplifying the signal using an analog amplifier, converting it into a digital signal, and extracting random numbers from the Labview environment. The data was tested to see if it conforms to the standard in a Python environment. The test results show that it is a true random number based on beta source.
Infinite Art Gallery: A Game World of Interactively Evolved Artwork

Procedural Content Generation (PCG) has been used extensively in video games as both a cost saving measure and a means to increase replayability. Evolutionary computation is an approach to PCG that has the ability to continually create new content tailored to particular users. This paper presents the Infinite Art Gallery, a game that uses established methods of evolving art with Compositional Pattern Producing Networks to allow users to explore a world of art tailored to their preferences. From a first-person perspective, users explore room after room of evolved paintings and sculptures. Each painting is a doorway that leads to a new room with art derived from a selected painting and sculpture in the previous room. Users have an inventory of collected paintings that can be used to return to old favorites, and they can influence the generation process with special item pickups. To gauge user response to the game, a human subject study was conducted with 30 users. Average overall enjoyment was 4.2 on a 5 point scale, indicating a general appreciation of the game. However, users also identified several areas in which the game could be improved, which will be pursued in future work.
A Generic Framework for Data Analysis in Privacy-Preserving Data Mining

Directions of Privacy-preserving data publishing are toward research and applications. Previous studies focus on static data sets and some experiments are on dynamic data sets too. The problem of continuous privacy-preserving publishing of data streams is not solved by too complex approaches. Privacy is achieved by applying security on dynamic data which is a challenging task. We propose a method that extends the scope of existing works with a different framework of building ensemble classifier on time window based samples on the data streams and applying perturbation using Perlin noise on selective tuples as selective perturbation.
An Inferable Representation Learning for Fraud Review Detection with Cold-start Problem

Fraud review significantly damages the business reputation and also customers' trust to certain products. It has become a serious problem existing on the current social media. Various efforts have been put in to tackle such problems. However, in the case of cold-start where a review is posted by a new user who just pops up on the social media, common fraud detection methods may fail because most of them are heavily depended on the information about the user's historical behavior and its social relation to other users, yet such information is lacking in the cold-start case. This paper presents a novel Joint-bEhavior-and-Social-relaTion-infERable (JESTER) embedding method to leverage the user reviewing behavior and social relations for cold-start fraud review detection. JESTER embeds the deep characteristics of existing user behavior and social relations of users and items in an inferable user-item-review-rating representation space where the representation of a new user can be efficiently inferred by a closed-form solution and reflects the user's most probable behavior and social relations. Thus, a cold-start fraud review can be effectively detected accordingly. Our experiments show JESTER (i) performs significantly better in detecting fraud reviews on four real-life social media data sets, and (ii) effectively infers new user representation in the cold-start problem, compared to three state-of-the-art and two baseline competitors.
A Heuristic for Task Allocation and Routing of Heterogeneous Robots while Minimizing Maximum Travel Cost

The article proposes a new heuristic for task allocation and routing of heterogeneous robots. Specifically, we consider a path planning problem where there are two (structurally) heterogeneous robots that start from distinctive depots and a set of targets to visit. The objective is to find a tour for each robot in a manner that enables each target location to be visited at least once by one of the robots while minimizing the maximum travel cost. A solution for Multiple Depot Heterogeneous Traveling Salesman Problem (MDHTSP) with min-max objective is in great demand with many potential applications, because it can significantly reduce the job completion duration. However, there are still no reliable algorithms that can run in short amount of time. As an initial idea of solving min-max MDHTSP, we present a heuristic based on a primal-dual technique that solves for a case involving two robots while focusing on task allocation. Based on computational results of the implementation, we show that the proposed algorithm produces a good quality of feasible solution within a relatively short computation time.
Bayesian Hyperparameter Estimation using Gaussian Process and Bayesian Optimization

To reduce the computational cost of Bayesian inference, we propose a method to estimate the distribution of the Bayesian posterior probability from a small number of samples using a Gaussian process and Bayesian optimization. Our aim was to estimate the hyperparameter distribution of a Markov random field (MRF) model that corresponds to a lattice gas model. We used a small number of samples for the Gaussian process and estimated the distribution. In addition, we applied Bayesian optimization as a sampler for the Gaussian process and estimated the distribution from fewer sampled points than random sampling. Numerical experiments showed that our method can estimate the distribution of the Bayesian posterior probability accurately while reducing the computational cost.
QUESt: new practices to represent hypotheses in experiment-driven software development

Recent studies have proposed the use of experiments to guide software development in order to build features that users really want. In this context, product assumptions should be taken as hypotheses to be tested through experiments. User stories (US) are broadly used in the agile context but current guidelines to write them, like INVEST (Independent, Negotiable, Valuable, Estimable, Small and Testable), are not suitable in a experiment-driven context. In this paper, we present the first cycle of a design science research to tackle this problem. We proposed QUESt, a quality guideline recommending US to have a Questioning sense, be Updatable, Evaluable and Straightforward, and a new template to write user stories. To evaluate these new artifacts, we performed a think-aloud evaluation with software development practitioners. Although the results do not confirm the artifacts effectiveness, they indicate that they have value and should be tested within a comprehensive framework complemented by other practices.
Lamarckian Inheritance in Neuromodulated Multiobjective Evolutionary Neurocontrollers

This paper presents a novel evolutionary multiobjective neurocontroller with unsupervised learning and Lamarckian inheritance for robot navigation. Multiobjective evolution of network weights and topologies (NEAT-MODS) is augmented with Lamarckian inherited neuromodulated learning. NEAT-MODS is an NSGA-II augmented multiobjective neurocon-troller that uses two conflicting objectives. NEAT-MODS uses a selection process that aims to ensure Pareto-optimal genotypic diversity and elitism. Neuromodulation is a biologically-inspired technique that can adapt the per-connection learning rates of synaptic plasticity. Effectiveness of the design is demonstrated using a series of experiments with a simulated robot traversing a simple maze containing target goals. It is shown that when Lamarckian inheritance is combined with evolved neuromodulated learning, neural controllers are synthesized in fewer generations than by neuromodulated evolution alone. The proposed Lamarckian neuromodulated approach is found to be statistically superior to neuromodulation alone when applied to solve a multiobjective navigation problem.
Optimization-Based Terrain Analysis and Path Planning in Unstructured Environments

Accurate environment representation is one of the key challenges in autonomous ground vehicle navigation in unstructured environments. We propose a real-time optimization-based approach to terrain modeling and path planning in off-road and rough environments. Our method uses an irregular, hierarchical, graph-like environment model. A space-dividing tree is used to define a compact data structure capturing vertex positions and establishing connectivity. The same unique underlying data structure is used for both terrain modeling and path planning without memory reallocation. Local plans are generated by graph search algorithms and are continuously regenerated for on-the-fly obstacle avoidance inside the scope of the local terrain map. We show that implementing a hierarchical model over a regular space division reduces graph edge expansions by up to 84%. We illustrate the applicability of the method through experiments with an unmanned ground vehicle in both structured and unstructured environments.
Visual Repetition Sampling for Robot Manipulation Planning

One of the main challenges in sampling-based motion planners is to find an efficient sampling strategy. While methods such as Rapidly-exploring Random Tree (RRT) have shown to be more reliable in complex environments than optimization-based methods, they often require longer planning times, which reduces their usability for real-time applications. Recently, biased sampling methods have shown to remedy this issue. For example Gaussian Mixture Models (GMMs) have been used to sample more efficiently in feasible regions of the configuration space. Once the GMM is learned, however, this approach does not adapt its biases to individual planning scene during inference. Hence, we propose in this work a more efficient sampling strategy to further bias the GMM based on visual input upon query. We employ an autoencoder trained entirely in simulation to extract features from depth images and use the latent representation to adjust the weights of each mixture components in the GMM. We show empirically that this improves the sampling efficiency of an RRT motion planner in both real and simulated scenes.
Depth Maps Restoration for Human Using RealSense

Recently, mobile devices such as iPhone X start to be equipped with depth cameras, and more applications based on captured depth maps are emerging. Among many depth cameras on the market, Intel RealSense has the ability to capture depth information and is expected to be widely used in mobile devices and laptops. However, depth maps captured by RealSense always suffer from severe holes and noises, which make it hard to be used in real applications. In this paper, we propose a method to fill holes and remove noises in depth maps captured by RealSense. This method includes two parts: human depth prediction and human depth optimization. Firstly, we propose a two-stage stacked hourglass network to predict human part-segmentation and human depth simultaneously based on RGB image. Then we use GradientFMM method to optimize captured depth maps with the guidance of the above human depth prediction. The RGB image and depth maps mentioned above are captured by the same RealSense device. Furthermore, in order to show the effectiveness of the proposed method, we register and measure human 3D models based on optimized depth maps. The experimental results show that our method can restore depth maps for human using RealSense effectively.
Application of K-means Algorithm in Image Compression

Abstract In machine learning problems, there are two main algorithms: supervised learning and unsupervised learning. Supervised learning algorithms can be used to classify data for tagged data; non-supervised learning algorithms can be used to cluster data for unlabeled data. This paper discusses the basic principles of clustering algorithm and selection of key parameters of clustering algorithm. The application of clustering algorithm in image compression is also analyzed. This paper also emphasizes the problems that should be paid attention to when using clustering. Finally, a practical case of image compression with K-means is given.
Efficient Attack Countermeasure Selection Accounting for Recovery and Action Costs

The losses arising from a system being hit by cyber attacks can be staggeringly high, but defending against such attacks can also be costly. This work proposes an attack countermeasure selection approach based on cost impact analysis that takes into account the impacts of actions by both the attacker and the defender.
Smart Speaker Privacy Control - Acoustic Tagging for Personal Voice Assistants

Personal Voice Assistants (PVAs) such as the Siri, Amazon Echo and Google Home are now commonplace.PVAs continuously monitor conversations which may be transported to a cloud back end where they are stored, processed and maybe even passed on to other service providers.A user has little control over this process.She is unable to control the recording behaviour of surrounding PVAs, unable to signal her privacy requirements to back-end systems and unable to track conversation recordings.In this paper we explore techniques for embedding additional information into acoustic signals processed by PVAs.A user employs a tagging device which emits an acoustic signal when PVA activity is assumed.Any active PVA will embed this tag into their recorded audio stream.The tag may signal a cooperating PVA or back-end system that a user has not given a recording consent.The tag may also be used to trace when and where a recording was taken.We discuss different tagging techniques and application scenarios, and we describe the implementation of a prototype tagging device based on PocketSphinx.Using the popular PVA Google Home Mini we demonstrate that the device can tag conversations and that the tagging signal can be retrieved from conversations stored in the Google back-end system.
Performance comparison of Apache Hadoop and Apache Spark

The term 'Big Data' is a broad term used for the data sets, which is enormous and traditional data processing applications find it hard to process. Both Apache Spark and Apache Hadoop are one of the significant parts of the big data family. Some of the researchers view both frameworks as the rivals but it is not that easy to compare these two as they perform numerous things same, but there are also some areas where both work differently. Still both Apache Hadoop and Apache Spark are comparable on different parameters. This research intends to compare these two popular frameworks and figure out their strengths, weaknesses, unique characteristics and try to answer whether Spark can replace hadoop or not.