Reviewerly/exHarmony huggingface dataset:
Aspect based Sentiment Analysis of Employee’s Review Experience

Background: Employees of technology companies evaluate their experience through online reviews. Online reviews of companies from employees or former employees help job seeker to find out the weaknesses and strengths of the companies. The reviews can be used as an evaluation tool for each technology company to understand their employee’s perceptions. However, most information on online reviews is not well responded since some of the detailed information of the company is missing. Objective: This study aims to generate an Aspect-based Sentiment Analysis using user review data. The review data were then extracted and classified into five aspects: work balance, culture value, career opportunities, company benefit, and management. The output of this study is the aspect score from each company.Methods: This study suggests a method to analyze online reviews from employees in detail, so it can prevent the missing of specific information. The analysis was sequentially carried out in five stages. First, user review data were crawled from Glassdoor and stored in a database. Second, the raw data were processed in the data pre-processing stage to delete the incomplete data. Third, the words other than noun keyword were eliminated using Standford POS Tagger. Fourth, the noun keywords were then classified into each aspect. Finally, the aspect score was calculated based on the aspect-based sentiment analysis.Results: Result showed that the proposed method managed to turn raw review data into five aspects based on user perception.Conclusion: The study provides information for two parties, job seeker and the company. The analysis of the review could help the job seeker to decide which company that suits his need and ability. For the companies, it can be a great assistance because they will be more aware of their strengths and weaknesses. This study could possibly also provide ratings to the companies based on the aspects that have been determined.
Two-way Smart Communication System for Deaf &amp; Dumb and Normal People

With regard to Deaf & Dumb individuals, communication with others is a way longer struggle for them. They are unable to speak with traditional individuals properly. They face difficulties in finding jobs and living a traditional life like others. In this paper, We are introducing a two-way smart communication system for Deaf & Dumb and also for Normal people. The system consists of two main parts: The first part is for Deaf & Dumb person to convey their messages to a normal person by using our hardware system and the second one is for a normal person who can also respond them easily without learning a sign language by using our Android Application. This ensures a two-way smart communication system and will make life less demanding for them. The overall accuracy of the system is 92.5%, with both the hands involved.
Evaluating mixed reality technology for architectural design and construction layout

Mixed reality (MR) technology has attracted increasing interest in the architecture and construction industry since the commercial availability of head-mounted MR devices in 2016.With its ability of overlaying digital building models in real construction sites, building owners, architects, engineers, and contractors have quickly recognized the value of MR technology in on-site architectural visualization, construction planning, and as-built verification.Although a few industry practitioners have piloted some initial tests, no thorough reviews on such uses have been reported in the existing literature.To bridge this gap in the body of knowledge, this study aims to evaluate the use of MR technology in architectural design and construction layout through a comparative review of commercially available MR applications.The study employed two HoloLens devices that were attached to a hard hat to meet construction safety regulations and identified nine MR applications developed for architectural and construction purposes.The capabilities of MR technology within these applications were evaluated and compared under different on-site conditions for architectural design and construction layout.It was found that among the various MR capabilities, while some are common features that offer basic information and navigation, several are unique but essential to provide an effective and successful onsite experience for architectural and construction uses.In addition, the limitations of MR technology and the head-mounted MR device for on-site uses were discussed.This paper provides timely and empirical information to help practitioners and researchers better understand MR technology for the architecture and construction industry.
Modeling POI-Specific Spatial-Temporal Context for Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation is a fundamental task in location-based social networks. Different from traditional item recommendation, POI recommendation is highly context-dependent: (1) geographical influence, e.g., users prefer to visit POIs that are not far away; (2) time-sensitivity, e.g., restaurants are preferred in dinner time; (3) dependency in a user's check-in sequence, e.g., POIs planned in a trip. Yet, existing methods either partially leverage such context information or combine different types of contexts using a global weighting scheme, failing to capture the phenomenon that the importance of each context is also context-dependent rather than the same for all recommendation. In this paper, we propose a model to exploit spatial-temporal contexts in a POI-guided attention mechanism for POI recommendation. Such an attention mechanism offers us high flexibility to capture the POI-specific importance of each context. Experimental results on two real-world datasets collected from Foursquare and Gowalla demonstrate that the POI-specific context importance significantly improves the performance of POI recommendation.
An overview of virtual reality within industrial design education

Purpose With the release of headsets such as HTC Vive and Oculus Rift in 2016, fully immersive virtual reality (VR) technology has become available for industrial designers to represent and communicate design ideas. However, how this development will affect industrial design education practice is not clear enough yet. The purpose of this study is to reveal and discuss the current status of using VR in industrial design education and potentials of it. Design/methodology/approach In the first part of the study, the use of computer technology in industrial design education and how VR can be positioned in the existing system is discussed by the acceptance of “design” as “representation”. In the second part, the literature review carried out to unveil and analyse the efforts for using VR in industrial design practice and education is presented. The results of the review are interpreted together with the design process in industrial design education. Findings VR has the potential for changing the operating ways of not only sketching, visualising, modelling, prototyping, presenting, demonstrating and evaluating design ideas, but also getting inspiration and collaborating in industrial design education. However, it is first necessary to solve the issue of how it will be integrated into industrial design education. Originality/value This paper presents the preliminary presumptions regarding the integration of VR into industrial design education that can contribute to future studies.
Neuroevolution of Generative Adversarial Networks

Generative Adversarial Networks (GAN) is an adversarial model that became relevant in the last years, displaying impressive results in generative tasks. A GAN combines two neural networks, a discriminator and a generator, trained in an adversarial way. The discriminator learns to distinguish between real samples of an input dataset and fake samples. The generator creates fake samples aiming to fool the discriminator. The training progresses iteratively, leading to the production of realistic samples that can mislead the discriminator. Despite the impressive results, GANs are hard to train, and a trial-and-error approach is generally used to obtain consistent results. Since the original GAN proposal, research has been conducted not only to improve the quality of the generated results but also to overcome the training issues and provide a robust training process. However, even with the advances in the GAN model, stability issues are still present in the training of GANs. Neuroevolution, the application of evolutionary algorithms in neural networks, was recently proposed as a strategy to train and evolve GANs. These proposals use the evolutionary pressure to guide the training of GANs to build robust models, leveraging the quality of results, and providing a more stable training. Furthermore, these proposals can automatically provide useful architectural definitions, avoiding the manual discovery of suitable models for GANs. We show the current advances in the use of evolutionary algorithms and GANs, presenting the state-of-the-art proposals related to this context. Finally, we discuss perspectives and possible directions for further advances in the use of evolutionary algorithms and GANs.
360° virtual reality project to inspire future educators to be creators

Pre-service students in an elementary education program were involved in an educational technology course project where they created a virtual field trip using 360° virtual reality (VR) technology. As a result of the project, they acknowledged the benefits of using a 360° VR virtual field trip (VFT) in the classroom instruction and their intentions to use VFTs in their future classroom were positively changed. Maintaining stable VR technology tools and better communicating the unique contributions of VR VFTs with pre-service teachers are necessary to improve the practice.
Fast Evolution of CNN Architecture for Image Classification

The performance improvement of Convolutional Neural Network (CNN) in image classification and other applications has become a yearly event. Generally, two factors are contributing to achieving this envious success: stacking of more layers resulting in gigantic networks and use of more sophisticated network architectures, e.g. modules, skip connections, etc. Since these state-of-the-art CNN models are manually designed, finding the most optimized model is not easy. In recent years, evolutionary and other nature-inspired algorithms have become human competitors in designing CNN and other deep networks automatically. However, one challenge for these methods is their very high computational cost. In this chapter, we investigate if we can find an optimized CNN model in the classic CNN architecture and if we can do that automatically at a lower cost. Towards this aim, we present a genetic algorithm for optimizing the number of blocks and layers and some other network hyperparameters in classic CNN architecture. Experimenting with CIFAR10, CIFAR100, and SVHN datasets, it was found that the proposed GA evolved CNN models which are competitive with the other best models available.
A deep learning-aided temporal spectral ChannelNet for IEEE 802.11p-based channel estimation in vehicular communications

Abstract In vehicular communications using IEEE 802.11p, estimating channel frequency response (CFR) is a remarkably challenging task. The challenge for channel estimation (CE) lies in tracking variations of CFR due to the extremely fast time-varying characteristic of channel and low density pilot. To tackle such problem, inspired by image super-resolution (ISR) techniques, a deep learning-based temporal spectral channel network (TS-ChannelNet) is proposed. Following the process of ISR, an average decision-directed estimation with time truncation (ADD-TT) is first presented to extend pilot values into tentative CFR, thus tracking coarsely variations. Then, to make tentative CFR values accurate, a super resolution convolutional long short-term memory (SR-ConvLSTM) is utilized to track channel extreme variations by extracting sufficiently temporal spectral correlation of data symbols. Three representative vehicular environments are investigated to demonstrate the performance of our proposed TS-ChannelNet in terms of normalized mean square error (NMSE) and bit error rate (BER). The proposed method has an evident performance gain over existing methods, reaching about 84.5% improvements at some high signal-noise-ratio (SNR) regions.
Kriging Predictor for Facial Emotion Recognition Using Numerical Proximities of Human Emotions

Emotion recognition from facial expressions has gained much interest over the last few decades.In the literature, the common approach, used for facial emotion recognition (FER), consists of these steps: image pre-processing, face detection, facial feature extraction, and facial expression classification (recognition).We have developed a method for FER that is absolutely different from this common approach.Our method is based on the dimensional model of emotions as well as on using the kriging predictor of Fractional Brownian Vector Field.The classification problem, related to the recognition of facial emotions, is formulated and solved.The relationship of different emotions is estimated by expert psychologists by putting different emotions as the points on the plane.The goal is to get an estimate of a new picture emotion on the plane by kriging and determine which emotion, identified by psychologists, is the closest one.Seven basic emotions (Joy, Sadness, Surprise, Disgust, Anger, Fear, and Neutral) have been chosen.The accuracy of classification into seven classes has been obtained approximately 50%, if we make a decision on the basis of the closest basic emotion.It has been ascertained that the kriging predictor is suitable for facial emotion recognition in the case of small sets of pictures.More sophisticated classification strategies may increase the accuracy, when grouping of the basic emotions is applied.
A Sentiwordnet Strategy for Curriculum Learning in Sentiment Analysis

Curriculum Learning (CL) is the idea that learning on a training set sequenced or ordered in a manner where samples range from easy to difficult, results in an increment in performance over otherwise random ordering. The idea parallels cognitive science’s theory of how human brains learn, and that learning a difficult task can be made easier by phrasing it as a sequence of easy to difficult tasks. This idea has gained a lot of traction in machine learning and image processing for a while and recently in Natural Language Processing (NLP). In this paper, we apply the ideas of curriculum learning, driven by SentiWordNet in a sentiment analysis setting. In this setting, given a text segment, our aim is to extract its sentiment or polarity. SentiWordNet is a lexical resource with sentiment polarity annotations. By comparing performance with other curriculum strategies and with no curriculum, the effectiveness of the proposed strategy is presented. Convolutional, Recurrence and Attention based architectures are employed to assess this improvement. The models are evaluated on standard sentiment dataset, Stanford Sentiment Treebank.
Optimal quantum phase estimation with generalized multi-component Schrödinger cat states

In this paper, we are interested in detecting the presence of a nearby phase-sensitive object, where traveling light works out under a low-photon loss rate. Here we investigate the optimal quantum phase estimation with generalized multi-component Schrodinger cat states. In addition, we show the optimal conditions of the generalized multi-component cat states for the phase estimation in a lossless scenario. We then demonstrate that the generalized multi-component cat states can beat the performances of the NOON and two-mode squeezed vacuum states in the presence of small loss, while maintaining the quantum advantage over the standard quantum limit, attainable by coherent states. Finally, we propose a generation scheme of the entangled multi-component cat states with current or near-term optical technologies.
Equivalence and characterizations of linear rank-metric codes based on invariants

We show that the sequence of dimensions of the linear spaces, generated by a given rank-metric code together with itself under several applications of a field automorphism, is an invariant for the whole equivalence class of the code. The same property is proven for the sequence of dimensions of the intersections of itself under several applications of a field automorphism. These invariants give rise to easily computable criteria to check if two codes are inequivalent. We derive some concrete values and bounds for these dimension sequences for some known families of rank-metric codes, namely Gabidulin and (generalized) twisted Gabidulin codes. We then derive conditions on the length of the codes with respect to the field extension degree, such that codes from different families cannot be equivalent. Furthermore, we derive upper and lower bounds on the number of equivalence classes of Gabidulin codes and twisted Gabidulin codes, improving a result of Schmidt and Zhou for a wider range of parameters. In the end we use the aforementioned sequences to determine a characterization result for Gabidulin codes.
Differentially Private Release of Datasets using Gaussian Copula

&#x0D; &#x0D; &#x0D; We propose a generic mechanism to efficiently release differentially private synthetic versions of high-dimensional datasets with high utility. The core technique in our mechanism is the use of copulas, which are functions representing dependencies among random variables with a multivariate distribution. Specifically, we use the Gaussian copula to define dependencies of attributes in the input dataset, whose rows are modelled as samples from an unknown multivariate distribution, and then sample synthetic records through this copula. Despite the inherently numerical nature of Gaussian correlations we construct a method that is applicable to both numerical and categorical attributes alike. Our mechanism is efficient in that it only takes time proportional to the square of the number of attributes in the dataset. We propose a differentially private way of constructing the Gaussian copula without compromising computational efficiency. Through experiments on three real-world datasets, we show that we can obtain highly accurate answers to the set of all one-way marginal, and two-and three-way positive conjunction queries, with 99% of the query answers having absolute (fractional) error rates between 0.01 to 3%. Furthermore, for a majority of two-way and three-way queries, we outperform independent noise addition through the well-known Laplace mechanism. In terms of computational time we demonstrate that our mechanism can output synthetic datasets in around 6 minutes 47 seconds on average with an input dataset of about 200 binary attributes and more than 32,000 rows, and about 2 hours 30 mins to execute a much larger dataset of about 700 binary attributes and more than 5 million rows. To further demonstrate scalability, we ran the mechanism on larger (artificial) datasets with 1,000 and 2,000 binary attributes (and 5 million rows) obtaining synthetic outputs in approximately 6 and 19 hours, respectively. These are highly feasible times for synthetic datasets, which are one-off releases.&#x0D; &#x0D; &#x0D;
Project “National Research University” – Driver of Russian Higher Education

The article presents the results of the national research universities (NRU) development programs implementation analysis for the period 2008–2018. The authors have held a comparative analysis of NRU activities with other strategic initiatives in the field of higher education in Russia, in particular, Russian Academic Excellence Project 5-100 on Competitiveness Enhancement of the Leading Russian Universities among Global Research and Education Centers; Project «Federal Universities»; support for strategic development programs of state educational institutions of higher education; sustainable relationships support between higher educational institutions and regional enterprises and organizations within the framework of the priority project “Universities as Innovation Drivers”. The analysis was based on 41 quantitative and 11 qualitative indicators. In the analysis, the following data sources were used: statistical forms 1-Monitoring, data from ranking agencies, etc. It has been determined that implementation of development programs for the entire NRU network is generally successful and positively affects the development of Russian higher education. The leading NRU – the participants of Project 5-100 have shown the greater impact on the enhancement of the Russian education in the world.
Optimization of parallel random forest algorithm based on distance weight

In order to overcome the problems of long execution time and low parallelism of existing parallel random forest algorithms, an optimization method for parallel random forest algorithm based on distance weights is proposed. The concept of distance weights is introduced to optimize the algorithm. Firstly, the training sample data are extracted from the original data set by random selection. Based on the extracted results, a single decision tree is constructed. The single decision tree is grouped together according to different grouping methods to form a random forest. The distance weights of the training sample data set are calculated, and then the weighted optimization of the random forest model is realized. The experimental results show that the execution time of the parallel random forest algorithm after optimization is 110 000 ms less than that before optimization, and the operation efficiency of the algorithm is greatly improved, which effectively solves the problems existing in the traditional random forest algorithm.
Fusion of Orthogonal Moment Features for Mammographic Mass Detection and Diagnosis

Masses are mammographic nonpalpable signs of breast cancer. These masses could be detected using screening mammography. This paper proposed a system utilizing orthogonal moment invariants (OMIs) features for mammographic masses detection and diagnosis. In this work, three sets of OMIs features were extracted. These OMIs features are Gaussian-Hermite moments (GHMs), Gegenbauer moments (GeMs), and Legendre moments (LMs). The extracted features are fused and presented to the particle swarm optimization (PSO) algorithm for feature selection. The classification step is achieved using the support vector machine (SVM). The proposed system is evaluated using 400 regions, extracted from the DDSM dataset. The obtained results reveal the promising application of OMIs features for masses detection and identification. It shows that fusing the OMIs features produces an acceptable detection performance where the area under the receiver operating characteristics (ROC) curve is $Az=0.969\pm 0.01$ and the best performance of OMIs features is $Az = 0.856\pm 0.053$ for characterizing the malignancy of masses.
HAEPG: An Automatic Multi-hop Exploitation Generation Framework

Automatic exploit generation for heap vulnerabilities is an open challenge. Current studies require a sensitive pointer on the heap to hijack the control flow and pay little attention to vulnerabilities with limited capabilities. In this paper, we propose HAEPG, an automatic exploit framework that can utilize known exploitation techniques to guide exploit generation. We implemented a prototype of HAEPG based on the symbolic execution engine S2E [15] and provided four exploitation techniques for it as prior knowledge. HAEPG takes crashing inputs, programs, and prior knowledge as input, and generates exploits for vulnerabilities with limited capabilities by abusing heap allocator's internal functionalities. We evaluated HAEPG with 24 CTF programs, and the results show that HAEPG is able to accurately reason about the type of vulnerability for 21 (87.5%) of them, and generate exploits that spawn a shell for 16 (66.7%) of them. All the exploits could bypass NX [25] and Full RELRO [28] security mechanisms.