Reviewerly/exHarmony huggingface dataset:
Temporal expert profiling: With an application to T-shaped expert finding

Cost optimization continues to be a critical concern for many human resources departments. The key is to balance between costs and business value. In particular, computer science organizations prefer to hire people who are expert in only one skill area and have a slight superficial knowledge in other areas that gives them the ability to collaborate across different aspects of project. Community Question Answering networks provide good platforms for people and organizations to share knowledge and find experts. An important issue in expert finding is that an expert has to constantly update his knowledge after being saturated in his field of expertise to still be identified as expert. A person who fails to preserve his expertise is likely to lose his expertise. This work justifies this question that does take the concept of time into account improve the quality of expertise retrieval. We propose a new method for T-shaped expert finding that is based on temporal expert profiling. The proposed method takes the temporal property of expertise into account to mine the shape of expertise for each candidate expert based on his profile. To this end, for each candidate expert, we take snapshots of his expertise trees at regular time intervals and learn the relation between temporal changes in different expertise trees and candidates' profile. Finally, we use a filtering technique that is applied on top of the profiling method, to find shape of expertise for candidate experts. Experimental results on a large test collection show the superiority of the proposed method in terms of quality of results in comparison with state-of-the-art.
ReviewModus: Text classification and sentiment prediction of unstructured reviews using a hybrid combination of machine learning and evaluation models

While research interest on product and service evaluation from unstructured text reviews is increasing, investigating the effectiveness of predictive analytical models in this context is still under-explored. With the advancement in machine learning research, an opportunity exists to bridge this gap using a model-based product and service evaluation. We propose in this article ReviewModus, a text mining and processing framework that (1) relies on the model structure and its corresponding assessment questions to train a machine learning algorithm to predict the classification of reviews around the model dimensions; (2) predicts the sentiments within the reviews based on external review training datasets; and (3) transforms the extracted measures from the reviews for further analysis. Our approach is evaluated in the context of 11 e-government services where the performance of the framework is compared to the manual processing of unstructured reviews cross-checked by three independent evaluators. Our study shows promising classification results with a micro-average F-score reaching 85.16%, and a high sentiment prediction correlation (71.44%) with the manually performed sentiment assessment.
Bio-Inspired Heuristics for VM Consolidation in Cloud Data Centers

In infrastructure-as-a-service environments, Cloud data centers employ virtualization technology to host various applications in virtual machines (VMs) and enable application isolation on shared physical resources. Additionally, live VM migration has been adopted to perform load balancing by moving VMs across distinct hosts. However, co-located VMs that show significant positive correlations on their CPU utilization patterns are at a higher risk of triggering overloading events and incurring performance degradation, even when their host is operating below its critical limits. To address this problem, a VM consolidation mechanism inspired by host-switching behaviors in symbiotic associates is proposed in this paper. In the proposed mechanism, hosts and VMs in Cloud data centers represent symbionts in an ecosystem. Two heuristic functions, inspired by host susceptibility and symbiotic coefficient among symbionts, are proposed to yield better resource utilization via VM consolidations. Experiment results demonstrate that the proposed mechanism can achieve reductions in both energy consumption and service level agreement violations of Cloud data centers.
A Fast Density-Grid Based Clustering Method

Clustering algorithms are a large area of focus in the world of data analytics, especially in the current age of big data sets that are large, multidimensional, and grow quickly. We also live in an age where parallel and distributed computing are at the forefront of any form of data analysis. Because of these two ideas, this paper discusses an algorithm that was designed to scale well with big data sets for fast run time, while also being highly scalable for a potential parallel version. This algorithm, the Fast Density-Grid Clustering Algorithm, works by dividing the data space into a grid structure and then assigning a density measurement to each grid cell. These spaces are then merged based on their densest neighbor in order to cluster the entire space. The experimental results for the serial version demonstrate that the algorithm is generally comparable to DBSCAN in accuracy, while also having a lower run time.
Deep Auto Encoder Model With Convolutional Text Networks for Video Recommendation

Collaborative filtering (CF) approach has been successfully used in recommender system (RS).Sparsity and cold start are two common phenomena in the CF algorithms nearly for each data set.Hence, these drawbacks of the classical CF algorithms have limited the recommendation performance.Deep learning theory is a very useful tool to mine the latent features in many scientific areas, such as image processing, video processing, and signal processing.In this paper, a novel deep learning-based recommendation model is introduced to solve the sparsity and cold start recommendation problems by mining the auxiliary data of users' viewing behavior datasets (e.g., the user attribute features information and video item attribute features information) and to deeply mine the latent information and their correlations of the user features and item features.First of all, the user features and video item features are processed and deeply mined by the data preprocessing layer, embedding dense layer, convolution network layer, share layer, and the auto encoder layer of our proposed model.After that, the final predictive rating process is conducted in multilayer perception by combining with the target rating vector data and the processed user and item feature data, which is deeply mined by the above-mentioned submodels of our proposed algorithm model.The extensive experiments have shown the benefits of the proposed algorithm in the measure of mean absolute error (MAE) and root mean square error (RMSE) compared with the state-of-the-art algorithms.Besides, the impact of choices of different components and parameters of our proposed algorithm model is also studied thoroughly.
Online Evolving Interval Type-2 Intuitionistic Fuzzy LSTM-Neural Networks for Regression Problems

Since the existence of fuzziness in the real world, uncertainty could be always present in the reasoning results.Therefore, how to tackle with the hesitation existing in the process of reasoning is a meaningful issue for the construction of the inference system.In this paper, a novel interval type-2 intuitionistic fuzzy neural network based on long-short term mechanism is proposed (LSTM-IT2IFNN).By means of interval type-2 intuitionistic set, the hesitation of reasoning is described and involved to determine fuzzy rules in inference system, by which the chosen of fuzzy rules not only considers the membership value but also non-membership degree.In order to handle the regression problems with long-term time dependency, long-short term mechanism is introduced into fuzzy neural networks, which can effectively improve the memory ability of long-term knowledge.Furthermore, by means of intuitionistic sets, an extended metacognitive learning mechanism is proposed for the adaptively online learning of network structure, where it achieves when, what, and how to learn from the sequential data.The performance of the LSTM-IT2IFNN is evaluated on different regression problems, including synthetic and real-world data.By comparing to existing algorithms, the experimental results show the efficiency and accuracy of the proposed scheme.
Experimental Realization of Robust Self-Testing of Bell State Measurements

Bell state measurements, of which the eigenvectors are in an entangled form, are crucial resources in the construction of quantum networks. Therefore, device-independent certification of a Bell state measurement has significance in the quantum information process because it satisfies the exact demand on security. In this study, we implement a proof-of-concept experiment to certify a Bell state measurement device independently in an entanglement swapping process, namely, self-testing. Instead of preparing a tensor product of two singlets with four photons, multiplex encoding in polarization and spatial modes is utilized to produce two pairs of entangled qubits. As a result, we implement a full Bell state measurement and achieve a high degree of Bell violation on the remaining two qubits, which are required for nontrivial self-testing of a Bell state measurement. Furthermore, our results combine the correlations before and after the swapping; thus, the quality of the performed Bell state measurement can be precisely inferred.
A Video Camera Model Identification System Using Deep Learning and Fusion

While significant work has been conducted to perform source camera model identification for images, little work has been done specifically for video camera model identification. This is problematic because different forensic traces may be left in digital images and videos captured by the same camera. As our experiments in this paper will show, a system trained to perform camera model identification for images yields unacceptably low performance when given video frames from the same cameras. To overcome this problem, new systems for identifying a videos source must be developed. In this paper, we propose a deep learning based system for determining the source camera model that captured a digital video. To do this, we use a convolutional neural network to produce camera model identification scores for small patches taken from video frames. These patches are chosen by a patch selection system that obtains patches from several appropriate frames temporally distributed throughout the video. Forensic information obtained by the CNN is provided to a fusion system, which combines it to produce a single, more accurate identification result. Through a series of experiments, we evaluate several system design choices and show that our system can achieve 95.9% video camera model identification accuracy.
Clustering and the Weekend Effect: Recommendations for the Use of Top Domain Lists in Security Research

Top domain rankings (e.g., Alexa) are commonly used in security research, such as to survey security features or vulnerabilities of "relevant" websites. Due to their central role in selecting a sample of sites to study, an inappropriate choice or use of such domain rankings can introduce unwanted biases into research results. We quantify various characteristics of three top domain lists that have not been reported before. For example, the weekend effect in Alexa and Umbrella causes these rankings to change their geographical diversity between the workweek and the weekend. Furthermore, up to 91% of ranked domains appear in alphabetically sorted clusters containing up to 87k domains of presumably equivalent popularity. We discuss the practical implications of these findings, and propose novel best practices regarding the use of top domain lists in the security community.
Technologizing agriculture

An array of technologies are making farms more efficient, safer, and profitable.
Blind Image Quality Assessment With Joint Entropy Degradation

Blind image quality assessment (BIQA) aims to evaluate the quality of an image without pristine image objectively, which is highly desired in many perception-oriented image processing systems. Distortions degrade the visual contents and cause image quality degradation. Moreover, the visual contents of an image suffer from individual degradations by different types and different levels of distortions, which makes us difficult to analyze the quality degradation. From the perspective of information theory, there is a decrease in the amount of the visual contents when images are distorted. Therefore, in this paper, the image quality is assessed through its visual entropy degradation. Researches on the neuroscience indicate that the simple cells in the local receptive field can be characterized as being spatially localized and oriented, then the local intensity, gradient, and orientation features are extracted to represent the visual contents of an image. By deducing the joint entropy equation, the joint entropy is related to the statistical distributions. Next, in order to measure the visual entropy, the joint statistical distributions of those features are calculated. Finally, by measuring the degradations on these distributions of distorted images, a novel BIQA method is proposed. The experimental results on the databases of LIVE, CSIQ, and TID2013 show that the proposed method has superior performance than other state-of-the-art BIQA methods.
Real-Time Multi-Gesture Recognition using 77 GHz FMCW MIMO Single Chip Radar

Innovations in CMOS radar has paved way for new functions like gesture-based human-machine interaction using radar for consumer and automotive electronics. Single chip radars which integrate the RF front-end and digital processing logic are fit for such applications due to their cost and form factor but are constrained in angular resolution, memory, and processing power. In this paper, we propose low complexity radar-based multi-gesture classification solution which overcomes these constraints to achieve 96% accuracy for 6 gestures generalized across 8 users. The algorithm developed was found to consume only 8.4% DSP cycles and 256KiB memory on Texas Instrument’s AWR1642.
Developing Virtual Reality Visualizations for Unsteady Flow Analysis of Dinosaur Track Formation using Scientific Sketching

We present the results of a two-year design study to developing virtual reality (VR) flow visualization tools for the analysis of dinosaur track creation in a malleable substrate. Using Scientific Sketching methodology, we combined input from illustration artists, visualization experts, and domain scientists to create novel visualization methods. By iteratively improving visualization concepts at multiple levels of abstraction we helped domain scientists to gain insights into the relationship between dinosaur foot movements and substrate deformations. We involved over 20 art and computer science students from a VR design course in a rapid visualization sketching cycle, guided by our paleontologist collaborators through multiple critique sessions. This allowed us to explore a wide range of potential visualization methods and select the most promising methods for actual implementation. Our resulting visualization methods provide paleontologists with effective tools to analyze their data through particle, pathline and time surface visualizations. We also introduce a set of visual metaphors to compare foot motion in relation to substrate deformation by using pathsurfaces. This is one of the first large-scale projects using Scientific Sketching as a development methodology. We discuss how the research questions of our collaborators have evolved during the sketching and prototyping phases. Finally, we provide lessons learned and usage considerations for Scientific Sketching based on the experiences gathered during this project.
Accounting and Cybersecurity Risk Management

SUMMARY As the number of cybersecurity incidents continue to rise and stakeholders are becoming increasingly concerned, companies are devoting considerable resources to their cybersecurity risk management efforts and related cybersecurity disclosures. This paper describes how accountants are uniquely positioned to assist companies with these efforts in advisory and assurance capacities. We present a model of effective cybersecurity risk management and discuss how accountants' core competencies can add significant value in each of the model's five stages. In addition, we use several recent high-profile cybersecurity incidents as illustrative examples in each of the five stages. We conclude by discussing implications for accountants.
Perceiving Motion From Dynamic Memory for Vehicle Detection in Surveillance Videos

Most existing video-based object detection methods utilize successful image-based object detector as a base network, and additionally exploit temporal information with either bounding-box post-processing or feature enhancement from multiple frames. However, little work has been done on directly modeling temporal motion in an efficient way for detection in surveillance videos. In this paper, a simple but effective module, denoted as motion-from-memory (MFM), is proposed to encode temporal context for improved detection in surveillance videos. With appearance features extracted from a base CNN, the MFM module maintains a dynamic memory for each input sequence and output motion features on each frame. This module costs minor additional model parameters and computations, but is very helpful for moving object detection, especially in surveillance videos. Thanks to the additional MFM module, the performance of a light-weight MobileNet-based Faster RCNN detector is boosted by 13.93% in mAP, achieving comparable performance to that of strong ResNet-50-based. When MFM is integrated into an even weaker but faster single-stage detector, it ranks the second best one among all published works when submitted to the DEETRAC vehicle detection benchmark, with 69.10% mAP, compared to 69.87% of the best one. However, when running speed is considered, the proposed method is the fastest one, running at 33 FPS with 540×960 surveillance videos on a moderate commercial GPU (NVIDIA GTX 1080Ti), which is about 3 times faster than the second fastest one.
Accelerated Bayesian inference using deep learning

ABSTRACT We present a novel Bayesian inference tool that uses a neural network (NN) to parametrize efficient Markov Chain Monte Carlo (MCMC) proposals. The target distribution is first transformed into a diagonal, unit variance Gaussian by a series of non-linear, invertible, and non-volume preserving flows. NNs are extremely expressive, and can transform complex targets to a simple latent representation. Efficient proposals can then be made in this space, and we demonstrate a high degree of mixing on several challenging distributions. Parameter space can naturally be split into a block diagonal speed hierarchy, allowing for fast exploration of subspaces where it is inexpensive to evaluate the likelihood. Using this method, we develop a nested MCMC sampler to perform Bayesian inference and model comparison, finding excellent performance on highly curved and multimodal analytic likelihoods. We also test it on Planck 2015 data, showing accurate parameter constraints, and calculate the evidence for simple one-parameter extensions to the standard cosmological model in ∼20D parameter space. Our method has wide applicability to a range of problems in astronomy and cosmology and is available for download from https://github.com/adammoss/nnest.
A novel Bagged Naïve Bayes-Decision Tree approach for multi-class classification problems

Breakthrough classification performances have been achieved by utilizing ensemble techniques in machine learning and data mining. Bagging is one such ensemble technique that has outperformed single models in obtaining higher predictive performances. This paper proposes an ensemble technique by util izing the basic bootstrap aggregating technique on hybridization of two base learners namely Naïve Bayes (NB) and Decision Tree (DT). Before induction of the DT, NB algorithm is employed for eliminating mislabeled or contradictory instances from the training set. Consequently, bagging approach is applied on hybrid NBDT as the base learner. The resultant Bagged Naïve Bayes-Decision Tree (BNBDT) algorithm is then used for improving the classification accuracy of various multi-class problems. This algorithm iteratively trains the base learner from random samples of the training set, and then performs majority voting of their predictions. The proposed algorithm is compared with both ensemble and single classification techniques such as Random Forest, Bagged NB, Bagged DT, NB, and DT. Experimental results over 52 UCI data sets with bag size 100 demonstrate that the proposed algorithm significantly outperforms the existing algorithms.
Technology Comprehension — Combining computing, design, and societal reflection as a national subject

This article considers the implementation of a new learning subject ”Technology Comprehension” into lower secondary schools in Denmark, as part of an initiative by the Danish Ministry of Education. The subject consists of learning objectives related to computing, design, and societal reflection and was first introduced as an elective course in 13 schools to investigate how it could be integrated into the Danish education system. We present four key findings based on school visits, interviews, an electronical survey, two questionnaires, and workshops including theme discussions: (1) teachers did not perceive Technology Comprehension as a distinct subject, but rather as a set of skills that can be integrated into other subjects; (2) teachers pointed out that Technology Comprehension opens up for interdisciplinary and engaging learning activities, but they need more scaffolding and support; (3) Technology Comprehension challenges teachers’ existing competencies and there is a need for a framework that takes into account computing, design, and societal reflection as a whole; (4) Technology Comprehension appealed to various kind of students, not only those who are enthusiastic about technical matters. This study contributes to the previous research on making and digital fabrication by addressing how these endeavours are implemented on a national level through engaging with local teachers. We call for more research on scaffolding and supporting teachers to orchestrate meaningful learning activities to successfully integrate Technology Comprehension into the Danish national education.
Influence of Real Exchange Rate on the Finance-Growth Nexus in the West African Region

This study examines the moderating effects of the real exchange rate and its volatility on the finance-growth nexus in the West African region. It also determines the marginal effects of financial development on economic growth at various levels of the real exchange rates and its volatility. The findings show that financial development has a long-term positive impact on economic growth, but this impact is weakened by real exchange rate and its volatility. The marginal effects of financial development on economic growth vary with the levels of the real exchange rate and its volatility. The higher the real exchange rate and its volatility, the less finance spurs growth. We also provide evidence of this scenario in individual specific countries in the region. The implication of this study is that the development of the financial sector would not provide the desirable economic benefits except it is accompanied by a reduction and stability in the real exchange rates. Based on the findings, the study makes some policy recommendations.